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Auditing the Hand-Written Inputs of Learned Access Control

Arsalan Jalil Sarghaleh, Mehdi Hazrati · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.31388 · cited by 0 works (OpenAlex) · open-access PDF

Japanese

Learned Loss Access Controlは通常、2つの手で作られたアーティファクトの後ろに展開されます:小さな信頼できるシールドによって強制されたハードネイセット、およびリージョンマップによって決定されるリクエストが採掘されたルールに応答し、学習者が答えるものを選択します。シールドを検証するか、測定した学習者はどちらも検証しません。我々は、5つの公的なABACラボポリシーの著者と3つの盲語モデルの両方を監査します。第一に、我々はハードネイサンスの制限を公式化し、リスク関係がすべてのルールの上に徹底的に検証します: 対例の997%は他のルールによって許可されています。 確実に明確な制限を書き換える

Machine translation (Meta M2M100); the English original follows.

Abstract

Learned access control is typically deployed behind two hand-built artefacts: a hard-deny set enforced by a small trusted shield, and a region map that decides which requests mined rules answer and which the learner answers. Verifying the shield or measuring the learner validates neither. We audit both on the five public ABAC Lab policies. First, we formalise hard-deny soundness and check it exhaustively over relations of up to 794,250 tuples. Clauses that faithfully transcribe an explicit restriction are unsound at rates of 0.50-0.70 across four elicitors - an author and three blind language-model elicitors - because prose scopes restrictions to a section while the grant relation is a disjunction over all rules: 97% of counterexamples are granted by other rules. Shipped unchecked, these deny sets would withhold 16-78% of everything the policies grant; certified, they withhold nothing. Vacuity, a second silent failure, also occurs. Second, hybrid routing needs a two-class, abstaining rule set that ABAC miners do not emit, and ranking a mixed-effect candidate pool under a cap is one-class in all 24 cells for the two commonest ranking keys. Third, for additive losses the hybrid-versus-learner effect factorises into covered mass times a rule-learner risk contrast that must be reported on both false-non-grant and false-permit loss: on two real policies, most selection rules give opposite verdicts on the two losses, and the one favourable on both holds for one policy, one learner configuration and a band of noise levels. A released artefact regenerates and checks every reported number.

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Topics: Soundness, Set (abstract data type), Ranking (information retrieval), Relation (database), Audit, Computer science

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