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
Wiz AI Security · April 30, 2026

The (In)security Landscape of AI-Powered GitHub Actions (Part 2/2)

analysis Agent SecurityAI Red TeamingAI Compliance
Why it matters

Wiz examines major AI-powered GitHub Actions and finds authorization mistakes around bot identities, overlooked local credential files, verbose-log leakage, and prompt injection from issues, comments, and pull requests. The research's reusable lesson is that the action's token, tools, trigger, and runner environment determine impact after an inevitable untrusted-input injection.

My takeaway: Authorize automation with stable actor IDs and repository permissions, minimize workflow-token scopes, and never combine pull_request_target privileges with an untrusted prompt. Separate authentication from the AI job, disable sensitive verbose logging, isolate runners, and test every issue, comment, diff, and file as attacker-controlled input.
Keep exploring

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

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.