Build in Public, Test in Private - on the Genbounty AI Bug Bounty Platform

AI is cumbersome to test, LLM applications can accept any input and produce any output, each response id probabilistic and the number of jailbreaks beyond the scope of internal testing teams.

Security leaders know they need external real world LLM testing to stress test their models. However, they face a massive dilemma. Opening a public bug bounty for an experimental AI feature invites immense PR risk and exposes unpolished logic to the entire internet.

Companies need the adversarial coverage of a bug bounty combined with the confidentiality of a closed-door audit.

A Stealth Approach to AI Security

To solve this, testing must be moved behind closed doors. This is exactly why we built Genbounty.

Genbounty operates as a strictly private, invite-only platform. Your AI programs remain entirely invisible to the public. You define your threat model, and we privately invite only the highest-ranked, fully vetted hacker squads to attack your specific challenge under strict NDAs. You control the scope, access, timing, and budget from day one.

The Flaw in Public AI Bug Bounties

Enterprises are rightly paranoid about consumer AI safety. Exposing an AI agent to thousands of random internet users often results in an overwhelming amount of low-quality noise. Security teams spend hours triaging duplicate reports of basic jailbreaks instead of fixing structural vulnerabilities. Furthermore, testing in public means any critical failure or leaked system prompt becomes public knowledge instantly.

Why Squads Outperform Solo Operators

Breaking complex AI integrations requires a highly multidisciplinary approach. Solo operators are incredibly talented, but modern AI systems demand combined expertise.

• Diverse Skill Sets: A successful exploit often requires the combined efforts of a creative prompt engineer, a Python automation specialist, and a logic-flaw analyst.
• Chained Exploits: Squads excel at finding multi-stage vulnerabilities. They provide cohesive reports that show exactly how several minor flaws can be chained together to compromise an AI agent.
• Faster Iteration: Teams collaborate to bypass defences much faster than isolated testers.

Securing the Entire AI Attack Surface

Modern AI applications are complex ecosystems that extend far beyond a simple chat interface. Vetted squads are equipped to test the full stack, including:

• LLM Vulnerabilities: Advanced jailbreaks, prompt injections, and training data extraction.
• Agent Tool Abuse: Unsafe autonomous actions, API manipulation, and logic bypasses.
• RAG Data Leakage: Unauthorized access to sensitive internal knowledge bases and document stores.
• MCP Integration Flaws: Exploits targeting third-party plugin connections and data pipelines.

Actionable Evidence for Risk Defence

When an invited squad successfully identifies a vulnerability, your engineering team receives a triaged report containing the severity, business impact, clear reproduction steps, and immediate mitigation guidance.

This documentation serves as critical evidence. It allows security, legal, and leadership teams to support launch readiness, complete customer security questionnaires, and make informed internal risk decisions.

Ready to Stress Test Your AI?

Stop guessing how your models will behave in the wild. If you are preparing to launch a new LLM integration or autonomous agent, you need to ensure it is secure before real users interact with it.

Explore Genbounty's Private AI Bug Bounty Programs and request a private consultation to secure and control your AI.