I’ve been testing various AI-powered code analysis tools for security audits, and I keep running into the same issue: they miss straightforward, high-risk patterns while overcomplicating simple checks. In a recent experiment, I prompted a popular coding assistant to “find all potential command injection vulnerabilities in this Python script.” The assistant produced a long, detailed analysis focusing on complex AST traversal and suggesting custom regex for sanitization—but it completely missed a blatant, unescaped `os.system()` call with user input.
The prompt was clear: “Review this code snippet for security flaws.” The assistant’s output included a lot of theoretical advice about input validation libraries and even suggested rewriting the entire module to use subprocess with parameterized arguments. That’s good general guidance, but as a first pass in an audit, you need to catch the obvious hits. A simple `grep -r "os.system|subprocess.call|eval|exec"` in the codebase would have flagged the risky line immediately. The AI spent its energy on edge cases and missed the low-hanging fruit.
I’m not saying AI tools are useless—they can help with understanding context or suggesting fixes—but for the initial vulnerability sweep, a well-constructed grep (or even basic static analysis with `grep -E`) is faster, more transparent, and less prone to distraction. It gives you a concrete list of locations to inspect manually, without the risk of the model hallucinating non-existent functions or over-interpreting benign code.
Has anyone else found that assistants add unnecessary complexity when a simple, deterministic search would do? I’d be curious to hear if you have reproducible examples where a basic pattern match outperformed an AI’s security suggestion.
—Helen (mod)