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Review an AI scanner finding as a hypothesis, not proof of a vulnerability. Before merging, trace the reported data flow through the code, verify that an attacker can reach the risky operation in your deployment, assess the actual impact, and review and test any proposed fix. A clean scan is useful evidence, but it does not replace human review of authorization, business logic, or the full change.
What should I do first when an AI scanner flags a pull request?
Start with the finding in the context of the pull request, then follow its evidence beyond the highlighted line. A flagged line can be risky, harmless, or only one link in a longer path; deciding from the annotation alone can lead to either a missed vulnerability or an unnecessary dismissal.
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- Classify the alert. Record the scanner and finding type, rule or category, affected file and changed lines, severity, and any CWE identifier provided. Note whether the result is a conventional code-scanning alert, an AI-generated finding, or a detector aimed at AI applications. These labels describe how a result was produced; they do not establish whether it is exploitable.
- Open the full finding. Read its explanation and data-flow evidence. Inspect relevant code around the flagged line, including callers, input handling, sanitizers, authorization checks, configuration, tests, and the operation that ultimately receives the data.
- Trace the path. Identify where the data originates, what transforms it, and where it ends up. Check whether the source is attacker-controlled, whether a control actually applies to this path, and whether the destination is reached in the deployed configuration.
- Assess impact and decide what happens next. Fix a valid issue, ask for specialist review when the change crosses a sensitive boundary, or document a defensible false-positive or accepted-risk decision. Then review and test the resulting patch.
For GitHub, its documentation describes AI Scan results as findings shown with an AI indicator in the pull request’s Conversation and Files changed tabs. Treat that indicator as information about the finding’s origin, not as a severity rating or security approval. Product coverage and merge-policy behavior can change, so verify current GitHub documentation and repository settings when relying on them.
How do I tell whether a finding is exploitable?
Determine whether an untrusted actor can cause the reported behavior, what permissions or conditions are required, and what the behavior would expose or change. A scanner’s explanation is a lead for that analysis, not a substitute for it.
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Trace the source, controls, and sink
Follow the complete source-to-sink path. A source might be a request, uploaded file, message, or other input; a sink is the sensitive operation or destination affected by that data. At each step, examine whether validation, encoding, authorization, or another control exists—and whether it is effective for the specific path the alert describes.
- Source: Can a user, external service, or other untrusted actor influence this value? Identify the actor and the route by which the value enters the system.
- Trust boundary: Where does data or control move between users, services, privilege levels, or environments? Ask whether assumptions made before the boundary still hold afterward.
- Authorization: Is access checked for the particular object and action, or does the code merely establish that the user is signed in? Follow the decision to where the protected operation occurs.
- Validation and transformation: Does the code constrain or safely handle the input for its eventual use? A check on a different field or an earlier, unrelated code path may not protect the sink.
- Sink and impact: What can the value cause the application to read, modify, execute, or disclose? Explain the consequence in terms of data or actions, rather than repeating the rule name.
Check reachability in the real deployment
Review callers, routing, feature flags, configuration, and deployment assumptions to see whether the vulnerable path can actually run. Determine what identity and permissions an attacker would need, whether the affected feature is exposed, and whether a compensating control meaningfully prevents the behavior. Do not dismiss an alert merely because a path seems unlikely: identify and document the evidence for that conclusion.
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Then judge whether the scanner’s severity matches the likely impact and prerequisites in this context. A rule label is a starting point; the review should explain what is at risk and under what conditions, without claiming greater certainty than the evidence supports.
What does an AI scanner miss that a human reviewer should check?
Automated scanners can identify known patterns, but they may not understand the application’s business rules or whether a particular user is allowed to perform an action. OWASP distinguishes this pattern-matching role from contextual human review, which can uncover missing authorization and business-logic bypasses.
Use scan results alongside review of the changed code and its security context. Require a security champion or trained reviewer for changes involving authentication, sessions, authorization, cryptography, input parsing, deserialization, file uploads, new public endpoints, third-party integrations, data stores, CORS, or network exposure. These areas can make a small-looking edit consequential because security depends on how the code interacts with the rest of the system.
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Infrastructure and CI changes need a separate look at what the new configuration allows. Check for newly public resources, broader IAM permissions, disabled encryption or logging, unpinned actions or images, secrets placed in workflow variables, and privileged-token execution of untrusted pull-request code. A source-code finding alone may not expose these risks.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchRun SAST, software-composition analysis, secret scanning, and other relevant checks on every pull request, but do not treat a clean report as proof that the change is safe. Scanners can find issues their rules cover; they do not establish that business permissions are correct, that every risky path was detected, or that the application behaves safely in its deployment context.
How should I review AI-generated code or an AI-proposed fix?
Give AI-generated code at least the scrutiny you would give code from an unknown contributor. A human reviewer who understands and can explain the security-relevant change must approve it; an AI review comment is not accountable approval.
Inspect the whole patch, not just the suggested lines
Look for missing authorization or input validation, weak or deprecated cryptography, queries assembled through string concatenation, and unfamiliar or potentially hallucinated dependencies. Check whether the author can explain what the code does and why the chosen control is appropriate. A plausible-looking patch can still introduce a second problem, weaken a nearby safeguard, or fail to address the original data flow.
Pay attention to edits to agent instruction files and hooks. These can alter how tools or agents behave beyond the immediate code path, so review what the changed instructions permit and which workflows they affect. Do not assume that a change is harmless because it is stored in a configuration or guidance file rather than application code.
