AI Red Team Engineering notes, research references, and field notes on AI systems, responsible AI, governance, and compliance.
Global search

Search AI Red Team

Search across the complete knowledge base.

Enter at least two characters to begin.
↑↓ select Enter open Browse the full research archive
Trail of Bits Blog · July 2, 2026

GPT-5.5-Cyber built a zlib fuzzing lab in a day

analysis AI Red TeamingAgent SecurityAdversarial ML
Why it matters

Trail of Bits describes supervising GPT-5.5-Cyber as it built ASan and UBSan variants, derived seed corpora, and wrote fuzz harnesses for roughly a dozen zlib entry points in one day. The useful result is the workflow and its emphasis on reachability and reportability; vulnerability details remain under coordinated disclosure and the speed comparison is the authors' estimate.

My takeaway: For AI-assisted fuzzing, define the vulnerability class and reportability criteria up front, require sanitizer-backed reproduction, test compile-time variants and realistic state transitions, and keep a human maintainer in the disclosure loop. Measure confirmed, reachable findings—not crashes or model-generated severity labels.
Keep exploring

More curated notes connected through AI Red Teaming and Agent Security.

OpenAI News · framework

OpenAI’s Frontier Governance Framework

OpenAI's 22-page Frontier Governance Framework maps its frontier-model processes to California's Transparency in Frontier AI Act and the EU AI Act's general-purpose AI code. It documents lifecycle risk assessment, cyber-offense and other risk tiers, mitigation and residual-risk decisions, critical-incident handling, security risk management, model reporting, external review, responsibility allocation, and change control.

OWASP GenAI Security Project · guide

OWASP Top 10 for Agentic Applications for 2026

OWASP's community guide organizes agentic-system risk into ten categories, including goal hijacking, tool misuse, identity and privilege abuse, memory poisoning, insecure inter-agent communication, cascading failures, and rogue-agent behavior. It provides a shared taxonomy and mitigation starting point rather than a certification checklist or evidence that a deployed system is secure.

OECD.AI Wonk · guide

A five-step roadmap to closing the AI evaluation gap

The roadmap addresses evaluation results that overstate real-world performance or fail to transfer across deployment contexts. Its five steps balance standardized and local tests, evaluate throughout the lifecycle, build qualified assurance and communication capacity, tailor tests to each value-chain actor and technology, and use a coordinated, trusted process for updating methods.