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Do not treat an AI-generated vulnerability finding as a confirmed flaw. Treat it as a claim: check that it applies to the code and configuration you actually run, verify the alleged behavior safely in an authorized environment, and establish whether that behavior creates the stated security impact. Then record the evidence and your decision so another reviewer can follow it.
What counts as evidence?
A convincing explanation, code snippet, or severity label is not proof. Look for evidence tied to the affected component and version, such as a reachable code path, relevant configuration, a controlled test result, or runtime behavior that supports the claimed impact. Record the conditions under which the evidence was gathered.
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OWASP’s Vulnerability Disclosure Cheat Sheet says reports should “provide sufficient details to allow the vulnerabilities to be verified and reproduced.” Use that as a practical standard for evaluating a finding: can a reviewer identify what is affected and understand how the claim was checked?
How to validate a finding
- Capture the claim. Preserve the affected component and version, code location, claimed weakness, preconditions, proposed attack path, stated impact, severity rationale, and any suggested exploit or fix. Mark which parts are AI-generated and which are supported by scanner output, source code, tests, or runtime observations.
- Check provenance, scope, and authorization. Confirm that the source material belongs to the project and version under review. Verify that the relevant code is present, reachable, and enabled in the actual configuration. Make sure any testing or probing is authorized. OWASP’s disclosure guidance advises researchers to understand applicable law and provide details that support verification and reproduction.
- Reproduce the behavior safely. Choose the least invasive test that can establish the claim, using a controlled, authorized environment. Save the test case or command, relevant request or input, observed output, and environment details. Do not run a model-suggested exploit against a live system just because it was proposed.
- Trace the security condition. Follow the relevant input or action through the code and controls. Check whether the required preconditions exist and whether authentication, authorization, validation, sandboxing, or other protections change the outcome. A risky-looking pattern is not, by itself, proof of a path to harm.
- Judge validity before severity. First determine whether the vulnerability exists. Then assess its impact and urgency in the environment where it occurs. A high severity label or confident explanation does not establish exploitability.
- Record a triage decision. Confirm and assign the issue, request missing evidence, or document why the finding is unsupported or excepted. Keep the scope and version reviewed, evidence, decision-maker, uncertainty, and a review point in the record.
- Retest after a fix. Check whether the original behavior is gone after remediation, and record what you tested and observed.
How to compare multiple findings
When a queue contains several AI-generated findings, compare them on the evidence and work needed to resolve uncertainty—not on how persuasive their wording sounds. These are practical comparison dimensions, not a published scoring rubric.
#1 Best Overall
| Dimension | What to examine |
|---|---|
| Evidence and provenance | Is the claim supported by source, configuration, test, or runtime evidence, and can you identify where that evidence came from? |
| Reproducibility | Can the behavior be reproduced under the stated version and configuration? |
| Reachability and preconditions | Can an attacker or other relevant actor reach the code path, and what must be true for it to execute? |
| Impact and affected assets | What security consequence is demonstrated, and which assets or users would be affected? |
| Report completeness | Does the report identify the affected component, location, conditions, and steps or evidence needed to verify it? |
| Remaining uncertainty | What is not yet established, and what effort or evidence would resolve it? |
What to do when a finding cannot be reproduced
Failure to reproduce is a result, not automatic proof that the report is wrong. The environment may differ, a precondition may be missing, or the report may not contain enough detail. Record what you tested, the version and configuration, and the observed result. If evidence remains incomplete, mark the finding unconfirmed or request clarification rather than calling it a false positive.
OWASP’s disclosure guidance notes that reports may include “a large number of junk or false positives.” That makes careful triage important, but it does not justify dismissing a specific claim without checking it.
How to document a false positive or exception
Use “false positive” only when investigation supports that decision. Preserve the basis for rejecting the claim, the scope and version reviewed, who made the decision, and what new evidence would trigger another review. OWASP’s Vulnerability Management Guide recommends obtaining evidence from the source, documenting false-positive submissions, and setting a timeframe for reevaluation.
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Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What AI changes—and what it does not
AI can help generate hypotheses or summarize tool output, but fluent, confident language is not independent evidence. OWASP’s GenAI Security Project identifies overreliance as trusting erroneous LLM output without oversight or confirmation, and recommends oversight and continuous validation. Keep the underlying evidence visible in the triage record, along with the human decision.
There is no universal AI-specific acceptance threshold or accuracy figure established for findings across different models, scanners, codebases, and configurations. NIST SP 800-216 offers process guidance for federal vulnerability disclosure handling; it is not an AI finding acceptance test. Its scope is systems under federal control. See NIST SP 800-216: Recommendations for Federal Vulnerability Disclosure Guidelines.
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