CVE-2026-105615 in PostgreSQL Anonymizer
Summary
by MITRE • 10/28/2026
PostgreSQL's optimized statistics on a table are visible as long as you have SELECT privilege on said table. A masked role can therefore read a limited subset of the data through the statistics view's histograms and mcvs. The vulnerability is resolved in PostgreSQL Anonymizer 3.3.0 and later versions.
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Analysis
by VulDB Data Team • 10/28/2026
The disclosed vulnerability highlights a significant information disclosure flaw within the PostgreSQL database ecosystem, specifically affecting implementations that utilize or are compatible with extensions like PostgreSQL Anonymizer prior to version 3.3.0. The core issue stems from the inherent design of PostgreSQL's statistics collection mechanism, which is optimized for query planning and performance tuning. These statistical aggregates, including histograms and most common values (MCVs), are stored in system catalog tables such as pg_statistic. Under standard database security models, access to these views is typically governed by the SELECT privilege on the underlying table rather than granular row-level or column-level permissions designed for data masking purposes.
This architectural oversight creates a scenario where users with limited privileges can inadvertently or intentionally extract sensitive information. Specifically, if an attacker possesses only the SELECT privilege on a given table but lacks explicit permission to view raw data rows due to other security controls like Row Level Security (RLS) or column-level grants, they may still query the statistics views associated with that table. By analyzing the distribution of values presented in histograms and MCVs, it is possible to infer the existence and approximate frequency of specific data points within masked columns. This constitutes a side-channel attack vector where statistical metadata serves as an oracle for reconstructing subsets of protected data, thereby bypassing intended access controls designed to anonymize or restrict visibility of sensitive fields.
The operational impact of this vulnerability extends beyond simple data leakage; it undermines the integrity of privacy-preserving measures and compliance frameworks that rely on strict data isolation. For organizations implementing PostgreSQL Anonymizer to mask personally identifiable information (PII) or other regulated data, this flaw allows malicious actors with low-level access rights to perform statistical inference attacks. Over time, by aggregating statistics from multiple queries or observing changes in distribution after updates, an attacker could potentially reconstruct significant portions of the underlying dataset. This compromises confidentiality and violates principles outlined in industry standards such as CWE-209 (Generation of Error Message Containing Sensitive Information) and CWE-613 (Exposure of Sensitive Data to Unauthorized Actors). Furthermore, this technique aligns with ATT&CK techniques related to Collection via Command or Scripting and potentially Taint: Shared Resource if the statistics are considered a shared resource leaking context.
To mitigate this risk, organizations must ensure they upgrade PostgreSQL Anonymizer to version 3.3.0 or later, where the developers have addressed the exposure of these statistical views to roles with only SELECT privileges. In addition to upgrading software components, database administrators should review their privilege assignment strategies. It is advisable to restrict access to system catalog tables containing statistics unless absolutely necessary for query optimization purposes. Implementing strict Row Level Security policies that account for metadata leakage and auditing queries against pg_statistic can provide additional layers of defense. Regular security assessments focusing on statistical inference vulnerabilities are recommended to ensure that anonymization techniques remain effective against sophisticated attackers who exploit indirect data channels.