Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsBefore merging AI-generated backend code, reconstruct the intended behavior, run the project’s normal checks, trace the change through the service, and independently scrutinize security-sensitive paths. Then fix root causes, rerun the relevant checks, and require an accountable human to approve the change. Passing tests, a scanner result, or an AI explanation is evidence to evaluate—not a transfer of responsibility.
1. Reconstruct what the change is supposed to do
Start with the issue or requirement, not the code’s comments or the assistant’s explanation. Read the relevant API contract, surrounding implementation, and architecture notes. Identify the expected inputs, outputs, side effects, and failure behavior, then compare those expectations with the pull request. GitHub’s guidance for reviewing AI-generated code likewise starts with functional checks and understanding the code’s context.
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- Does the diff solve the requested problem, rather than a nearby or broader one?
- Does it preserve existing API and data contracts?
- Does it follow local patterns for validation, errors, persistence, and observability?
- Are unrelated edits or new dependencies necessary to the change?
If the intended behavior is unclear, resolve that ambiguity before judging whether the implementation is correct.
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2. Establish a baseline with the project’s ordinary checks
Build or compile the service, run the existing unit and integration tests, inspect new warnings, and run the project’s static analysis. Compare failures with the baseline so pre-existing problems are not mistaken for regressions. These checks can expose functional and basic quality issues; they do not establish that the code is secure.
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- Run the repository’s documented build and test commands from the same branch and configuration used by the team.
- Review failures and warnings, including changes in test behavior that may have been obscured by generated test edits.
- Run the configured static analysis and inspect findings in the context of the changed code.
GitHub’s review guidance treats functional verification as an important early step, while still requiring review of the code’s context and behavior.
3. Trace the diff through the backend service
Review the request path end to end, rather than treating each changed function in isolation. Follow data from parsing and validation through authorization, business logic, storage, and response handling. Check that the change fits the service’s established contracts at each boundary.
- Input and validation: Are types, formats, lengths, and allowed values checked before use?
- Authorization: Is access checked for the specific resource and operation, not merely inferred from authentication?
- Persistence: Do transaction boundaries preserve consistency? Can retries or concurrent requests cause duplicate or lost work?
- Errors and responses: Are failures handled consistently without leaking internal details or turning errors into success?
- External calls: Are timeouts, partial failures, retries, and response validation handled according to local practice?
- Logging: Does the code avoid recording credentials, tokens, or sensitive payloads?
These are practical review questions, not a substitute for understanding the service’s own design. A plausible implementation can still violate an invariant that only becomes visible when you follow the full request or data flow.
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Give authentication, authorization, input validation, cryptography, and deserialization extra scrutiny. OWASP’s Secure Coding with AI Cheat Sheet warns against relying on tests produced by the same agent that generated the implementation. Such tests may encode the same mistaken assumption or miss adversarial cases.
Write or independently verify tests that exercise both expected behavior and rejection paths. Choose cases relevant to the changed endpoint or component, such as:
- Unauthenticated requests and users lacking permission for the particular resource.
- Expired or otherwise invalid tokens when token handling changes.
- Malformed, oversized, or unexpected payloads, including deserialization failures.
- Boundary values and invalid inputs around length, range, format, and optional fields.
- Concurrent requests or retries where the change touches shared state or transactions.
Tests demonstrate only the cases they actually assert. A green suite is not proof that untested conditions are safe.
5. Run security and dependency checks as normal pull-request gates
Code origin should not determine whether a pull request receives security checks. Run the team’s established static application security testing (SAST), software composition analysis (SCA), and secret scanning. OWASP’s AISVS guidance for AI-assisted secure coding also identifies dynamic testing and infrastructure-as-code scanning as relevant controls. Use the team’s severity thresholds and escalation rules rather than treating any one scanner as a verdict.
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If the change adds a package, verify that it exists, is the intended project and version, and is appropriate for the service. Check its dependency risks through the team’s normal process. Review security findings against the actual code and deployment context; a clean scan does not prove that business logic is correct.
6. Keep the coding agent’s access within deliberate boundaries
Security review includes how the code was produced. OWASP cautions that untrusted repository content can steer an agent. Issue text, README files, dependency notes, and repository instruction files should therefore be treated as inputs that may influence its output—not as inherently safe instructions.
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Consider what repository context the assistant receives, particularly secrets and sensitive code. For agents with shell, network, or CI access, constrain permissions and credentials, and require approval for consequential actions. OWASP’s DevSecOps guidance for IDE and AI-assisted development discusses scanner and dependency guardrails, agent controls, and triage. The relevant control is the access actually granted in your environment, not the label attached to the tool.
7. Fix the cause, then verify the change
When a test or scanner finds a problem, first determine what failed and why. Make the smallest correction that restores the intended behavior and security property. Do not accept a generated patch merely because it quiets a scanner or sounds convincing.
- Reproduce the failure and identify the affected code path or violated invariant.
- Assess whether the finding is valid in the service’s context, and record the reasoning for any disposition required by team policy.
- Make a focused fix; add a regression test where it will reliably catch the same failure.
- Rerun the relevant tests, static analysis, and security checks against the updated diff.
- Request independent human review for sensitive paths before merge.
OWASP notes that AI-assisted triage can help investigate findings, but engineers should understand suggested fixes before applying them. Its Secure Coding with AI Cheat Sheet puts the relationship plainly: “Treat AI as a tool, not a colleague.”
Best Value
What the checks can—and cannot—establish
| Control | What it helps assess | What it does not replace |
|---|---|---|
| Unit and integration tests | Behavior exercised by the cases the tests actually run. | Independent tests for omitted or adversarial cases; review of untested behavior. |
| SAST | Potential weaknesses identifiable from source-code analysis. | Dependency, runtime, configuration, or business-logic review. |
| SCA | Risks associated with software components and dependencies. | Verification that a new package is necessary and suitable for this service. |
| Secret scanning | Potential exposed secrets detected by the configured checks. | Review of access boundaries and sensitive data handling. |
| Dynamic testing and infrastructure-as-code scanning | Runtime paths and infrastructure configuration within their coverage. | Independent human assessment of security-critical business logic. |
| Human review | Whether the change meets the requirement and fits service context, contracts, and risk. | Automated tests and scans that provide complementary evidence. |
NIST’s SP 800-218A announcement describes an AI-focused community profile that augments the Secure Software Development Framework (SSDF) in SP 800-218. NIST released SP 800-218A on July 26, 2024; the page records an update on June 25, 2025. It is guidance for development practices, not a substitute for the service’s pull-request review and security controls.
Before merging: a practical checklist
- The requirement and expected behavior are clear, and the diff is limited to the intended change.
- The service builds; existing tests and static analysis have been run and their results understood.
- The changed request and data paths fit the architecture and preserve contracts, error handling, and state invariants.
- Security-sensitive behavior has independent tests, including relevant negative and boundary cases.
- Configured SAST, SCA, and secret checks have run; any added dependency has been verified.
- Agent context and permissions were appropriate for the repository and task.
- Findings were fixed or dispositioned based on their root cause, and relevant checks were rerun.
- A responsible human reviewer has approved the final diff.
An AI agent is not the human reviewer. The engineer or reviewer accountable for the change owns the merge decision, regardless of whether the code, tests, or proposed fixes came from an assistant.
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