FlagThis

Abstract

Objectives: As autonomous vehicles reach public roads, their software becomes safety-critical. A defect reachable from an attacker input can change how a vehicle steers or brakes. Static analysis flags many candidate sites, but confirming one is reachable and exploitable needs executable artifacts whose manual construction is the bottleneck. We ask whether large language models (LLMs) can automate this for the open-source stack Autoware. Methods: We perform a compiler-precise static analysis of Autoware (185 packages), recovering 1,375 decision rules, 2,274 validation checks, and 482 input-to-safety-output flows, from which we derive a weakness taxonomy and a sample of 740 reachable sites. For each site, 2 local open-weight LLMs, a no-static-context ablation, and a naive-template baseline generate artifacts. All 3,700 sets are compiled against the real build under sanitizers, repaired via a compiler in-the-loop stage, and fuzzed when they compile. Findings: The principal result is a build-integration failure taxonomy that locates the binding constraint one stage before the fuzzer: 80% of first-shot compile failures arise from dependency-wiring rather than program logic. The reasoning model compiled 64% of its harnesses first, against 6% for the code-specialized model, and repair reached full object-compileability for the reasoning model only by stubbing the real target; consequently, under half of its harnesses reached the fuzzer and all 37 crashes came from a stub. Within budget, no candidate weakness was dynamically confirmed, reflecting this build-integration barrier rather than a benign attack surface. Novelty: The first end-to-end feasibility study of LLM-assisted dynamic analysis on a full AV stack, pairing repositoryscale static candidate identification with LLM-generated confirmation and pinpointing where the automation fails. Practical Applications: Scalable software-safety assurance is a prerequisite for automated driving. Unaided LLMassisted dynamic analysis is not yet a trustworthy assurance stage; effort is better spent on build integration than on prompt design, while the static analysis already guides safety review.