How to Run a 100% Private, Local AI Code Security Reviewer So Your Source Code Never Leaves Your PC

Model Selection

Download a security-tuned open-weights model such as 'deepseek-coder-v2:lite' or 'qwen2.5-coder:7b' using Ollama. These models fit on consumer GPUs with 8GB to 16GB of VRAM.

Setting Up the CLI Script

Write a simple shell script that pipes git diffs or file contents into Ollama with a strict security prompt: 'Analyze this code for OWASP vulnerabilities, authorization bypasses, and insecure memory calls. Return findings in markdown.'

Integrating with Git Hooks

Add a pre-commit hook in your .git/hooks directory that triggers the local model before any commit is finalized. If the model spots an exposed API key or critical flaw, the commit is blocked locally.

Zero Network Leakage

Disconnect your Wi-Fi or monitor network traffic with Little Snitch to verify that zero packets leave your machine during the audit.

RunAnAi Takeaway

Running local models for code reviews gives you total peace of mind. Your intellectual property stays safe on your hard drive while you still benefit from automated AI inspection.