Introducing the OpenAI Safety Bug Bounty Program
OpenAI has launched a Safety Bug Bounty program targeting AI-specific abuse and safety risks. The program focuses on agentic vulnerabilities, prompt injection, and data exfiltration scenarios. This extends traditional security bug bounty models into AI safety territory, incentivizing external researchers to surface novel attack vectors.
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OpenAI Launches Bug Bounty Program
OpenAI announced a formal bug bounty program to crowdsource security vulnerability discovery across its products and services. The initiative is framed as part of OpenAI's broader commitment to building secure and trustworthy AI systems. Researchers who find and responsibly disclose vulnerabilities will be eligible for rewards.
Announcing the OpenAI Safety Fellowship
OpenAI has announced a Safety Fellowship, described as a pilot program aimed at supporting independent safety and alignment research while developing the next generation of AI safety talent. The announcement is sparse on details but signals a structured investment in external safety research capacity. This follows broader industry trends of labs funding independent safety work to build the research ecosystem.
Anthropic expands model safety bug bounty to target universal jailbreaks in CBRN and cybersecurity domains
Anthropic is expanding its HackerOne-partnered bug bounty program to offer up to $15,000 for novel universal jailbreak attacks against a next-generation safety mitigation system not yet publicly deployed. The program specifically targets high-risk domains including CBRN (chemical, biological, radiological, nuclear) and cybersecurity, with participants given early access to test the new safeguards before release. The initiative begins as invite-only and aligns with Anthropic's commitments under the White House Voluntary AI Commitments and G7 Hiroshima Process Code of Conduct.
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.
GPT-5.5 Bio Bug Bounty
OpenAI has launched a red-teaming bug bounty program specifically targeting biosafety risks in GPT-5.5, offering rewards up to $25,000. The program focuses on finding universal jailbreaks that could bypass biological safety guardrails. This represents a structured external adversarial evaluation of a frontier model's safety properties in a high-stakes domain.
OpenAI and Anthropic Share Findings from Joint Safety Evaluation
OpenAI and Anthropic conducted a first-of-its-kind cross-lab safety evaluation, testing each other's frontier models across dimensions including misalignment, instruction following, hallucinations, and jailbreaking resistance. The collaboration represents a novel form of inter-lab safety research cooperation. Findings highlight both progress and ongoing challenges in AI safety, and establish a potential template for future cross-organizational evaluations.
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.
Our approach to AI safety
OpenAI published a high-level overview of its approach to AI safety, framing safe development and deployment as central to its mission. The post appears to be a brief, top-level statement rather than a detailed technical or policy document. It signals OpenAI's public positioning on safety at a time of growing regulatory and public scrutiny.