Scalable Dynamic Analysis of Binary Code

To use dynamic slicing, detailed information about a program execution must first be recorded. Since the amount of information is often too large to fit in main memory, existing dynamic slicing methods apply various time-versus-space trade-offs to reduce memory requirements. However, these trade-offs result in very high time overheads, limiting the usefulness of dynamic slicing in practice. In this thesis, we show that the speed of dynamic slicing can be greatly improved by carefully designing data structures and algorithms to exploit temporal locality of programs. This allows avoidance of the expensive trade-offs used in earlier methods by accessing recorded runtime information directly from secondary storage without significant random-access overhead. In addition to being a standalone contribution, scalable dynamic slicing also forms integral parts of our contributions to fuzzing. Our first contribution uses dynamic slicing and binary code mutation to automatically turn an existing executable into a test generator. In our experiments, this new approach to fuzzing achieved about an order of magnitude better code coverage than traditional mutational fuzzing and found several bugs in popular Linux software. The second work on fuzzing presented in this thesis uses dynamic slicing to accelerate the state-of-the-art fuzzer AFL by focusing the fuzzing effort on previously unexplored parts of the input space.

For the second dynamic analysis technique whose scalability we sought to improve — instruction trace alignment — we employed techniques used in speech recognition and information retrieval to design what is, to the best of our knowledge, the first general approach to aligning realistically long program traces. We show in our experiments that this method is capable of producing meaningful alignments even in the presence of significant syntactic differences stemming from, for example, the use of different compilers or optimization levels.