Stop trusting AI to "just find the bugs" in your pull requests. I

If you want a real AI code security review, you have to stop treating the LLM like a magic oracle and start treating it like a junior dev who is overconfident and prone to hallucinating security patches.

Stop using generic prompts for security audits

The biggest mistake I see is developers pasting a file and asking, "Are there any security vulnerabilities here?" You'll get a generic list of "ensure input is sanitized" and "use strong passwords." That's useless fluff.

I switched to a "Threat Model First" approach. Instead of asking for bugs, I tell the AI exactly what the attacker's goal is.

The "Generic" Way (Bad):
"Review this Python FastAPI code for security issues."
Result: "Make sure you use HTTPS and validate your inputs." (Yawn).

The "Attack Vector" Way (Better):
"Act as a security researcher. The goal is to achieve Remote Code Execution (RCE) through the /upload endpoint. Review this code specifically for unsafe deserialization or path traversal. If you find nothing, explain why the current implementation prevents these specific attacks."

When you force the AI to prove a negative, it actually looks at the logic instead of guessing based on common patterns.

Fix the "Hallucinated Patch" loop

We've all been there. The AI finds a legitimate SQL injection, suggests a fix, you apply it, and then it suggests another fix for the code it just wrote because it realized the first fix broke the type system.

To kill this loop, I use a "Verification Step" config. I don't let the AI write the fix in the same prompt it uses for the review.

| Step | Action | Tooling/Prompt |
| :--- | :--- | :--- |
| 1. Identification | Find the flaw | "Identify the vulnerability. Do NOT provide code yet." |
| 2. Proof of Concept | Simulate attack | "Write a curl command or a python script that would trigger this flaw." |
| 3. Remediation | Fix it | "Now provide the fix based on the PoC results." |

Last month, using this flow on a legacy Node.js project saved me from merging a "fix" that actually introduced a prototype pollution bug because the AI was rushing to be helpful.

Use MCP to feed the AI actual context

A security review is worthless if the AI doesn't know your environment. If it doesn't know you're running on an outdated version of Alpine Linux or using a specific middleware, its "security" advice is just a guess.

I've started using Model Context Protocol (MCP) servers to pull in my actual dependency tree (package-lock.json or go.mod) and my Dockerfile. When the AI can see that I'm running as root in the container, it stops suggesting generic "user permission" tips and tells me exactly which line in the Dockerfile to change to USER node.

If you're tired of manually uploading files, look into Workflows to automate how context is fed into your security prompts. It turns a 10-minute setup into a 2-second trigger.

When to ignore the AI's security warnings

AI is paranoid. It will flag every single eval() or dangerouslySetInnerHTML as a critical risk, even if the data is hardcoded and safe.

The trick is to create a "Security Baseline" document. I keep a .md file in my repo listing known acceptable risks (e.g., "We use eval here because we are building a DSL for internal use only"). I feed this to the AI at the start of the session.

Before: AI spends 40% of the chat telling me to remove a specific library it thinks is deprecated.
After: AI ignores the "known risks" and finds a logic flaw in my JWT expiration check that actually mattered.

Leverage the community for vetted prompts

You don't need to invent these prompt structures from scratch. I've found that browsing Prompt Sharing saves me hours of tweaking. Instead of guessing if "Act as a CISSP" or "Act as a penetration tester" works better, you can see which personas actually yield fewer false positives in real-world codebases.

One specific tip: if the AI is being too vague, tell it to "Output the results in a table with: Vulnerability, Severity (Low/Med/High), CVSS Score estimate, and Remediation." Forcing it into a structured format kills the "conversational fluff" and makes the review actionable.

The actual cost of AI security reviews

Don't assume this is free or "cheap." If you're using a high-token window model (like Claude 3.5 or GPT-4o) and feeding it 20 files for a comprehensive review, you'll hit the rate limits or burn through credits fast.

In my experience, reviewing a 500-line module with deep context costs about $0.15 to $0.40 per run. That's nothing compared to a production breach, but if you're running this on every single save via a plugin, the cost adds up. I recommend running "Security Mode" only on PRs, not on every keystroke.

The most expensive mistake is blindly merging an AI-suggested "security fix" without running it through a linter and a test suite. AI is a great reviewer, but a mediocre coder when it comes to edge-case security. Trust the identification, verify the fix.