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AI can help generate hypotheses, inspect code, or organize evidence, but it does not grant permission to test a hosted model, cloud service, SaaS application, or dependency. Authorization still comes from the system owner, an authorized intermediary, a contract, or a clearly applicable public policy. The framework below adapts established coordinated vulnerability disclosure (CVD) practice to AI-assisted work while separating settled guidance from emerging proposals.
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What a responsible disclosure policy must decide
A vulnerability disclosure policy (VDP) tells a researcher what good-faith testing the organization permits and how to notify it when something goes wrong. CISA’s federal guidance describes a VDP as authorizing good-faith research and setting expectations for reporters. That makes the policy a boundary around permission, not a general immunity from criminal law, civil claims, contracts, or the terms of a third-party provider.
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For AI-assisted research, the policy has two related but distinct jobs:
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- Control the test. Define the systems, accounts, methods, traffic levels, data handling, and actions that are allowed.
- Control the information flow. Explain how a report is received, protected, assessed, coordinated with vendors or intermediaries, remediated, and eventually communicated.
Decide explicitly whether the policy covers only conventional security vulnerabilities or also AI-system issues such as harmful model behavior, unintended data disclosure, prompt-injection pathways, unsafe tool use, or reliability failures. These categories can require different owners, evidence, severity criteria, and fixes. Treating every surprising model output as a security vulnerability can overwhelm a security queue; excluding consequential model behavior can leave a serious safety issue without a route to action.
The 2024 preprint Coordinated Flaw Disclosure for AI: Beyond Security Vulnerabilities proposes an AI/ML-specific framework. It is useful for designing questions your policy should answer, but it is a proposal, not an adopted standard or proof of any provider’s current rules.
How do I define authorization and scope?
Start with an inventory that a researcher can read without guessing. Name the domains, applications, APIs, model endpoints, versions, environments, and test accounts that are in scope. State exclusions in the same level of detail. If an asset is ambiguous, provide a way to ask before testing it and identify who can give a binding answer.
List assets and boundaries
- Identify production, staging, demonstration, and sandbox systems separately.
- Include model endpoints, plugins, retrieval services, agent tools, mobile applications, and supporting APIs when they are covered.
- State whether a vulnerability in a dependency should be reported to your organization, the dependency owner, or both.
- List excluded domains, customer-owned environments, employee accounts, physical facilities, and systems operated by another organization.
- Give the policy’s effective date and a contact for scope questions so researchers can rely on the current version.
Handle third-party systems explicitly
Use of a vendor’s cloud, hosted model, SaaS platform, data store, or security service does not transfer authorization to the customer. Verify who owns the system and who can authorize testing. Check the provider’s contract, acceptable-use terms, vulnerability disclosure policy, or written permission before sending probes, prompts, payloads, or automated traffic. A customer policy cannot promise protection for conduct that violates a provider’s terms.
Where authorization depends on an intermediary, say whether the researcher should report to your organization, the intermediary, or both. Record the authorization path internally so the triage team can contact the correct party without asking the reporter to repeat sensitive testing.
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Which testing methods should be allowed?
Permit only validation needed to establish a finding, and make the stopping point clear. CISA describes good faith as following the policy, testing to investigate a flaw, disclosing consistently with the policy, and avoiding harm. Turn those principles into operational rules that a researcher can follow during an uncertain result.
Allowed validation
- Use accounts created for testing or accounts for which you have explicit permission.
- Send the smallest number of requests, prompts, files, or transactions needed to reproduce the issue.
- Use non-sensitive test data whenever possible.
- Record timestamps, request identifiers, model or software versions, and the minimum output needed to explain the result.
- Stop once the evidence demonstrates impact; do not continue to explore an exposed system merely to map its contents.
Prohibited or restricted activity
- Denial-of-service, stress testing, traffic floods, or actions likely to degrade availability.
- Persistence, privilege expansion beyond what is needed to prove the flaw, destructive changes, or deletion of data.
- Social engineering, phishing, impersonation, or physical intrusion unless a separate written engagement authorizes it.
- Accessing, copying, or retaining personal, confidential, regulated, or customer data beyond the minimum accidental exposure.
