Cyber Resilience

CVE-2025-26527

Moodle 4.1.0 – 4.1.16

Published
24 February 2025
Modified
08 August 2025
Patch / advisory
CVSS Score v3.1 5.3
Click a component to see what it means
Raw vectorCVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:N
EPSS Score 0.0035 28th percentile
Risk Priority 44 floored blend · peak EPSS

Summary

CVE-2025-26527 is a medium-severity Exposure of Sensitive Information Through Metadata (CWE-1230) vulnerability in Moodle Moodle. Its CVSS base score is 5.3 (Medium).

Operationally, exploitation aligns with the MITRE ATT&CK technique Data from Local System (T1005); ranked at the 28th 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

Tags not expected to be visible to a user could still be discovered by them via the tag search page or in the tags block.

CWE(s)

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.
T1025 Data from Removable Media Collection
Adversaries may search connected removable media on computers they have compromised to find files of interest.
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.
T1217 Browser Information Discovery Discovery
Adversaries may enumerate information about browsers to learn more about compromised environments.
T1592 Gather Victim Host Information Reconnaissance
Adversaries may gather information about the victim's hosts that can be used during targeting.
Derived from this CVE’s CWE(s) via the direct CWE→ATT&CK cross-walk.

CVEs Like This One

CVE-2025-62396Same product: Moodle Moodle
CVE-2024-48896Same product: Moodle Moodle
CVE-2025-62397Same product: Moodle Moodle
CVE-2023-1402Same product: Moodle Moodle
CVE-2025-62401Same product: Moodle Moodle
CVE-2024-48901Same product: Moodle Moodle
CVE-2024-48897Same product: Moodle Moodle
CVE-2024-43429Same product: Moodle Moodle
CVE-2024-34002Same product: Moodle Moodle
CVE-2024-34005Same product: Moodle Moodle

Affected Assets

moodle
moodle
4.1.0 — 4.1.16 · 4.3.0 — 4.3.10 · 4.4.0 — 4.4.6

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

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

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

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.

mitigates

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

mitigates

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

degrades

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

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.

finds

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

References