Ethics & Responsible Disclosure

Ethical frameworks for AI red teaming, responsible disclosure processes for AI vulnerabilities, when and how to report findings, and navigating bug bounty programs.

Legal compliance sets the floor for acceptable behavior; ethics sets the ceiling. Many activities that are technically legal during an AI red teaming engagement can still be ethically problematic. Professional ethics in AI security research require active consideration of harm, proportionality, and societal impact.

Ethical Frameworks for AI Red Teaming

The Proportionality Principle

Every test should balance the severity of the vulnerability being investigated against the potential harm of the test itself.

The Dual-Use Dilemma

AI red teaming research is inherently dual-use: the same techniques that help defenders also enable attackers. Ethical considerations for publishing and sharing findings:

1. 1

   Assess the attack-defense balance

   Will publishing this technique help defenders more than attackers? If the vulnerability is already being exploited in the wild, disclosure accelerates defense. If it is novel, consider coordinated disclosure.

2. 2

   Evaluate reproducibility

   How difficult is it for a less-skilled attacker to reproduce the finding? If the technique requires significant expertise or resources, publication is lower risk. If it provides a copy-paste attack, consider restricting details.

3. 3

   Consider the affected population

   How many systems and users are affected? A vulnerability in a single deployment is different from one affecting all instances of a widely-used model.

4. 4

   Apply staged disclosure

   Share the vulnerability class and mitigation guidance first. Provide full technical details after patches are available.

Responsible Disclosure for AI Vulnerabilities

AI vulnerabilities differ from traditional software vulnerabilities in important ways that affect the disclosure process.

How AI Vulnerabilities Differ

Disclosure Process

1. 1

   Document the vulnerability

   Record the exact conditions, inputs, outputs, and reproduction steps. Note the success rate (AI vulnerabilities are often probabilistic) and any model-specific conditions.

2. 2

   Identify the disclosure recipient

   Determine who should receive the report: the model provider, the application deployer, or both. For vulnerabilities in the underlying model, the model provider must be notified even if you were engaged by the deployer.

3. 3

   Make initial contact

   Use the provider's security reporting channel (security@, bug bounty platform, or dedicated AI safety reporting). If no channel exists, contact the security team through general channels. Encrypt sensitive details.

4. 4

   Set a disclosure timeline

   Standard practice is 90 days for traditional vulnerabilities. For AI vulnerabilities requiring model retraining, 120-180 days may be more appropriate. Agree on the timeline with the vendor.

5. 5

   Coordinate public disclosure

   Work with the vendor on a joint advisory if possible. Share mitigation guidance first, technical details second. Credit the researcher.

When to Disclose Immediately

Some situations warrant accelerated disclosure:

• Active exploitation of the vulnerability in the wild
• The vulnerability poses an immediate risk to physical safety
• The vendor has been notified but refuses to act after a reasonable period
• The vulnerability affects critical infrastructure or public safety systems

Bug Bounty Programs for AI Systems

Major AI providers have established bug bounty programs, though their scope for AI-specific issues varies significantly.

Current AI Bug Bounty Landscape

What AI Bug Bounties Typically Cover

• Authentication and authorization bypasses in AI APIs
• Data exposure through AI system misconfiguration
• Server-side vulnerabilities in AI infrastructure
• Rate limiting and resource exhaustion issues
• Some model safety issues (varies significantly by provider)

What They Typically Exclude

• Jailbreaks and prompt injection (treated as known limitations)
• Model hallucination and factual errors
• Bias and fairness issues (often handled through separate channels)
• Content policy violations that do not indicate a security flaw

Common Ethical Dilemmas

Dilemma 1: Discovering Unrelated Vulnerabilities

During an authorized engagement, you discover a critical vulnerability in a system outside your scope.

Dilemma 2: Client Wants to Suppress Findings

The client asks you not to include a critical vulnerability in your final report.

The ethical obligation is clear: findings should be accurately reported. If the client does not want to remediate, that is their risk decision, but the report should reflect reality. Include the finding with a risk acceptance note if the client provides written acknowledgment.

Dilemma 3: Vulnerability Affects Other Organizations

A vulnerability in a shared model or platform affects organizations beyond your client.

Coordinate with your client to determine appropriate disclosure. If the vulnerability is in the underlying model, the model provider must be notified. Your engagement contract should include provisions for this scenario (see authorization and contracts).

Dilemma 4: Testing Produces Genuinely Harmful Content

Your testing produces detailed, actionable harmful content (e.g., real synthesis instructions).

Treat such outputs as sensitive data. Do not include verbatim harmful content in reports -- summarize the category and severity. Securely delete the raw outputs after documenting the vulnerability. Report the safety gap to the model provider.

Related Topics

• Legal Frameworks for AI Red Teaming -- the legal requirements that complement ethical obligations
• Authorization, Contracts & Liability -- contractual provisions for disclosure and data handling
• AI Safety Benchmarks & Evaluation -- systematic approaches to evaluating AI safety
• Report Writing -- how to document and present findings professionally

References

• "Coordinated Vulnerability Disclosure Guidelines" - CERT/CC, Carnegie Mellon University (2024) - Standard framework for responsible vulnerability disclosure adapted for AI systems
• "The Menlo Report: Ethical Principles for ICT Research" - Department of Homeland Security (2012) - Foundational ethical principles for information and communications technology research
• "AI Red Teaming: Ethical Considerations" - Partnership on AI (2024) - Ethical guidelines specific to adversarial testing of AI systems
• "Responsible Disclosure for AI Vulnerabilities" - OWASP Foundation (2024) - Community guidelines for disclosing AI-specific security findings