Offensive testing for AI systems, not just applications
Full-scope AI penetration testing combining automated adversarial suites with manual expert exploitation — covering the complete OWASP LLM Top 10, AI agent compromise, and RAG pipeline attacks.
What is AI Penetration Testing — Offensive Security for AI Systems?
AI penetration testing is a structured offensive security engagement that systematically attempts to exploit vulnerabilities in AI and LLM systems. It goes beyond automated scanning to include manual expert exploitation of prompt injection, model access abuse, RAG pipeline attacks, agent hijacking, and data exfiltration via LLM outputs — producing a risk-ranked findings report with exploitability evidence.
Why AI systems need dedicated penetration testing
- Standard web application penetration tests do not include AI-layer testing — OWASP LLM Top 10 vulnerabilities are outside the scope of conventional pen test methodologies
- Automated AI scanners have limited coverage — novel attack chains, multi-turn exploitation, and context-dependent vulnerabilities require human expertise
- AI systems are deployed and updated rapidly — penetration testing must keep pace rather than being a one-time pre-launch activity
- AI pen test findings require AI-specific remediation guidance — standard remediation recommendations from web pen tests do not apply to LLM vulnerabilities
A four-step operational model
Scoping & Reconnaissance
Define AI systems in scope, enumerate model endpoints, identify input channels and tool integrations, and map the attack surface for the engagement.
- AI system scope definition
- Endpoint enumeration
- Input channel and tool mapping
Automated Adversarial Suite
Run comprehensive automated test suites across all in-scope AI endpoints — covering 100+ prompt injection scenarios, jailbreak techniques, and OWASP LLM Top 10 test cases.
- 100+ injection test cases
- OWASP LLM Top 10 automated testing
- Jailbreak resistance benchmarking
Manual Expert Exploitation
Human penetration testers attempt novel attack chains: multi-turn manipulation, indirect injection via data sources, agent tool chain exploitation, and model data extraction.
- Novel multi-turn attack chains
- Indirect injection via realistic sources
- Agent tool chain exploitation
Findings Report
Risk-ranked report with exploitability evidence, CVSS-equivalent scoring, business impact assessment, and specific remediation guidance — plus optional retest.
- Risk-ranked findings with evidence
- OWASP LLM Top 10 mapping
- Specific AI remediation guidance
Outcomes for security teams
AI pen testing finds what automated scanners miss
Novel attack chains, context-dependent vulnerabilities, and multi-turn exploitation require human expertise and cannot be fully automated.
Exploitability evidence changes remediation priority
A pen test provides evidence of actual exploitability — not just theoretical vulnerability — enabling accurate risk prioritisation and justified remediation investment.
Required by EU AI Act for high-risk AI systems
EU AI Act conformity assessments for high-risk AI systems require evidence of security testing — AI penetration testing provides that evidence in an auditor-acceptable format.
Direct answers
What is AI penetration testing?+
A structured offensive security engagement that attempts to exploit vulnerabilities in AI and LLM systems — combining automated adversarial testing with manual expert exploitation to produce a risk-ranked findings report.
What is included in an AI penetration test?+
Scope includes: prompt injection testing (direct and indirect), jailbreak resistance evaluation, model access control testing, RAG pipeline attack scenarios, agent tool chain exploitation, data exfiltration via LLM outputs, and model extraction detection.
How is AI pen testing different from AI red teaming?+
AI penetration testing follows a structured, time-boxed methodology with a defined scope and a formal findings report. AI red teaming is broader — it may include safety evaluation, novel attack research, and longer-horizon adversarial campaigns beyond the technical vulnerability scope.
How often should AI systems be penetration tested?+
At minimum: before initial production deployment, after significant model updates, and annually. High-risk AI systems under EU AI Act may require more frequent testing to maintain conformity assessment validity.