OpenAI Red Teaming Network
OpenAI is launching an open call for a Red Teaming Network, inviting domain experts to participate in ongoing safety evaluations of its models. The initiative aims to build a structured community of external red teamers who can help identify risks and failure modes across OpenAI's model releases. This represents a formalization of OpenAI's external adversarial testing program beyond one-off pre-release red teaming exercises.
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Advancing Red Teaming with People and AI
OpenAI published a blog post describing advances in their red teaming methodology, combining human red teamers with AI-assisted approaches. The post outlines how AI tools are being integrated into the red teaming pipeline to improve coverage and efficiency of safety evaluations. This represents an evolution in OpenAI's pre-deployment safety testing practices.
Red-Teaming Large Language Models
This Hugging Face blog post introduces red-teaming as a safety evaluation methodology for large language models, explaining how adversarial testing can surface harmful outputs, biases, and failure modes before deployment. It covers techniques for systematically probing LLMs to elicit problematic behaviors and discusses the role of red-teaming in responsible AI development. The post serves as an educational overview aimed at practitioners working on LLM safety.
OpenAI Expands External Safety Testing Ecosystem
OpenAI published a post describing its use of independent experts to evaluate frontier AI systems through third-party testing. The initiative aims to strengthen safety validation, verify safeguards, and increase transparency around capability and risk assessments. The announcement signals a continued push toward external accountability mechanisms for frontier model evaluation.
OpenAI Launches Preparedness Team and Challenge for Catastrophic Risk
OpenAI announced the formation of a dedicated Preparedness team focused on evaluating and mitigating catastrophic risks from highly capable AI systems. The initiative includes a challenge to solicit external input on frontier risk scenarios. This represents a formal organizational commitment to tracking and preparing for severe AI safety risks beyond existing red-teaming efforts.
Introducing the Red-Teaming Resistance Leaderboard
Hugging Face and Haize Labs have launched a Red-Teaming Resistance Leaderboard to systematically benchmark how well AI models resist adversarial prompting and jailbreak attempts. The leaderboard provides a standardized evaluation framework for comparing model robustness against red-teaming attacks. This fills a gap in the evaluation ecosystem where safety and adversarial robustness metrics have been less formalized than capability benchmarks.
Anthropic details red teaming methods and calls for standardized AI testing practices
Anthropic published a detailed overview of red teaming approaches used to test Claude and other AI systems, covering domain-specific expert testing, automated red teaming, multilingual/multicultural testing, and multimodal red teaming. The post documents empirical findings about when each method is appropriate, highlights partnerships with organizations like Thorn, Institute for Strategic Dialogue, and Singapore's IMDA, and closes with policy recommendations for building a standardized AI testing ecosystem. The piece is notable for its operational specificity and its explicit call for industry-wide standards to enable cross-system safety comparisons.
Deep Research System Card
OpenAI has published the system card for its Deep Research capability, detailing pre-release safety work including external red teaming and frontier risk evaluations conducted under the Preparedness Framework. The document outlines identified risk areas and the mitigations implemented before deployment. This is the formal safety disclosure accompanying the Deep Research product launch.
OpenAI and Los Alamos National Laboratory Announce Research Partnership on Biosafety Evaluations
OpenAI and Los Alamos National Laboratory (LANL) have announced a research partnership focused on developing safety evaluations for frontier AI models. The collaboration specifically targets assessing and measuring biological capabilities and risks. LANL brings national-lab-level biosecurity expertise to the effort, which aligns with OpenAI's broader preparedness framework for catastrophic risk domains.