ColorGo: Directed Concolic Execution
Jia Li, Jiacheng Shen, Yuxin Su, Michael R. Lyu

TL;DR
ColorGo is a novel directed whitebox fuzzer that combines concolic execution with incremental coloration techniques to achieve high scalability and precision, significantly outperforming existing methods like AFLGo in reaching targets.
Contribution
This paper introduces ColorGo, a new directed fuzzing approach that integrates compilation-based concolic execution with incremental coloration for improved efficiency and effectiveness.
Findings
ColorGo outperforms AFLGo by up to 100x in reaching target sites.
ColorGo effectively reproduces target crashes in diverse real-world programs.
ColorGo maintains high precision while achieving scalability.
Abstract
Directed fuzzing is a critical technique in cybersecurity, targeting specific sections of a program. This approach is essential in various security-related domains such as crash reproduction, patch testing, and vulnerability detection. Despite its importance, current directed fuzzing methods exhibit a trade-off between efficiency and effectiveness. For instance, directed grey-box fuzzing, while efficient in generating fuzzing inputs, lacks sufficient precision. The low precision causes time wasted on executing code that cannot help reach the target site. Conversely, interpreter- or observer-based directed symbolic execution can produce high-quality inputs while incurring non-negligible runtime overhead. These limitations undermine the feasibility of directed fuzzers in real-world scenarios. To kill the birds of efficiency and effectiveness with one stone, in this paper, we involve…
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Taxonomy
TopicsSoftware Testing and Debugging Techniques · Security and Verification in Computing · Advanced Malware Detection Techniques
