[2603.30016] Architecting Secure AI Agents: Perspectives on System-Level Defenses Against Indirect Prompt Injection Attacks

Computer Science > Cryptography and Security arXiv:2603.30016 (cs) [Submitted on 31 Mar 2026] Title:Architecting Secure AI Agents: Perspectives on System-Level Defenses Against Indirect Prompt Injection Attacks Authors:Chong Xiang, Drew Zagieboylo, Shaona Ghosh, Sanjay Kariyappa, Kai Greshake, Hanshen Xiao, Chaowei Xiao, G. Edward Suh View a PDF of the paper titled Architecting Secure AI Agents: Perspectives on System-Level Defenses Against Indirect Prompt Injection Attacks, by Chong Xiang and 7 other authors View PDF HTML (experimental) Abstract:AI agents, predominantly powered by large language models (LLMs), are vulnerable to indirect prompt injection, in which malicious instructions embedded in untrusted data can trigger dangerous agent actions. This position paper discusses our vision for system-level defenses against indirect prompt injection attacks. We articulate three positions: (1) dynamic replanning and security policy updates are often necessary for dynamic tasks and realistic environments; (2) certain context-dependent security decisions would still require LLMs (or other learned models), but should only be made within system designs that strictly constrain what the model can observe and decide; (3) in inherently ambiguous cases, personalization and human interaction should be treated as core design considerations. In addition to our main positions, we discuss limitations of existing benchmarks that can create a false sense of utility and security. We also hig...