Graph-level anomaly detection (GLAD) aims to identify graphs that deviate from the majority in a dataset of graphs. Existing methods typically adopt either a global aggregation perspective that summarizes nodes within a graph into a representation vector, or a subgraph-oriented perspective which regards certain local s...
Ge Zhang, Hui-Mei Li, Guo-Hao Sun et al.· Proceedings of the Thirty-Fi...· 0 citations
This work presents SkillSentry, a dynamic safety-testing framework based on adaptive honey worlds, which infers the intended capability boundary of a skill, constructs an LLM-simulated environment with controlled decoy resources, and adaptively generates tasks to explore its behavioral states.
Nizhang Li, Zong-Hao Ying, Xiang-Fan Wu et al.· 0 citations
SafeFlow is proposed, a defense framework for multi-agent systems that formalizes malicious cross-agent propagation as a semantic information-flow problem and reduces attack success rates compared to undefended baselines and external defenses while retaining high benign task completion and a high paired safe--harm succ...
Haowen Dai, Zonghao Ying, Wenfeng Li et al.· arXiv.org· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
