Papers › TrustAgent: Towards Safe and Trustworthy LLM-based Agents

TrustAgent: Towards Safe and Trustworthy LLM-based Agents

2 Feb 2024arXiv:2402.01586archive 2025-07-28

Wenyue Hua, Xianjun Yang, Mingyu Jin, Zelong Li, Wei Cheng, Ruixiang Tang, Yongfeng Zhang

The rise of LLM-based agents shows great potential to revolutionize task planning, capturing significant attention. Given that these agents will be integrated into high-stake domains, ensuring their reliability and safety is crucial. This paper presents an Agent-Constitution-based agent framework, TrustAgent, with a particular 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. Our experimental results demonstrate that the proposed framework can effectively enhance an LLM agent's safety across multiple domains by identifying and mitigating potential dangers during the planning. Further analysis reveals that the framework not only improves safety but also enhances the helpfulness of the agent. Additionally, we highlight the importance of the LLM reasoning ability in adhering to the Constitution. This paper sheds light on how to ensure the safe integration of LLM-based agents into human-centric environments. Data and code are available at https://github.com/agiresearch/TrustAgent.

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construct_training_data agiresearch/trustagent/safeagi/utils/finetune_opensource.py official repository ran no licence file found · pointer only · f9d42a2023173572 · report
conversation_generation agiresearch/trustagent/safeagi/utils/finetune_file_generation.py official repository ran fingerprinted no licence file found · pointer only · bafda5afdb78051c · report
embedding_computation_contriever agiresearch/trustagent/safety_regulation/safety_regulation_embedding_computation.py official repository ran no licence file found · pointer only · ec0dbc9e05378be1 · report
embedding_computation_mistral agiresearch/trustagent/safety_regulation/safety_regulation_embedding_computation.py official repository ran no licence file found · pointer only · 802baabb2774f437 · report
extract_paraphrases agiresearch/trustagent/safeagi/utils/finetune_file_generation.py official repository ran fingerprinted no licence file found · pointer only · 46bd3d334302f05f · report
training_steps agiresearch/trustagent/safeagi/utils/finetune_opensource.py official repository ran fingerprinted no licence file found · pointer only · 189c775601bf5130 · report
get_openai_embedding agiresearch/trustagent/safety_regulation/safety_regulation_embedding_computation.py official repository unverified no licence file found · pointer only · a004841bb25367cf · report
run_openai agiresearch/trustagent/running/evaluation.py official repository unverified no licence file found · pointer only · 6a805bd7270ee7e4 · report
run_openai agiresearch/trustagent/safeagi/utils/finetune_file_generation.py official repository unverified no licence file found · pointer only · b18d631010b50266 · report

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