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LLM (Large Language Models) Pentesting

LLM (Large Language Models) Pentesting is presented here as a field note for offensive security work. The emphasis is on attack surface, validation logic, common failure patterns, operator choices and the public references worth keeping nearby during a live assessment.

field noteassessment referencepublic sources

Why it matters in practice

LLM (Large Language Models) Pentesting matters because it shapes how an operator scopes the work, chooses validation steps, prioritizes evidence and explains risk. The point is not to accumulate trivia; it is to understand which control boundary is in play and how that boundary can fail under realistic pressure.

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 items below mark the main workflows, concepts, tools and validation themes that repeatedly matter when working through llm (large language models) pentesting.

  • OWASP llm applications
  • What are llms?
  • Komponenten by llms
  • Anwendungsfaelle by llms:
  • Damnvulnerablellmproject
  • LLM01: prompt injection
  • LLM02: insecure output handling
  • LLM03: training data poisoning
  • LLM04: model denial of service
  • LLM05: supply chain vulnerabilities

Selected public references