← Back to Library

CAPTURE: Context-Aware Prompt Injection Testing and Robustness Enhancement

Authors: Gauri Kholkar, Ratinder Ahuja

Published: 2025-05-18

arXiv ID: 2505.12368v2

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

Red Teaming

📄 Abstract

Prompt injection remains a major security risk for large language models. However, the efficacy of existing guardrail models in context-aware settings remains underexplored, as they often rely on static attack benchmarks. Additionally, they have over-defense tendencies. We introduce CAPTURE, a novel context-aware benchmark assessing both attack detection and over-defense tendencies with minimal in-domain examples. Our experiments reveal that current prompt injection guardrail models suffer from high false negatives in adversarial cases and excessive false positives in benign scenarios, highlighting critical limitations. To demonstrate our framework's utility, we train CaptureGuard on our generated data. This new model drastically reduces both false negative and false positive rates on our context-aware datasets while also generalizing effectively to external benchmarks, establishing a path toward more robust and practical prompt injection defenses.

🔍 Key Points

  • Introduction of CAPTURE, a novel context-aware benchmark for prompt injection testing targeting both attack detection and over-defense tendencies.
  • Highlighting the limitations of existing guardrail models, which suffer from high false negatives (missing adversarial attacks) and excessive false positives (flagging benign interactions).
  • Development of CaptureGuard, a model trained on CAPTURE's generated data, which significantly reduces false negative and false positive rates compared to prior models.
  • Demonstration of CaptureGuard's effectiveness not only on context-aware datasets but also effectively generalizing to external benchmarks, enhancing overall robustness.
  • Presentation of empirical results emphasizing the necessity of context-aware mechanisms for reliable prompt injection defenses in large language models.

💡 Why This Paper Matters

The CAPTURE framework represents an important step forward in the security of large language models by systematically addressing the shortcomings of traditional prompt injection defenses. Its focus on context awareness and the development of CaptureGuard provide critical insights into improving robustness and reliability, which are essential in sustaining the safe deployment of AI systems in real-world applications.

🎯 Why It's Interesting for AI Security Researchers

This paper is highly relevant to AI security researchers due to its direct confrontation of the pressing vulnerability posed by prompt injection attacks. The innovative approaches introduced, including the CAPTURE benchmark and CaptureGuard model, offer the potential for significant improvements in the security of deployed AI systems. By addressing previously overlooked aspects of attack detection and defense balance, researchers in the field can leverage these findings to develop more resilient AI security protocols, combating emerging threats in the rapidly evolving landscape of AI technologies.

📚 Read the Full Paper

📄 View on arXiv