Yanhao Wang (Institute of Software, Chinese Academy of Sciences), Xiangkun Jia (Pennsylvania State University), Yuwei Liu (Institute of Software, Chinese Academy of Sciences), Kyle Zeng (Arizona State University), Tiffany Bao (Arizona State University), Dinghao Wu (Pennsylvania State University), Purui Su (Institute of Software, Chinese Academy of Sciences)

Coverage-based fuzzing has been actively studied and widely adopted for finding vulnerabilities in real-world software applications. With code coverage, such as statement coverage and transition coverage, as the guidance of input mutation, coverage-based fuzzing can generate inputs that cover more code and thus find more vulnerabilities without prerequisite information such as input format. Current coverage-based fuzzing tools treat covered code equally. All inputs that contribute to new statements or transitions are kept for future mutation no matter what the statements or transitions are and how much they impact security. Although this design is reasonable from the perspective of software testing, which aims to full code coverage, it is inefficient for vulnerability discovery since that 1) current techniques are still inadequate to reach full coverage within a reasonable amount of time, and that 2) we always want to discover vulnerabilities early so that it can be patched promptly. Even worse, due to the non-discriminative code coverage treatment, current fuzzing tools suffer from recent anti-fuzzing techniques and become much less effective in finding real-world vulnerabilities.

To resolve the issue, we propose coverage accounting, an innovative approach that evaluates code coverage by security impacts. Based on the proposed metrics, we design a new scheme to prioritize fuzzing inputs and develop TortoiseFuzz, a greybox fuzzer for memory corruption vulnerabilities. We evaluated TortoiseFuzz on 30 real-world applications and compared it with 5 state-of-the-art greybox and hybrid fuzzers (AFL, AFLFast, FairFuzz, QSYM, and Angora). TortoiseFuzz outperformed all greybox fuzzers and most hybrid fuzzers. It also had comparative results for other hybrid fuzzers yet consumed much fewer resources. Additionally, TortoiseFuzz found 18 new real-world vulnerabilities and has got 8 new CVEs so far. We will open source TortoiseFuzz to foster future research.

View More Papers

Designing a Better Browser for Tor with BLAST

Tao Wang (Hong Kong University of Science and Technology)

Read More

A Practical Approach for Taking Down Avalanche Botnets Under...

Victor Le Pochat (imec-DistriNet, KU Leuven), Tim Van hamme (imec-DistriNet, KU Leuven), Sourena Maroofi (Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG), Tom Van Goethem (imec-DistriNet, KU Leuven), Davy Preuveneers (imec-DistriNet, KU Leuven), Andrzej Duda (Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG), Wouter Joosen (imec-DistriNet, KU Leuven), Maciej Korczyński (Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG)

Read More

When Match Fields Do Not Need to Match: Buffered...

Jiahao Cao (Tsinghua University; George Mason University), Renjie Xie (Tsinghua University), Kun Sun (George Mason University), Qi Li (Tsinghua University), Guofei Gu (Texas A&M University), Mingwei Xu (Tsinghua University)

Read More

Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning

Harsh Chaudhari (Indian Institute of Science, Bangalore), Rahul Rachuri (Aarhus University, Denmark), Ajith Suresh (Indian Institute of Science, Bangalore)

Read More