ubuntu-cve-2025-10990
Vulnerability from osv_ubuntu
Published
2026-02-27 14:16
Modified
2026-07-16 18:13
Summary
Details
A flaw was found in REXML. A remote attacker could exploit inefficient regular expression (regex) parsing when processing hex numeric character references (&#x...;) in XML documents. This could lead to a Regular Expression Denial of Service (ReDoS), impacting the availability of the affected component. This issue is the result of an incomplete fix for CVE-2024-49761.
Severity
7.5 (High)
N/A (UNKNOWN)
References
{
"affected": [
{
"ecosystem_specific": {
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{
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}
]
},
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},
"ranges": [
{
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}
],
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]
},
{
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},
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},
{
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"2.3.1-2~ubuntu16.04.16+esm6",
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"2.3.1-2~ubuntu16.04.16+esm10",
"2.3.1-2~ubuntu16.04.16+esm11",
"2.3.1-2~ubuntu16.04.16+esm12",
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{
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"2.7.0-5ubuntu1.18+esm4",
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},
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},
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},
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}
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},
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},
"package": {
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{
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{
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},
"package": {
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"name": "ruby3.3",
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},
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}
],
"aliases": [],
"details": "A flaw was found in REXML. A remote attacker could exploit inefficient regular expression (regex) parsing when processing hex numeric character references (\u0026#x...;) in XML documents. This could lead to a Regular Expression Denial of Service (ReDoS), impacting the availability of the affected component. This issue is the result of an incomplete fix for CVE-2024-49761.",
"id": "UBUNTU-CVE-2025-10990",
"modified": "2026-07-16T18:13:20Z",
"published": "2026-02-27T14:16:00Z",
"references": [
{
"type": "REPORT",
"url": "https://ubuntu.com/security/CVE-2025-10990"
},
{
"type": "REPORT",
"url": "https://www.cve.org/CVERecord?id=CVE-2025-10990"
},
{
"type": "REPORT",
"url": "https://access.redhat.com/errata/RHSA-2025:17606"
},
{
"type": "REPORT",
"url": "https://access.redhat.com/errata/RHSA-2025:17613"
},
{
"type": "REPORT",
"url": "https://access.redhat.com/errata/RHSA-2025:17693"
},
{
"type": "REPORT",
"url": "https://access.redhat.com/security/cve/CVE-2025-10990"
}
],
"related": [],
"schema_version": "1.7.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
},
{
"score": "medium",
"type": "Ubuntu"
}
],
"upstream": [
"CVE-2025-10990"
]
}
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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