mlsanityData to 14 Oct 2026

Attesting Outputs and Delegation Ancestry in Multi-Agent AI Systems

arXiv 2608.30387 ↗First posted 31 Aug 2026cs.CRcited by 1

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

Abstract (arXiv)

Multi-agent applications delegate work across independently operated deployers. After an incident, a verifier must answer two questions: which deployer released the reported bytes, and whether each cross-deployer edge was authorized. Credentials establish who may act, but need not bind them to later output bytes or prove both deployers authorized a dynamically created edge. We present a two-layer attestation design for dynamic delegation without a shared authority, public log, or precommitted workflow. A trusted deployer runtime signs a hash of each released output; this records released bytes but does not prevent prompt injection. Ancestry evidence records edge authorization. Under a unified threat model, we compare a signed linked list, a Merkle-chain variant, and a co-signed DAG. The primitives are standard; the contribution is deployer-side binding and the evidence needed for the two questions. After child-key compromise, the single-signer designs permit an unauthorized parent binding, whereas the co-signed DAG rejects it because the parent must authorize the edge. Fixed adversary matrices and regression tests validate the composed verifier. On an Apple M1 Pro, ancestry-only checks take 24.3-499.2us per hop. In a live local multi-service workflow, a parent discovers the child's A2A Agent Card; the child calls an MCP tool and releases local-LLM output: all 30 signed-DAG tasks passed complete verification, while a controlled child-key-only claim was rejected; its mean end-to-end latency was 813.1ms versus 770.8ms without evidence. In a complementary three-availability-zone AWS deployment, all 1,000 valid co-signed-DAG paths verified; issuance averaged 3.651ms and complete verification 5.015ms. The cloud result excludes TLS/mTLS, KMS, and model-serving latency.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 10 of its 24 references that are in the map. First posted on arXiv 31 Aug 2026.

Builds on

10 of 24 references.

Closest ancestors · 5

Same direction, newest first

  1. 2026Mandato: Protocol-Level Enforcement of Digitally Signed Mandates on AI Agent Actions with Cryptographically Chained Audit Trails14 Aug
  2. 2026When AUC 0.998 Is Not Enough: A Candidate Evaluation Protocol for Hidden-State Probes of Indirect Prompt Injection in Multimodal Computer-Use Agents22 Jun
  3. 2026MemLineage: Lineage-Guided Enforcement for LLM Agent Memory14 May
  4. 2026HDP: A Lightweight Cryptographic Protocol for Human Delegation Provenance in Agentic AI Systems6 Apr
  5. 2026AIP: Agent Identity Protocol for Verifiable Delegation Across MCP and A2A25 Mar

Other references · 5

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,988
  3. 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,443
  4. 2024Visibility into AI Agentscited by 79
  5. 2025Authenticated Delegation and Authorized AI Agentscited by 65

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

Similar, not cited

Close in wording, no citation either way

  1. 2026Observability for Delegated Execution in Agentic AI Systems
  2. 2026Delegation Without Trust: An Empirical Gap Analysis of Identity, Authorization, and Runtime Governance in Multi-Agent LLM Systems
  3. 2026Cryptographically verifiable authorization for autonomous AI agents: a falsifiable hypothesis and proof of concept
  4. 2026Authorization Propagation in Multi-Agent AI Systems: Identity Governance as Infrastructure
  5. 2026CAVA: Canonical Action Verification and Attestation for Runtime Governance of Agentic AI Systems
  6. 2026NostrAgent: A Decentralized Identity and Delegation Architecture for Sovereign Agentic Systems
  7. 2025Verifiability-First Agents: Provable Observability and Lightweight Audit Agents for Controlling Autonomous LLM Systems
  8. 2026SentinelAgent: Intent-Verified Delegation Chains for Securing Federal Multi-Agent AI Systems