AI Agent & LLM Penetration Testing
If your product has an AI that can take real actions — sending emails, looking things up, moving money — someone will try to talk it into doing something it shouldn't. That's our job before they get to it.
AI/LLM penetration testing, defined
AI agent and LLM penetration testing is adversarial testing for LLM-powered products — simulating prompt injection and jailbreak techniques, tool-call hijacking and excessive agency abuse, RAG and vector-store exfiltration, and system prompt or training-data leakage.
Unlike a conventional application, the caller of your backend logic is a language model whose behavior is influenced by whatever content it just read — a document, an email, a tool response. That's a fundamentally different attack surface than a human user with a browser.
What we test
- Prompt injection and jailbreak resistance — direct and indirect injection through content the agent reads at runtime
- Excessive agency — whether the agent can be manipulated into taking actions beyond its intended scope
- Tool-call hijacking — redirecting legitimate tool access toward unauthorized outcomes
- RAG and vector-store exfiltration — extracting data the retrieval system wasn't meant to expose
- System prompt and training-data leakage — extracting internal instructions or sensitive training content
Mapped to the OWASP LLM Top 10 & Agentic (ASI) Top 10
Findings map to both the OWASP Top 10 for LLM Applications (2025) and the OWASP Top 10 for Agentic Applications (2026).
How ComplyArmor runs AI/LLM penetration testing
We attempt real jailbreaks and tool-call boundary violations against a live target, not a static prompt-injection checklist — see our full guide to agentic AI security for the complete methodology. Delivered as Smart PTaaS — continuous rather than a one-off engagement — and following our testing methodology: discover, scan, exploit, validate. Every finding maps to your compliance requirements.
Frequently asked questions
What is AI/LLM penetration testing?
Adversarial testing for AI agents and LLM-powered products — attempting prompt injection, jailbreaks, tool-call hijacking, and excessive-agency abuse against a live target, mapped to the OWASP LLM Top 10 and Agentic (ASI) Top 10.
What is the difference between LLM testing and AI agent testing?
LLM testing focuses on the model's input-output behavior — jailbreaks, data leakage. AI agent testing extends that to autonomous, tool-using systems — goal hijack, tool misuse, memory poisoning — risks that only exist because the system can take real actions, not just generate text.
Can automated tools fully test AI agent security?
No. The highest-impact failures are sequence-level — each individual tool call looks authorized and legitimate, and only the combination is an attack. That requires a human adversary who understands the business logic, not just a prompt-injection pattern matcher.
Do you test MCP servers as part of AI/LLM testing?
MCP servers are tested as a related but distinct surface — see our dedicated MCP server pentesting methodology for the protocol-layer testing that complements AI agent behavior testing.