Code Auditor
This series describes agent-assisted code review in which scanner alerts enter an isolated workspace for reachability, impact, and reproduction tests. An alert is a candidate; a finding is a claim another reviewer can reproduce.
For security and release teams, the articles show what evidence supports escalating a risk, rejecting a false positive, or continuing an investigation.
Start here: AI Code Audit: From Scanner Alert to Reproducible Evidence. Then continue in the order below.
AI Code Audit: From Scanner Alert to Reproducible Evidence
An AI code audit turns scanner alerts into findings by testing reachability in an isolated environment, recording impact, and preserving rejected candidates.
AI Security Audit: Make Every Finding Reproducible
An AI security audit becomes useful when each finding carries its code location, exploit path, command output, severity reasoning, and fix.
Automated Smart Contract Audit: Prove High-Severity Findings
An automated smart contract audit should validate high-severity findings with a test, simulation, trace, or proof of concept before assigning severity.
AI Vulnerability Scanner vs. Agent Audit: Choose by the Risk
An AI vulnerability scanner finds possible issues across code; an agent audit tests reachability, validates impact, removes duplicates, and explains the fix.