- Testing excluded systems or third-party infrastructure without that owner’s authorization.
- Using an AI tool to send secrets, credentials, personal data, or proprietary material to a model provider unless the data owner has authorized that transfer.
Require an immediate stop when further validation could expose people, increase access to sensitive data, or disrupt service. The policy should tell the researcher to preserve only minimal evidence, avoid changing the system, and report what happened through the stated channel.
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Yes, where the underlying test is authorized and the policy permits the tool. AI assistance does not change the authorization boundary. A researcher may use a model to generate hypotheses, suggest test cases, analyze code they are allowed to possess, compare logs, or help draft a report. The human researcher remains responsible for selecting targets, approving actions, checking results, and complying with the policy.
Write clear AI-use expectations
A practical policy should answer four questions:
- What uses are permitted? State whether AI may assist with hypothesis generation, static analysis, test-case creation, output classification, code review, or report drafting.
- What requires human review? Require a person to verify every material finding, reproduce the behavior where safe, remove unsupported claims, and approve the final report.
- When must assistance be disclosed? Ask the reporter to identify material AI assistance when it affects reproducibility, interpretation, or the provenance of evidence. This is a policy-design recommendation, not a general rule imposed by the reviewed official guidance.
- What data may enter a model? Prohibit sending secrets, personal information, customer content, credentials, unreleased code, or regulated data to an AI provider unless the owner and provider terms authorize it.
Preserve reproducibility
AI-generated results can vary with the model version, system prompt, temperature or equivalent settings, tools, retrieval corpus, and conversation history. Ask reporters to identify the model or service, relevant version or date, material system instructions, tools enabled, and the prompts or inputs needed to reproduce the issue. They should redact secrets and personal data before submission and explain any transformation that could change the result.
For an organization, log the model and tool versions used in internal analysis, restrict sensitive reports to approved environments, and separate exploratory AI assistance from automated actions against production systems. Do not allow an agent to execute generated commands or send generated payloads without a human-controlled approval step.
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What should happen if AI finds sensitive data?
Tell researchers to stop as soon as they have enough evidence to establish exposure. Do not download full records, enumerate additional people, or paste the material into another model for analysis. Capture only a minimal sample, preferably synthetic or redacted, that demonstrates the field, access condition, and scope of the exposure.
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Researcher procedure
- Stop the query, crawl, prompt sequence, or tool action that revealed the data.
- Do not alter, delete, share, or forward the exposed material.
- Record the affected asset, time, account, request or prompt, and a redacted description of what was visible.
- Secure any local evidence, limit access to it, and delete unnecessary copies according to the policy.
- Report immediately through the organization’s security channel and mark the report as sensitive or urgent.
Organization procedure
Route an exposure to the incident or privacy owner as well as the vulnerability triage owner. Preserve relevant logs, restrict access to the report, and determine whether the data belongs to the organization, a customer, or a third party. The disclosure policy should explain that an accidental out-of-scope discovery is still reportable when it occurs during authorized work, while deliberately testing an excluded system is not treated the same way under CISA’s federal guidance. That federal guidance is not a universal legal determination.
How should the reporting channel work?
A policy fails if a researcher cannot deliver a report safely. Provide a monitored security contact or secure form, describe acceptable attachments, and publish an alternate route if the primary channel is unavailable. CISA notes that a web form can structure reports but should not be the sole way an organization receives notice.
Minimum channel requirements
- A security mailbox, encrypted form, or other monitored intake with named ownership inside the organization.
- Instructions for encryption and a warning not to include secrets or unnecessary personal data.
- A subject convention or field for urgent exploitation, active harm, or sensitive-data exposure.
- A backup contact or method for outages, blocked mail, or a compromised primary channel.
- Language and accessibility support appropriate to the researchers you expect to receive.
Report contents
Request the target asset, affected version or endpoint, prerequisites, concise reproduction steps, expected and actual behavior, impact, evidence, timestamps, and the researcher’s preferred contact. Ask whether AI assistance was material to the finding and, if so, for the model or tool context needed to interpret the evidence. Do not require a reporter to provide personal data that is irrelevant to triage.
