ResearchResearch paperSafety & Alignment · Agents & Tool Use1 source · Oct 16, 2026

Who Speaks, Who Authorizes, Who Acts? Understanding Trust Boundaries in Multi-Agent LLMs

Multi-agent LLM systems exchange messages before acting through tools, but message delivery, signing authority, and tool-side enforcement are separate trust boundaries.

Key points

  • We represent them as communication, authorization, and execution structures, and use the authority cut to characterize the smallest independently compromised signer coalition that can construct a policy-valid false-claim proof under stated assumptions.
  • First, no fixed-message routing contrast across four models meets the prespecified large-effect criterion; when relays rewrite messages, executed false-state action rates vary by model and route configuration.
  • Second, presented source-identity cues do not uniformly increase protected-action selection across three models.
  • Third, on invalid-proof cases, Nemotron and Llama request protected actions in 52.1% and 54.2% of trials, respectively, yet a deterministic tool gate commits none: request-level and commitment-level safety readouts diverge.

Sources (1)

  • [1]Who Speaks, Who Authorizes, Who Acts? Understanding Trust Boundaries in Multi-Agent LLMs
    arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 10:49 PM
    Multi-agent LLM systems exchange messages before acting through tools, but message delivery, signing authority, and tool-side enforcement are separate trust boundaries.
    We represent them as communication, authorization, and execution structures, and use the authority cut to characterize the smallest independently compromised signer coalition that can construct a policy-valid false-claim proof under stated assumptions.

Extractive summary: sentences quoted from the sources.

Before this

  1. Oct 16, 2026Scaling Indic Pretraining: 2.8 Trillion Tokens and an Open Data Generation Pipeline
  2. Oct 16, 2026OR-License: Evaluating LLM Agents as Real-World Operations Research Practitioners
  3. Oct 16, 2026Language Models Learn to Improve Language Models
  4. Oct 15, 2026NVIDIA Nemotron 3.5 Super VL Powers Specialized Agents with Multimodal Reasoning
  5. Oct 15, 2026GlobeBench: Evaluating Text-Based World Model Capabilities for Scalable Environment Simulations
  6. Oct 15, 2026Librarian Agents: Training LLMs to Curate Long-Context Workspaces that Anticipate Future Queries

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