mlsanityData to 14 Oct 2026

Verifier-Bound Communication for LLM Agents: Certified Bounds on Covert Signaling

arXiv 2603.00381 ↗First posted 27 Feb 2026cs.CR cs.AI cs.SYcited by 0

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

Abstract (arXiv)

Colluding language-model agents can hide coordination in messages that remain policy-compliant at the surface level. We present CLBC, a protocol where generation and admission are separated: a message is admitted to transcript state only if a small verifier accepts a proof-bound envelope under a pinned predicate Pi. The predicate binds policy hash, public randomness schedule, transcript chaining, latent schema constraints, canonical metadata/tool fields, and deterministic rejection codes. We show how this protocol yields an upper bound on transcript leakage in terms of latent leakage plus explicit residual channels, derive adaptive composition guarantees, and state a semantic lower bound when policy-valid alternatives remain choosable. We report extensive empirically grounded evidence: aggregate evaluation satisfies all prespecified thresholds; strict lane decoder advantage is bounded at 0.0000 with MI proxy 0.0636; adaptive-colluder stress tests remain below attacker thresholds; and baseline separation shows large gaps between reject-by-default semantics and audit-only controls. We further quantify operational tradeoffs. Strict full-proof mode has median turn latency 27.53s (p95 28.08s), while sampled proving reduces non-proved-turn latency to 0.327ms. The central finding is that bottlenecks alone are insufficient: security claims depend on verifiable admission semantics that are online, deterministic, and fail-closed.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 15 of its 43 references that are in the map. First posted on arXiv 27 Feb 2026.

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15 of 43 references.

References · 15

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  3. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,988
  4. 2023CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Societycited by 1,395
  5. 2016Deep Variational Information Bottleneckcited by 930
  6. 2016Learning to Communicate with Deep Multi-Agent Reinforcement Learningcited by 575
  7. 2016Learning Multiagent Communication with Backpropagationcited by 366
  8. 2017Emergence of Grounded Compositional Language in Multi-Agent Populationscited by 263
  9. 2019Learning Efficient Multi-agent Communication: An Information Bottleneck Approachcited by 61
  10. 2021Provably Secure Generative Linguistic Steganographycited by 34
  11. 2025Odysseus: Jailbreaking Commercial Multimodal LLM-integrated Systems via Dual Steganographycited by 8
  12. 2025"Show Me You Comply... Without Showing Me Anything": Zero-Knowledge Software Auditing for AI-Enabled Systemscited by 6
3 more
  1. 2024Provably Secure Disambiguating Neural Linguistic Steganographycited by 5
  2. 2025Trusted Compute Units: A Framework for Chained Verifiable Computationscited by 1
  3. 2025Defining Cost Function of Steganography with Large Language Modelscited by 1

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  6. 2026Beyond the Transcript: Detecting Covert Co ordination in Latent Multi-Agent Communication
  7. 2026AgentLeak: A Benchmark for Internal-Channel Privacy Leakage in Multi-Agent LLM Systems
  8. 2026DP-MemView: A Memory Interface for Attribute-Level Transcript Privacy in Long-Term LLM Agents