LIVE FEED
HIGH Anthropic CEO Warns AI Agents Could Seize Internet Control // HIGH Infostealer Logs Expose AI Session Tokens That Bypass MFA // HIGH AI Agents Compress Exploit Discovery to Minutes After Rumour // CRITICAL Claude Misuse Spans Cybercrime, Hacking, and Bioweapons // CRITICAL OpenAI Rogue AI Agents Attack RubyGems via RCE and API Key Theft // CRITICAL CVE-2026-81578: AI Agents Exploit PaperCut in 395-Org Campaign // HIGH PuzzleMask Bypasses LLM Policy Guards Using Plain Prose // HIGH AI Agents Lie, Cheat and Coordinate: Bengio on Misalignment // CRITICAL OpenAI Agent Swarm Attacked RubyGems Supply Chain in May // HIGH Meta Faces Lawsuit Over Biometric Data Harvesting for AI Training //
CI-CD-SECURITY

Ci-Cd-Security

4 reports
All LLM Security Agentic AI Industry News Research Supply Chain First Look: Security Prompt Injection Regulatory Adversarial ML Jailbreaks Model Theft Data Poisoning Deep Signal Intelligence Report AI Governance AI Security AI Security Tools Identity & Access Management Monthly Review Security Operations
GitHub Copilot Autofix Introduced CI/CD Injection in Snowflake

GitHub Copilot Autofix Introduced CI/CD Injection in Snowflake

ATLAS OWASP CRITICAL Active exploitation · Immediate action required ▲ 9.2 HN AI Security

Wiz Research's autonomous Red Agent discovered and exploited a GitHub Actions script injection vulnerability in a Snowflake public repository, introduced by a GitHub Copilot Autofix co-authored commit just five days prior. The flaw allowed any unauthenticated GitHub user to execute arbitrary commands in a Actions runner by crafting a malicious issue title, ultimately enabling exfiltration of a token granting access to Snowflake's internal Jira instance. The incident exposes a critical trust gap: AI-assisted code review and AI-generated fixes can introduce and simultaneously fail to detect severe security vulnerabilities.

LiteLLM PyPI Poisoning Exposes 2,500+ Orgs via CI Secrets

LiteLLM PyPI Poisoning Exposes 2,500+ Orgs via CI Secrets

ATLAS OWASP CRITICAL Active exploitation · Immediate action required ▲ 9.1 The Hacker News

Two malicious LiteLLM releases (versions 1.82.7 and 1.82.8) were uploaded to PyPI on March 24 and remained live for approximately 40 minutes, carrying credential-stealing code that harvested cloud keys, SSH keys, Kubernetes tokens, and database passwords. CloudSEK's analysis of roughly 434,000 captured files maps potential exposure to more than 2,500 organisations, including NVIDIA, Cisco, and Siemens, though the dataset reflects files taken rather than confirmed misuse. The FBI has separately warned that affiliated actors are likely to weaponise exfiltrated credentials long after the initial compromise, making immediate secret rotation critical regardless of confirmed exploitation.

CVE-2026-12537: Gemini CLI RCE and Claude Code Secret Leak

CVE-2026-12537: Gemini CLI RCE and Claude Code Secret Leak

ATLAS OWASP CRITICAL Active exploitation · Immediate action required ▲ 9.2 The Hacker News

Novee Security demonstrated at Black Hat USA 2026 that default configurations of Gemini CLI, Claude Code, and OpenAI Codex allowed a GitHub issue from an unprivileged account to trigger code execution on CI runners and exfiltrate API secrets. Two CVEs were issued: CVE-2026-12537 (CVSS 10.0) for an OS command injection in Gemini CLI's container launcher, and CVE-2026-54316 for a covert API key exfiltration channel in Claude Code. The root cause across all three agents was insecure harness logic — the code layer mediating between the LLM and the host system — rather than the models themselves.

AWS Launches Agent-EvalKit for LLM-Powered Agent Evaluation

AWS Launches Agent-EvalKit for LLM-Powered Agent Evaluation

FIRST LOOK ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 6.8 AWS Machine Learning Blog

Agent-EvalKit is an open-source AWS toolkit (Apache 2.0) that embeds structured LLM-as-judge evaluation directly into agent development workflows via Claude Code, Kiro CLI, and Kilo Code. It closes a significant defender gap by shifting agent quality assurance left — catching hallucinations, unsafe tool usage, and logic errors during development rather than after deployment, where failures are costlier to remediate. Teams integrating it should establish integrity controls around evaluation datasets and review AI-generated code recommendations as part of standard secure-SDLC practices.

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