SafeAgentBench: A Benchmark for Safe Task Planning of Embodied LLM Agents

• URL: http://arxiv.org/abs/2412.13178v2
• Date: Wed, 18 Dec 2024 14:00:02 GMT
• Title: SafeAgentBench: A Benchmark for Safe Task Planning of Embodied LLM Agents
• Authors: Sheng Yin, Xianghe Pang, Yuanzhuo Ding, Menglan Chen, Yutong Bi, Yichen Xiong, Wenhao Huang, Zhen Xiang, Jing Shao, Siheng Chen,
• Abstract summary: We present SafeAgentBench -- a new benchmark for safety-aware task planning of embodied LLM agents. Best-performing baseline gets 69% success rate for safe tasks, but only 5% rejection rate for hazardous tasks.
• Score: 42.69984822098671
• License:
• Abstract: With the integration of large language models (LLMs), embodied agents have strong capabilities to execute complicated instructions in natural language, paving a way for the potential deployment of embodied robots. However, a foreseeable issue is that those embodied agents can also flawlessly execute some hazardous tasks, potentially causing damages in real world. To study this issue, we present SafeAgentBench -- a new benchmark for safety-aware task planning of embodied LLM agents. SafeAgentBench includes: (1) a new dataset with 750 tasks, covering 10 potential hazards and 3 task types; (2) SafeAgentEnv, a universal embodied environment with a low-level controller, supporting multi-agent execution with 17 high-level actions for 8 state-of-the-art baselines; and (3) reliable evaluation methods from both execution and semantic perspectives. Experimental results show that the best-performing baseline gets 69% success rate for safe tasks, but only 5% rejection rate for hazardous tasks, indicating significant safety risks. More details and codes are available at https://github.com/shengyin1224/SafeAgentBench.

Related papers

• AGrail: A Lifelong Agent Guardrail with Effective and Adaptive Safety Detection [47.83354878065321]
  We propose AGrail, a lifelong guardrail to enhance agent safety. AGrail features adaptive safety check generation, effective safety check optimization, and tool compatibility and flexibility.
  arXiv  Detail & Related papers  (2025-02-17T05:12:33Z)
• Agent-SafetyBench: Evaluating the Safety of LLM Agents [72.92604341646691]
  We introduce Agent-SafetyBench, a comprehensive benchmark to evaluate the safety of large language models (LLMs) Agent-SafetyBench encompasses 349 interaction environments and 2,000 test cases, evaluating 8 categories of safety risks and covering 10 common failure modes frequently encountered in unsafe interactions. Our evaluation of 16 popular LLM agents reveals a concerning result: none of the agents achieves a safety score above 60%.
  arXiv  Detail & Related papers  (2024-12-19T02:35:15Z)
• AgentHarm: A Benchmark for Measuring Harmfulness of LLM Agents [84.96249955105777]
  LLM agents may pose a greater risk if misused, but their robustness remains underexplored. We propose a new benchmark called AgentHarm to facilitate research on LLM agent misuse. We find leading LLMs are surprisingly compliant with malicious agent requests without jailbreaking.
  arXiv  Detail & Related papers  (2024-10-11T17:39:22Z)
• Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents [32.62654499260479]
  Agent Security Bench (ASB) is a framework designed to formalize, benchmark, and evaluate the attacks and defenses of LLM-based agents. We benchmark 10 prompt injection attacks, a memory poisoning attack, a novel Plan-of-Thought backdoor attack, a mixed attack, and 10 corresponding defenses across 13 LLM backbones. Our benchmark results reveal critical vulnerabilities in different stages of agent operation, including system prompt, user prompt handling, tool usage, and memory retrieval.
  arXiv  Detail & Related papers  (2024-10-03T16:30:47Z)
• AgentMonitor: A Plug-and-Play Framework for Predictive and Secure Multi-Agent Systems [43.333567687032904]
  AgentMonitor is a framework that integrates at the agent level to capture inputs and outputs, transforming them into statistics for training a regression model to predict task performance. It can further apply real-time corrections to address security risks posed by malicious agents, mitigating negative impacts and enhancing MAS security.
  arXiv  Detail & Related papers  (2024-08-27T11:24:38Z)
• TrustAgent: Towards Safe and Trustworthy LLM-based Agents [50.33549510615024]
  This paper presents an Agent-Constitution-based agent framework, TrustAgent, with a focus on improving the LLM-based agent safety. The proposed framework ensures strict adherence to the Agent Constitution through three strategic components: pre-planning strategy which injects safety knowledge to the model before plan generation, in-planning strategy which enhances safety during plan generation, and post-planning strategy which ensures safety by post-planning inspection.
  arXiv  Detail & Related papers  (2024-02-02T17:26:23Z)
• Identifying the Risks of LM Agents with an LM-Emulated Sandbox [68.26587052548287]
  Language Model (LM) agents and tools enable a rich set of capabilities but also amplify potential risks. High cost of testing these agents will make it increasingly difficult to find high-stakes, long-tailed risks. We introduce ToolEmu: a framework that uses an LM to emulate tool execution and enables the testing of LM agents against a diverse range of tools and scenarios.
  arXiv  Detail & Related papers  (2023-09-25T17:08:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.