Data processing oriented software, especially machine learning applications, are heavily dependent on standard frameworks/libraries such as TensorFlow and OpenCV. As those frameworks have gained significant popularity, the exploitation of vulnerabilities in the frameworks has become a critical security concern. While software isolation can minimize the impact of exploitation, existing approaches suffer from difficulty analyzing complex program dependencies or excessive overhead, making them ineffective in practice.

Decompilation is a crucial capability in forensic analysis, facilitating analysis of unknown binaries. The recent rise of Python malware has brought attention to Python decompilers that aim to obtain source code representation from a Python binary. However, Python decompilers fail to handle various binaries, limiting their capabilities in forensic analysis.This paper proposes a novel solution that transforms a decompilation error-inducing Python binary into a decompilable binary. Our key intuition is that we can resolve the decompilation errors by transforming error-inducing code blocks in the input binary into another form. The core of our approach is the concept of Forensically Equivalent Transformation (FET) which allows non-semantic preserving transformation in the context of forensic analysis. We carefully define the FETs to minimize their undesirable consequences while fixing various error-inducing instructions that are difficult to solve when preserving the exact semantics. We evaluate the prototype of our approach with 17,117 real-world Python malware samples causing decompilation errors in five popular decompilers. It successfully identifies and fixes 77,022 errors. Our approach also handles anti-analysis techniques, including opcode remapping, and helps migrate Python 3.9 binaries to 3.8 binaries.

Inspired by swarms in nature, swarm robotics have been developed to conduct various challenging tasks such as environmental monitoring, disaster recovery, logistics, and even military operations. Despite the significant potential impact of the swarm on society, relatively little attention is given to adversarial scenarios against swarm robotics. In this paper, we explore a systematic approach to find logical flaws of the swarm robotics algorithms that adversaries can exploit. Specifically, we develop an automated testing system, SWARMFLAWFINDER, for swarm algorithms. We identify and overcome various challenges in understanding and reasoning about the swarm algorithm execution. In particular, we propose a novel abstraction of robotics behavior, which we call the degree of causal contribution (DCC), based on the idea of counterfactual causality. Then, we build a feedback guided greybox fuzz testing system called SWARMFLAWFINDER, leveraging DCC as a feedback metric. We evaluate SWARMFLAWFINDER with four swarm algorithms conducting navigating, searching, and rescuing missions. SWARMFLAWFINDER discovers 42 logic flaws (and all of them have been acknowledged by the developers) in the swarm algorithms. Our analysis of the flaws reveals that the swarm algorithms have critical logic errors/bugs or suffer from incomplete implementations that can be exploited by adversaries.

Injection attacks have been a major threat to web applications. Despite the significant effort in thwarting injection attacks, protection against injection attacks remains challenging due to the sophisticated attacks that exploit the existing protection techniques' design and implementation flaws. In this paper, we develop Spinner, a system that provides general protection against input injection attacks, including OS/shell command, SQL, and XXE injection. Instead of focusing on detecting malicious inputs, Spinner constantly randomizes underlying subsystems so that injected inputs (e.g., commands or SQL queries) that are not properly randomized will not be executed, hence prevented. We revisit the design and implementation choices of previous randomization-based techniques and develop a more robust and practical protection against various sophisticated input injection attacks. To handle complex real-world applications, we develop a bidirectional analysis that combines forward and backward static analysis techniques to identify in-tended commands or SQL queries to ensure the correct execution of the randomized target program. We implement Spinner for the shell command processor and two different database engines(MySQL and SQLite) and in diverse programming languages including C/C++, PHP, JavaScript and Lua. Our evaluation results on 42real-world applications including 27 vulnerable ones show that it effectively prevents a variety of input injection attacks with low runtime overhead (around 5%).

Swarm robotics collectively solve problems that are challenging for individual robots, from environmental monitoring to entertainment. The algorithms enabling swarms allow individual robots of the swarm to plan, share, and coordinate their trajectories and tasks to achieve a common goal. Such algorithms rely on a large number of configurable parameters that can be tailored to target particular scenarios. This large configuration space, the complexity of the algorithms, and the dependencies with the robots’ setup and performance make debugging and fixing swarms configuration bugs extremely challenging. This paper proposes Swarmbug, a swarm debugging system that automatically diagnoses and fixes buggy behaviors caused by misconfiguration. The essence of Swarmbug is the novel concept called the degree of causal contribution (Dcc), which abstracts impacts of environment configurations (e.g., obstacles) to the drones in a swarm via behavior causal analysis. Swarmbug automatically generates, validates, and ranks fixes for configuration bugs. We evaluate Swarmbug on four diverse swarm algorithms. Swarmbug successfully fixes four configuration bugs in the evaluated algorithms, showing that it is generic and effective. We also conduct a real-world experiment with physical drones to show the Swarmbug’s fix is effective in the real-world.