Trust propagation and structural containment in Multi-agent LLM pipelines

Research identifies a critical security vulnerability in multi-agent Large Language Model (LLM) systems where compromised lower-privilege agents can influence higher-privilege agents, potentially leading to unauthorized actions. The study empirically examines attack propagation within a four-agent LLM pipeline, evaluating shared-memory poisoning and indirect prompt injection. It proposes and compares the effectiveness of an independent authorization layer using task-bound signed tokens and a separately verified policy oracle against a Validator agent's judgment to mitigate these risks.

The proliferation of multi-agent LLM systems across various operations introduces new attack vectors and necessitates robust security architectures. Understanding and mitigating these propagation risks is crucial for maintaining system integrity, preventing unauthorized actions, and ensuring the trustworthy deployment of AI technologies in sensitive or critical functions.

Multi-agent LLM systems with differing privilege levels inherently face security risks, particularly from lower-privilege agents influencing higher-privilege ones.

Forward consideration, not a verified fact.