arXiv subjects

Tai Yue

Publications and source records attributed to Tai Yue.

4 recordsLinked to original sources

arXiv2024

INSTILLER: Towards Efficient and Realistic RTL Fuzzing

Bugs exist in hardware, such as CPU. Unlike software bugs, these hardware bugs need to be detected before deployment. Previous fuzzing work in CPU bug detection has several disadvantages, e.g., the length of RTL input instructions keeps growing, and longer inputs are ineffective for fuzzing. In this paper, we propose INSTILLER (Instruction Distiller), an RTL fuzzer based on ant colony optimization (ACO). First, to keep the input instruction length short and efficient in fuzzing, it distills input instructions with a variant of ACO (VACO). Next, related work cannot simulate realistic interruptions well in fuzzing, and INSTILLER solves the problem of inserting interruptions and exceptions in generating the inputs. Third, to further improve the fuzzing performance of INSTILLER, we propose hardware-based seed selection and mutation strategies. We implement a prototype and conduct extensive experiments against state-of-the-art fuzzing work in real-world target CPU cores. In experiments, INSTILLER has 29.4% more coverage than DiFuzzRTL. In addition, 17.0% more mismatches are detected by INSTILLER. With the VACO algorithm, INSTILLER generates 79.3% shorter input instructions than DiFuzzRTL, demonstrating its effectiveness in distilling the input instructions. In addition, the distillation leads to a 6.7% increase in execution speed on average.

cs.CR↗

arXiv2024

MobFuzz: Adaptive Multi-objective Optimization in Gray-box Fuzzing

Coverage-guided gray-box fuzzing (CGF) is an efficient software testing technique. There are usually multiple objectives to optimize in CGF. However, existing CGF methods cannot successfully find the optimal values for multiple objectives simultaneously. In this paper, we propose a gray-box fuzzer for multi-objective optimization (MOO) called MobFuzz. We model the multi-objective optimization process as a multi-player multi-armed bandit (MPMAB). First, it adaptively selects the objective combination that contains the most appropriate objectives for the current situation. Second, our model deals with the power schedule, which adaptively allocates energy to the seeds under the chosen objective combination. In MobFuzz, we propose an evolutionary algorithm called NIC to optimize our chosen objectives simultaneously without incurring additional performance overhead. To prove the effectiveness of MobFuzz, we conduct experiments on 12 real-world programs and the MAGMA data set. Experiment results show that multi-objective optimization in MobFuzz outperforms single-objective fuzzing in the baseline fuzzers. In contrast to them, MobFuzz can select the optimal objective combination and increase the values of multiple objectives up to 107%, with at most a 55% reduction in the energy consumption. Moreover, MobFuzz has up to 6% more program coverage and finds 3x more unique bugs than the baseline fuzzers. The NIC algorithm has at least a 2x improvement with a performance overhead of approximately 3%.

cs.CR↗

arXiv2020

UltraFuzz: Towards Resource-saving in Distributed Fuzzing

Recent research has sought to improve fuzzing performance via parallel computing. However, researchers focus on improving efficiency while ignoring the increasing cost of testing resources. Parallel fuzzing in the distributed environment amplifies the resource-wasting problem caused by the random nature of fuzzing. In the parallel mode, owing to the lack of an appropriate task dispatching scheme and timely fuzzing status synchronization among different fuzzing instances, task conflicts and workload imbalance occur, making the resource-wasting problem severe. In this paper, we design UltraFuzz, a fuzzer for resource-saving in distributed fuzzing. Based on centralized dynamic scheduling, UltraFuzz can dispatch tasks and schedule power globally and reasonably to avoid resource-wasting. Besides, UltraFuzz can elastically allocate computing power for fuzzing and seed evaluation, thereby avoiding the potential bottleneck of seed evaluation that blocks the fuzzing process. UltraFuzz was evaluated using real-world programs, and the results show that with the same testing resource, UltraFuzz outperforms state-of-the-art tools, such as AFL, AFL-P, PAFL, and EnFuzz. Most importantly, the experiment reveals certain results that seem counter-intuitive, namely that parallel fuzzing can achieve ``super-linear acceleration'' when compared with single-core fuzzing. We conduct additional experiments to reveal the deep reasons behind this phenomenon and dig deep into the inherent advantages of parallel fuzzing over serial fuzzing, including the global optimization of seed energy scheduling and the escape of local optimal seed. Additionally, 24 real-world vulnerabilities were discovered using UltraFuzz.

cs.CR↗

arXiv2020

The Progress, Challenges, and Perspectives of Directed Greybox Fuzzing

Greybox fuzzing is a scalable and practical approach for software testing. Most greybox fuzzing tools are coverage-guided as reaching high code coverage is more likely to find bugs. However, since most covered codes may not contain bugs, blindly extending code coverage is less efficient, especially for corner cases. Unlike coverage-guided greybox fuzzing which increases code coverage in an undirected manner, directed greybox fuzzing (DGF) spends most of its time allocation on reaching specific targets (e.g., the bug-prone zone) without wasting resources stressing unrelated parts. Thus, DGF is particularly suitable for scenarios such as patch testing,bug reproduction, and special bug detection. For now, DGF has become an active research area. However, DGF has general limitations and challenges that are worth further studying. Based on the investigation of 42 state-of-the-art fuzzers that are closely related to DGF, we conduct the first in-depth study to summarize the empirical evidence on the research progress of DGF. This paper studies DGF from a broader view, which takes into account not only the location-directed type that targets specific code parts, but also the behavior-directed type that aims to expose abnormal program behaviors. By analyzing the benefits and limitations of DGF research, we try to identify gaps in current research, meanwhile, reveal new research opportunities, and suggest areas for further investigation.

cs.CR↗