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CVE-2025-8713


PostgreSQL optimizer statistics allow a user to read sampled data within a view that the user cannot access. Separately, statistics allow a user to read sampled data that a row security policy intended to hide. PostgreSQL maintains statistics for tables by sampling data available in columns; this data is consulted during the query planning process. Prior to this release, a user could craft a leaky operator that bypassed view access control lists (ACLs) and bypassed row security policies in partitioning or table inheritance hierarchies. Reachable statistics data notably included histograms and most-common-values lists. CVE-2017-7484 and CVE-2019-10130 intended to close this class of vulnerability, but this gap remained. Versions before PostgreSQL 17.6, 16.10, 15.14, 14.19, and 13.22 are affected.


Security Impact Summary

This vulnerability carries a LOW severity rating with a CVSS v3.1 score of 3.1, indicating it can be exploited remotely over the network but requires specific conditions to be met without requiring user interaction requiring only low-level privileges . The vulnerability impacts limited data confidentiality, for affected systems.

Historical Context

Reported in 2025, this vulnerability emerged during an era marked by increased sophistication in supply chain attacks, cloud infrastructure vulnerabilities, and software-as-a-service (SaaS) security challenges. Security practices during this period emphasized zero-trust architectures, container security, and API protection.


Published

2025-08-14T13:15:37.543

Last Modified

2025-08-15T13:13:07.817

Status

Awaiting Analysis

Source

f86ef6dc-4d3a-42ad-8f28-e6d5547a5007

Severity

CVSSv3.1: 3.1 (LOW)

Weaknesses
  • Type: Secondary
    CWE-1230

Affected Vendors & Products

-


References

How SecUtils Interprets This CVE

SecUtils normalizes and enriches National Vulnerability Database (NVD) records by standardizing vendor and product identifiers, aggregating vulnerability metadata from both NVD and MITRE sources, and providing structured context for security teams. For affected software, we extract Common Platform Enumeration (CPE) data, Common Weakness Enumeration (CWE) classifications, CVSS severity metrics, and reference data to enable rapid vulnerability prioritization and asset correlation. This record contains no exploit code, proof-of-concept instructions, or attack methodologies—only defensive intelligence necessary for patch management, risk assessment, and security operations.