Abstract

Cyber-physical systems (CPS) play a pivotal role in modern critical infrastructure, spanning sectors such as energy, transportation, healthcare, and manufacturing. These systems combine digital and physical elements, making them susceptible to a new class of threats known as cyber kinetic vulnerabilities. Such vulnerabilities can exploit weaknesses in the cyber world to force physical consequences and pose significant risks to both human safety and infrastructure integrity. This paper presents a novel tool, named Rampo, that can perform binary code analysis to identify cyber kinetic vulnerabilities in CPS. The proposed tool takes as input a Signal Temporal Logic (STL) formula that describes the kinetic effect—i.e., the behavior of the “physical” system—that one wants to avoid. The tool then searches the possible “cyber” trajectories in the binary code that may lead to such “physical” behavior. This search integrates binary code analysis tools and hybrid systems falsification tools using a Counter-Example Guided Abstraction Refinement (CEGAR) approach. In particular, Rampo starts by analyzing the binary code to extract symbolic constraints that represent the different paths in the code. These symbolic constraints are then passed to a Satisfiability Modulo Theories (SMT) solver to extract the range of control signals that can be produced by each of the paths in the code. The next step is to search over possible “physical” trajectories using a hybrid systems falsification tool that adheres to the behavior of the “cyber” paths and yet leads to violations of the STL formula. Since the number of “cyber” paths that need to be explored increases exponentially with the length of “physical” trajectories, we iteratively perform refinement of the “cyber” path constraints based on the previous falsification result and traverse the abstract path tree obtained from the control program to explore the search space of the system. To illustrate the practical utility of binary code analysis in identifying cyber kinetic vulnerabilities, we present case studies from diverse CPS domains, showcasing how they can be discovered in their control programs. In particular, compared to off-the-shelf tools, our tool could compute the same number of vulnerabilities while leading to a speedup that ranges from 3× to 98×.