Augment Code Reviews with an AI-Specific Validation Checklist
Update all code review standards to include a mandatory checklist for "AI-native" vulnerabilities. Traditional checklists are necessary but insufficient, as they are not designed to catch the subtle, context-deficient flaws that AI-generated code introduces. This augmentation is essential to maintain code quality and security in an AI-assisted environment.
Augment your team's existing pull request (PR) checklists with a new, required section for "AI-Specific Validation." This new checklist should force the human reviewer to pause and explicitly check for common AI-generated flaws, such as omission of security controls, subtle logic errors, and "hallucinated" dependencies.
This is the single most important human-in-the-loop defense for pain-point-01-almost-correct-code. AI models are "unaware of your application's risk model" and are incentivized to find the "shortest path to a passing result," which leads to a new class of "AI-native" vulnerabilities. These flaws are dangerous because the code looks plausible and often passes basic checks, but it contains critical flaws, such as: Omission of Security Controls: The AI forgets to add input validation, sanitization, or authorization checks because they were not explicitly in the prompt. Subtle Logic Errors: The AI introduces a bug that looks correct, such as using if user.role == "admin" (which fails for multi-role users) instead of if "admin" in user.roles (which is correct). Optimization Shortcuts: The AI uses a dangerous but functional shortcut, like eval(expression), which solves the prompt but opens a remote code execution vulnerability. Hallucinated Dependencies: The AI "invents" a package name. An attacker can then register this package name ("slopsquatting") and publish malicious code, which your developer then installs. Architectural Drift: The AI non-deterministically swaps a critical library (e.g., a cryptography library) or removes an access control check, breaking security invariants.
This augmented checklist should be a mandatory part of every code review for any team that uses AI coding assistants. It is not optional. It should be physically added to the pull request template in your code host (e.g., GitHub, GitLab). This is especially critical for PRs that are "AI-heavy" or touch critical code paths (auth, payments, data migrations).
Augment your existing PR checklist with a new, mandatory "AI-Specific Validation" section. Part 1: Standard Code Review Checklist - [ ] Functionality: Does the code meet all requirements and handle edge cases? [ ] Readability & Style: Does it follow team coding standards? [ ] Design: Does it follow established architectural patterns? [ ] Performance: Does it introduce any bottlenecks? [ ] Error Handling: Are errors handled gracefully? [ ] Testing: Are there sufficient unit and integration tests? (See Rec 7) [ ] Documentation: Is the code (and PR) adequately documented? (See Rec 19) Part 2: MANDATORY AI-Specific Validation Checklist - [ ] Omission Check: What security controls is this code missing? (Check for input validation, output encoding, and authorization.) [ ] Logic Check: Is there a subtle logic error? (Check logic, e.g., == vs. in, or off-by-one.) [ ] Dependency Check: Are all new packages real, secure, and approved? (Check for "hallucinated dependencies".) [ ] Context Check: Did the AI take a dangerous "shortcut" (like eval()) that violates our security posture? [ ] Drift Check: Did the AI change any existing security-critical code (e.g., auth, crypto) that was outside the PR's main scope? Finally, this review process must create a feedback loop. When a reviewer finds a common AI mistake, they should "Document Common AI Mistakes" and "Refine Your Prompts". This finding should be given to the AI Champion (Rec 11) and shared in the CoP (Rec 12) to update the central process-optimization/structure-your-ai-prompt-library.
Workflows that implement or support this recommendation.
- The Most Common Security Vulnerabilities in AI-Generated Code ... - https://www.endorlabs.com/learn/the-most-common-security-vulnerabilities-in-ai-generated-code
AI models are "unaware of your application's risk model" and are incentivized to find the "shortest path to a passing result," which leads to "AI-native" vulnerabilities. - How to Review AI-Generated Code: A Guide for Developers - Arsturn - https://www.arsturn.com/blog/the-essential-guide-to-reviewing-ai-generated-code
AI-specific validation checklist for code reviews, including checks for omitted security controls, subtle logic errors, and hallucinated dependencies.
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