LIVE FEED
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 // CRITICAL Claude Weaponised by State Hackers for Automated Data Theft // LOW Hugging Face security.txt Redirects AI Agents Away From Live Systems //
GENERATIVE-AI

Generative-Ai

2 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
Meta Faces Lawsuit Over Biometric Data Harvesting for AI Training

Meta Faces Lawsuit Over Biometric Data Harvesting for AI Training

ATLAS OWASP HIGH Significant risk · Prioritise patching ▲ 6.5 Wired Security

A proposed class action alleges Meta illegally extracted biometric data from Facebook and Instagram photos to train AI image-generation models (Emu and Muse Image) and to build its unreleased NameTag facial recognition system for smart glasses. The case highlights systemic risks around unconsented biometric data collection embedded in large-scale AI training pipelines, raising serious privacy and data governance concerns. The lawsuit invokes Illinois and California privacy laws, underscoring the growing regulatory pressure on AI vendors over training data provenance.

Cisco AI Agents Vulnerable to Prompt Injection Honeypots

Cisco AI Agents Vulnerable to Prompt Injection Honeypots

ATLAS OWASP MEDIUM Moderate risk · Monitor closely ▲ 7.2 Cisco Talos

Cisco Talos researcher Martin Lee demonstrates how generative AI can be used to rapidly deploy adaptive honeypot systems that deceive and study AI-driven attack agents. The technique exploits a fundamental weakness in AI agents — their lack of situational awareness — causing them to interact with simulated vulnerable systems as if they were real targets. This defensive approach shifts the paradigm from passive detection to active manipulation, giving defenders new insight into automated threat actor methodologies.

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