Cyber Resilience

CVE-2025-8713

Published
14 August 2025
Modified
17 June 2026
CVSS Score v3.1 3.1
Click a component to see what it means
Raw vectorCVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:U/C:L/I:N/A:N
EPSS Score 0.0022 13th percentile
Risk Priority 27 floored blend · peak EPSS

Summary

CVE-2025-8713 is a low-severity Exposure of Sensitive Information Through Metadata (CWE-1230) vulnerability in Postgresql (inferred from references). Its CVSS base score is 3.1 (Low).

Operationally, exploitation aligns with the MITRE ATT&CK technique Data from Local System (T1005); ranked at the 13th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.

The strongest mitigations our analysis identified map to AC-4 (Information Flow Enforcement) and AC-16 (Security and Privacy Attributes) — see the control section below for these in your framework.

EU & UK References

Vulnerability Data

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…

more

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.

CWE(s)
CWE-1230

Related Threats

MITRE ATT&CK Enterprise Techniques

T1005 Data from Local System Collection
Adversaries may search local system sources, such as file systems, configuration files, local databases, virtual machine files, or process memory, to find files of interest and sensitive data prior to Exfiltration.
attack.mitre.org →
T1025 Data from Removable Media Collection
Adversaries may search connected removable media on computers they have compromised to find files of interest.
attack.mitre.org →
T1083 File and Directory Discovery Discovery
Adversaries may enumerate files and directories or may search in specific locations of a host or network share for certain information within a file system.
attack.mitre.org →
T1217 Browser Information Discovery Discovery
Adversaries may enumerate information about browsers to learn more about compromised environments.
attack.mitre.org →
T1592 Gather Victim Host Information Reconnaissance
Adversaries may gather information about the victim's hosts that can be used during targeting.
attack.mitre.org →
Derived from this CVE’s CWE(s) via the direct CWE→ATT&CK cross-walk.

CVEs Like This One

CVE-2024-8910Shared CWE-1230
CVE-2024-10324Shared CWE-1230
CVE-2025-30038Shared CWE-1230
CVE-2023-50458Shared CWE-1230
CVE-2024-9447Shared CWE-1230
CVE-2026-49270Shared CWE-1230
CVE-2025-59601Shared CWE-1230
CVE-2023-32488Shared CWE-1230
CVE-2026-29055Shared CWE-1230
CVE-2025-48941Shared CWE-1230

Affected Assets

Postgresql
—
inferred from references and description; NVD did not file a CPE for this CVE

Mitigating Controls

Control response

Prevent
Stop it (NIST 800-53)

—

Detect
Catch it (NIST detect / respond)

—

Harden
Shrink the surface (DISA STIG)
  • 1 hardening rule · 1 OS baseline
Validate
Prove the fix (OWASP ASVS)
  • V3.4.2
  • V12.1.5
  • V13.3.2
  • V14.2.4

Mitigating Controls (NIST 800-53 r5) AI

AC-4 Information Flow Enforcement good match

Information flow enforcement can be configured to treat metadata as protected information and block its unauthorized release.

AC-16 Security and Privacy Attributes partial match

Associating and enforcing security/privacy attributes on objects directly governs what metadata may be exposed.

SC-16 Transmission of Security and Privacy Attributes partial match

Requiring security attributes to accompany transmitted data prevents sensitive values from leaking through metadata channels.

Mitigating Controls (NIST CSF 2.0) AI

Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.

ID.AM-07 mostly match
prevents

Explicit inventorying of metadata enables identification and subsequent protection of sensitive derived information.

PR.AA-05 mostly match
prevents

Least-privilege access policies applied to metadata directly prevent unauthorized exposure of sensitive derived data.

Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI

Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.

A.5.12 Classification of information partial match
mitigates

Classification helps identify metadata that may need protection, but does not directly limit its exposure.

A.5.13 Labelling of information partial match
mitigates

Labelling can flag sensitive metadata, yet does not enforce technical controls to prevent its disclosure.

A.5.14 Information transfer partial match
degrades

Transfer policies can require stripping or protecting metadata, but coverage is indirect.

A.5.15 Access control partial match
mitigates

Access-control rules can be extended to metadata objects, though the control is not metadata-specific.

A.8.11 Data masking partial match
mitigates

Data-masking techniques can obscure sensitive metadata values, providing partial mitigation.

A.8.12 Data leakage prevention partial match
finds

DLP solutions can detect and block metadata leakage, yet are not designed solely for this weakness.

References