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Coverage-guided fuzzing's aggressive, high-volume testing has helped reveal tens of thousands of software security flaws. While executing billions of test cases mandates fast code coverage tracing, the nature of binary-only targets leads to reduced tracing performance. A recent advancement in binary fuzzing performance is Coverage-guided Tracing (CGT), which brings orders-of-magnitude gains in throughput by restricting the expense of coverage tracing to only when new coverage is guaranteed. Unfortunately, CGT suits only a basic block coverage granularity---yet most fuzzers require finer-grain coverage metrics: edge coverage and hit counts. It is this limitation which prohibits nearly all of today's state-of-the-art fuzzers from attaining the performance benefits of CGT.This paper tackles the challenges of adapting CGT to fuzzing's most ubiquitous coverage metrics. We introduce and implement a suite of enhancements that expand CGT's introspection to fuzzing's most common code coverage metrics, while maintaining its orders-of-magnitude speedup over conventional always-on coverage tracing. We evaluate their trade-offs with respect to fuzzing performance and effectiveness across 12 diverse real-world binaries (8 open- and 4 closed-source). On average, our coverage-preserving CGT attains near-identical speed to the present block-coverage-only CGT, UnTracer; and outperforms leading binary- and source-level coverage tracers QEMU, Dyninst, RetroWrite, and AFL-Clang by 2--24x, finding more bugs in less time. 

Coverage-guided fuzzing is one of the most effective software security testing techniques. Fuzzing takes on one of two forms: compiler-based or binary-only, depending on the availability of source code. While the fuzzing community has improved compiler-based fuzzing with performance- and feedback-enhancing program transformations, binary-only fuzzing lags behind due to the semantic and performance limitations of instrumenting code at the binary level. Many fuzzing use cases are binary-only (i.e., closed source). Thus, applying fuzzing-enhancing program transformations to binary-only fuzzing—without sacrificing performance—remains a compelling challenge. This paper examines the properties required to achieve compiler-quality binary-only fuzzing instrumentation. Based on our findings, we design ZAFL: a platform for applying fuzzing-enhancing program transformations to binary-only targets—maintaining compiler-level performance. We showcase ZAFL's capabilities in an implementation for the popular fuzzer AFL, including five compiler-style fuzzing-enhancing transformations, and evaluate it against the leading binary-only fuzzing instrumenters AFL-QEMU and AFL-Dyninst. Across LAVA-M and real-world targets, ZAFL improves crash-finding by 26–96% and 37–131%; and throughput by 48– 78% and 159–203% compared to AFL-Dyninst and AFL-QEMU, respectively—while maintaining compiler-level of overhead of 27%. We also show that ZAFL supports real-world open- and closed-source software of varying size (10K– 100MB), complexity (100–1M basic blocks), platform (Linux and Windows), and format (e.g., stripped and PIC).