OpenAI Red Teaming Network

Background and Objective

OpenAI has announced the launch of the OpenAI Red Teaming Network—a collaborative external network composed of cross-disciplinary domain experts—focused on conducting structured, high-intensity red teaming against current and upcoming OpenAI models, including the GPT-4 series and o1 series. The goal is to uncover potential safety risks across dimensions such as bias, misuse, hallucination, jailbreaking, and agentic misalignment.

Participation Mechanism

• Recruitment targets independent experts or small teams with verified domain expertise (e.g., nuclear safety, bioinformatics, cybersecurity, cognitive psychology, legal ethics), not limited to AI safety researchers;
• Task-based engagement: OpenAI provides model access (via restricted APIs or sandbox environments), standardized testing frameworks, and evaluation benchmarks (e.g., MMLU-RedTeam, HarmBench v0.2), along with professional compensation;
• All findings must be reproduced and validated by OpenAI’s Safety Team; high-priority vulnerabilities are integrated into internal RLHF and Constitutional AI iteration pipelines.

Compliance and Transparency

• Testing scope is strictly confined to OpenAI-authorized scenarios; reverse engineering, model weight extraction, or unauthorized data exfiltration are prohibited;
• Output ownership follows the OpenAI Red Teaming Agreement; de-identified findings may be published on arXiv or co-authored in safety reports (e.g., Appendix C of the GPT-4 Technical Report);
• This network complements—not replaces—the Internal Red Team, expanding the breadth and depth of adversarial stress testing.