Using AI for Source Code Vulnerability Analysis

The SEC543 Field Guide, Using AI for Source Code Vulnerability Analysis, published by SANS Institute, provides a workflow for security teams directing large language models to discover and exploit vulnerabilities during source code review. The guide is tied to SANS course SEC543: AI-Assisted Source Code Analysis and Exploitation for Penetration Testers, and outlines a five-step process along with prompt patterns, hallucination defenses, and pre-upload checks for using AI safely and effectively in penetration testing engagements.

Key findings:

• The core workflow follows a five-step loop: Map, Slice, Interrogate, Verify, and Exploit
• The Verify step is explicitly human-owned — AI-claimed findings must be reproduced by hand before they're trusted
• Adversarial framing (asking the AI pointed, challenging questions) produces actionable findings, while descriptive framing tends to produce rewrites of the code instead
• Useful prompt patterns include threat-modeling a code slice, tracing authorization checks, differential reading of similar handlers, negative-space searches for unchecked state-changing operations, constraint prompting to bound AI scope, and tool generation for custom scanners or parsers
• Every AI-produced finding should be treated as a hypothesis until reproduced, with citations required (a file:line reference for each claim, or the claim is discarded)
• A single fabricated function or vulnerability identifier is treated as contaminating the entire session, requiring a reset
• Important findings should be crosschecked against a second model from a different vendor
• Before any code leaves a reviewer's machine, four checks apply: confirming written permission to send client source to a third-party model, checking internal data classification and acceptable use policy, understanding the AI provider's data retention practices, and defaulting to a local model or zero-retention enterprise tenant when any of these are unclear
• Cloud-hosted models are assumed to process, and potentially retain visibility into, anything a reviewer uploads

The guide's central framing is that AI accelerates the mapping and hypothesis-generation stages of source code review, but verification and exploitation confirmation remain squarely human responsibilities. Skipping steps in the five-step loop, or accepting AI findings without file:line-level reproduction, produces results that won't survive exploitation attempts or report review. Methodology: This is a course-affiliated field guide rather than a research survey; it distills workflow guidance developed for SANS SEC543 rather than empirical study findings.