Plaintext Recovery Against Post-Filtering Access Control

• URL: http://arxiv.org/abs/2608.11730v1
• Date: Wed, 12 Aug 2026 07:11:18 GMT
• Title: Plaintext Recovery Against Post-Filtering Access Control
• Abstract summary: Fine-level access control (RLS) and document-level security (DLS) are widely deployed in databases to restrict access to data stored in physical indexing structures shared by multiple users.<n>FGAC implementations often use post-filtering where untrusted queries run over all data and private results are redacted afterwards.<n>While damaging, prior attacks do not enable the efficient recovery of rich, high-entropy data like full records or text documents.
• Score: 1.261497003127225
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fine-grained access control (FGAC) mechanisms such as row-level security (RLS) and document-level security (DLS) are widely deployed in databases to restrict access to data stored in physical indexing structures shared by multiple users (e.g., in multi-tenant databases, or in the implementation of least-privilege within an organization). FGAC implementations often use post-filtering where untrusted queries run over all data and private results are redacted afterwards. Prior work shows this approach can lead to side-channels that enable attackers to test if a chosen value exists in unseen data. While damaging, prior attacks do not enable the efficient recovery of rich, high-entropy data like full records or text documents. We show these side-channels are more damaging than previously thought. Using rich query interfaces (e.g., range, prefix, and conjunctive predicates), we amplify existence leakage into reconstruction attacks. We do this in two settings: - PostgreSQL (RLS timing). We exploit a timing side-channel and expressive SQL queries (e.g., ranges, conjunctions) to enumerate unknown attribute values and, in turn, full records via binary search over large domains. - Elasticsearch/OpenSearch (DLS scoring). We exploit scoring and prefix-expansion side-channels to recover indexed terms from documents. In some cases, we can extract $n$-grams in the corpus to recover approximate text. Our results show that FGAC side-channels must be evaluated in the presence of rich predicates, which can turn membership tests into scalable reconstruction of high-entropy records.

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