Agent Flight Recorder: Tamper-Evident Audit Trails with On-Chain Anchoring for Long-Horizon Tool-Using Agents
cs.CR, cs.MA
Submitted: 2026-09-01
Updated: 2026-09-01
Comments: 9 pages, 1 figure, 4 tables. Accepted at BCCA 2026 (IEEE International Conference on Blockchain Computing and Applications)
Code: https://github.com/mpi-dsg/agent-flight-recorder
License: http://creativecommons.org/licenses/by/4.0/
The gist: Long-horizon agents execute thousands of actions, resulting in sequential failures rather than isolated errors.
Terminology
Abstract
Long-horizon agents execute thousands of actions, resulting in sequential failures rather than isolated errors. When a coding agent deletes a production database or a prompt injection spreads across agents, the incident raises questions of causality, authority, and non-repudiable third-party verification. The Agent Flight Recorder captures each agent action as a structured, canonically serialized event binding eight semantic fields from intent through execution to provenance. Hash chaining and Merkle batching provide tamper evidence and compact inclusion proofs. For cross-organizational disputes where no party's infrastructure qualifies as neutral ground, periodic on-chain anchoring of epoch roots lets any verifier with the disclosed payload and Merkle proof check the record independently, without pre-agreeing on a trusted intermediary. The on-chain footprint is minimal: each anchor stores a 32-byte epoch root and a back-pointer, and no event content touches the chain. We evaluate the system across five cumulative ablation configurations on synthetic agent workloads. The full system adds 48 microseconds median per-event latency and 512 bytes per event. L2 anchoring costs 2.30 per 100K events at 100-event epochs. The full integrity stack detects edit, delete, reorder, and fork tampering at 100% with zero false positives. Structured forensic queries achieve 1.0 precision on guardrail and delegation lookups where unstructured text search yields 0.013 and 0.077 respectively.
Sources
- Audit Trails for Accountability in Large Language Models
- SoK: Security and Privacy of AI Agents for Blockchain
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