CISA’s federal FAQ captures the usability goal: “It ought to be easier to report a vulnerability to your agency than it is to tweet about it.” Design the channel so a researcher can submit a useful first report without completing an unnecessarily long form.
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What happens after a report arrives?
A report is the beginning of coordinated work, not the end of the researcher’s responsibility or the organization’s. NIST Special Publication 800-216, published in 2023, provides a federal framework for receiving, assessing, managing, and communicating vulnerability disclosures. Its stated scope is systems under federal control, but its process ideas are useful for other organizations. CERT/SEI’s evolving guide, based on its 2017 special report, describes stakeholder roles and coordination phases.
Recommended intake workflow
- Acknowledge receipt. Confirm that the report reached a monitored team, provide a tracking identifier, and state when the reporter should expect the next update.
- Protect the evidence. Restrict access, preserve the original submission, and move sensitive attachments into an approved case system.
- Validate safely. Reproduce the issue with the least-impact method available. Do not ask the reporter to perform riskier tests just to satisfy an internal checklist.
- Assess severity and scope. Determine affected assets, prerequisites, confidentiality or integrity impact, availability risk, and whether exploitation is active or imminent.
- Assign ownership. Name the engineering, product, privacy, safety, legal, or incident-response owners needed to act.
- Coordinate dependencies. Notify the responsible vendor, cloud provider, model host, or coordinator when the organization cannot remediate the root cause itself.
- Remediate and verify. Develop a fix or mitigation, test it, and confirm that the reported behavior no longer occurs or that residual risk is understood.
- Communicate closure. Tell the researcher what can be shared about the outcome, credit them if requested and appropriate, and document any coordinated public communication.
Use an intermediary when direct coordination is impractical
Some disclosures involve several organizations, a widely used dependency, or a provider that receives reports through a coordinator. CISA’s 2026 joint program guidance describes coordinated vulnerability disclosure program design and possible intermediary involvement. Treat an intermediary as a defined role with authority, confidentiality expectations, escalation paths, and a clear handoff, rather than as an informal forwarding address.
How should disclosure timing be set?
State how the organization and reporter will coordinate public release, but do not promise a universal number of days unless the team can meet it and has defined exceptions. The reviewed guidance establishes coordination and communication principles, not one deadline that fits every vulnerability, provider, or jurisdiction.
Include decision points
- Routine findings: agree on a target for updates and a release plan after remediation or an understood mitigation.
- Delayed remediation: explain how the organization will update the reporter, reassess risk, and discuss a revised release date.
- Active exploitation or imminent harm: permit accelerated escalation, emergency mitigation, and communication with affected parties.
- Multi-party flaws: identify who coordinates the timeline and how disagreements are handled.
- Nonresponsive parties: describe the escalation route and the information that may be disclosed when coordination fails.
Keep the reporter informed even when there is no new technical detail. Silence encourages duplicate reports, premature publication, and unsafe follow-up testing.
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Does a vulnerability disclosure policy protect security researchers?
It can express an organization’s commitment not to pursue certain claims against good-faith research that stays within the policy, but it cannot erase every legal or contractual risk. The reviewed U.S. federal guidance does not resolve an individual’s legal position across countries, providers, target systems, or contracts.
Write a bounded safe-harbor statement
Describe the organization’s intended treatment of research that satisfies the policy’s scope, methods, data-handling, and reporting requirements. Explain exclusions, including deliberate testing of excluded systems, harmful activity, violation of third-party terms, and misuse of data. Say which organization the commitment covers and whether it extends to contractors, subsidiaries, or systems operated by another party.
Avoid language such as “you are legally safe” or “no action can ever be taken.” Tell researchers to obtain written authorization for a concrete engagement and to consider applicable law, jurisdiction, contracts, and provider terms. A private policy is an organizational commitment, not a universal license.
How should AI-specific flaws be triaged?
Keep security vulnerabilities and broader AI-system flaws connected but distinguishable. A model may disclose protected data through an access-control weakness, which belongs in security triage. It may also produce dangerous or discriminatory content without exposing a system or bypassing an authorization control, which may require a safety, policy, or product review.
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- Security issue: identify the asset, authorization boundary, exploit path, affected data or capability, and reproducible impact.
