Guide for penetration testing of
Large Language Models (LLMs)
Artificial intelligence has long since become part of corporate reality – but with it comes new security risks. Large language models (LLMs), such as those used in chatbots, assistance systems, and automated processes, pose new challenges for IT security and data protection.
How can these systems be tested? Which attack scenarios are realistic? And how do you test a learning model that behaves dynamically?
These are precisely the questions addressed in the Guide for Penetration Testing of Large Language Models (LLMs), which was developed in collaboration with the Federal Office for Information Security (BSI). Blackfort Technology was a co-author and significantly involved in the development of the content.
The aim of this guide is to provide a structured approach to security testing of LLM-based systems – practical, methodically comprehensible, and tailored to real-world threat scenarios. It covers, among other things:
Attack vectors on LLMs and their infrastructure
Test methods for prompt injection, model manipulation, data leaks
Differences to classic penetration tests
Recommendations for integration into existing security processes
The guide is aimed at security teams, developers, those responsible for AI strategies, and service providers in the areas of testing and compliance.
Current status:
The publication of the document is currently in preparation.
As soon as the guide is available, we will make it available for download here.
Would you like to be informed as soon as the guide is available?
Contact us.