Trustworthy LLM Agents: Threats and Countermeasures

Large Language Model (LLM) agents represent a transformative evolution in artificial intelligence, extending beyond traditional conversational AI to autonomous systems capable of reasoning, planning, and executing complex tasks through tool integration and environmental interaction. As these systems are rapidly deployed across critical domains including healthcare, finance, and autonomous systems, they introduce unprecedented security challenges that traditional cybersecurity frameworks are inadequate to address. This paper provides a comprehensive analysis of the threat landscape facing LLM agents, examining vulnerabilities across multiple dimensions including prompt injection, multi-agent exploitation, memory poisoning, and supply chain attacks. We present a taxonomy of 15 distinct threat categories identified by recent research and standardization efforts, analyze their technical mechanisms and potential impacts, and evaluate current countermeasure frameworks. Our researchreveals that while 41.2% of LLMagents succumbto direct prompt injection attacks, 82.4%canbecompromised through inter-agent trust exploitation, with only 5.9% of tested models proving resistant to all attack vectors. We examine defensemechanisms including multi-layered securityarchitectures, automated red teamingmethodologies, and governance frameworks, whileidentifying significant gaps in current protectionstrategies. This paper contributes to the emergingfield of trustworthy AI by providing practitionersand researchers with a comprehensive referenceforunderstanding, assessing, and mitigating securityrisks in LLM agent deployments.