We provide artifacts to reproduce the evaluation results of our article: “NSFuzz: Towards Efficient and State-Aware Network Service Fuzzing”. The provided artifacts can be downloaded from https://zenodo.org/record/7134490 . It includes 14 docker containers, several scripts for execution and analysis, one additional proof for the crash results, and six related documents for the running of experiments. We claim for all three badges, i.e., Available, Functional, and Reusable. This report gives instructions on how to reproduce the answers which mainly involve basic operations on the Ubuntu operating system.

As an essential component responsible for communication, network services are security critical, thus, it is vital to find their vulnerabilities. Fuzzing is currently one of the most popular software vulnerability discovery techniques, widely adopted due to its high efficiency and low false positives. However, existing coverage-guided fuzzers mainly aim at stateless local applications, leaving stateful network services underexplored. Recently, some fuzzers targeting network services have been proposed but have certain limitations, for example, insufficient or inaccurate state representation and low testing efficiency. In this article, we propose a new fuzzing solution NSFuzz for stateful network services. We studied typical implementations of network service programs to determine how they represent states and interact with clients. Accordingly, we propose (1) a program variable–based state representation scheme and (2) an efficient interaction synchronization mechanism to improve fuzzing efficiency. We implemented a prototype of NSFuzz, which uses static analysis and annotation application programming interfaces (APIs) to identify synchronization points and state variables within the services. It then achieves fast I/O synchronization and accurate service state tracing to carry out efficient state-aware fuzzing via lightweight compile-time instrumentation. The evaluation results show that compared with other network service fuzzers, including AFL net and S tate AFL, our solution NSFuzz could infer a more accurate state model during fuzzing and improve fuzzing throughput by up to 200×. In addition, NSFuzz could improve code coverage by up to 25% and trigger more crashes in less time. We also performed a fuzzing campaign to find new bugs in the latest version of the target services; 8 zero-day vulnerabilities have been found by NSFuzz.

Linux drivers share the same address space and privilege with the core of the kernel but have a much larger code base and attack surface. The Linux drivers are not well tested and have weaker security guarantees than the kernel. Missing support from hardware devices, existing fuzzing solutions fail to cover a large portion of the driver code, e.g., the initialization code and interrupt handlers. In this paper, we present PrIntFuzz, an efficient and universal fuzzing framework that can test the overlooked driver code, including the PRobing code and INTerrupt handlers. PrIntFuzz first extracts knowledge from the driver through inter-procedural field-sensitive, path-sensitive, and flow-sensitive static analysis. Then it utilizes the information to build a flexible and efficient simulator, which supports device probing, hardware interrupts emulation and device I/O interception. Lastly, PrIntFuzz applies a multi-dimension fuzzing strategy to explore the overlooked code. We have developed a prototype of PrIntFuzz and successfully simulated 311 virtual PCI (Peripheral Component Interconnect) devices, 472 virtual I2C (Inter-Integrated Circuit) devices, 169 virtual USB (Universal Serial Bus) devices, and found 150 bugs in the corresponding device drivers. We have submitted patches for these bugs to the Linux kernel community, and 59 patches have been merged so far. In a control experiment of Linux 5.10-rc6, PrIntFuzz found 99 bugs, while the state-of-the-art fuzzer only found 50. PrIntFuzz covers 11,968 basic blocks on the latest Linux kernel, while the state-of-the-art fuzzer Syzkaller only covers 2,353 basic blocks.

A growing number of bugs have been reported by vulnerability discovery solutions. Among them, some bugs are hard to diagnose or reproduce, including data race bugs caused by thread interleavings. Few solutions are able to well address this issue, due to the huge space of interleavings to explore. What’s worse, in security analysis scenarios, analysts usually have no access to the source code of target programs and have troubles in comprehending them.

Security vulnerability is one of the root causes of cyber-security threats. To discover vulnerabilities and fix them in advance, researchers have proposed several techniques, among which fuzzing is the most widely used one. In recent years, fuzzing solutions, like AFL, have made great improvements in vulnerability discovery. This paper presents a summary of the recent advances, analyzes how they improve the fuzzing process, and sheds light on future work in fuzzing. Firstly, we discuss the reason why fuzzing is popular, by comparing different commonly used vulnerability discovery techniques. Then we present an overview of fuzzing solutions, and discuss in detail one of the most popular type of fuzzing, i.e., coverage-based fuzzing. Then we present other techniques that could make fuzzing process smarter and more efficient. Finally, we show some applications of fuzzing, and discuss new trends of fuzzing and potential future directions.