AI Code Review: How to Actually Review Code an Agent Wrote | Blog | Endor Labs

What it is

An Endor Labs explainer on reviewing AI-agent-generated code. Beyond the general premise the summary states, the concrete carry-over is a taxonomy of recurring flaws (with CWE IDs) and hard metrics quantifying how often AI code is insecure — the numbers a reader would want to justify adding a review step.

Common flaws to check for (with CWEs)

The page names five recurring flaw classes in AI-generated code:

• Missing input validation — injection risks (CWE-20, CWE-89, CWE-78)
• Hardcoded secrets — keys/passwords in source (CWE-798)
• Broken authentication / access control — endpoints shipping without permission checks (CWE-306, CWE-284)
• Hallucinated dependencies — packages that don't exist or are outdated
• Design drift — structural changes with no obvious bug

The metrics (the reason to act)

• Georgetown CSET tested five LLMs: ~half of code snippets contained security weaknesses.
• Endor Labs' 2025 State of Dependency Management: only 1 in 5 AI-recommended dependency versions is safe; 34% of AI-recommended packages are hallucinated.
• Veracode 2025 GenAI report: 45% of code samples failed security tests, introducing OWASP Top 10 vulnerabilities.
• Faros AI telemetry: 31% more PRs merging without any review — the gap where risk enters.
• Stack Overflow 2025 survey: 84% of respondents use or plan to use AI tools.

What to do

The page's operational suggestions, not commands:

• Apply existing cybersecurity standards to model-written code — CSET's line is that "authorship does not change the standard."
• Prioritize change-driven risk over CVE scanning: the riskiest issues are subtle edits (a change to who can access an endpoint) that vulnerability scanners don't flag, not renamed variables.
• Review each change from three angles — developer (is it correct?), architect (blast radius / structural drift?), security (exploitable? impact?) — since single-pass pattern tools miss issues between these views.
• Use reachability analysis to suppress false positives (findings that aren't actually exploitable) so reviewers keep reading results.

Caveats

This is a vendor post that concludes by pitching Endor Labs' own multi-agent review product; treat the framing as promotional. It states AI review complements rather than replaces human reviewers — a person still makes the final call. The fetched text is truncated mid-sentence in the reachability section, so that mechanism is only partially described.