The Tool Desk
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Start by understanding the change
For an ordinary pull request, begin with a diff-based review: identify the files that changed, why they changed, which components they affect, and what existing security controls may be touched. OWASP’s Secure Code Review Cheat Sheet distinguishes this focused approach from a baseline review of an entire codebase.
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Before evaluating implementation details, establish the intended behavior. Ask what the code is supposed to do, which inputs it accepts, what data it reads or changes, and what should happen when an operation fails. Trace the change into its callers and surrounding controls rather than treating the diff as an isolated patch. A reviewer should be able to explain what the change does; OWASP advises that code its author cannot explain is not ready to merge.
Check behavior and security in context
Tests and scanners can direct attention, but they cannot determine whether a change is correct for the product’s rules. Manual review is especially important for business logic and context-specific vulnerabilities that automated tools may not recognize.
#1 Best Overall
- Authentication and authorization: Verify that every relevant path checks who is acting and whether that person may access or modify the resource. Look for missing checks, including in new endpoints and alternate paths.
- Input and data flow: Confirm that untrusted inputs are validated for their intended use and that sensitive data is handled appropriately as it moves through the code.
- Queries and cryptography: Inspect how queries are constructed; string-built queries can create injection risks. Check that cryptographic choices are appropriate and not weak or deprecated.
- Errors and edge cases: Examine failure handling and boundary conditions, not only the happy path. Confirm that errors do not bypass controls or expose information they should not.
- Explainability: Ask the author to account for non-obvious logic and design choices. If no responsible person can explain the change, pause the merge.
These checks apply to AI-generated and human-written code alike. The reason to inspect a particular area is its security impact, not the identity of the code’s author.
Verify new dependencies before installing them
For each added package, confirm that it exists, is the intended project, and is appropriate for the application. Do not assume a package name suggested in generated code is genuine: OWASP warns that an attacker may register a hallucinated package name. Review the dependency change and its implications before running an installation command or accepting it into the project.
Give build and pipeline changes extra scrutiny
Changes outside application logic can execute automatically or with elevated permissions. Review package scripts, CI workflows, Dockerfiles, build configuration, and deployment files for what will run, when it will run, and which permissions it receives. In particular, look for newly added network access or shell execution, privileged triggers, widened permissions, and third-party actions that are not pinned.
Also inspect modifications to agent instruction files and hooks. Those files can influence later generated work, so an apparently small change may alter future development behavior. If an AI tool is used during coding, consider what context it receives: credentials, personal data, or proprietary code may be sensitive. Review the tool’s settings and exclusions for sensitive files.
Rank #3
Use automated checks as separate signals
Run the project’s normal pull-request checks regardless of whether a person or an AI produced the code. OWASP recommends using static application security testing (SAST), software composition analysis (SCA) for dependencies, and secret scanning on every pull request. Tests check specified behavior; scanners flag patterns, dependencies, or credentials. None substitutes for the others or for contextual review.
A passing test suite is not proof of security. Tests can encode the wrong expectation, and a pass rate alone does not establish confidence in a change. Likewise, a clean scan is the beginning of review: automated tools may miss broken access control and business-logic flaws. OWASP describes manual secure review as complementary to automated testing.
Rank #4
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AI-assisted findings and fixes are candidates to evaluate, not decisions to accept automatically. GitHub documents AI-assisted capabilities for CodeQL alert fixes, generic secret detection, custom secret-pattern generation, and code-quality analysis. Its documentation says users should review suggestions, verify they match expectations, and run CI tests after applying fixes. Those are product capabilities as described by GitHub, not independent measurements of how reliably they find or fix defects. Inspect the proposed diff and verify its behavior before accepting a suggestion.
Choose the review scope that fits the work
| Review scope | What it covers | Best suited to | Main emphasis |
|---|---|---|---|
| Diff-based | Changed files and the controls they may affect | Routine pull requests and commits | Understand the change and assess its impact on existing controls |
| Baseline | The application and its dependencies across the codebase | New applications, major releases, legacy-system onboarding, compliance work, or post-incident analysis | Architecture, boundaries, dependencies, security history, and broader coverage |
These approaches serve different purposes and can coexist. A focused pull-request review does not replace a codebase-wide baseline when the project or situation calls for one.
Make approval and ownership explicit
Assign a responsible owner to each AI-generated change and require that person to understand and explicitly approve it before merge. AI-generated review comments do not transfer accountability. Apply the same baseline gates to all changes, then add enhanced review for sensitive modules or high-risk paths. If generated changes arrive in volumes that overwhelm existing review practices, ownership rules and risk-based review help keep attention on the changes that matter most.
The National Institute of Standards and Technology’s SP 800-218A, published July 26, 2024, augments SSDF version 1.1 with practices and recommendations specific to generative AI and dual-use foundation models. NIST intends it to be used alongside SP 800-218; it provides broader secure-development context rather than a substitute for reviewing an individual pull request.
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