mlsanityData to 14 Oct 2026

The Proxy Knows Too Much: Sealing LLM API Routers with Attested TEEs

arXiv 2606.16358 ↗First posted 15 Jun 2026cs.CR cs.AI cs.ETcited by 1

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Abstract (arXiv)

Agents increasingly access large language models (LLMs) through API routers. A router terminates the client's transport-layer security session and opens a separate upstream session, so it holds the full interaction in plaintext. This makes the router an application-layer man-in-the-middle: it can rewrite agent tool calls, swap dependencies for typosquatted packages, trigger attacks only under audit-evading conditions, and passively exfiltrate secrets. Existing client-side defenses are evadable. We propose AEGIS, a provider-transparent attested API router whose data path is a client-verified faithful passthrough. AEGISconfines plaintext handling to a small hardware-enclave component while leaving authentication, scheduling, accounting, and management on the untrusted host. The client verifies the enclave before releasing plaintext. The host can neither read nor alter the interaction, and plaintext leaves only toward destinations fixed by the measured image. We show that all four malicious-router attack classes succeed against a plaintext-access baseline and are blocked by AEGIS, including adaptive tests against the same boundary. The trusted path is 851 lines, carries three provider-native APIs without conversion, and completes every request under real-provider workload and concurrency. In a seeded audit pilot, two commodity coding agents find eight and ten of ten planted invariant violations. The local relay overhead is about six milliseconds per request.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 14 of its 51 references that are in the map. First posted on arXiv 15 Jun 2026.

Builds on

14 of 51 references.

References · 14

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2024SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineeringcited by 1,737
  3. 2025Defeating Prompt Injections by Designcited by 297
  4. 2025LlamaFirewall: An open source guardrail system for building secure AI agentscited by 127
  5. 2018Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardwarecited by 102
  6. 2024What Was Your Prompt? A Remote Keylogging Attack on AI Assistantscited by 26
  7. 2023No Privacy Left Outside: On the (In-)Security of TEE-Shielded DNN Partition for On-Device MLcited by 21
  8. 2026Your Agent Is Mine: Measuring Malicious Intermediary Attacks on the LLM Supply Chaincited by 13
  9. 2024PipeLLM: Fast and Confidential Large Language Model Services with Speculative Pipelined Encryptioncited by 10
  10. 2018Integrating Remote Attestation with Transport Layer Securitycited by 9
  11. 2026AEX: Non-Intrusive Multi-Hop Attestation and Provenance for LLM APIscited by 3
  12. 2025Attestable Builds: Compiling Verifiable Binaries on Untrusted Systems using Trusted Execution Environmentscited by 2
2 more
  1. 2020On the TOCTOU Problem in Remote Attestationcited by 1
  2. 2022Nitriding: A tool kit for building scalable, networked, secure enclavescited by 1

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Citing papers per year: 2026: 1.

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  1. 2026AgentVisor: Defending LLM Agents Against Prompt Injection via Semantic Virtualization
  2. 2026API Secrets Should Never Become Tokens in the LLM's Vocabulary: A Threat Analysis of API Credential Handling in LLM Agent Systems and an Empirical Evaluation of a Vault-Mediated Execution Boundary
  3. 2026Beyond Max Tokens: Stealthy Resource Amplification via Tool Calling Chains in LLM Agents
  4. 2026Securing LLM Agents Need Intent-to-Execution Integrity
  5. 2026Prompt Control-Flow Integrity: A Priority-Aware Runtime Defense Against Prompt Injection in LLM Systems
  6. 2026TraceGrant: A Contract-Governed Security Framework for the Task-Effect Lifecycle of Networked LLM Agents
  7. 2026Early Detection of Distributed Backdoors in Multi-Agent LLM Systems: A Characterization Study
  8. 2026MIRROR: Multipath Quorum Integrity for LLM Multi-Agent Communication