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The rise of Agent AI and Large Language Model-powered Multi-Agent Systems (LLM-MAS) has underscored the need for responsible and dependable system operation. These advancements introduce critical challenges: LLM agents exhibit inherent unpredictability, and uncertainties in their outputs can compound, threatening system stability. To address these risks, a human-centered design approach with active dynamic moderation is essential.
arXiv Detail & Related papers (2025-02-03T16:04:30Z)
Safety at Scale: A Comprehensive Survey of Large Model Safety [298.05093528230753]
We present a comprehensive taxonomy of safety threats to large models, including adversarial attacks, data poisoning, backdoor attacks, jailbreak and prompt injection attacks, energy-latency attacks, data and model extraction attacks, and emerging agent-specific threats. We identify and discuss the open challenges in large model safety, emphasizing the need for comprehensive safety evaluations, scalable and effective defense mechanisms, and sustainable data practices.
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Defining and Evaluating Physical Safety for Large Language Models [62.4971588282174]
Large Language Models (LLMs) are increasingly used to control robotic systems such as drones. Their risks of causing physical threats and harm in real-world applications remain unexplored. We classify the physical safety risks of drones into four categories: (1) human-targeted threats, (2) object-targeted threats, (3) infrastructure attacks, and (4) regulatory violations.
arXiv Detail & Related papers (2024-11-04T17:41:25Z)
Recent Advances in Attack and Defense Approaches of Large Language Models [27.271665614205034]
Large Language Models (LLMs) have revolutionized artificial intelligence and machine learning through their advanced text processing and generating capabilities. Their widespread deployment has raised significant safety and reliability concerns. This paper reviews current research on LLM vulnerabilities and threats, and evaluates the effectiveness of contemporary defense mechanisms.
arXiv Detail & Related papers (2024-09-05T06:31:37Z)
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Large Language Models (LLMs) have revolutionized various applications by providing advanced natural language processing capabilities. This paper explores the threat modeling and risk analysis specifically tailored for LLM-powered applications.
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arXiv Detail & Related papers (2023-10-16T13:23:54Z)
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arXiv Detail & Related papers (2023-08-24T14:45:50Z)
On the Risk of Misinformation Pollution with Large Language Models [127.1107824751703]
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arXiv Detail & Related papers (2023-05-23T04:10:26Z)

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