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Palisade -- Prompt Injection Detection Framework

Authors: Sahasra Kokkula, Somanathan R, Nandavardhan R, Aashishkumar, G Divya

Published: 2024-10-28

arXiv ID: 2410.21146v1

Added to Library: 2025-11-11 14:07 UTC

📄 Abstract

The advent of Large Language Models LLMs marks a milestone in Artificial Intelligence, altering how machines comprehend and generate human language. However, LLMs are vulnerable to malicious prompt injection attacks, where crafted inputs manipulate the models behavior in unintended ways, compromising system integrity and causing incorrect outcomes. Conventional detection methods rely on static, rule-based approaches, which often fail against sophisticated threats like abnormal token sequences and alias substitutions, leading to limited adaptability and higher rates of false positives and false negatives.This paper proposes a novel NLP based approach for prompt injection detection, emphasizing accuracy and optimization through a layered input screening process. In this framework, prompts are filtered through three distinct layers rule-based, ML classifier, and companion LLM before reaching the target model, thereby minimizing the risk of malicious interaction.Tests show the ML classifier achieves the highest accuracy among individual layers, yet the multi-layer framework enhances overall detection accuracy by reducing false negatives. Although this increases false positives, it minimizes the risk of overlooking genuine injected prompts, thus prioritizing security.This multi-layered detection approach highlights LLM vulnerabilities and provides a comprehensive framework for future research, promoting secure interactions between humans and AI systems.

🔍 Key Points

  • Introduction of the 'distraction effect' concept, revealing how specific attention heads in LLMs are manipulated during prompt injection attacks.
  • Development of Attention Tracker ( extbackslash attn), a training-free method for detecting prompt injection attacks by analyzing attention patterns, achieving significant AUROC improvements compared to existing methods.
  • Demonstration of the robustness and generalizability of the distraction effect across different LLMs, attack types, and datasets, making the detection method applicable in diverse contexts.
  • Validation of the effectiveness of extbackslash attn on both large and small LMs, addressing limitations of previous detection methods that required larger models for accuracy.
  • Extensive experimental evaluations confirming the higher detection accuracy, with improvements ranging from 3.1% to 10.0% in AUROC scores over current baselines.

💡 Why This Paper Matters

This paper is relevant due to its novel approach to detecting prompt injection attacks in LLMs, a critical vulnerability in AI systems. By uncovering the distraction effect and utilizing it for real-time attack detection without needing additional training or costly inference, it presents a significant advancement in securing AI applications. The findings contribute to the ongoing conversation on AI safety and robustness, especially as LLMs become more integrated into sensitive and critical applications.

🎯 Why It's Interesting for AI Security Researchers

The paper is of great interest to AI security researchers as it tackles a pressing and emerging threat within the realm of large language models. Researchers can benefit from understanding the dynamics of attention mechanisms under attack conditions, which is essential for developing more resilient AI systems. Furthermore, the practical implications of the Attention Tracker in real-world applications enhance its appeal, providing a framework that can be adapted and scaled to safeguard various LLM-integrated systems.

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