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Rapid Taint Assisted Concolic Execution (TACE)

Authors: Ridhi Jain; Norbert Tihanyi; Mthandazo Ndhlovu; Mohamed Amine Ferrag; Lucas C. Cordeiro;

Rapid Taint Assisted Concolic Execution (TACE)

Abstract

While fuzz testing is a popular choice for testing open-source software, it might not effectively detect bugs in programs that feature many symbols due to the significant increase in exploration of the program executions. Fuzzers can be more effective when they concentrate on a smaller and more relevant set of symbols, focusing specifically on the key executions. We present rapid Taint Assisted Concolic Execution (TACE), which utilizes the concept of taint in symbolic execution to identify all sets of dependent symbols. TACE can evaluate a subset of these sets with a significantly reduced testing effort by concretizing some symbols from selected subsets. The remaining subsets are explored with symbolic values. TACE significantly enhances speed, achieving a 50x constraint-solving time improvement over SymQEMU in binary applications. In our fuzing campaign, we tested five open-source libraries (minizip-ng, TPCDump, GifLib, OpenJpeg, bzip2) and identified a new heap buffer overflow in the latest version of GifLib 5.2.1 with an assigned CVE-2023-48161 number. Under identical conditions and hardware environments, SymCC could not identify the same issue, underscoring TACE’s enhanced capability in quickly discovering real-world vulnerabilities. A demo video for TACE is available at: https://www.youtube.com/watch?v=FZpxPNsp IE.

Country
United Kingdom
Related Organizations
Keywords

Fuzzing, symbolic execution, bug hunting

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Top 10%
Average
Average
Green