Matteo Marini (Sapienza University of Rome), Daniele Cono D'Elia (Sapienza University of Rome), Mathias Payer (EPFL), Leonardo Querzoni (Sapienza University of Rome)

Fuzzing evolved into the most popular technique to detect bugs in software. Its combination with sanitizers has shown tremendous efficacy in uncovering memory safety errors, such as buffer overflows, that haunt C and C++ programmers. However, an important class of such issues, the so-called use-of-uninitialized-memory (UUM) errors, struggles to gain similar benefits from fuzzing endeavors. The only fuzzer-compatible UUM sanitizer available to date, MSan, requires that all libraries are fully instrumented. Unlike address sanitization, for which partial instrumentation results in false negatives (missed detection of bugs), UUM sanitizers require complete instrumentation to avoid false positives, hampering testing at scale. Yet, full-stack compiler-based instrumentation can be a daunting prospect for compatibility and practicality. As a result, many programs are left untested for UUM bugs.

In this paper, we propose an efficient multi-layer, opportunistic design that does not require (source-based) recompilation of all code without harming accuracy. The multiplicity of executions when fuzzing offers us the opportunity to learn what any encountered false positive looks like, and later ignore them when we meet them again with new test cases. Such an avenue is feasible only if one can resort to fast techniques to effectively discriminate candidate errors, or false negatives will then occur.

We show how to realize this design by using the dynamic binary translation of QEMU for compatibility and lightweight code analysis techniques to achieve scalability and accuracy. As a result, we obtain a fuzzer-friendly, performant sanitizer, QMSan, that effectively tackles current practicality challenges of UUM error detection. On a collection of 10 open-source and 5 proprietary programs, QMSan exposed 44 new UUM bugs. In our tests, QMSan incurs slowdowns of 1.51x over QEMU and 1.55x over the compiler-based instrumentation of MSan, showing no false positives and false negatives. QMSan is open-source.

View More Papers

EAGLEYE: Exposing Hidden Web Interfaces in IoT Devices via...

Hangtian Liu (Information Engineering University), Lei Zheng (Institute for Network Sciences and Cyberspace (INSC), Tsinghua University), Shuitao Gan (Laboratory for Advanced Computing and Intelligence Engineering), Chao Zhang (Institute for Network Sciences and Cyberspace (INSC), Tsinghua University), Zicong Gao (Information Engineering University), Hongqi Zhang (Henan Key Laboratory of Information Security), Yishun Zeng (Institute for Network Sciences…

Read More

Incorporating Gradients to Rules: Towards Lightweight, Adaptive Provenance-based Intrusion...

Lingzhi Wang (Northwestern University), Xiangmin Shen (Northwestern University), Weijian Li (Northwestern University), Zhenyuan LI (Zhejiang University), R. Sekar (Stony Brook University), Han Liu (Northwestern University), Yan Chen (Northwestern University)

Read More

BitShield: Defending Against Bit-Flip Attacks on DNN Executables

Yanzuo Chen (The Hong Kong University of Science and Technology), Yuanyuan Yuan (The Hong Kong University of Science and Technology), Zhibo Liu (The Hong Kong University of Science and Technology), Sihang Hu (Huawei Technologies), Tianxiang Li (Huawei Technologies), Shuai Wang (The Hong Kong University of Science and Technology)

Read More

EMIRIS: Eavesdropping on Iris Information via Electromagnetic Side Channel

Wenhao Li (Shandong University), Jiahao Wang (Shandong University), Guoming Zhang (Shandong University), Yanni Yang (Shandong University), Riccardo Spolaor (Shandong University), Xiuzhen Cheng (Shandong University), Pengfei Hu (Shandong University)

Read More