StatSym: Vulnerable Path Discovery through Statistics-Guided Symbolic Execution

Identifying vulnerabilities in software systems is crucial to minimizing the damages that result from malicious exploits and software failures. This often requires proper identification of vulnerable execution paths that contain program vulnerabilities or bugs. However, with rapid rise in software complexity, it has become notoriously difficult to identify such vulnerable paths through exhaustively searching the entire program execution space. In this paper, we propose StatSym, a novel, automated Statistics-Guided Symbolic Execution framework that integrates the swiftness of statistical inference and the rigorousness of symbolic execution techniques to achieve precision, agility and scalability in vulnerable program path discovery. Our solution first leverages statistical analysis of program runtime information to construct predicates that are indicative of potential vulnerability in programs. These statistically identified paths, along with the associated predicates, effectively drive a symbolic execution engine to verify the presence of vulnerable paths and reduce their time to solution. We evaluate StatSym on four real-world applications including polymorph, CTree, Grep and thttpd that come from diverse domains. Results show that StatSym is able to assist the symbolic executor, KLEE, in identifying the vulnerable paths for all of the four cases, whereas pure symbolic execution fails in three out of four applications due to memory space overrun.