Many assisting exploration strategies have been proposed to assist grey-box fuzzers in exploring program states guarded by tight and complex branch conditions such as equality constraints. Although they have shown promising results in their original papers, their evaluations seldom follow equivalent protocols, e.g., they are rarely evaluated on identical benchmarks. Moreover, there is a lack of sufficient investigations on the specifics of the program states explored by these strategies which can obfuscate the future application and development of such strategies. Consequently, there is a pressing need for a comprehensive study of assisting exploration strategies on their effectiveness, versatility, and limitations to enlighten their future development. To this end, we perform the first comprehensive study about the assisting exploration strategies for grey-box fuzzers. Specifically, we first collect nine recent fuzzers representing the mainstream assisting exploration strategies as our studied subjects and 21 real-world projects to form our benchmark suite. After evaluating the subjects on the benchmark suite, we then surprisingly find that the dictionary strategy is most promising since it not only achieves similar or even slightly better performance over the other studied assisting exploration strategies in terms of exploring program states but also is more practical to be enhanced. Accordingly, we propose CDFUZZ, which generates a customized dictionary for each seed upon the baseline fuzzer AFL to improve over the original dictionary strategy. The evaluation results demonstrate that CDFUZZ increases the edge coverage by 16.1% on average for all benchmark projects over the best performer in our study (i.e., AFL++ with the dictionary strategy). CDFUZZ also successfully exposed 37 previously unknown bugs, with nine confirmed and seven fixed by the corresponding developers.

Taint analysis, i.e., labeling data and propagating the labels through data flows, has been widely used for analyzing program information flows and ensuring system/data security. Due to its important applications, various taint analysis techniques have been proposed, including static and dynamic taint analysis. However, existing taint analysis techniques can be hardly applied to the rising microservice systems for industrial applications. To address such a problem, in this paper, we proposed the first practical non-intrusive dynamic taint analysis technique MirrorTaint for extensively supporting microservice systems on JVMs. In particular, by instrumenting the microservice systems, MirrorTaint constructs a set of data structures with their respective policies for labeling/propagating taints in its mirrored space. Such data structures are essentially non-intrusive, i.e., modifying no program meta-data or runtime system. Then, during program execution, MirrorTaint replicates the stack-based JVM instruction execution in its mirrored space on-the-fly for dynamic taint tracking. We have evaluated MirrorTaint against state-of-the-art dynamic and static taint analysis systems on various popular open-source microservice systems. The results demonstrate that MirrorTaint can achieve better compatibility, quite close precision and higher recall (97.9%/100.0%) than state-of-the-art Phosphor (100.0%/9.9%) and FlowDroid (100%/28.2%). Also, MirrorTaint incurs lower runtime overhead than Phosphor (although both are dynamic techniques). Moreover, we have performed a case study in Ant Group, a global billion-user FinTech company, to compare MirrorTaint and their mature developer-experience-based data checking system for automatically generated fund documents. The result shows that the developer experience can be incomplete, causing the data checking system to only cover 84.0% total data relations, while MirrorTaint can automatically find 99.0% relations with 100.0% precision. Lastly, we also applied MirrorTaint to successfully detect a recently wide-spread Log4j2 security vulnerability.

For coverage-guided fuzzers, many of their adopted seeds are usually underused by exploring limited program states since essentially all their executions have to abide by rigorous program dependencies while only limited seeds are capable of accessing dependencies. Moreover, even when iteratively executing such limited seeds, the fuzzers have to repeatedly access the covered program states before uncovering new states. Such facts indicate that exploration power on program states of seeds has not been sufficiently leveraged by the existing coverage-guided fuzzing strategies. To tackle these issues, we propose a coverage-guided fuzzer, namely MirageFuzz, to mitigate the program dependencies when executing seeds for enhancing their exploration power on program states. Specifically, MirageFuzz first creates a “phantom” program of the target program by reducing its program dependencies corresponding to conditional statements while retaining their original semantics. Accordingly, MirageFuzz performs dual fuzzing, i.e., the source fuzzing to fuzz the original program and the phantom fuzzing to fuzz the phantom program simultaneously. Then, MirageFuzz applies the taint-based mutation mechanism to generate a new seed by updating the target conditional statement of a given seed from the source fuzzing with the corresponding condition value derived by the phantom fuzzing. To evaluate the effectiveness of MirageFuzz, we build a benchmark suite with 18 projects commonly adopted by recent fuzzing papers, and select seven open-source fuzzers as baselines for performance comparison with MirageFuzz. The experiment results suggest that MirageFuzz outperforms our baseline fuzzers from 13.42% to 77.96% averagely. Furthermore, MirageFuzz exposes 29 previously unknown bugs where 4 of them have been confirmed and 3 have been fixed by the corresponding developers.