LLM-Assisted Dynamic Threat Analysis for Attacker-Reachable Software Weaknesses in Autonomous Vehicles
Why AI struggles to test hidden dangers in self-driving car software
Researchers asked AI models to automatically create test programs that could confirm exploitable weaknesses in Autoware, an open-source autonomous vehicle system. The AI successfully generated candidate test harnesses, but only 64% compiled on the first try, and ultimately none revealed genuine vulnerabilities—not because the AI couldn't identify weak points, but because integrating those tests into the massive, interdependent codebase proved vastly harder than generating the tests themselves.
Autonomous vehicles must be rigorously tested for security flaws that could affect steering, braking, and safety-critical decisions. This work reveals that the bottleneck isn't finding weaknesses or writing test code—it's the engineering overhead of actually running those tests against real production software. The finding redirects attention from AI capability to the real barrier: building testing infrastructure that works with complex vehicle software stacks.