# Binary-level Directed Fuzzing for Use-After-Free Vulnerabilities

arXiv: https://arxiv.org/abs/2002.10751 · HTML: https://mlsanity.com/paper/2002.10751/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2020.

## Built on by (most cited first, 4 of 4)

- [Locus: Agentic Predicate Synthesis for Directed Fuzzing](https://mlsanity.com/paper/2508.21302/) (2025)
- [Enhancing Reverse Engineering: Investigating and Benchmarking Large Language Models for Vulnerability Analysis in Decompiled Binaries](https://mlsanity.com/paper/2411.04981/) (2024)
- [NeuDep: Neural Binary Memory Dependence Analysis](https://mlsanity.com/paper/2210.02853/) (2022)
- [DeepGo: Predictive Directed Greybox Fuzzing](https://mlsanity.com/paper/2507.21952/) (2025)

## Abstract (arXiv)

Directed fuzzing focuses on automatically testing specific parts of the code by taking advantage of additional information such as (partial) bug stack trace, patches or risky operations. Key applications include bug reproduction, patch testing and static analysis report verification. Although directed fuzzing has received a lot of attention recently, hard-to-detect vulnerabilities such as Use-After-Free (UAF) are still not well addressed, especially at the binary level. We propose UAFuzz, the first (binary-level) directed greybox fuzzer dedicated to UAF bugs. The technique features a fuzzing engine tailored to UAF specifics, a lightweight code instrumentation and an efficient bug triage step. Experimental evaluation for bug reproduction on real cases demonstrates that UAFuzz significantly outperforms state-of-the-art directed fuzzers in terms of fault detection rate, time to exposure and bug triaging. UAFuzz has also been proven effective in patch testing, leading to the discovery of 30 new bugs (7 CVEs) in programs such as Perl, GPAC and GNU Patch. Finally, we provide to the community a large fuzzing benchmark dedicated to UAF, built on both real codes and real bugs.