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Multimodal Prompt Injection Attacks: Risks and Defenses for Modern LLMs

Authors: Andrew Yeo, Daeseon Choi

Published: 2025-09-07

arXiv ID: 2509.05883v1

Added to Library: 2025-09-09 04:01 UTC

Red Teaming Safety

📄 Abstract

Large Language Models (LLMs) have seen rapid adoption in recent years, with industries increasingly relying on them to maintain a competitive advantage. These models excel at interpreting user instructions and generating human-like responses, leading to their integration across diverse domains, including consulting and information retrieval. However, their widespread deployment also introduces substantial security risks, most notably in the form of prompt injection and jailbreak attacks. To systematically evaluate LLM vulnerabilities -- particularly to external prompt injection -- we conducted a series of experiments on eight commercial models. Each model was tested without supplementary sanitization, relying solely on its built-in safeguards. The results exposed exploitable weaknesses and emphasized the need for stronger security measures. Four categories of attacks were examined: direct injection, indirect (external) injection, image-based injection, and prompt leakage. Comparative analysis indicated that Claude 3 demonstrated relatively greater robustness; nevertheless, empirical findings confirm that additional defenses, such as input normalization, remain necessary to achieve reliable protection.

🔍 Key Points

  • Systematic evaluation of vulnerabilities in eight Large Language Models (LLMs) to prompt injection attacks, revealing exploitable weaknesses across multiple injection types.
  • Identification of four specific categories of prompt injection: direct injection, external injection, image-based injection, and prompt leakage, each demonstrating varying levels of model susceptibility.
  • Comparison of LLM robustness, with Claude 3 shown to be the most resilient, while underscoring the necessity for comprehensive defense mechanisms like input normalization and proactive security measures.
  • Discussion of implications in sensitive domains like healthcare and enterprise, highlighting the risks of data exfiltration and the need for rigorous security protocols to comply with regulations such as HIPAA and GDPR.
  • Proposal of a structured framework for categorizing injection vulnerabilities and defenses—this serves as a practical guide for developers and practitioners seeking to enhance the security of LLM implementations.

💡 Why This Paper Matters

This paper is relevant because it addresses the critical security risks posed by prompt injection attacks on LLMs, presenting empirical evidence of vulnerabilities and emphasizing the importance of proactive defenses to mitigate potential exploitation. Its structured approach to analyzing attack types and providing recommendations for enhanced security will aid developers and organizations in safeguarding their AI systems against emerging threats.

🎯 Why It's Interesting for AI Security Researchers

AI security researchers will find this paper of significant interest due to its comprehensive examination of prompt injection attacks, a growing threat vector in LLM deployments. The identification of vulnerabilities and clear categorization of attack methods contribute to a deeper understanding of how adversaries exploit these models, providing a foundation for developing more effective security protocols and defensive strategies in the rapidly evolving landscape of AI.

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