LLM-Assisted Dynamic Threat Analysis for Attacker-Reachable Software Weaknesses in Autonomous Vehicles

2026-08-13

AI Summary

This study addresses the challenge of validating exploitability of safety-critical vulnerabilities in autonomous driving systems such as Autoware, where manually crafting executable test artifacts is prohibitively expensive. The authors propose an automated approach that integrates compiler-level static analysis with large language models (LLMs) to generate compilable and executable test cases through a feedback loop involving compiler diagnostics, dependency stubbing, and fuzzing. Their systematic evaluation reveals that reasoning-capable LLMs achieve a 64% first-time compilation success rate—substantially higher than the 6% attained by code-specialized models—with 80% of failures attributed to missing dependencies. Notably, all 37 observed crashes originated from stub code, and no genuine exploitable vulnerabilities were reproduced within the testing budget, highlighting test artifact integration as the primary bottleneck in dynamic exploitability assessment.

Abstract

Autonomous vehicles depend on large safety-critical software stacks, where weaknesses reachable from adversarial inputs may affect steering, braking, or other control decisions. Static analysis can identify candidate sites, but dynamically confirming exploitability requires executable test artifacts that are difficult to construct manually. We investigate whether large language models (LLMs) can automate this process for Autoware, an open-source autonomous-driving stack. We perform compiler-precise static analysis across 185 packages, identifying 1,375 decision rules, 2,274 validation checks, and 482 input-to-safety-output flows, from which we derive a weakness taxonomy and sample 740 reachable sites. Two local open-weight LLMs, a no-static-context ablation, and a naive-template baseline generate 3,700 artifact sets, which are compiled against the real build under sanitizers, repaired through compiler-in-the-loop feedback, and fuzzed when executable. The main result is a build-integration failure taxonomy showing that 80% of first-shot compilation failures arise from dependency wiring rather than program logic. The reasoning model compiled 64% of harnesses on the first attempt, compared with 6% for the code-specialized model. Repair achieved full object-compileability for the reasoning model only through extensive stubbing; fewer than half of its harnesses reached the fuzzer, and all 37 observed crashes originated in stubbed code rather than Autoware. No candidate weakness was dynamically confirmed within budget. These results show that build integration, not candidate generation or fuzzing, is the primary barrier to reliable LLM-assisted dynamic analysis of full autonomous-vehicle software stacks.

Problem

• autonomous vehicles
• software weaknesses
• dynamic analysis
• exploitability
• build integration

Innovation

• LLM-assisted dynamic analysis
• build integration
• compiler-in-the-loop repair
• attacker-reachable weaknesses
• autonomous vehicle software