From Intent to Execution Grant: An Execution-Boundary Conformance Profile for High-Risk AI Actions
Researchers specify EBL-Core, an execution-boundary conformance profile binding AI agent intents, policies, and evidence into verifiable execution grants, validated with bounded tests.
The paper defines EBL-Core, a conformance profile deciding whether one fully materialized AI-generated candidate action may receive action-scoped execution authority. It binds a structured intent object, Root and Operational Policies, typed evidence, and a verifiable Decision Derivation through an Execution Release Contract, with lifecycle rules for Redemption and Revocation. Evaluation included 34 static vectors, 15 lifecycle checks, and 100 trials of 32 concurrent Redemption attempts yielding exactly one winner per trial. The authors state these bounded results demonstrate executability of the specified subset, not production readiness or complete mediation.
- EBL-Core defines conditions for granting AI-generated candidate actions scoped execution authority
- Execution Release Contracts bind intent, policies, evidence, and verifiable decision derivation
- Tests include 34 static vectors, 15 lifecycle checks, and 100 concurrent Redemption races
- Authors note bounded results show executability, not production security or intent correctness
From the original article
1 min read at arxiv.orgAI agents increasingly propose actions with external consequences, including financial transfers, infrastructure changes, software deployments, disclosures, and physical actuation.
By Mengting Wu · arxiv.org
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