OpenAI has expanded its bug-hunting initiatives with the launch of a dedicated Safety Bug Bounty program, designed specifically to capture risks that fall outside the lines of traditional cybersecurity vulnerabilities. While their existing security program focuses on infrastructure flaws, this new effort invites researchers to stress-test AI-specific failure modes, such as malicious prompt injection and account integrity manipulation.

The initiative places a heavy emphasis on agentic risks, which involve autonomous AI systems (agents) being tricked into performing harmful actions or leaking sensitive user data. A key area of focus is the Model Context Protocol (MCP), a standard used to connect AI models with external data sources. By incentivizing the discovery of flaws where an agent might reliably hijack a user’s browser or chat interface, OpenAI aims to shore up its defenses as AI becomes more integrated into daily digital workflows.

Interestingly, the program draws a clear distinction between linguistic jailbreaks and actionable safety threats. Purely creative bypasses—like making a model use rude language or bypass simple filters—are deemed out of scope unless they lead to tangible, reproducible harm. This refined focus signals a shift toward prioritizing functional safety over surface-level content filtering, encouraging the ethical hacking community to hunt for structural flaws in how AI agents interact with the world.

OpenAI has expanded its bug-hunting initiatives with the launch of a dedicated Safety Bug Bounty program, designed specifically to capture risks that fall outside the lines of traditional cybersecurity vulnerabilities. While their existing security program focuses on infrastructure flaws, this new effort invites researchers to stress-test AI-specific failure modes, such as malicious prompt injection and account integrity manipulation.

The initiative places a heavy emphasis on agentic risks, which involve autonomous AI systems (agents) being tricked into performing harmful actions or leaking sensitive user data. A key area of focus is the Model Context Protocol (MCP), a standard used to connect AI models with external data sources. By incentivizing the discovery of flaws where an agent might reliably hijack a user’s browser or chat interface, OpenAI aims to shore up its defenses as AI becomes more integrated into daily digital workflows.

Interestingly, the program draws a clear distinction between linguistic jailbreaks and actionable safety threats. Purely creative bypasses—like making a model use rude language or bypass simple filters—are deemed out of scope unless they lead to tangible, reproducible harm. This refined focus signals a shift toward prioritizing functional safety over surface-level content filtering, encouraging the ethical hacking community to hunt for structural flaws in how AI agents interact with the world.