OpenAI has launched its first public Safety Bug Bounty program — and it’s squarely focused on the attack surfaces that matter most for agentic AI: prompt injection, MCP-based hijacks, data exfiltration from ChatGPT Agent, and platform integrity flaws. Top reward: $100,000 for critical safety vulnerabilities.
This isn’t a standard security bounty. It’s specifically designed to capture the class of AI-native risks that traditional vulnerability disclosure programs aren’t built for — the kind of things that don’t show up in CVE databases but can cause real harm at scale when AI agents are acting in the world.
What’s In Scope
According to OpenAI’s program documentation and coverage from HelpNetSecurity, the Safety Bug Bounty focuses on three primary risk categories:
Agentic risks — Cases where attacker-controlled text can hijack an agent, causing it to perform harmful actions or expose sensitive user information. This explicitly includes browser-based agents and ChatGPT Agent. The behavior must be reproducible at least 50% of the time to qualify. MCP (Model Context Protocol) risk testing is included, but researchers must comply with third-party terms of service when testing.
OpenAI proprietary information risks — Vulnerabilities where model outputs reveal internal reasoning, system prompts, or other confidential information. This is a category that has generated significant research interest as models have become more capable.
Account and platform integrity risks — Weaknesses in systems that enforce rules and protect accounts. This includes bypassing anti-automation measures, manipulating trust signals, or evading restrictions like suspensions and bans.
Notably, jailbreaks are explicitly out of scope for this program. OpenAI says they run separate private campaigns for specific harm categories (like biorisk) and invite researchers to apply when those arise.
Why This Is Significant for the Agentic AI Community
The agentic security problem is real, documented, and growing. As AI agents gain the ability to browse the web, execute code, send emails, manage files, and call external APIs, the attack surface for prompt injection expands dramatically. A malicious instruction embedded in a webpage can now hijack an agent into doing things the user never intended.
OpenAI putting a $100K price tag on critical agentic vulnerabilities does two things: it creates economic incentives for security researchers to focus on AI-specific attack patterns, and it signals that OpenAI itself is treating agentic risk as a first-class security problem rather than an edge case.
For organizations building on top of OpenAI’s platform, this is also a transparency signal. A public bug bounty means vulnerabilities get disclosed in a structured way rather than going directly to press or exploit markets.
The MCP Angle
The explicit inclusion of MCP (Model Context Protocol) risk is particularly notable. MCP has become the dominant standard for connecting AI agents to external tools and data sources, but it also creates new attack vectors: a malicious MCP server could inject instructions that cause a connected agent to exfiltrate data, perform unauthorized actions, or escalate privileges.
This is an area where the research community has been ahead of the vendors — papers documenting MCP injection attacks predate any formal bug bounty program. OpenAI formalizing MCP scope in their safety bounty is a meaningful step toward industry-wide security norms for the agentic protocol layer.
Reward Structure
While the full tiered reward structure wasn’t available in early coverage, the program sets a $100,000 ceiling for critical safety vulnerabilities. Given that traditional security bug bounties often top out at $20,000-$50,000 for critical flaws, the $100K ceiling signals that OpenAI is treating top-tier agentic safety issues as genuinely high-priority.
What This Means for AI Security Researchers
If you’re working in AI security and haven’t looked at agentic attack surfaces yet, this is the moment to start. The combination of real money, explicit MCP scope, and a major platform’s backing creates favorable conditions for high-quality research.
OpenAI’s program also complements existing efforts: Google, Anthropic, and others have security disclosure programs, but none have been this explicit about agentic-specific risk categories. Expect others to follow.
Sources
- HelpNetSecurity — Make OpenAI’s models misbehave and earn a reward
- ITBrief Asia, CybersecurityNews, Moneycontrol, Digital Watch Observatory — corroborating coverage
- Cryptika — additional security press coverage
Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260326-2000
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