AI can do the heavy lifting on this SQL injection audit, but a human must own the sign-off.

SQL injection patterns in Laravel (raw queries, DB::statement, lack of bindings) are structurally consistent and well-documented, making detection repeatable. However, each codebase has unique query construction patterns, custom abstractions, and edge cases that require fresh judgment per instance.

The core success criteria are reasonably crisp — parameterized queries, no raw interpolation, middleware added, report produced. But 'similar patterns' and severity scoring involve judgment calls about risk context, business logic, and what counts as exploitable, which are not fully deterministic.

The agent needs full read access to the codebase and write access to make changes, which is achievable with a code agent setup. However, it cannot run the app, execute tests, or observe runtime behavior without additional tooling, limiting its ability to verify fixes don't break functionality.

A missed vulnerability leaves a critical security hole in production; an incorrect query rewrite could silently break business logic or cause data corruption. Both failure modes are serious and potentially irreversible in their consequences, even if the code change itself is technically reversible via version control.

Pattern detection and parameterization rewrites are largely mechanical and well within current AI capability. But assessing true exploitability, prioritizing severity in business context, and deciding whether a middleware approach is architecturally appropriate for this specific app require a security engineer's judgment.