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

Can a Cybersecurity Question Answering Assistant Help Change User...

Lea Duesterwald (Carnegie Mellon University), Ian Yang (Carnegie Mellon University), Norman Sadeh (Carnegie Mellon University)

Read More

Siniel: Distributed Privacy-Preserving zkSNARK

Yunbo Yang (The State Key Laboratory of Blockchain and Data Security, Zhejiang University), Yuejia Cheng (Shanghai DeCareer Consulting Co., Ltd), Kailun Wang (Beijing Jiaotong University), Xiaoguo Li (College of Computer Science, Chongqing University), Jianfei Sun (School of Computing and Information Systems, Singapore Management University), Jiachen Shen (Shanghai Key Laboratory of Trustworthy Computing, East China Normal…

Read More

MALintent: Coverage Guided Intent Fuzzing Framework for Android

Ammar Askar (Georgia Institute of Technology), Fabian Fleischer (Georgia Institute of Technology), Christopher Kruegel (University of California, Santa Barbara), Giovanni Vigna (University of California, Santa Barbara), Taesoo Kim (Georgia Institute of Technology)

Read More