Xin Wang

Zihao Rao, Jiping Zhou, Hongliang Tian et al.

In Rust, unsafe code is the sole source of potential undefined behaviors. To avoid misuse, Rust developers should clarify the safety properties for each unsafe API. However, the community currently lacks a key standard for safety documentation: existing safety comments in the source code and safety documentation can be ad hoc and incomplete. This paper presents a tag-centric methodology for auditing the consistency and completeness of safety documentation. We first derive a taxonomy of Safety Tags to formalize natural-language requirements. Second, because API soundness frequently relies on struct invariants, we propose a set of empirical rules to systematically audit the structural consistency of safety documentation. We implemented this methodology in safety-tool, a static linter that automatically enforces structural consistency between local safety annotations and callee requirements. Our approach was applied to the Rust standard library, fixing documentation issues on 27 APIs with 61 safety tags and identifying safety tags that are applicable to 96.1% of the public unsafe APIs in libstd. Furthermore, we have formalized the tagging idea through a Rust RFC to the wider community. We believe that the approach establishes a standardized practice of safety documentation and helps significantly reduce safety perils.

Rui Miao, Yixin Liu, Yili Wang et al.

The security of LLM-based multi-agent systems (MAS) is critically threatened by propagation vulnerability, where malicious agents can distort collective decision-making through inter-agent message interactions. While existing supervised defense methods demonstrate promising performance, they may be impractical in real-world scenarios due to their heavy reliance on labeled malicious agents to train a supervised malicious detection model. To enable practical and generalizable MAS defenses, in this paper, we propose BlindGuard, an unsupervised defense method that learns without requiring any attack-specific labels or prior knowledge of malicious behaviors. To this end, we establish a hierarchical agent encoder to capture individual, neighborhood, and global interaction patterns of each agent, providing a comprehensive understanding for malicious agent detection. Meanwhile, we design a corruption-guided detector that consists of directional noise injection and contrastive learning, allowing effective detection model training solely on normal agent behaviors. Extensive experiments show that BlindGuard effectively detects diverse attack types (i.e., prompt injection, memory poisoning, and tool attack) across MAS with various communication patterns while maintaining superior generalizability compared to supervised baselines. The code is available at: https://github.com/MR9812/BlindGuard.