The industrial automation giant has fixed security holes in Logix, CompactLogix, Flex, RSLinx, and FactoryTalk products.
The post Rockwell Automation Patches Vulnerabilities in ICS Controllers and Software appeared first on SecurityWeek.
Oracle has released its June 2026 Critical Security Patch Update to fix vulnerabilities in Communications, EBS, Enterprise Manager and other products.
The post Oracle’s Second Monthly Security Updates Deliver 245 Patches appeared first on SecurityWeek.
The browser updates address multiple memory safety bugs that could potentially lead to remote code execution.
The post Chrome and Firefox Updated to Patch Critical, High-Severity Vulnerabilities appeared first on SecurityWeek.
Unit 42 discovered a Vertex AI Python SDK vulnerability that allows remote code execution via bucket squatting. Read the article for more.
The post Pickle in the Middle – Hijacking Vertex AI Model Uploads for Cross-Tenant RCE appeared first on Unit 42.
Microsoft Threat Intelligence discovered that Anthropic’s Claude Code GitHub Action could expose CI/CD workflow secrets when AI agents process untrusted GitHub content, including issue bodies, pull request descriptions, and comments. We found that while Claude Code Action supported environment scrubbing for subprocess execution paths such as Bash, the Read tool was not subject to the same sandboxing model. It was eventually authorized to access /proc/self/environ, reading the workflow’s ANTHROPIC_API_KEY and potentially other credentials available to the runner.
Following our responsible disclosure, Anthropic mitigated this issue in Claude Code version 2.1.128 by blocking access to sensitive /proc files. Defenders should treat AI workflows that process untrusted GitHub content as high-risk when they also have access to secrets, file-read tools, or external communication channels.
We began this research after observing prompt injection attempts in public repositories using AI-assisted GitHub workflows across multiple vendors, where attacker-controlled issue or PR content is processed by the AI agent and could influence its tool use. For example:
The injection payload was placed inside an HTML comment (<!– –>), making it invisible when the issue is rendered in the browser but still visible to the AI model which reads the raw markdown:
The target repository – fork of a major open-source documentation project – used a highly permissive GitHub Actions workflow to automate issue resolution. We believe the actor is using a fork to test which payloads work before disclosing or exploiting them.
Whenever a user opened a new issue, an AI bot interpreted the request and was granted robust operational tools to resolve it:
This tool chain, operating without external oversight, provided an unauthorized user with the exact high-level primitives needed to plant malware without directly possessing write access.
Disguising the attack as a legitimate feature request for “diagnostic telemetry”, the payload provided the AI with a precise sequence of commands rather than a standard conversational prompt. It instructed the bot to search for a specific markdown heading, read the target file’s contents, append an exact block of malicious HTML, and immediately invoke the pull request tool to commit the newly poisoned file, effectively steering the AI step-by-step through a supply-chain compromise.
The attack vector successfully coerced the bot into locating the target documentation file and appending an invisible XSS image tag:
Had this PR been merged by a maintainer or by automated CI/CD automation, rendering the documentation site would execute JavaScript on visitors’ machines to silently exfiltrate their session tokens to the attacker’s endpoint.
This same trust boundary is what makes the Read tool vulnerability exploitable: once an attacker can influence the agent, they might be able to steer it toward sensitive files available inside the CI runner environment.
To understand the vulnerability described in this blog, it helps to first understand the environment in which they operate. GitHub Actions workflows were designed for deterministic automation—running tests, deploying builds, and enforcing policy. But as AI-powered tools like Claude Code Action have entered that environment, they’ve brought up a fundamentally different execution model: one where natural language can be treated as instruction. The sections below walk through how that model works, where the security boundaries are drawn, and critically, why those boundaries fail.
GitHub Actions is GitHub’s native automation and CI/CD platform. A workflow is a YAML configuration file that defines jobs to run when repository events occur, such as pull_request, issue_comment, scheduled runs, or manual dispatch.
