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
METR · August 13, 2025

Passing repository tests did not establish that AI patches were mergeable

analysis Model EvaluationAI Engineering
Why it matters

METR’s August 2025 follow-up contrasts algorithmic scoring with human review of repository work. It evaluated 18 tasks drawn from two projects using Claude 3.7 Sonnet and a basic agent scaffold. Automated scoring credited some solutions, while none of the 15 manually assessed submissions met the study’s holistic mergeability standard. Missing documentation, inadequate tests and other maintenance requirements help explain the gap. The selected tasks, limited elicitation and small sample prevent a general estimate of coding-agent usefulness; the manual assessment also was not a direct measurement of maintainers’ repair time.

My takeaway: Define acceptance criteria beyond passing tests, including documentation, maintainability and regression coverage. Sample completed patches for independent review and measure repair effort separately from benchmark success.
Keep exploring

More curated notes connected through Model Evaluation and AI Engineering.

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.

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.

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.