Securing AI Agents Against Prompt Injection Attacks

TL;DR

This paper introduces a comprehensive benchmark and a multi-layered defense framework to mitigate prompt injection attacks in retrieval-augmented generation AI systems, significantly improving security without sacrificing performance.

Contribution

It provides the first extensive benchmark for prompt injection risks in RAG systems and proposes an effective multi-layered defense framework validated across multiple models.

Findings

Benchmark includes 847 adversarial test cases across five attack types.

Defense mechanisms reduce attack success rate from 73.2% to 8.7%.

Maintains 94.3% of baseline task performance.

Abstract

Retrieval-augmented generation (RAG) systems have become widely used for enhancing large language model capabilities, but they introduce significant security vulnerabilities through prompt injection attacks. We present a comprehensive benchmark for evaluating prompt injection risks in RAG-enabled AI agents and propose a multi-layered defense framework. Our benchmark includes 847 adversarial test cases across five attack categories: direct injection, context manipulation, instruction override, data exfiltration, and cross-context contamination. We evaluate three defense mechanisms: content filtering with embedding-based anomaly detection, hierarchical system prompt guardrails, and multi-stage response verification, across seven state-of-the-art language models. Our combined framework reduces successful attack rates from 73.2% to 8.7% while maintaining 94.3% of baseline task performance. We release our benchmark dataset and defense implementation to support future research in AI agent security.