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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

Indonesian

Learned Loss Access Control biasanya didistribusikan di belakang dua artefak buatan tangan: satu set hard-denied yang dipaksakan oleh perisai kecil yang dipercaya, dan sebuah peta wilayah yang memutuskan permohonan aturan tambang yang dijawab dan yang dipelajari menjawab. Memeriksa perisai atau mengukur siswa tidak memvalidasi keduanya. Kami mengaudit pada lima kebijakan ABAC Lab publik dan tiga model buta. Pertama, kami memformalisasi pembatasan hard-denied soundness dan memeriksa secara menyeluruh di atas hubungan yang dirilis hingga 794.250 tuples. Klausa yang setia mengkonfigurasi pembatasan eksplisit tidak terdengar pada tingkat 0.50-0.70 di seluruh empat elicitors - seorang penulis dan tiga elicitors-model buta - karena prosa membatasi ke bagian sementara hubungan sumbangan adalah aturan pelanggaran di seluruh: 97% dari

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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