This work presents a unified comparison of large language models, AI agents, and embodied agents, introducing a taxonomy of risks spanning data, models, systems, content, and applications, alongside a catalog of 24 specific threats.
Large language models (LLMs) are increasingly embedded as core components of data-centric systems, supporting analytical decision making, and automated reasoning over large-scale, heterogeneous datasets. Yet their deployment in open-world environments raises fundamental challenges to security and trustworthiness: LLMs...
Lu Lin, Jinghui Chen, Ting Wang et al.· Proceedings of the 32nd ACM...· 0 citations
The 2nd SeT-LLM Workshop on Secure and Trustworthy Large Language Models brings together researchers and practitioners from data mining, machine learning, security, and responsible AI to address issues from a data-centric, system-level perspective.
Lu Lin, Jinghui Chen, Ting Wang et al.· Proceedings of the 32nd ACM...· 0 citations
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