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AI can help you spot and understand potential security flaws in code you own or are authorized to review, but its response is a lead to investigate—not proof that a vulnerability exists or that your code is secure. Give it a focused piece of code and enough context, ask for evidence and a traceable exploit path, then verify any finding or fix with code review, tests, and security tools.
Use AI as one part of a security review
A conversational assistant can examine selected code, point out suspicious patterns, explain how they might be abused, and suggest changes. It may also miss vulnerabilities or flag code that is safe in context. GitHub cautions that Copilot Chat can help find some common vulnerabilities but should not be relied on for a comprehensive security analysis (GitHub Docs: Finding existing vulnerabilities in code).
Automated code scanning provides a complementary, more systematic check where supported. GitHub describes code scanning as offering more thorough security assurance than relying on Copilot Chat alone. Neither a conversational review nor a scan replaces human review of the code’s behavior and context.
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Choose the code and provide context
Start with a function, endpoint, recent diff, or another clearly bounded part of the codebase instead of asking an assistant to assess a large repository without direction. Include relevant call sites, data models, framework details, and security assumptions when they affect how the code works. State that you own the code or are authorized to review it.
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Identify where inputs come from and what sensitive operations the code performs. For example, tell the assistant whether a value can come from a user, whether it crosses a trust boundary, and whether the code writes to a database, renders HTML, accesses files, or makes network requests. This context helps you judge whether a suspicious line is actually reachable and exploitable.
Ask for evidence, not just a list of labels
Request the affected input, relevant trust boundary, path from input to risky operation, and assumptions behind the severity assessment. Ask for a minimal proposed fix, potential behavior changes, and explicit uncertainty. Tell the assistant not to call something a vulnerability unless it can explain how the relevant code path could be reached.
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Try a concrete prompt
GitHub’s documented example is: Analyze this code for potential security vulnerabilities and suggest fixes.
A more useful version for a focused review is:
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Review this code for potential security vulnerabilities. I own this code or am authorized to assess it. Identify each concern and cite the relevant lines or operation. For each finding, explain the affected input, trust boundary, and plausible path to exploitation; state your severity rationale and assumptions; and suggest the smallest fix that preserves intended behavior. If you cannot trace a path or lack context, say what is uncertain rather than claiming a vulnerability. Also note what tests or security checks would help verify the finding and the fix.A prompt can guide the review, but it cannot make the assistant’s analysis complete or independently validate its conclusions.
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Review each finding in the actual code path
- Locate the cited code. Confirm that the assistant is discussing the current version and the correct function, call site, or diff.
- Trace the data. Follow the potentially attacker-controlled value from its source to the operation the assistant identifies. Check whether the path is reachable and whether earlier validation, encoding, authorization, or other safeguards change the analysis.
- Test the claim against the design. Use the application’s actual framework, data flow, permissions, and expected behavior to decide whether the proposed exploit makes sense. Ask for relevant files or call sites if repository context is missing.
- Assess the suggested change. Check that the fix addresses the cause rather than merely hiding a symptom, and that it does not break legitimate behavior or introduce another security problem.
For instance, GitHub’s tutorial shows JavaScript inserting a name parameter through innerHTML. Replacing that assignment with textContent avoids interpreting the supplied value as HTML in that example. That is a context-specific illustration, not a rule to replace every rendering operation without checking how the application uses the value.
Verify fixes with tests and security tools
Run functional checks
Add or run tests for the relevant behavior, including cases that exercise the risky input and the expected safe outcome. GitHub’s guidance on reviewing AI-generated code recommends functional checks before relying on generated changes (Review AI-generated code).
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Use complementary scanning and dependency checks
Run the static analysis or code-scanning tools appropriate to your repository and language. Where supported, CodeQL can provide a systematic code-scanning layer. Also review dependency alerts and proposed dependency changes; check that any suggested package exists and assess its maintenance, origin, and license before adopting it. GitHub’s security-validation guidance discusses CodeQL, dependency advisory checks, and secret scanning (Risks and mitigations for GitHub Copilot cloud agent).
Consider secret scanning where appropriate to the repository, alongside dependency review and code scanning. These checks address different risks and complement—not certify—the assistant’s analysis.
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Choose the right kind of AI-assisted review
Tools differ in what code they can see and how their results fit into a development workflow. Compare them on the context they inspect, supported languages and frameworks, whether they explain a traceable path, how you can test or rescan a fix, access requirements, and the amount of human review still needed.
| Approach | What it is suited to | Important qualification |
|---|---|---|
| Conversational review | Interactive questions about a selected snippet or code path, with explanations and suggested fixes. | It may miss issues and is not a comprehensive security analysis. |
| Automated code scanning | Systematic analysis of supported code using a security scanner such as CodeQL. | Coverage depends on tool support and configuration; review findings and verify changes. |
| Workflow-integrated AI review | Security feedback attached to a change or pull-request workflow. | Availability, eligibility, policy controls, and whether findings block a merge vary by feature. |
GitHub announced the /security-review command for in-flight changes in the Copilot app as a public preview on July 14, 2026. Its announcement describes findings and suggested fixes for selected common vulnerability classes (GitHub Changelog). GitHub also announced AI-powered security detections on pull requests in public preview on that date. The announcement says those findings are informational and do not block merges; preview eligibility includes Code Security, policy, CodeQL setup, and Copilot or AI-credit conditions (GitHub Changelog). Check the linked official documentation for current availability and requirements because preview status and eligibility can change. GitHub’s Copilot code-review documentation provides additional workflow context (About GitHub Copilot code review).
Know what kinds of issues may be surfaced
GitHub’s tutorial gives cross-site scripting (XSS), SQL injection, and cross-site request forgery (CSRF) as common vulnerability examples. Its 2026 security-review announcements identify additional targeted areas such as injection, insecure data handling, path traversal, weak cryptography, hardcoded credentials, authentication and CORS failures, server-side request forgery (SSRF), misconfiguration, supply-chain risks, and prompt-injection risks in code that integrates large language models.
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Keep a review record
For changes that matter, retain the prompt, the commit or diff reviewed, findings you accepted or rejected, tests run, and scanner results. That record helps another reviewer understand what was examined and why a proposed change was made; the assistant’s response itself is not evidence that a defect has been fixed.
Quick Recap
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