This research introduces a systematic framework for addressing the complex trustworthiness challenges that emerge when LLMs are empowered with additional capabilities like memory, tools, and multi-agent interactions.

Key Findings:

• LLM agents face unique security threats beyond those affecting standalone LLMs, requiring specialized countermeasures
• The TrustAgent framework provides a comprehensive approach covering both intrinsic and extrinsic trustworthiness dimensions
• Security vulnerabilities increase as LLM agents gain more capabilities and interact with other agents
• Existing defense mechanisms can be adapted and extended to protect LLM-based agent systems

Business Impact: As organizations deploy increasingly sophisticated LLM agents, understanding and mitigating these security threats becomes crucial for responsible AI implementation and avoiding potential harms or misuse.

A Survey on Trustworthy LLM Agents: Threats and Countermeasures