Understanding AI Vulnerability Scanner Output
AI-powered vulnerability scanners produce fundamentally different output than traditional scanners. This lesson teaches you to read, interpret, and prioritize their findings.
How AI Scanners Differ
- Context-aware: AI scanners understand code semantics, not just patterns
- Chain-aware: They identify multi-step exploit paths, not isolated bugs
- Confidence-scored: Each finding includes a probability of exploitability
- Proof-of-concept: Many findings include working PoC code
Triage Framework
- Severity — CVSS score + AI confidence rating
- Exploitability — Does the AI provide a working PoC?
- Exposure — Is the vulnerable component internet-facing?
- Business Impact — What data/systems are at risk?
- Patch Availability — Is a fix available or must one be developed?
Common False Positive Patterns
- Theoretical vulnerabilities in dead code paths
- Findings mitigated by existing security controls
- Version-specific bugs in already-patched dependencies
Best Practice
Always validate AI findings before deploying patches. AI scanners are powerful but not infallible — human judgment