mlsanityData to 7 Oct 2026

Zero-Knowledge Audit for Internet of Agents: Privacy-Preserving Communication Verification with Model Context Protocol

arXiv 2512.14737 ↗First posted 11 Dec 2025cs.CR cs.AIcited by 1

On the map: LLM agents and tool use → Agent threats, guardrails and governance

Abstract (arXiv)

Existing agent communication frameworks face critical limitations in providing verifiable audit trails without compromising the privacy and confidentiality of agent interactions. The protection of agent communication privacy while ensuring auditability emerges as a fundamental challenge for applications requiring accurate billing, compliance verification, and accountability in regulated environments. We introduce a framework for auditing agent communications that keeps messages private while still checking they follow expected rules. It pairs zero-knowledge proofs with the existing Model Context Protocol (MCP) so messages can be verified without revealing their contents. The approach runs in lightweight networks, stays compatible with standard MCP exchanges, and adds asynchronous audit verification to confirm format and general message types without exposing specifics. The framework enables mutual audits between agents: one side can check communication content and quality while the other verifies usage metrics, all without revealing sensitive information. We formalize security goals and show that zk-MCP provides data authenticity and communication privacy, achieving efficient verification with negligible latency overhead. We fully implement the framework, including Circom-based zero-knowledge proof generation and an audit protocol integrated with MCP's bidirectional channel, and, to our knowledge, this is the first privacy-preserving audit system for agent communications that offers verifiable mutual auditing without exposing message content or compromising agent privacy.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 6 of its 28 references that are in the map. First posted on arXiv 11 Dec 2025.

Builds on

6 of 28 references.

References · 6

  1. 2025Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directionscited by 257
  2. 2023LongLoRA: Efficient Fine-tuning of Long-Context Large Language Modelscited by 243
  3. 2025Enterprise-Grade Security for the Model Context Protocol (MCP): Frameworks and Mitigation Strategiescited by 42
  4. 2025Advancing Multi-Agent Systems Through Model Context Protocol: Architecture, Implementation, and Applicationscited by 23
  5. 2024A Survey on the Applications of Zero-Knowledge Proofscited by 5
  6. 2025Distributed Non-Interactive Zero-Knowledge Proofscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

Similar, not cited

Close in wording, no citation either way

  1. 2026Agentic Witnessing: Pragmatic and Scalable TEE-Enabled Privacy-Preserving Auditing
  2. 2026Zero-Knowledge Predicate Proofs Between AI Agents: A Measured, Cross-Protocol Gateway and the Source-Integrity Gap ↗
  3. 2026AgentLeak: A Benchmark for Internal-Channel Privacy Leakage in Multi-Agent LLM Systems
  4. 2026SS-ZKR: Spatial-Semantic Zero-Knowledge Routing for Privacy-Preserving Multi-Agent Collaboration
  5. 2026InterSAGE: The Secure and Verifiable Interoperability Protocol for An Internet of Agents
  6. 2025Fortifying the Agentic Web: A Unified Zero-Trust Architecture Against Logic-layer Threats
  7. 2025AgentCrypt: Advancing Privacy and (Secure) Computation in AI Agent Collaboration
  8. 2025Privacy in Action: Towards Realistic Privacy Mitigation and Evaluation for LLM-Powered Agents