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Securing AI Agents Against Prompt Injection Attacks

Authors: Badrinath Ramakrishnan, Akshaya Balaji

Published: 2025-11-19

arXiv ID: 2511.15759v1

Added to Library: 2025-11-21 03:05 UTC

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📄 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.

🔍 Key Points

  • Development of a comprehensive benchmark dataset with 847 adversarial test cases categorized into five types of prompt injection attacks, providing a systematic way to evaluate vulnerabilities in RAG systems.
  • Proposal of a multi-layered defense framework that includes content filtering, hierarchical prompt guardrails, and multi-stage response verification, significantly reducing prompt injection attack success rates from 73.2% to 8.7%.
  • Evaluation of the defense framework across seven state-of-the-art language models, showcasing varied vulnerabilities and demonstrating that it's possible to maintain 94.3% of baseline task performance while achieving high security against prompt injection attacks.
  • The introduction of novel mechanisms like embedding anomaly detection and structured prompt construction that help in distinguishing between benign and malicious content effectively.
  • Discussion of broader implications for AI security, emphasizing the need for depth in defenses against prompt injection vulnerabilities as a fundamental challenge in deploying AI models.

💡 Why This Paper Matters

This paper is relevant and important as it addresses a crucial vulnerability in AI systems incorporating language models, specifically in RAG frameworks. By establishing a robust benchmark and a practical defense strategy against prompt injection attacks, it contributes significant advancements to the field of AI security, illustrating that effective protective measures can coexist with practical performance standards.

🎯 Why It's Interesting for AI Security Researchers

This paper would be of interest to AI security researchers as it provides insights into the vulnerabilities of retrieval-augmented generation systems, a prevalent architecture in modern AI applications. Furthermore, the proposed defenses are empirically evaluated, and the findings reveal critical implications for enhancing the security of AI agents, which align with ongoing research efforts to improve resilience against sophisticated adversarial attacks.

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