AI Penetration Testing: From Red Team to Audit Report

wasaconf.org hosted the WASA Conference on security analytics. Today: AI penetration testing is structured red teaming—finding vulnerabilities, grading risk, and building evidence for the audit report.

Historical Domain: wasaconf.org
Original Focus: WASA Conference (Wireless Algorithms, Systems and Applications)
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Why It Matters

From Software Analysis to AI Security Testing

WASA brought together researchers on software analysis, network algorithms, and security testing. The same rigorous methodology applies to AI: automated testing of model behavior, vulnerability discovery, and risk classification. Penetration testing an AI system is software analysis applied to ML.

Core Concepts

The foundations that connect historical research to modern AI security.

Structured Red Team Workflow

Penetration testing follows a structured process: reconnaissance (probe behavior), exploitation (trigger failure), and validation (prove the impact). Each step is methodical and repeatable—not ad hoc fuzzing.

  • Reconnaissance: model capabilities and constraint discovery
  • Exploitation: targeted attack campaigns
  • Validation: reproducible failure cases and risk evidence

Risk Grading & Audit Evidence

A finding is only valuable if it maps to business risk and provides actionable evidence. Severity depends on context and impact. A prompt injection on a content moderator differs from one on a financial decision system. The audit report must explain the risk, cost of remediation, and detection strategy.

  • Finding severity mapped to CVSS and OWASP LLM Top 10
  • Reproducible test cases and step-by-step exploitation
  • Remediation roadmap with cost-benefit analysis

On This Site

Related pages that build on these principles.

AI Agent Security Audit →

Structured audit frameworks, risk classification, and evidence collection.

LLM Red Teaming →

Structured attack campaigns: reconnaissance, exploitation, validation.