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If an AI tool proposes a patch, review the complete diff, modify it if needed, and run the project’s normal unit, integration, and security tests. Inspect dependency and configuration changes as well as application code. For Copilot Autofix, GitHub’s documented flow calls for assessing the proposal, testing or modifying it, committing and pushing the change, and confirming that tests pass and the alert closes. Alert closure is evidence that the finding was addressed according to the tool’s workflow; it is not a substitute for reviewing the final patch.
How should I resolve or document a finding?
Choose an outcome supported by the evidence: fix a real vulnerability, obtain the right specialist review, or record why the alert is a false positive or accepted risk. A dismissal without an explanation leaves future reviewers unable to tell whether the path was investigated.
| Outcome | When it fits | What to leave in the review record |
|---|---|---|
| Fix | The path is reachable and the impact is security-relevant, or the change can safely remove the underlying risk. | The relevant change, test evidence, and any necessary follow-up scan or alert status. |
| Request specialist review | The finding involves a sensitive security area, trust boundary, or impact that the current reviewer cannot confidently assess. | The unresolved question and the area that needs an accountable security review. |
| Dismiss as a false positive | The evidence shows that the rule does not describe a real risky path in this context. | The specific code, control, or reachability fact that makes the alert inapplicable. |
| Accept risk with follow-up | The risk is understood but is not being fixed in this change. | The reason, responsible owner, and follow-up issue or action. |
GitHub’s code-scanning workflow supports comments, issue links, dismissal, and reruns after a fix. Use those capabilities to preserve the reasoning and connect the alert to its resolution; the repository still needs a human decision about whether the result is acceptable.
How do I make sure the finding actually affects merge decisions?
Set merge controls in the repository’s actual policy rather than assuming that every AI finding blocks a pull request. GitHub’s AI Scan documentation describes its findings as advisory and says they cannot currently be used directly as ruleset requirements; this product behavior is subject to change, so check the current documentation before designing a gate around it.
Configure code-scanning checks, branch protection, required human reviews, and severity thresholds to match the repository’s risk policy. Decide which checks must pass and which changes require specialist approval, then verify that the rules apply to the branches and pull requests you intend to protect. A visible alert is not itself proof that a required status check or approval is enforced.
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GitHub documents AI Scan as a pull-request detector rather than a full-repository scanner, and says it excludes pull requests from forks and Dependabot. AI applications scanning is a separate detector; its documented public-preview coverage focuses on indirect prompt injection. These distinctions matter when interpreting a quiet result: absence of an alert does not mean every repository file, contribution type, or AI-application risk was scanned.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should I check for in AI-agent changes?
Review an AI agent’s inputs, access, and possible actions as well as the conventional code risks. For AI applications, explicitly consider indirect prompt injection, sensitive-information disclosure, and excessive agency—the possibility that an agent can take actions beyond what its task requires.
Ask what untrusted content the agent can read, whether that content can influence its instructions, which sensitive information is available to it, and what tools or operations it can invoke. Review whether permissions and available actions are limited to what the application needs. The scanner’s coverage of an AI-specific risk may be narrow; a detector focused on indirect prompt injection should not be interpreted as a complete assessment of agent security.
How should I choose or improve a scanner workflow?
Compare tools and workflows against the risks and controls your repository actually needs, rather than choosing by an unexplained alert count or an unsupported accuracy claim. OWASP also recommends using custom rules and review-derived automation to turn recurring findings into durable checks.
- Coverage: Which languages and frameworks are supported? Does the workflow cover pull requests, the full repository, dependencies, secrets, infrastructure as code, or AI-agent risks?
- Evidence: Does the finding show useful data flow and reachability context, or only a matched line and rule name?
- Review workflow: Can reviewers provide feedback, dismiss findings with a reason, link issues, and track fixes or reruns?
- Policy integration: Can the result participate in severity thresholds, required checks, and branch protection, or is it advisory?
- Remediation and accountability: Are suggested fixes reviewable and testable? Can the process identify the human responsible for approving the security-relevant change?
- Maintainability: Can the team add custom rules for recurring problems and keep those rules aligned with application behavior?
- Operational fit: Consider licensing and the effort required to triage, maintain, and act on results.
No universal false-positive rate or cross-tool benchmark is established by the cited guidance, so do not infer that one scanner is more accurate from a broad numerical claim that lacks comparable test conditions. Evaluate evidence quality and the fit with your own code and merge policy.
Frequently Asked Questions
Can an AI scanner prove that my application has no vulnerabilities?
No. A scanner reports what its rules and coverage can detect; it cannot prove the absence of flaws outside that coverage or establish that business permissions are correct. Treat its results as one input to a broader secure development process.
What should an accepted-risk note include?
State the risk being accepted, why it is acceptable for now, who owns the decision, and what follow-up will revisit it. Keep the note connected to the finding or an issue so a later reviewer can see whether the conditions have changed.
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Should I block every pull request that has an AI finding?
Not automatically: findings need triage, and some will be inapplicable or require a documented exception. Define which checks and severity levels block merging, and confirm those controls are actually enforced by the repository.
Why might a pull request show no AI Scan alert even though it contains risky code?
A detector may not cover the code path, language, contribution type, or risk involved. GitHub’s documented AI Scan scope has specific exclusions and is not a full-repository scan, so a quiet result is not a security guarantee.
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