uafuzz

UAFuzz: Binary-level Directed Fuzzing for Use-After-Free Vulnerabilities Directed Greybox Fuzzing DGF like AFLGo aims to perform stress testing on pre-selected potentially vulnerable target locations, with applications to different security contexts: 1 bug reproduction, 2 patch testing or 3 stati...

uafuzz

UAFuzz：针对释放后使用漏洞的二进制级定向模糊测试 定向灰盒模糊测试 （DGF）如 AFLGo 旨在对预先选定的潜在易受攻击目标位置进行压力测试，适用于不同的安全场景：（1）漏洞复现，（2）补丁测试或（3）静态分析报告验证。近期有更多研究工作提升了定向模糊测试的有效性和效率（参见 awesome-directed-fuzzing）。 我们提出 UAFuzz ，这是一个 专注于二进制级释放后使用（UAF）漏洞的定向模糊器 ，通过精心调整定向模糊测试的关键组件，以适应此类漏洞的具体特征。UAF 漏洞在堆元素被释放后被使用时出现。检测 UAF 漏洞很困难：（1）复杂性...

DIG: Oracle-Guided Directed Input Generation for One-Day Vulnerabilities

One-day vulnerabilities pose significant risks due to delayed or incomplete patch adoption. Generating proof-of-concept PoC inputs is therefore essential for assessing real-world impact. The key challenge is identifying necessary constraints for triggering the vulnerability and solving them...

Rust and Go Directed Fuzzing with LibAFL-DiFuzz

In modern SSDLC, program analysis and automated testing are essential for minimizing vulnerabilities before software release, with fuzzing being a fast and widely used dynamic testing method. However, traditional coverage-guided fuzzing may be less effective in specific tasks like verifying stati...

AFLGopher: Accelerating Directed Fuzzing Via Feasibility-Aware Guidance

Directed fuzzing is a useful testing technique that aims to efficiently reach target code sites in a program. The core of directed fuzzing is the guiding mechanism that directs the fuzzing to the specified target. A general guiding mechanism adopted in existing directed fuzzers is to calculate th...

Intelligent Graybox Fuzzing Via ATPG-Guided Seed Generation and Submodule Analysis

Hardware Fuzzing emerged as one of the crucial techniques for finding security flaws in modern hardware designs by testing a wide range of input scenarios. One of the main challenges is creating high-quality input seeds that maximize coverage and speed up verification. Coverage-Guided Fuzzing CGF...

ATLANTIS: AI-Driven Threat Localization, Analysis, and Triage Intelligence System

We present ATLANTIS, the cyber reasoning system developed by Team Atlanta that won 1st place in the Final Competition of DARPA's AI Cyber Challenge AIxCC at DEF CON 33 August 2025. AIxCC 2023-2025 challenged teams to build autonomous cyber reasoning systems capable of discovering and patching...

Locus: Agentic Predicate Synthesis for Directed Fuzzing

Directed fuzzing aims to find program inputs that lead to specified target program states. It has broad applications, such as debugging system crashes, confirming reported bugs, and generating exploits for potential vulnerabilities. This task is inherently challenging because target states are...

Hybrid Approach to Directed Fuzzing

Program analysis and automated testing have recently become an essential part of SSDLC. Directed greybox fuzzing is one of the most popular automated testing methods that focuses on error detection in predefined code regions. However, it still lacks ability to overcome difficult program...

Directed Greybox Fuzzing Via Large Language Model

Directed greybox fuzzing DGF focuses on efficiently reaching specific program locations or triggering particular behaviors, making it essential for tasks like vulnerability detection and crash reproduction. However, existing methods often suffer from path explosion and randomness in input mutatio...