LLM pentesting checklist: key techniques to quickly verify safety and security

Companies worldwide are rushing to integrate Large Language Models (LLMs) into their processes and products. Meanwhile, security team are scrambling to keep up with the pace of LLM adoption. In this context, penetration testing of LLMs continues to remain a crucial step to ensuring robust LLM deployments.

While pre-development security measures, such as threat modelling and secure coding practices, are also critical, they cannot fully account for the dynamic risks that emerge once an LLM is live and interacting with users. The OWASP Top 10 for LLM Applications highlights these evolving threats, including prompt injection, data leakage and insecure plugin integrations. These risks often surface only during runtime, making post-deployment testing a vital component.

Pentesting checklists that can be quickly used in the field can be incredibly useful for lean security teams that cannot engage specialist testers. In this post, we discuss how to build a practical, reusable LLM pentesting checklist to help security teams rapidly validate deployed models. Building on the foundational LLM Security Checklist, we'll cover the fundamental testing techniques to identify and mitigate real-world vulnerabilities before they are exploited.

LLM security frameworks and research literature

Several established frameworks address LLM and AI security, but they stop short of providing penetration testers with a practical step-by-step guide.

• OWASP Top 10 for LLM Applications (2025): Defines core risks such as prompt injection, data leakage, insecure output handling and supply chain compromise. It is excellent for understanding what to test but not how to test in a live environment.
• MITRE ATLAS: Provides a taxonomy of adversarial techniques against machine learning systems, useful for mapping attack surfaces and enriching threat intelligence. However, it lacks reproducible pentesting procedures for individual models.
• NIST AI Risk Management Framework: Frames risks across governance and provides measurement techniques and mitigation functions. It helps organisations align AI security with enterprise risk processes but it is too high-level for practitioners running rapid security assessments.

To find better guidance, we need to consult adversarial machine learning, data poisoning, evasion attacks and model theft research papers. These journals offer, in fact, deep insights into potential threat vectors. For example, recent studies demonstrate how data poisoning during instruction tuning can embed stealthy backdoors using gradient triggers.