Verify AI-generated code the way you would any other change: understand the full diff, check it against expected behavior, run tests and security checks, and have a human reviewer approve code they can explain. Passing tests or an AI security review are evidence—not proof that the change is correct or safe.
1. Bound the change before reviewing it
Start with the intended behavior, not the agent’s summary. Identify which components should change, what must remain true, and which trust boundaries the code crosses—for example, authentication, authorization, input validation, or access to sensitive data. Then compare that expected scope with the actual diff.
Review every changed file, including files that do not look like application code. A small source change can arrive with consequential edits to tests, dependency lockfiles, package scripts, CI workflows, Dockerfiles, deployment settings, or assistant rule files. OWASP distinguishes diff-based review for routine changes from baseline review of an application or major release; its Secure Code Review Cheat Sheet explains both approaches.
- Read the complete diff, not only the main implementation file.
- Check whether the changes match the requested scope; investigate unrelated edits.
- Trace important changes into callers, configuration, tests, build steps, and deployment behavior.
- Look for removed files, altered permissions, new network access, and commands that will execute automatically.
2. Establish expected behavior independently
Decide what correct behavior means using requirements, API contracts, invariants, and security policy—not just the implementation or tests generated alongside it. If the expected behavior is ambiguous, resolve that before treating a passing check as meaningful.
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Run the existing test suite and inspect changed tests. Then add or run cases that challenge the implementation’s assumptions. Choose cases that fit the feature, such as malformed input, boundary values, expired credentials, unauthorized access, concurrent requests, and failures from downstream services. Pay particular attention to deleted tests, weakened assertions, mocks that replace the behavior you need to verify, and tests that merely confirm what the generated code already does.
Tests written by the same agent as the implementation can be useful, but they are not independent confirmation. OWASP advises: “Measure security confidence by adversarial testing results and independent analysis, not by "all tests pass."” Its Secure Coding with AI Cheat Sheet discusses risks such as agents making CI green by changing tests rather than fixing the underlying behavior.
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3. Run layered automated checks
Use the checks that fit the project and the risk of the change. A green result means a particular tool found no issue within its coverage and configuration; it does not establish that the code meets every requirement or is safe in context.
| Check | What it can help find | What still needs review |
|---|---|---|
| Project tests | Regressions and incorrect behavior covered by the test suite. | Uncovered cases, incorrect expectations, weakened tests, and behavior outside the test environment. |
| Linting and static analysis | Style violations and some detectable code or security patterns. | Business-logic errors, context-specific flaws, and findings that depend on runtime behavior. |
| Dependency auditing | Known advisories associated with packages and versions in the audit source. | Typosquatting, package legitimacy, unsafe package behavior, and vulnerabilities not represented in the audit data. |
| Secret scanning | Credentials or other sensitive strings matching the scanner’s detection rules. | Secrets it does not recognize, exposed credentials outside the scanned scope, and whether a suspected secret has already been used. |
| Dynamic or security testing | Some issues observable when the application or service runs under selected conditions. | Unexercised paths, missing scenarios, and flaws requiring an understanding of business rules or deployment context. |
Run the project’s normal tests and linting, then add suitable static analysis, dependency checks, and secret scanning. Use dynamic or security testing where the application’s architecture and risk justify it. Investigate findings and their context instead of treating any scanner’s output as a binary guarantee. OWASP’s review guidance describes manual analysis as complementary to automated security testing, particularly for business logic and context-specific vulnerabilities.
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4. Check dependencies and executable configuration
For each new or changed package, verify that the package exists in the intended public or private registry, that its name is spelled correctly, and that its source and maintainers make sense for your project. Check the selected version for known advisories using an appropriate dependency audit. A plausible-sounding suggestion from an AI model is not evidence that a package exists or is trustworthy; models can suggest nonexistent package names or stale versions.
Review files that can execute code or affect what runs in CI and production. That includes package scripts, build hooks, GitHub Actions, Dockerfiles, Makefiles, and deployment configuration. Give extra scrutiny to commands that run on pull requests or with elevated privileges. Where applicable, pin third-party GitHub Actions to commit SHAs rather than relying on a movable reference. OWASP covers these dependency and execution risks in its Secure Coding with AI Cheat Sheet.
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5. Account for agent permissions and untrusted context
Coding agents can be influenced by material they encounter while working. Treat repository issues, pull-request descriptions and comments, READMEs, dependency changelogs, error output, fetched web content, and MCP tool responses as untrusted input—not as trusted instructions. Review assistant rule files as security-relevant configuration, too.
Reduce the possible impact of a mistaken or manipulated action by limiting the agent’s context and permissions to what the task requires. Where possible, restrict network access and credentials, exclude secrets and sensitive directories from model context, and sandbox execution for higher-risk work. Understand what code or terminal context is sent to the provider, and inspect unexpected agent actions after it processes external content. OWASP’s AI secure-coding guidance describes these agent-specific risks and mitigations.
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6. Treat built-in validation as one layer, not approval
Some platforms can run checks on agent-authored changes, but coverage depends on the product and its configuration. GitHub’s March 18, 2026 changelog says Copilot coding agent runs project tests and a linter, as well as CodeQL, GitHub Advisory Database checks, secret scanning, and Copilot code review; administrators can configure which validation tools run. These are product-specific checks, so verify what is enabled in the repository rather than assuming every check ran. See GitHub’s validation-tools announcement.
GitHub’s June 9, 2026 announcement describes security validation for changes from third-party coding agents: CodeQL analysis, checks of newly introduced dependencies against the GitHub Advisory Database, and secret scanning. The announcement says these validations follow repository Copilot settings and do not require a GitHub Advanced Security license. That description does not establish what is available or enabled for every repository; check current configuration and availability before relying on it. See GitHub’s third-party agent validation announcement.
GitHub also announced agentic autofix for code-scanning alerts in public preview on July 10, 2026. The described workflow explores relevant files, proposes a fix, reruns the original CodeQL analysis, iterates, and opens a draft pull request for human review. The announcement lists GitHub Code Security or GitHub Advanced Security and a Copilot license with cloud agent enabled as access requirements; during preview, it uses AI Credits and GitHub Actions minutes. This is an example of rerunning a check after a proposed fix, not proof that the fix is correct in every application context. Preview access and billing terms can change; consult the announcement for its dated details.
7. Require a human reviewer who owns the change
Static analysis, security scanners, test automation, and AI code review can help find issues or prioritize inspection. None can replace a reviewer who understands the change’s behavior and security implications. OWASP notes that manual review complements automated SAST and DAST by focusing on business logic, complex security implementations, and context-specific vulnerabilities in its Secure Code Review Cheat Sheet.
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