Cascading Prompt Injection

This research paper examines cascading prompt injection in cooperative multi-agent artificial intelligence systems, focusing on how a compromised sub-agent can introduce malicious instructions into shared context and influence the behavior of other agents in an enterprise workflow. The study explores potential attack pathways, trust boundaries, context contamination, inter-agent communication risks, and the challenges of detecting and containing malicious instructions across connected AI agents. It also discusses potential mitigation strategies, including input validation, instruction provenance tracking, least-privilege access, isolation of agent contexts, output verification, and continuous monitoring. The objective is to improve understanding of cascading prompt injection risks and support the development of more secure, reliable, and resilient multi-agent AI systems. Author: Aaditya Vishwakarma.