- Model-behavior issue: identify the model or version, relevant instructions or policy context, input conditions, output pattern, severity rationale, and reproducibility across runs.
- Data-exposure issue: identify the access condition and data class while minimizing or redacting the exposed content.
- Tool or agent issue: identify the tool permission, action taken, confirmation boundary, and whether the behavior could affect external systems.
Assign an owner who can make the right decision. Security engineering may own an authorization bypass; privacy may own personal-data exposure; model safety or trust teams may own harmful behavior; product engineering may own a faulty control. One intake channel can serve all of them if routing rules and response times are clear.
Policy models: baseline CVD and an AI-extended program
The following comparison shows what changes when an organization adds AI-assisted research to an established CVD program. The baseline column reflects conventional CVD practice; the AI-extended column adds controls that address model behavior, data exposure, reproducibility, and disclosure of assistance.
| Policy area | Baseline CVD policy | AI-extended policy |
|---|---|---|
| Asset and third-party scope | Domains, services, versions, exclusions, and authorization contacts are named. | Add model endpoints, agent tools, retrieval systems, training or evaluation environments, and the provider responsible for each dependency. |
| Permitted testing | Low-impact validation is allowed; denial-of-service, destructive activity, and unauthorized access are prohibited. | Add limits on automated agents, generated payloads, prompt volume, external tool actions, and submission of sensitive data to AI providers. |
| Evidence | Reproduction steps, affected asset, impact, and minimal supporting data. | Add model or tool version, material prompts or instructions, run conditions, and the human checks used to confirm an AI-generated finding. |
| Report channel | Monitored security contact or secure form with an alternate route. | Add a sensitive-data route, redaction guidance, and routing for security, privacy, safety, and model-behavior reports. |
| Triage and remediation | Acknowledge, assess, assign, coordinate with dependencies, fix, verify, and communicate. | Add owners for harmful outputs, data leakage, agent actions, evaluation failures, and provider-specific mitigations. |
| Disclosure timing | Parties coordinate release and explain delays or urgent exceptions. | Consider whether public examples could reproduce harmful behavior or expose prompts, data, or bypass techniques. |
| Safe-harbor language | Commit to good-faith research within the stated scope and explain limits. | Clarify that AI assistance neither expands authorization nor overrides provider terms, data restrictions, or prohibited methods. |
Implementation checklist for organizations
Before publishing, test the policy as if you were a researcher who has just found an unexpected result.
- Can a reader tell exactly which host, endpoint, model, version, and environment are in scope?
- Is there a named process for ambiguous assets and third-party authorization?
- Are low-impact methods, rate limits, account rules, and stop conditions concrete?
- Does the policy prohibit unnecessary collection of personal or confidential data?
- Does it explain whether AI may assist with analysis, coding, testing, or report drafting?
- Does it require human review without demanding disclosure of irrelevant personal information?
- Does it tell researchers what to do with secrets or sensitive data discovered accidentally?
- Are the security contact, secure-submission method, attachment rules, and backup route monitored?
- Can the organization acknowledge, triage, assign, coordinate, remediate, and communicate, rather than merely receive mail?
- Does the disclosure section address delays, active exploitation, imminent harm, and multi-party coordination?
- Does the safe-harbor language identify its covered organization and its limits?
- Are security vulnerabilities separated from broader model-safety or harmful-output reports where their handling differs?
What researchers should do before testing
- Read the current policy and verify that the exact asset and activity are in scope.
- Confirm authorization for every cloud, hosted model, SaaS service, dependency, or customer-owned system involved.
- Prepare a low-impact test plan with a clear stopping point and non-sensitive test data.
- Configure AI tools so secrets, personal data, and proprietary material cannot be uploaded without authorization.
- Keep enough context to reproduce the result, including versions, prompts, timestamps, and tool settings.
- Stop at the first convincing proof of impact, preserve minimal evidence, and use the stated reporting route.
CISA’s FAQ summarizes the expected posture: “A hallmark of good faith activity is a factual, timely report of a vulnerability on a system authorized for testing, sent directly to the organization in accordance with a VDP; however, a person could conduct research in good faith and have no reportable findings.” In practical terms, careful authorized testing and an honest report matter more than producing a finding at any cost.
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