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TaintScope: A Checksum-Aware Directed Fuzzing Tool for Automatic Software Vulnerability Detection
Abstract
Fuzz testing has proven successful in finding security vulnerabilities in large programs. However, traditional fuzz testing tools have a well-known common drawback: they are ineffective if most generated malformed inputs are rejected in the early stage of program running, especially when target programs employ checksum mechanisms to verify the integrity of inputs. In this paper, we present TaintScope, an automatic fuzzing system using dynamic taint analysis and symbolic execution techniques, to tackle the above problem. TaintScope has several novel contributions: 1) TaintScope is the first checksum-aware fuzzing tool to the best of our knowledge. It can identify checksum fields in input instances, accurately locate checksum-based integrity checks by using branch profiling techniques, and bypass such checks via control flow alteration. 2) TaintScope is a directed fuzzing tool working at X86 binary level (on both Linux and Window). Based on fine-grained dynamic taint tracing, TaintScope identifies which bytes in a well-formed input are used in security-sensitive operations (e.g., invoking system/library calls) and then focuses on modifying such bytes. Thus, generated inputs are more likely to trigger potential vulnerabilities. 3) TaintScope is fully automatic, from detecting checksum, directed fuzzing, to repairing crashed samples. It can fix checksum values in generated inputs using combined concrete and symbolic execution techniques. We evaluate TaintScope on a number of large real-world applications. Experimental results show that TaintScope can accurately locate the checksum checks in programs and dramatically improve the effectiveness of fuzz testing. TaintScope has already found 27 previously unknown vulnerabilities in several widely used applications, including Adobe Acrobat, Google Picasa, Microsoft Paint, and ImageMagick. Most of these severe vulnerabilities have been confirmed by Secunia and oCERT, and assigned CVE identifiers (such as CVE-2009-1882, CVE-2009-2688). Corresponding patches from vendors are released or in progress based on our reports.
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Cited by
Böhme, M., Pham, V.-T., Nguyen, M.-D., & Roychoudhury, A. (2017) · Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/3133956.3134020 · Cited by 747
Coverage-based Greybox Fuzzing as Markov Chain
Böhme, M., Pham, V.-T., & Roychoudhury, A. (2016) · Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2976749.2978428 · Cited by 649
(2021) · doi:10.1109/tse.2019.2946563 · Cited by 574
FairFuzz: a targeted mutation strategy for increasing greybox fuzz testing coverage
Lemieux, C., & Sen, K. (2018) · Proceedings of the 33rd ACM/IEEE International Conference on Automated Software Engineering · doi:10.1145/3238147.3238176 · Cited by 477
LAVA: Large-Scale Automated Vulnerability Addition
Dolan-Gavitt, B., Hulin, P., Kirda, E., Leek, T., Mambretti, A., Robertson, W., Ulrich, F., & Whelan, R. (2016) · 2016 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2016.15 · Cited by 300
Metadata
- Authors
- Wang, T., Wei, T., Gu, G., & Zou, W.
- Venue
- 2010 IEEE Symposium on Security and Privacy
- Publisher
- IEEE
- Published
- 2010
- Pages
- 497-512
- ISSN
- 1081-6011 (ISSN-L)
- OpenAlex
- W1976878954
- Cited by
- 380
- References
- 0
- FWCI
- 10.59
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Wang, T., Wei, T., Gu, G., & Zou, W. (2010). TaintScope: A Checksum-Aware Directed Fuzzing Tool for Automatic Software Vulnerability Detection. 2010 IEEE Symposium on Security and Privacy. https://doi.org/10.1109/sp.2010.37
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