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.

The delta debugging problem concerns how to reduce an object while preserving a certain property, and widely exists in many applications, such as compiler development, regression fault localization, and software debloating. Given the importance of delta debugging, multiple algorithms have been proposed to solve the delta debugging problem efficiently and effectively. However, the efficiency and effectiveness of the state-of-the-art algorithms are still not satisfactory. For example, the state-of-the-art delta debugging tool, CHISEL, may take up to 3 hours to reduce a single program with 14,092 lines of code, while the reduced program may be up to 2 times unnecessarily large.

Integer Linear Programming (ILP) formulations of multi-label Markov random fields (MRFs) models with global connectivity priors were investigated previously in computer vision. In these works, only Linear Programming (LP) relaxations [1] or simplified versions [2] of the problem were solved. This paper investigates the ILP of MRF with exact connectivity priors via a branch-and-cut method, which provably finds globally optimal solutions. It enforces connectivity priors iteratively by a cutting plane method, and provides feasible solutions with a guarantee on sub-optimality even if we terminate it earlier. The proposed ILP can be applied as a post-processing method on top of any existing multi-label segmentation approach. As it provides globally optimal solution, it can be used off-line to serve as quality check for any fast on-line algorithm. Furthermore, the scribble based model presented in this paper could be potentially used to generate ground-truth proposals for any deep learning based segmentation. We demonstrate the power and usefulness of our model by extensive experiments on the BSDS500 and PASCAL VOC dataset. The experiments show that our proposed model achieves great performance, yielding provably global optimum in most instances and that provably good optimization solutions also provide good segmentation accuracy, even with the limited computing time of few seconds.