Predictive Context-sensitive Fuzzing

Abstract—Coverage-guided fuzzers expose bugs by progressively mutating testcases to drive execution to new program locations. Code coverage is currently the most effective and popular exploration feedback. For several bugs, though, also how execution reaches a buggy program location may matter: for those, only tracking what code a testcase exercises may lead fuzzers to overlook interesting program states. Unfortunately, context-sensitive coverage tracking comes with an inherent state explosion problem. Existing attempts to implement context- sensitive coverage-guided fuzzers struggle with it, experiencing non-trivial issues for precision (due to coverage collisions) and performance (due to context tracking and queue/map explosion). In this paper, we show that a much more effective approach to context-sensitive fuzzing is possible. First, we propose function cloning as a backward-compatible instrumentation primitive to enable precise (i.e., collision-free) context-sensitive coverage tracking. Then, to tame the state explosion problem, we argue to account for contextual information only when a fuzzer explores contexts selected as promising. We propose a prediction scheme to identify one pool of such contexts: we analyze the data-flow diversity of the incoming argument values at call sites, exposing to the fuzzer a contextually refined clone of the callee if the latter sees incoming abstract objects that its uses at other sites do not. Our work shows that, by applying function cloning to pro- gram regions that we predict to benefit from context-sensitivity, we can overcome the aforementioned issues while preserving, and even improving, fuzzing effectiveness. On the FuzzBench suite, our approach largely outperforms state-of-the-art coverage-guided fuzzing embodiments, unveiling more and different bugs without incurring explosion or other apparent inefficiencies. On these heavily tested subjects, we also found 8 enduring security issues in 5 of them, with 6 CVE identifiers issued.