mlsanityData to 14 Oct 2026

Operationalizing CaMeL: Strengthening LLM Defenses for Enterprise Deployment

arXiv 2505.22852 ↗First posted 28 May 2025cs.CR cs.AIcited by 1

On the map: LLM safety and security → Prompt injection and agent security

Abstract (arXiv)

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 facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Prompt injection and agent security, one of 12 directions in LLM safety and security. Builds on 12 of its 32 references that are in the map. First posted on arXiv 28 May 2025.

Builds on

12 of 32 references.

Closest ancestors · 1

Same direction, newest first

  1. 2022Red Teaming Language Models with Language Models7 Feb

Other references · 11

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  3. 2023Universal and Transferable Adversarial Attacks on Aligned Language Modelscited by 3,104
  4. 2022Constitutional AI: Harmlessness from AI Feedbackcited by 3,050
  5. 2017A Broad-Coverage Challenge Corpus for Sentence Understanding through Inferencecited by 2,682
  6. 2023Poisoning Language Models During Instruction Tuningcited by 269
  7. 2025Alignment, Agency and Autonomy in Frontier AI: A Systems Engineering Perspectivecited by 7
  8. 2025Transforming Cyber Defense: Harnessing Agentic and Frontier AI for Proactive, Ethical Threat Intelligencecited by 4
  9. 2025Engineering Risk-Aware, Security-by-Design Frameworks for Assurance of Large-Scale Autonomous AI Modelscited by 4
  10. 2025CyberSentinel: An Emergent Threat Detection System for AI Securitycited by 3
  11. 2025The Cyber Immune System: Harnessing Adversarial Forces for Security Resiliencecited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026A Five-Plane Reference Architecture for Runtime Governance of Production AI Agentscited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2025Defeating Prompt Injections by Design
  2. 2026CaMeLoT: CaMeL orchestrated with Temporal logic for static verification and liveness
  3. 2025A Multi-Agent LLM Defense Pipeline Against Prompt Injection Attacks
  4. 2025Bridging AI and Software Security: A Comparative Vulnerability Assessment of LLM Agent Deployment Paradigms
  5. 2025MCP Security Bench (MSB): Benchmarking Attacks Against Model Context Protocol in LLM Agents
  6. 2026Prompt Control-Flow Integrity: A Priority-Aware Runtime Defense Against Prompt Injection in LLM Systems
  7. 2026AgentVisor: Defending LLM Agents Against Prompt Injection via Semantic Virtualization
  8. 2026Evaluation of Prompt Injection Defenses in Large Language Models