From Prompt Injections to Protocol Exploits: Threats in LLM-Powered AI Agents Workflows

Understand

Autonomous AI agents powered by large language models (LLMs) with structured function-calling interfaces enable real-time data retrieval, computation, and multi-step orchestration.

• However, the rapid growth of plugins, connectors, and inter-agent protocols has outpaced security practices, leading to brittle integrations that rely on ad-hoc authentication, inconsistent schemas, and weak validation.
• This survey introduces a unified end-to-end threat model for LLM-agent ecosystems, covering host-to-tool and agent-to-agent communications.
• We systematically categorize more than thirty attack techniques spanning input manipulation, model compromise, system and privacy attacks, and protocol-level vulnerabilities.