When a workflow is triggered, GitHub executes its jobs on a runner: an ephemeral virtual machine, or in some cases a self-hosted environment. That runner is not just executing code in isolation. Depending on the workflow configuration, it may receive repository contents, issue and pull request metadata, environment variables, the GITHUB_TOKEN, cloud credentials, package publishing tokens, and third-party API keys.
GitHub workflows were built for deterministic automation: run tests, build artifacts, deploy code, label issues, or enforce repository policy. AI-powered workflows change that model. Instead of only executing predefined logic, they ingest repository context, interpret natural-language input, and decide which actions to take next.
A common example is AI-based pull request review. Tools such as Anthropic’s Claude Code GitHub Action can trigger on pull requests, read the diff, title, description, and comments, then post review feedback or security findings. In more advanced configurations, the same agent can modify files, create commits, or open follow-up pull requests from inside the CI runner.
Despite differences between vendors and implementations, the security pattern is consistent:
These integrations are not necessarily careless. Most include system prompts, filters, and policy logic intended to separate user content from control instructions. But when those boundaries fail, the workflow is no longer just automation. It becomes an AI agent embedded inside the repository, and its prompt construction, tool permissions, and runtime isolation become part of the security perimeter.
Claude Code Action is a GitHub action that runs Claude inside your CI runner. Under the hood, it’s a wrapper around the Claude Agent SDK (software development kit). The Claude Code Action handles GitHub-specific concerns (parsing the event, fetching issue/PR context, building the prompt, wiring up MCP (Model Context Protocol) servers, managing tracking comments) and then calls the SDK’s query function to drive Claude. Tool permissions, model selection, and most other runtime behavior are SDK options that the action is responsible for setting.
When Anthropic designed Claude Code Actions, they knew the risks. For the Bash tool, they support Bubblewrap (namespace-based Linux sandbox) with a scrubbed environment (enforced by CLAUDE_CODE_SUBPROCESS_ENV_SCRUB , auto enabled for actions that can be triggered by non-write users).
This is a solid defense. However, a gap exists: the Read tool is not subject to the same isolation.
Rather than routing Read operations through the same secure isolation boundary as Bash, these operations represent direct, in-process calls. They inherently bypass the Bubblewrap sandbox, operating with full access to the process’s environment variables.
To confirm the exploitability of this gap, we constructed a prompt injection payload. We tested this in a lab environment, specifically a non-write user enabled, which forces the CLAUDE_CODE_SUBPROCESS_ENV_SCRUB mitigation active.
We then injected this malicious prompt, the kind that naturally flows through issue bodies, PR comments, or other input:
This prompt defeats two distinct layers of defense:
In figure 4, the prompt injection succeeds; Claude confidently invokes the Read tool directly against /proc/self/environ (taken from the GitHub’s action logs).
The returned environ blob contains the unscrubbed ANTHROPIC_API_KEY. If Read ran inside the same Bubblewrap subprocess that Bash uses, it would not contain this key in the process’s environment variable.
From there, the attacker has their pick of exfiltration channels based on the target workflow configuration (which is publicly visible, since it’s stored in the repository under . github/workflows/). They can use an adversary-controlled domain via WebFetch or Bash, post it in an issue comment using GitHub MCP, or echo it to the Action log (if show_full_output is enabled in the target workflow). The attacker can then prepend “sk-ant-“ to the leaked string to reconstruct the full Anthropic API key.
May 5, 2026: Anthropic mitigated this issue in Claude Code 2.1.128. The mitigation strengthened the Read tool by unconditionally rejecting a number of files in /proc/ in order to protect those files from exfiltration.
April 29, 2026: reported to Anthropic via HackerOne.
The good news for defenders: controls already exist. Below is an actionable hardening guide:
Resource Development
Execution
Defense Evasion
Credential Access
Exfiltration
To conduct AI-driven black-box research on Claude Code Action, we built a GitHub workflow configured with the Bash tool and a system prompt designed to initiate a reverse shell. To bypass Sonnet’s refusal safety mechanisms, we obscured the shell payload behind a response from our controlled domain. We also enabled the workflow to be triggered by users with no “write” permissions to ensure Anthropic’s environment variables scrub mitigations were active during our tests.
