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
Adversa AI Trusted AI Blog · June 3, 2026

The AI risk quadrant for agents: scoring 100 digital workers nobody secured

framework Agent SecurityAI Red TeamingAI Compliance
Why it matters

Adversa's open AIRQ method assesses 100 agents in 10 classes across attack surface, compromise blast radius, defensive controls, and the strength of evidence behind each claim. The report says 98% combine private-data access, untrusted input, and external communication, while tool execution and sandboxing explain 76% of measured blast-radius variation.

My takeaway: Reuse the factor lists for agent inventory, threat modeling, red-team scope, and procurement, but validate the weights and vendor-specific evidence independently. Score both the vendor default and your configured deployment, require source or test evidence for claimed controls, prioritize tool authority and containment, and reassess whenever connectors, permissions, or defaults change.
Keep exploring

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

OpenAI News · framework

Pacing model development in an era of cyber-critical capabilities

OpenAI says preliminary evidence that Astra may meet its Critical cybersecurity threshold led it to pause frontier reinforcement-learning work for two weeks and keep its largest planned run on hold. New safeguards include stronger workload and network isolation, continuous boundary testing, token-level monitoring that escalates suspicious tool activity, and broader alignment checks for deception, reward hacking, and unauthorized access.

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

A blueprint for democratic governance of frontier AI

OpenAI proposes a three-part U.S. frontier-AI governance model: harmonize emerging state safety laws into a federal baseline, strengthen CAISI as an evaluation and standards institution, and coordinate a broader resilience program. Proposed controls include severe-risk evaluations, transparency reports, independent audits, safety-incident reporting, model-weight security, whistleblower protection, and periodic technical assessments.