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Operationalizing CaMeL: Strengthening LLM Defenses for Enterprise Deployment

Authors: Krti Tallam, Emma Miller

Published: 2025-05-28

arXiv ID: 2505.22852v1

Added to Library: 2025-05-30 03:01 UTC

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📄 Abstract

CaMeL (Capabilities for Machine Learning) introduces a capability-based sandbox to mitigate prompt injection attacks in large language model (LLM) agents. While effective, CaMeL assumes a trusted user prompt, omits side-channel concerns, and incurs performance tradeoffs due to its dual-LLM design. This response identifies these issues and proposes engineering improvements to expand CaMeL's threat coverage and operational usability. We introduce: (1) prompt screening for initial inputs, (2) output auditing to detect instruction leakage, (3) a tiered-risk access model to balance usability and control, and (4) a verified intermediate language for formal guarantees. Together, these upgrades align CaMeL with best practices in enterprise security and support scalable deployment.

🔍 Key Points

  • Introduction of engineering improvements for CaMeL to enhance threat coverage against prompt injection attacks.
  • Proposed methods include initial prompt screening, output auditing, a tiered-risk access model, and the use of a verified intermediate language for formal guarantees.
  • Identification and mitigation of side-channel risks such as loop counting attacks, exception-based information leaks, and timing channels.
  • A focus on balancing security guarantees with usability through a tiered-risk policy model that adapts security based on the context of actions taken within enterprise settings.
  • Recommendations for architectural improvements to reduce performance overhead associated with the dual-LLM design of CaMeL.

💡 Why This Paper Matters

The paper is a significant contribution to the field of AI security, as it not only identifies critical gaps in the existing CaMeL framework but proposes comprehensive engineering solutions to bolster its defenses against various advanced threats in enterprise deployment contexts. By addressing both threat model limitations and practical usability challenges, this work lays a foundation for developing a more robust security framework for large language models.

🎯 Why It's Interesting for AI Security Researchers

This paper is particularly relevant to AI security researchers due to its focus on real-world applications of LLMs in enterprise environments and the rigorous examination of security vulnerabilities associated with prompt injection attacks. By proposing new techniques for threat mitigation and usability enhancements, it opens up new avenues for research and development in secure AI deployments, making it a crucial reference for advancing security methodologies.

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