Recent years have witnessed a wide array of results in software testing, exploring different approaches and methodologies ranging from fuzzers to symbolic engines, with a full spectrum of instances in between such as concolic execution and hybrid fuzzing. A key ingredient of many of these tools is Satisfiability Modulo Theories (SMT) solvers, which are used to reason over symbolic expressions collected during the analysis. In this paper, we investigate whether techniques borrowed from the fuzzing domain can be applied to check whether symbolic formulas are satisfiable in the context of concolic and hybrid fuzzing engines, providing a viable alternative to classic SMT solving techniques. We devise a new approximate solver, FUZZY-SAT, and show that it is both competitive with and complementary to state-of-the-art solvers such as Z3 with respect to handling queries generated by hybrid fuzzers.

Modern software systems require the support of automatic program analyses to answer questions about their correctness, reliability, and safety. In recent years, symbolic execution techniques have played a pivotal role in this field, backing research in different domains such as software testing and software security. Like other powerful machine analyses, symbolic execution is often affected by efficiency and scalability issues that can be mitigated when a domain expert interacts with its working, steering the computation to achieve the desired goals faster. In this paper we explore how visual analytics techniques can help the user to grasp properties of the ongoing analysis and use such insights to refine the symbolic exploration process. To this end, we discuss two real-world usage scenarios from the malware analysis and the vulnerability detection domains, showing how our prototype system can help users make a wiser use of symbolic exploration techniques in the analysis of binary code.

Summary Symbolic execution is a popular program analysis technique that allows seeking for bugs by reasoning over multiple alternative execution states at once. As the number of states to explore may grow exponentially, a symbolic executor may quickly run out of space. For instance, a memory access to a symbolic address may potentially reference the entire address space, leading to a combinatorial explosion of the possible resulting execution states. To cope with this issue, state‐of‐the‐art executors either concretize symbolic addresses that span memory intervals larger than some threshold or rely on advanced capabilities of modern satisfiability modulo theories solvers. Unfortunately, concretization may result in missing interesting execution states, for example, where a bug arises, while offloading the entire problem to constraint solvers can lead to very large query times. In this article, we first contribute to systematizing knowledge about memory models for symbolic execution, discussing how four mainstream symbolic executors deal with symbolic addresses. We then introduce MemSight, a new approach to symbolic memory that reduces the need for concretization: rather than mapping address instances to data as previous approaches do, our technique maps symbolic address expressions to data, maintaining the possible alternative states resulting from the memory referenced by a symbolic address in a compact, implicit form. Experiments on prominent programs show that MemSight, which we implemented in both Angr and Klee, enables the exploration of states that are unreachable for memory models that perform concretization and provides a performance level comparable with memory models relying on advanced solver theories.