RVFuzzer: Finding Input Validation Bugs in Robotic Vehicles through Control-Guided Testing
Proceedings of the 28th USENIX Security Symposium (USENIX Security) 2019.
areas
Security,
Cyber-Physical Systems,
Program Analysis,
Software Testing
abstract
Robotic vehicles (RVs) are being adopted in a variety of application domains. Despite their increasing deployment, many security issues with RVs have emerged, limiting their wider deployment. In this paper, we address a new type of vulnerability in RV control programs, called input validation bugs, which involve missing or incorrect validation checks on control parameter inputs. Such bugs can be exploited to cause physical disruptions to RVs which may result in mission failures and vehicle damages or crashes. Furthermore, attacks exploiting such bugs have a very small footprint: just one innocent-looking ground control command, requiring no code injection, control flow hijacking or sensor spoofing. To prevent such attacks, we propose RVFuzzer, a vetting system for finding input validation bugs in RV control programs through control-guided input mutation. The key insight behind RVFuzzer is that the RV control model, which is the generic theoretical model for a broad range of RVs, provides helpful semantic guidance to improve bug-discovery accuracy and efficiency. Specifically, RVFuzzer involves a control instability detector that detects control program misbehavior, by observing (simulated) physical operations of the RV based on the control model. In addition, RVFuzzer steers the input generation for finding input validation bugs more efficiently, by leveraging results from the control instability detector as feedback. In our evaluation of RVFuzzer on two popular RV control programs, a total of 89 input validation bugs are found, with 87 of them being zero-day bugs.
related project
Robotic vehicles (also known as drones) are facing various threats of cyber-physical attacks that exploit their security vulnerabilities. RetroV develops automated analysis tools to find such vulnerabilities in existing robotic vehicle systems and retrofit their design against advanced cyber-physical attacks.