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
Cloudflare AI Security · September 25, 2026

Turnstile Spin: verify the backend check in agent-generated bot protection

analysis AI EngineeringAgent Security
Why it matters

Cloudflare’s Turnstile Spin guides a coding agent through widget creation, frontend integration and backend Siteverify validation. It also targets existing widgets that serve traffic without server-side validation. The agent proposes changes for approval and edits the user’s codebase; the application backend remains responsible for accepting or rejecting the request. The practical security issue is an incomplete integration: rendering a challenge widget alone does not protect the operation behind it.

My takeaway: Trace each protected action through backend token validation and test that missing, invalid or failed tokens prevent that action. Review the agent’s frontend and backend changes together, and keep the widget secret on the server. Treat widget-creation counts as adoption data, not evidence that a site is protected.
Keep exploring

More curated notes connected through AI Engineering and Agent Security.

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.

OpenAI News · framework

Frontier training safety cases: connect evidence to enforced pause and rollback controls

OpenAI proposes training-run safety cases combining alignment evaluations, containment and monitoring with explicit operational ownership. Concrete measures include immutable transcripts, held-out incident tests, checks for evaluation gaming, response deadlines and fail-closed monitoring. Independent internal challenge, leadership vetoes and tracking downstream uses support stopping a run and reversing affected work. The article describes recommendations still being implemented, rather than audited proof that every safeguard already operates or that residual risk has been eliminated.

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.