{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/full-speed-fuzzing-reducing-fuzzing-overhead","title":"Full-speed Fuzzing: Reducing Fuzzing Overhead through Coverage-guided Tracing","arxiv_id":"1812.11875","date":"2018-12-31","proceeding":null,"authors":["Stefan Nagy","Matthew Hicks"],"abstract":"Of coverage-guided fuzzing's three main components: (1) testcase generation, (2) code coverage tracing, and (3) crash triage, code coverage tracing is a dominant source of overhead. Coverage-guided fuzzers trace every testcase's code coverage through either static or dynamic binary instrumentation, or more recently, using hardware support. Unfortunately, tracing all testcases incurs significant performance penalties---even when the overwhelming majority of testcases and their coverage information are discarded because they do not increase code coverage. To eliminate needless tracing by coverage-guided fuzzers, we introduce the notion of coverage-guided tracing. Coverage-guided tracing leverages two observations: (1) only a fraction of generated testcases increase coverage, and thus require tracing; and (2) coverage-increasing testcases become less frequent over time. Coverage-guided tracing works by encoding the current frontier of code coverage in the target binary so that it self-reports when a testcase produces new coverage---without tracing. This acts as a filter for tracing; restricting the expense of tracing to only coverage-increasing testcases. Thus, coverage-guided tracing chooses to tradeoff increased coverage-increasing-testcase handling time for the ability to execute testcases initially at native speed. To show the potential of coverage-guided tracing, we create an implementation based on the static binary instrumentor Dyninst called UnTracer. We evaluate UnTracer using eight real-world binaries commonly used by the fuzzing community. Experiments show that after only an hour of fuzzing, UnTracer's average overhead is below 1%, and after 24-hours of fuzzing, UnTracer approaches 0% overhead, while tracing every testcase with popular white- and black-box-binary tracers AFL-Clang, AFL-QEMU, and AFL-Dyninst incurs overheads of 36%, 612%, and 518%, respectively.","url_abs":"https://arxiv.org/abs/1812.11875v2","url_pdf":"https://arxiv.org/pdf/1812.11875v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"full-speed-fuzzing-reducing-fuzzing-overhead","repo_url":"https://github.com/FoRTE-Research/FoRTE-FuzzBench","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"full-speed-fuzzing-reducing-fuzzing-overhead","repo_url":"https://github.com/FoRTE-Research/UnTracer-AFL","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"full-speed-fuzzing-reducing-fuzzing-overhead","repo_url":"https://github.com/FoRTE-Research/afl-fid","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}