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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsVerify AI-generated code the way you would any other change: understand the full diff, check it against independently established requirements, run tests that challenge its assumptions, inspect dependencies and executable configuration, and require an accountable human review. A green test suite or an AI security review is useful evidence—not proof that the change is correct or safe.
How do I verify AI-generated code before shipping it?
Use a repeatable review flow, starting with the change itself and ending with a human who can explain and own it. Do not begin and end with the agent’s summary: inspect what changed, including supporting files and configuration that may affect how the code is built or run.
1. Bound the change and inspect the full diff
Write down the intended behavior, the components expected to change, and any trust boundaries involved—for example, where user input, credentials, or external services enter the system. Compare that scope with the actual diff. Review every changed file, not just the main implementation: tests, lockfiles, package scripts, CI workflows, Dockerfiles, deployment settings, and assistant rule files can all alter behavior or risk.
For a routine pull request, a diff-based review focuses on the changes and their effects. A new application or major release may call for a broader baseline review. OWASP describes both approaches in its Secure Code Review Cheat Sheet.
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2. Establish expected behavior independently
Derive what the change should do from the requirements, API contracts, invariants, and security policy—not from the generated implementation or tests. Trace important effects through callers, data handling, error paths, and relevant configuration. If the intended behavior cannot be stated clearly enough to check, the change is not ready for approval.
Are passing tests enough to trust AI-generated code?
No. Passing tests show that the code satisfied the cases those tests exercised; they do not establish that the cases were complete, independent, or aligned with the requirements. OWASP recommends measuring security confidence through adversarial testing and independent analysis rather than treating “all tests pass” as proof.
Run the existing suite, then challenge its assumptions
Run the project’s normal test suite and inspect tests changed or added with the code. Add independent cases where appropriate, especially for:
- Malformed or unexpected input and boundary values.
- Unauthorised access, expired credentials, and other security-relevant states.
- Failure paths, retries, and partial results from dependent services.
- Concurrency or ordering behavior where simultaneous operations matter.
Look closely for deleted tests, assertions weakened to accept more outcomes, mocks that replace the behavior you need to verify, and tests that merely encode the generated implementation’s assumptions. Tests written by the same agent can help, but they are not independent confirmation; check the expected result against the requirement or contract.
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Which automated checks should I run?
Run the project’s tests and linting, then select additional checks that fit the language, architecture, and risk of the change. Common layers include static analysis, dependency vulnerability checks, secret scanning, and—when appropriate—dynamic or security testing. Treat findings as leads to investigate, not a binary guarantee: automated tools can miss business-logic errors and vulnerabilities that depend on the application’s context.
What agent-run validation does—and does not—establish
GitHub’s March 18, 2026 changelog says Copilot coding agent can run 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. GitHub’s June 9, 2026 announcement says changes from third-party coding agents can receive CodeQL analysis, checks of newly introduced dependencies against the GitHub Advisory Database, and secret scanning. It describes those validations as following repository Copilot settings and not requiring a GitHub Advanced Security license. See GitHub’s announcements on Copilot coding-agent validation tools and security validation for third-party coding agents.
Those are product-specific descriptions, not evidence that every repository has every check enabled or that every defect will be detected. Confirm the current configuration and availability for your repository, and inspect the results yourself.
When an AI proposes a security fix
GitHub announced agentic autofix for code-scanning alerts in public preview on July 10, 2026. The described process explores relevant files, proposes a fix, reruns the original CodeQL analysis, iterates, and opens a draft pull request for human review. The announcement says access requires GitHub Code Security or GitHub Advanced Security and a Copilot license with cloud agent enabled; during preview, it uses AI Credits and GitHub Actions minutes. Preview access and billing terms can change. Rerunning the original analysis is useful evidence about that finding, but it does not prove the fix is correct in every context or that it introduced no other issue. Check the announcement for current details.
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How do I check AI-suggested dependencies and executable configuration?
Inspect newly introduced packages and changes to scripts or workflows as code that may execute in your environment—not as harmless implementation details. A plausible-looking package suggestion can still be misspelled, nonexistent, stale, or unrelated to the intended project.
Check each new package
- Verify the exact package name on the registry the project is meant to use, including whether the dependency should come from a public or private registry.
- Check that its source and maintainers make sense for the project and that the selected version has no known advisories.
- Review the lockfile change as well as the manifest; confirm what will actually be installed and whether the change adds or alters transitive dependencies.
- Run the project’s dependency auditing tools and investigate their findings rather than assuming a clean result settles every supply-chain concern.
Inspect what will run automatically
Pay particular attention to package lifecycle scripts and build hooks, GitHub Actions, Dockerfiles, Makefiles, and deployment configuration. Such changes may run automatically or with access to sensitive credentials. Check their commands, permissions, triggers, and side effects; pin third-party GitHub Actions to commit SHAs where applicable. OWASP’s Secure Coding with AI Cheat Sheet recommends scrutinising dependencies and executable configuration rather than relying on an agent’s assurance.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What security risks are specific to coding agents?
An agent may read repository files, issue descriptions, pull-request comments, changelogs, terminal output, fetched web pages, or tool responses while deciding what to do. Treat that material as untrusted: it can contain malicious or misleading instructions. The risk is greater when an agent can edit broadly, access the network or credentials, or run actions in CI. OWASP discusses these agent-specific concerns in its Secure Coding with AI guidance.
Reduce exposure before the agent starts
- Limit the files and permissions available to the agent to what the task needs.
- Restrict network access and credentials where possible; exclude secrets and sensitive directories from model context.
- For higher-risk work, sandbox execution and consider what code, terminal context, or repository content is sent to the model provider.
- Review assistant rule files as security-relevant configuration, not as automatically trustworthy instructions.
Inspect actions as well as output
Check unexpected file access, network use, commands, and other actions taken after the agent processes external content. Review the resulting diff and execution effects even if the agent reports that it followed instructions. Limiting access reduces potential impact; it does not remove the need to review the change.
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Can static analysis or AI code review replace human review?
No. Static analysis and AI review can help identify issues or direct attention, but they cannot take responsibility for deciding whether a change matches the product’s requirements, security policy, and context. OWASP says manual review complements SAST and DAST by focusing on business-logic validation, complex security implementations, and context-specific vulnerabilities.
Make approval an ownership check
The person approving and committing the code should be able to explain its behavior, tests, dependencies, and security implications. If the reviewer cannot do that, ask for clarification or changes rather than approving an agent summary or another AI review. OWASP Top 10:2025 guidance states that developers should be able to read and fully understand code they submit, including AI-written code, and remain responsible for what they commit. See the OWASP Top 10:2025 guidance.
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