Over months of vulnerability research, I found LLM agents lose track of established facts during long investigations, causing hallucinations. Traditional memory stores old transcripts but don't maintain current knowledge state. I wanted LLMs to update conclusions automatically when facts change, like program analysis where rules derive new facts and only affected results need updating.

This led me to build a Datalog engine for LLMs. Datalog is a declarative logic language where facts and rules derive new facts; if an input fact changes, only dependent conclusions are invalidated instead of restarting from scratch. I implemented this after observing that attacker controls object_a, object_a points to object_b, and object_b is a kernel object, allowing attacker control of a kernel object. Later, discovering object_a doesn't point to object_b invalidated prior conclusions.

Maintaining a dynamic knowledge base feels familiar to program analysis, where fixed points calculate derivable facts and incremental updates optimize performance. This approach promises more reliable LLM assistance in complex exploit development by preserving only valid, current state rather than reconstructing history repeatedly.