Operationalizing CaMeL: Strengthening LLM Defenses for Enterprise Deployment
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916765397680128 |
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| author | Tallam, Krti Miller, Emma |
| author_facet | Tallam, Krti Miller, Emma |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_22852 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Operationalizing CaMeL: Strengthening LLM Defenses for Enterprise Deployment Tallam, Krti Miller, Emma Cryptography and Security Artificial Intelligence 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. |
| title | Operationalizing CaMeL: Strengthening LLM Defenses for Enterprise Deployment |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2505.22852 |