CVE-2025-66578
Dec. 9, 2025, 6:36 p.m.
6.0
Medium
Description
xmlseclibs is a library written in PHP for working with XML Encryption and Signatures. Versions 3.1.3 contain an authentication bypass vulnerability due to a flaw in the libxml2 canonicalization process during document transformation. When libxml2’s canonicalization is invoked on an invalid XML input, it may return an empty string rather than a canonicalized node. xmlseclibs then proceeds to compute the DigestValue over this empty string, treating it as if canonicalization succeeded. This issue is fixed in version 3.1.4. Workarounds include treating canonicalization failures (exceptions or nil/empty outputs) as fatal and aborting validation, and/or adding explicit checks to reject when canonicalize returns nil/empty or raises errors.
Product(s) Impacted
| Vendor | Product | Versions |
|---|---|---|
| Xmlseclibs |
|
|
Weaknesses
Common security weaknesses mapped to this vulnerability.
CWE-248
Uncaught Exception
An exception is thrown from a function, but it is not caught.
*CPE(s)
Affected systems and software identified for this CVE.
| Type | Vendor | Product | Version | Update | Edition | Language | Software Edition | Target Software | Target Hardware | Other Information |
|---|---|---|---|---|---|---|---|---|---|---|
| a | xmlseclibs | xmlseclibs | 3.1.3 | / | / | / | / | / | / | / |
| a | xmlseclibs | xmlseclibs | <3.1.4 | / | / | / | / | / | / | / |
Tags
CVSS Score
CVSS Data - 3.1
- Attack Vector: NETWORK
- Attack Complexity: LOW
- Privileges Required: HIGH
- Scope: UNCHANGED
- Confidentiality Impact: HIGH
- Integrity Impact: LOW
- Availability Impact: LOW
CVSS:3.1/AV:N/AC:L/PR:H/UI:N/S:U/C:H/I:L/A:L
Timeline
Published: Dec. 9, 2025, 4:18 p.m.
Last Modified: Dec. 9, 2025, 6:36 p.m.
Last Modified: Dec. 9, 2025, 6:36 p.m.
Status : Awaiting Analysis
CVE has been marked for Analysis. Normally once in this state the CVE will be analyzed by NVD staff within 24 hours.
More infoSource
security-advisories@github.com
*Disclaimer: Some vulnerabilities do not have an associated CPE. To enhance the data, we use AI to infer CPEs based on CVE details. This is an automated process and might not always be accurate.