mlsanityData to 7 Oct 2026

VET Your Agent: Towards Host-Independent Autonomy via Verifiable Execution Traces

arXiv 2512.15892 ↗First posted 17 Dec 2025cs.CR cs.AIcited by 4

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

Abstract (arXiv)

Recent advances in large language models (LLMs) have enabled a new generation of autonomous agents that operate over sustained periods and manage sensitive resources on behalf of users. Trusted for their ability to act without direct oversight, such agents are increasingly considered in high-stakes domains including financial management, dispute resolution, and governance. Yet in practice, agents execute on infrastructure controlled by a host, who can tamper with models, inputs, or outputs, undermining any meaningful notion of autonomy. We address this gap by introducing VET (Verifiable Execution Traces), a formal framework that achieves host-independent authentication of agent outputs and takes a step toward host-independent autonomy. Central to VET is the Agent Identity Document (AID), which specifies an agent's configuration together with the proof systems required for verification. VET is compositional: it supports multiple proof mechanisms, including trusted hardware, succinct cryptographic proofs, and notarized TLS transcripts (Web Proofs). We implement VET for an API-based LLM agent and evaluate our instantiation on realistic workloads. We find that for today's black-box, secret-bearing API calls, Web Proofs appear to be the most practical choice, with overhead typically under 3× compared to direct API calls, while for public API calls, a lower-overhead TEE Proxy is often sufficient. As a case study, we deploy a verifiable trading agent that produces proofs for each decision and composes Web Proofs with a TEE Proxy. Our results demonstrate that practical, host-agnostic authentication is already possible with current technology, laying the foundation for future systems that achieve full host-independent autonomy.

Key facts. Cited by 4 papers 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 9 of its 60 references that are in the map. First posted on arXiv 17 Dec 2025.

Builds on

9 of 60 references.

Closest ancestors · 2

Same direction, newest first

  1. 2025The AI Agent Index3 Feb
  2. 2024Enhancing Trust in Autonomous Agents: An Architecture for Accountability and Explainability through Blockchain and Large Language Models14 Mar

Other references · 7

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  2. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,203
  3. 2025Vending-Bench: A Benchmark for Long-Term Coherence of Autonomous Agentscited by 75
  4. 2018Spectre Attacks: Exploiting Speculative Executioncited by 47
  5. 2025A Survey of Zero-Knowledge Proof Based Verifiable Machine Learningcited by 21
  6. 2024opML: Optimistic Machine Learning on Blockchaincited by 20
  7. 2017Consensus in the Age of Blockchainscited by 10

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 4.

4 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026Verifiable Agentic Infrastructure: Proof-Derived Authorization for Sovereign AI Systems
  2. 2025Verifiability-First Agents: Provable Observability and Lightweight Audit Agents for Controlling Autonomous LLM Systems
  3. 2026From Capability to Assurance in Autonomous Penetration-Testing Harnesses: A Framework and Reference Implementation
  4. 2026Delegation Without Trust: An Empirical Gap Analysis of Identity, Authorization, and Runtime Governance in Multi-Agent LLM Systems
  5. 2026Toward Securing AI Agents Like Operating Systems
  6. 2025SIEVE: Selective Integrity Verification and Escalation for Defending LLM Agents against Indirect Prompt Injection
  7. 2026Securing LLM Agents Need Intent-to-Execution Integrity
  8. 2026AgentVisor: Defending LLM Agents Against Prompt Injection via Semantic Virtualization