A bug bounty program is more than just an open invitation to “hack us.” It is a structured, strategic initiative designed to crowdsource vulnerability discovery. For AI systems, where the attack surface is novel and constantly evolving, a well-defined program structure is not just beneficial—it’s essential for managing risk, fostering a positive relationship with the security research community, and systematically improving your model’s resilience.
Core Structural Components
A successful AI bug bounty program is built upon several interconnected pillars. Neglecting any one of these can lead to confusion for researchers, overwhelming internal teams, or failing to attract the right talent. Think of these components as the architectural blueprint for your program.
1. Scope Definition: The Boundaries of Engagement
The scope is your most critical document. It tells researchers precisely what they are permitted to test and what types of vulnerabilities you are interested in. For AI programs, this requires more nuance than traditional web application bounties.
- In-Scope Assets: Clearly list API endpoints, model versions, web interfaces, or specific applications hosting the AI. For example:
api.yourai.com/v2/generate. - Out-of-Scope Assets: Be explicit about what is forbidden. This often includes corporate infrastructure, third-party services, or older, unsupported model versions.
- AI-Specific Vulnerabilities: Define the classes of bugs you are seeking. This is your chance to guide research towards your biggest concerns, such as:
- Prompt Injections (Direct and Indirect)
- Model Evasion / Security Policy Bypasses
- Training Data Extraction
- Denial of Service (e.g., resource exhaustion via specific queries)
- Harmful Content Generation (Jailbreaks)
- Resource Limits: Specify any rate limits or query caps. This prevents abuse and ensures fair access for all researchers. E.g., “Maximum 100 queries per hour per researcher.”
2. Policy and Rules of Engagement
Your policy is the legal and ethical framework of the program. It protects both you and the researchers. It must be clear, fair, and easy to understand.
- Safe Harbor Statement: A crucial clause that states you will not pursue legal action against researchers who adhere to the policy in good faith.
- Disclosure Policy: Outlines the process and timeline for public disclosure. Will you allow it? If so, after how many days post-patch?
- Confidentiality: Specifies how researchers must handle any sensitive data they might encounter during testing.
- Prohibited Actions: Explicitly forbid actions like social engineering, phishing, physical attacks, or any testing that could degrade service for other users.
3. Submission and Triage Process
This is the operational heart of your program. A chaotic submission process frustrates researchers and burns out your internal team. A structured workflow is key.
- Submission Platform: Where will researchers submit reports? (e.g., a dedicated email, a form, or a third-party platform).
- Required Information: Define what constitutes a valid report. For AI, this should include the full prompt, model parameters (temperature, etc.), model response, and an explanation of the security impact.
- Triage Workflow: A clear internal process for receiving, validating, prioritizing, and assigning reports to the correct engineering teams.
- Communication SLAs: Set expectations for response times (e.g., “First response within 48 hours,” “Triage decision within 5 business days”).
4. Rewards and Recognition
While covered in detail in the next chapter, the reward structure is a fundamental part of your program’s architecture. It directly influences researcher motivation and the types of vulnerabilities you receive.
- Monetary Rewards: Tiered payouts based on severity.
- Non-Monetary Rewards: Public recognition (Hall of Fame), exclusive access to beta models, or branded merchandise (“swag”).
Choosing Your Program Model
How you run your program is as important as its policies. There are three primary models, each with distinct advantages and disadvantages. The right choice depends on your organization’s maturity, resources, and goals.
| Factor | Self-Hosted Program | Platform-Managed Program |
|---|---|---|
| Control | Full control over policies, branding, and researcher communication. | Operate within the platform’s framework. Less control over some processes. |
| Researcher Pool | You must build your own community and attract researchers. Can be slow initially. | Instant access to a large, diverse, and pre-vetted pool of global security researchers. |
| Overhead | High. Requires dedicated staff for triage, payments, and program management. | Lower internal overhead. The platform handles payments, initial triage, and disputes. |
| Cost Structure | Direct costs are limited to bounties and staff salaries. No platform fees. | Platform fees (often a percentage of bounties paid) in addition to the bounty rewards. |
| Best For |
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A Hybrid Model is also common, where an organization might run a private, self-hosted program for highly sensitive models while using a public platform for its general-purpose APIs. This approach balances control with broad reach.
Ultimately, the structure you choose should serve the program’s primary goal: to systematically reduce the security risk of your AI systems by collaborating with the global security community.