Why it matters in practice
Within Advanced Pentesting, LLM (Large Language Models) Pentesting changes how scope, prerequisites and evidence should be handled. Focus on the concrete asset or data flow under test, record the assumptions that make the condition exploitable and connect verified behavior to an operational consequence and remediation decision.
This note keeps LLM (Large Language Models) Pentesting tied to offensive workflow: what to observe, what to prove, what usually goes wrong, and which references remain useful once an assessment moves from planning into active validation.
Primary coverage
The coverage below turns LLM (Large Language Models) Pentesting into an assessment map for Advanced Pentesting: identify the relevant system boundary, select a safe validation path, preserve evidence and record uncertainty before drawing a conclusion.
- OWASP LLM Applications
- What are LLMs?
- LLM components
- LLM use cases:
- Damn Vulnerable LLM Project
- LLM01: prompt injection
- LLM02: insecure output handling
- LLM03: training data poisoning
- LLM04: model denial of service
- LLM05: supply chain vulnerabilities
Selected public references
- OWASP Top 10 for LLM ApplicationsGenerative-AI application risk model
- NIST AI Risk Management FrameworkAI risk governance and measurement
- MITRE ATLASAdversary tactics and techniques for AI systems
- OWASP AI Testing GuideStructured testing guidance for AI systems