Gaining an interactive foothold on the runner, we initially deployed a frontier AI model for automated, black-box research. When an hour of automated analysis produced no actionable findings, we pivoted.
We adopted a white-box approach, feeding the AI model the Claude Code Actions codebase and the obfuscated @anthropic-ai/claude-agent-sdk. Through this human-AI collaboration, where we actively directed the model, analyzed its findings, and tested variations, we uncovered the necessary exploit chains and responsibly disclosed them to Anthropic.
The integration of AI into GitHub Actions isn’t just a productivity improvement, it is a fundamental rewrite of the CI/CD security model. Right now, development is moving faster than defense.
Even when AI agents are deployed with safety prompts, permission scopes, and platform-level defenses (such as the secret scanner we reviewed), a determined attacker can potentially bypass these controls. We are entering an era where natural language is executable code, and untrusted inputs like GitHub issues must be treated as hostile by default. A single, carefully crafted comment combined with a misunderstood trust boundary is all it takes to walk away with production credentials.
We encourage maintainers to stay alert, keep up with the latest security updates, and implement the safeguards outlined in our mitigation guide to protect their repositories against this emerging class of attack.
For the latest security research from the Microsoft Threat Intelligence community, check out the Microsoft Threat Intelligence Blog.
To get notified about new publications and to join discussions on social media, follow us on LinkedIn, X (formerly Twitter), and Bluesky.
To hear stories and insights from the Microsoft Threat Intelligence community about the ever-evolving threat landscape, listen to the Microsoft Threat Intelligence podcast.
Review our documentation to learn more about our real-time protection capabilities and see how to enable them within your organization.
The post Securing CI/CD in an agentic world: Claude Code Github action case appeared first on Microsoft Security Blog.
The flaw allows attackers to execute arbitrary code remotely and has been exploited in the wild for two months.
The post Everest Forms Vulnerability Exploited to Hack WordPress Sites appeared first on SecurityWeek.
Unauthenticated attackers can exploit the flaw via specially crafted POST requests that crash the Serv-U service.
The post SolarWinds Serv-U Vulnerability Exploited in the Wild appeared first on SecurityWeek.
Microsoft Threat Intelligence discovered that Anthropic’s Claude Code GitHub Action could expose CI/CD workflow secrets when AI agents process untrusted GitHub content, including issue bodies, pull request descriptions, and comments. We found that while Claude Code Action supported environment scrubbing for subprocess execution paths such as Bash, the Read tool was not subject to the same sandboxing model. It was eventually authorized to access /proc/self/environ, reading the workflow’s ANTHROPIC_API_KEY and potentially other credentials available to the runner.
Following our responsible disclosure, Anthropic mitigated this issue in Claude Code version 2.1.128 by blocking access to sensitive /proc files. Defenders should treat AI workflows that process untrusted GitHub content as high-risk when they also have access to secrets, file-read tools, or external communication channels.
We began this research after observing prompt injection attempts in public repositories using AI-assisted GitHub workflows across multiple vendors, where attacker-controlled issue or PR content is processed by the AI agent and could influence its tool use. For example:
The injection payload was placed inside an HTML comment (<!– –>), making it invisible when the issue is rendered in the browser but still visible to the AI model which reads the raw markdown:
The target repository – fork of a major open-source documentation project – used a highly permissive GitHub Actions workflow to automate issue resolution. We believe the actor is using a fork to test which payloads work before disclosing or exploiting them.
Whenever a user opened a new issue, an AI bot interpreted the request and was granted robust operational tools to resolve it:
This tool chain, operating without external oversight, provided an unauthorized user with the exact high-level primitives needed to plant malware without directly possessing write access.
Disguising the attack as a legitimate feature request for “diagnostic telemetry”, the payload provided the AI with a precise sequence of commands rather than a standard conversational prompt. It instructed the bot to search for a specific markdown heading, read the target file’s contents, append an exact block of malicious HTML, and immediately invoke the pull request tool to commit the newly poisoned file, effectively steering the AI step-by-step through a supply-chain compromise.
