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Finding collision-free paths is crucial for autonomous multi-robots (AMRs) to complete assigned missions, ranging from search operations to military tasks. To achieve this, AMRs rely on collaborative collision avoidance algorithms. Unfortunately, the robustness of these algorithms against false data injection attacks (FDIAs) remains unexplored. In this paper, we introduce Raven, a tool to identify effective and stealthy semantic attacks (eg, herding). Effective attacks minimize positional displacement and the number of false data injections by using temporal logic and stochastic optimization techniques. Stealthy attacks remain within sensor noise ranges and maintain spatiotemporal consistency. We evaluate Raven against two state-of-the-art collision avoidance algorithms, ORCA and GLAS. Our results show that a single false data injection impacts multi-robot systems by causing position deviation or even collisions. We evaluate Raven on three testbeds–a numerical simulator, a high-fidelity simulator, and Crazyflie drones. Our results reveal five design flaws in these algorithms and underscore the importance of developing robust defenses against FDIAs. Finally, we propose countermeasures to mitigate the attacks we have uncovered. 

Language model approaches have recently been integrated into binary analysis tasks, such as function similarity detection and function signature recovery. These models typically employ a two-stage training process: pre-training via Masked Language Modeling (MLM) on machine code and fine-tuning for specific tasks. While MLM helps to understand binary code struc- tures, it ignores essential code characteristics, including control and data flow, which negatively affect model generalization. Recent work leverages domain-specific features (e.g., control flow graphs and dynamic execution traces) in transformer-based approaches to improve binary code semantic understanding. However, this approach involves complex feature engineering, a cumbersome and time-consuming process that can introduce predictive uncertainty when dealing with stripped or obfuscated code, leading to a performance drop. In this paper, we introduce PROTST, a novel transformer-based methodology for binary code embedding. PROTST employs a hierarchical training process based on a unique tree-like structure, where knowledge progressively flows from fundamental tasks at the root to more specialized tasks at the leaves. This progressive teacher-student paradigm allows the model to build upon previously learned knowledge, resulting in high-quality embeddings that can be effectively leveraged for diverse downstream binary analysis tasks. The effectiveness of PROTST is evaluated in seven binary analysis tasks, and the results show that PROTST yields an average validation score (F1, MRR, and Recall@1) improvement of 14.8% compared to traditional two-stage training and an average validation score of 10.7% compared to multimodal two-stage frameworks. 

In Voice Assistant (VA) platforms, when users add devices to their accounts and give voice commands, complex interactions occur between the devices, skills, VA clouds, and vendor clouds. These interactions are governed by the device management capabilities (DMC) of VA platforms, which rely on device names, types, and associated skills in the user account. Prior work studied vulnerabilities in specific VA components, such as hidden voice commands and bypassing skill vetting. However, the security and privacy implications of device management flaws have largely been unexplored. In this paper, we introduce DMC-Xplorer, a testing framework for the automated discovery of VA device management flaws. We first introduce VA description language (VDL), a new domain-specific language to create VA environments for testing, using VA and skill developer APIs. DMC-Xplorer then selects VA parameters (device names, types, vendors, actions, and skills) in a combinatorial approach and creates VA environments with VDL. It issues real voice commands to the environment via developer APIs and logs event traces. It validates the traces against three formal security properties that define the secure operation of VA platforms. Lastly, DMC-Xplorer identifies the root cause of property violations through intervention analysis to identify VA device management flaws. We exercised DMC-Xplorer on Amazon Alexa and Google Home and discovered two design flaws that can be exploited to launch four attacks. We show that malicious skills with default permissions can eavesdrop on privacy-sensitive device states, prevent users from controlling their devices, and disrupt the services on the VA cloud.