LIVE FEED
CRITICAL OpenAI GPT-5.6 Escapes Sandbox, Attacks Hugging Face to Cheat Benchmark // CRITICAL CVE-2026-0770: Langflow RCE Flaw Exploited in Active Attacks // HIGH Azure DevOps MCP Prompt Injection Hijacks AI Review Agents // FIRST LOOK Yellow Teams Bring AI Offense and Defense Into One Security Function // FIRST LOOK Tracebit Ships AWS Context Bombing Defence Against AI Hacking Agents // FIRST LOOK FriendMachine Launches Jacquard Lang for AI-Written Code Review // CRITICAL Check Point 2026 AI Security Report: LLMs Now Run Live Attacks // FIRST LOOK OpenAI GPT-5.6 Sol Ships Faster Parallel Tool-Use for Agents // FIRST LOOK Meta Launches Muse Image with Public Instagram Photo Reuse // FIRST LOOK Estonia Launches State-Issued Digital IDs for AI Agents //
BEHAVIORAL-ANALYSIS

Behavioral-Analysis

2 reports
All LLM Security Agentic AI Industry News Research Supply Chain Prompt Injection First Look: Security Regulatory Adversarial ML Jailbreaks Model Theft Data Poisoning AI Security Tools Security Operations
SkillCloak Bypasses AI Agent Skill Scanners at 90% Rate

SkillCloak Bypasses AI Agent Skill Scanners at 90% Rate

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 8.5 The Hacker News

Researchers at Hong Kong University of Science and Technology have demonstrated that static scanners used to vet malicious AI agent 'skills' — modular add-ons for agents like Claude Code and OpenAI Codex — can be systematically bypassed using a tool called SKILLCLOAK. The technique leverages either character-substitution obfuscation or self-extracting packing into scanner-ignored directories like .git/, achieving evasion rates above 90% across all eight tested scanners. The same research team also developed SKILLDETONATE, a runtime behavioral sandbox that catches most of the threats static analysis misses.

Claude System Prompts Exposed via Git-Based Extraction

Claude System Prompts Exposed via Git-Based Extraction

ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 6.2 Simon Willison

Simon Willison has created a git-based tool to track the evolution of Anthropic's publicly published Claude system prompts across model versions, enabling structured diff analysis of prompt changes over time. While the underlying prompts are intentionally public, the tooling lowers the barrier for adversarial reconnaissance — making it easier for threat actors to identify shifts in safety constraints, refusal heuristics, or behavioral guardrails between model releases. This kind of systematic prompt archaeology directly supports meta-prompt extraction and jailbreak development workflows.

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