The attack vector successfully coerced the bot into locating the target documentation file and appending an invisible XSS image tag:
Had this PR been merged by a maintainer or by automated CI/CD automation, rendering the documentation site would execute JavaScript on visitors’ machines to silently exfiltrate their session tokens to the attacker’s endpoint.
This same trust boundary is what makes the Read tool vulnerability exploitable: once an attacker can influence the agent, they might be able to steer it toward sensitive files available inside the CI runner environment.
To understand the vulnerability described in this blog, it helps to first understand the environment in which they operate. GitHub Actions workflows were designed for deterministic automation—running tests, deploying builds, and enforcing policy. But as AI-powered tools like Claude Code Action have entered that environment, they’ve brought up a fundamentally different execution model: one where natural language can be treated as instruction. The sections below walk through how that model works, where the security boundaries are drawn, and critically, why those boundaries fail.
GitHub Actions is GitHub’s native automation and CI/CD platform. A workflow is a YAML configuration file that defines jobs to run when repository events occur, such as pull_request, issue_comment, scheduled runs, or manual dispatch.
When a workflow is triggered, GitHub executes its jobs on a runner: an ephemeral virtual machine, or in some cases a self-hosted environment. That runner is not just executing code in isolation. Depending on the workflow configuration, it may receive repository contents, issue and pull request metadata, environment variables, the GITHUB_TOKEN, cloud credentials, package publishing tokens, and third-party API keys.
GitHub workflows were built for deterministic automation: run tests, build artifacts, deploy code, label issues, or enforce repository policy. AI-powered workflows change that model. Instead of only executing predefined logic, they ingest repository context, interpret natural-language input, and decide which actions to take next.
A common example is AI-based pull request review. Tools such as Anthropic’s Claude Code GitHub Action can trigger on pull requests, read the diff, title, description, and comments, then post review feedback or security findings. In more advanced configurations, the same agent can modify files, create commits, or open follow-up pull requests from inside the CI runner.
Despite differences between vendors and implementations, the security pattern is consistent:
These integrations are not necessarily careless. Most include system prompts, filters, and policy logic intended to separate user content from control instructions. But when those boundaries fail, the workflow is no longer just automation. It becomes an AI agent embedded inside the repository, and its prompt construction, tool permissions, and runtime isolation become part of the security perimeter.
Claude Code Action is a GitHub action that runs Claude inside your CI runner. Under the hood, it’s a wrapper around the Claude Agent SDK (software development kit). The Claude Code Action handles GitHub-specific concerns (parsing the event, fetching issue/PR context, building the prompt, wiring up MCP (Model Context Protocol) servers, managing tracking comments) and then calls the SDK’s query function to drive Claude. Tool permissions, model selection, and most other runtime behavior are SDK options that the action is responsible for setting.
When Anthropic designed Claude Code Actions, they knew the risks. For the Bash tool, they support Bubblewrap (namespace-based Linux sandbox) with a scrubbed environment (enforced by CLAUDE_CODE_SUBPROCESS_ENV_SCRUB , auto enabled for actions that can be triggered by non-write users).
This is a solid defense. However, a gap exists: the Read tool is not subject to the same isolation.
Rather than routing Read operations through the same secure isolation boundary as Bash, these operations represent direct, in-process calls. They inherently bypass the Bubblewrap sandbox, operating with full access to the process’s environment variables.
To confirm the exploitability of this gap, we constructed a prompt injection payload. We tested this in a lab environment, specifically a non-write user enabled, which forces the CLAUDE_CODE_SUBPROCESS_ENV_SCRUB mitigation active.
We then injected this malicious prompt, the kind that naturally flows through issue bodies, PR comments, or other input:
This prompt defeats two distinct layers of defense:
In figure 4, the prompt injection succeeds; Claude confidently invokes the Read tool directly against /proc/self/environ (taken from the GitHub’s action logs).
The returned environ blob contains the unscrubbed ANTHROPIC_API_KEY. If Read ran inside the same Bubblewrap subprocess that Bash uses, it would not contain this key in the process’s environment variable.
