Benchmarking Input-Sanitization Frameworks Against Indirect Prompt Injection in Autonomous LLM Agents

Abstract

Autonomous Large Language Model (LLM) agents are rapidly moving into production systems,handling tasks through Retrieval-Augmented Generation (RAG) and direct API interactions. However,granting models execution capabilities opens up a major vulnerability: Indirect Prompt Injection (IPI).This occurs when untrusted external text contains malicious commands that hijack the agent's controlflow. In this paper, I evaluate how well current defensive input-sanitization frameworks resist theseattacks. Using a dataset of 50 indirect injection payloads across data exfiltration, tool manipulation,and system overrides, I tested an agent powered by LLaMA-3-8B-Instruct under three defensepostures: baseline (no filtering), system prompt instruction, and active evaluation using Llama Guard.The results show that relying on system prompt guardrails fails against 62% of structured payloads.While an active evaluation layer drastically reduces the attack success rate to 14%, it incurs an averagelatency penalty of 182 ms. I conclude by outlining concrete design patterns for building safer agentarchitectures.