LLM Red Teaming: A Complete Guide To Testing And Securing AI Applications

Your AI model can pass every internal safety test, but you can’t guarantee full safety when a real user tries to break it. A hacker will manage to manipulate the algorithm and bypass the preloaded instruction set. That would give the hacker access to important information as well as the opportunity to perform prohibited actions. That is why LLM red teaming becomes relevant.

Instead of just determining if the LLM can be jailbroken, security testers advance the attack by one step and check whether it is possible to actually break the trust boundary. The research on AI agent hijacking conducted by NIST has shown that the baseline attack success rate was at least 11%, while for red team attacks the rate was 81%.

This guide is going to cover the practical side of LLM red teaming. We are going to talk about prompt injection, RAG attacks, data exposure, misuse of agents and tools, attack chaining, and impact validation and remediation.

Source: https://qualysec.com/llm-re ...