From there, the attacker has their pick of exfiltration channels based on the target workflow configuration (which is publicly visible, since it’s stored in the repository under . github/workflows/). They can use an adversary-controlled domain via WebFetch or Bash, post it in an issue comment using GitHub MCP, or echo it to the Action log (if show_full_output is enabled in the target workflow). The attacker can then prepend “sk-ant-“ to the leaked string to reconstruct the full Anthropic API key.
May 5, 2026: Anthropic mitigated this issue in Claude Code 2.1.128. The mitigation strengthened the Read tool by unconditionally rejecting a number of files in /proc/ in order to protect those files from exfiltration.
April 29, 2026: reported to Anthropic via HackerOne.
The good news for defenders: controls already exist. Below is an actionable hardening guide:
Resource Development
Execution
Defense Evasion
Credential Access
Exfiltration
To conduct AI-driven black-box research on Claude Code Action, we built a GitHub workflow configured with the Bash tool and a system prompt designed to initiate a reverse shell. To bypass Sonnet’s refusal safety mechanisms, we obscured the shell payload behind a response from our controlled domain. We also enabled the workflow to be triggered by users with no “write” permissions to ensure Anthropic’s environment variables scrub mitigations were active during our tests.
Gaining an interactive foothold on the runner, we initially deployed a frontier AI model for automated, black-box research. When an hour of automated analysis produced no actionable findings, we pivoted.
We adopted a white-box approach, feeding the AI model the Claude Code Actions codebase and the obfuscated @anthropic-ai/claude-agent-sdk. Through this human-AI collaboration, where we actively directed the model, analyzed its findings, and tested variations, we uncovered the necessary exploit chains and responsibly disclosed them to Anthropic.
The integration of AI into GitHub Actions isn’t just a productivity improvement, it is a fundamental rewrite of the CI/CD security model. Right now, development is moving faster than defense.
Even when AI agents are deployed with safety prompts, permission scopes, and platform-level defenses (such as the secret scanner we reviewed), a determined attacker can potentially bypass these controls. We are entering an era where natural language is executable code, and untrusted inputs like GitHub issues must be treated as hostile by default. A single, carefully crafted comment combined with a misunderstood trust boundary is all it takes to walk away with production credentials.
We encourage maintainers to stay alert, keep up with the latest security updates, and implement the safeguards outlined in our mitigation guide to protect their repositories against this emerging class of attack.
For the latest security research from the Microsoft Threat Intelligence community, check out the Microsoft Threat Intelligence Blog.
To get notified about new publications and to join discussions on social media, follow us on LinkedIn, X (formerly Twitter), and Bluesky.
To hear stories and insights from the Microsoft Threat Intelligence community about the ever-evolving threat landscape, listen to the Microsoft Threat Intelligence podcast.
Review our documentation to learn more about our real-time protection capabilities and see how to enable them within your organization.
The post Securing CI/CD in an agentic world: Claude Code Github action case appeared first on Microsoft Security Blog.
We include indicators of activity and mitigations for PAN-OS vulnerability CVE-2026-0257.
The post Threat Brief: Active Exploitation of PAN-OS CVE-2026-0257 appeared first on Unit 42.
Imagine handing your smartphone over for repair. A couple of days later, you pick it up — and great, it’s working again! But you won’t even realize that your device has been injected with malicious code, allowing attackers to access your smartphone even when it’s locked.
This is the beginning of the story shared by Kaspersky ICS CERT researchers, Alexander Kozlov and Sergey Anufrienko, at the Black Hat Asia 2026 conference. They managed to uncover a vulnerability that flips conventional assumptions about smartphone and IoT security on their head. Its core lies at the very heart of Qualcomm chips.
To grasp the severity of this discovery, we first need to look at how a modern device powered by a Qualcomm chip boots up. Think of it as a fortress with multiple layers of security. Each subsequent layer verifies the pass issued by the previous one. The bedrock foundation — the most trusted layer of them all — is the BootROM, a read-only memory baked directly into the silicon that can’t be modified once it comes off the fab.
