Hallucination-Resilient LLM-Driven Sound and Tunable Static Analysis: A Case of Higher-Order Control-Flow Analysis

We argue that soundness remains essential for LLM-driven static analysis and discuss hallucination-resilient approaches in combining LLMs with static analysis that ensure soundness while improving precision. We propose to use LLMs as a way of meta-analysis and investigate this approach in higher-order control-flow analysis, building on the abstracting abstract machine framework and delegating abstract address allocation to an LLM. Our analyzer LLMAAM maintains soundness regardless of LLM behavior, while adaptively tuning analysis precision. We report promising preliminary results and outline broader opportunities for sound LLM-driven analysis.