The BootROM is the very first thing to run when a device powers on. It verifies the signature of the next bootloader, which in turn verifies the next, building a chain of trust all the way up to the operating system. If an attacker can compromise this chain at the BootROM level, it’s game over: the malicious code will execute before the main operating system even has a chance to load.
This is exactly what attackers can do by exploiting the CVE-2026-25262 vulnerability discovered by Kaspersky ICS CERT researchers.
The research began with a protocol called Sahara. This is a component of Emergency Download Mode (EDL). Manufacturers and service centers use it to revive bricked devices: the phone is connected to a computer via USB, and a special utility program signed by the manufacturer (in this case, Qualcomm) is uploaded to it.
Sahara is implemented directly within the ARM PBL (Primary Boot Loader) — the BootROM itself. This means the protocol runs before any operating system boots, before any user access privileges are checked, and before any security controls are activated. The device simply waits for a USB connection, ready to accept data.
The communication scheme looks simple: the device sends a handshake (HELLO) to the computer, the computer selects the mode, a cycle begins to upload the utility program in chunks, and finally, the device executes the uploaded code. And it was within the verification logic of these very file chunks that the vulnerability was identified.
In technical terms, the bug introduced by the developers is classified as CWE-123: Write-What-Where Condition. This is about as bad as it gets when it comes to flaws in low-level programming. An attacker can write arbitrary data to an arbitrary address in the device memory.
Without diving too deep into the technical weeds, suffice it to say that by exploiting the discovered vulnerability, attackers can gain access to any data on the device, including user-entered passwords, files, contacts, geolocation data, as well as the hardware sensors like the camera and microphone. In certain scenarios, complete control over the device is possible. Just a few minutes of physical access to the device via a cable connection, and the gadget has been compromised. This creates a risk if you hand your smartphone over to a repair shop, pass it to someone else to set up and install apps on, or just leave it unattended.
The CVE-2026-25262 vulnerability affects the following Qualcomm chip series: MDM9x07, MDM9x45, MDM9x65, MSM8909, MSM8916, MSM8952, and SDX50 — every single version released to date, until the vulnerability is patched by the manufacturer.
These are no obsolete museum pieces. The MDM9207, which we used for the bulk of our research, is integrated into modem modules for the internet of things (IoT), industrial equipment, smart home devices, healthcare monitoring systems, logistics trackers, and banking terminals. The MSM8916 powers many budget smartphones, while the SDX50 is used in automotive control units.
The catch is that the attacker needs physical access to the device to pull this off. In the real world, this translates to:
With just a few minutes of physical access to the device an attacker can plant a backdoor so deep inside that standard research tools won’t even detect it in most cases.
Qualcomm was notified of the discovery in March 2025 and confirmed the vulnerability in its chips. To identify it, the vendor reserved CVE-2026-25262, and on April 20, 2026, Kaspersky ICS CERT published technical information on the vulnerability and recommendations for users.
Qualcomm included this vulnerability in its May security bulletin. While fixing already-made devices is fundamentally impossible, the company promised to make all future chips without this vulnerability.
If you currently own a device with an affected chip, use our recommendations below to help mitigate the risk of infection.
If you notice that your gadget with a vulnerable Qualcomm chip starts acting up — overheating when idle, reporting unexpected spikes in network traffic, or exhibiting strange app behavior — you may have fallen victim to this vulnerability. You can wipe the malicious code and reset your device to its baseline state simply by completely cutting its power. This means either pulling the battery or letting it drain all the way to zero until the gadget shuts down entirely. In this case, the malicious code will most likely not persist on the device — during our research, we were unable to confirm that it could achieve persistence in non-volatile memory.
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The researcher who found it says the vulnerability could have been chained with a prompt injection to exfiltrate data.
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Drupal says attackers may develop an exploit for the vulnerability within hours or days.
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The security defect can be exploited remotely, without authentication, to execute arbitrary code and leak sensitive information.
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The vulnerability, CVE-2026-8153, affects Universal Robots PolyScope 5 and it can be exploited for OS command injection.
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Four vulnerabilities in OpenClaw can be chained together to steal credentials, escape the sandbox, and plant persistent backdoors.
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