GHSA-337M-MW94-2V6G
Vulnerability from github – Published: 2026-05-14 12:30 – Updated: 2026-05-20 15:36
VLAI
Summary
Apache Commons Configuration: StackOverflowError for YAML input with cycles
Details
Uncontrolled Recursion vulnerability in Apache Commons.
When processing an untrusted configuration file, Commons Configuration will throw a StackOverflowError for YAML input with cycles. This issue affects Apache Commons: from 2.2 before 2.15.0.
Users are recommended to upgrade to version 2.15.0, which fixes the issue.
Severity
5.3 (Medium)
{
"affected": [
{
"package": {
"ecosystem": "Maven",
"name": "org.apache.commons:commons-configuration2"
},
"ranges": [
{
"events": [
{
"introduced": "2.2"
},
{
"fixed": "2.15.0"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2026-45205"
],
"database_specific": {
"cwe_ids": [
"CWE-674"
],
"github_reviewed": true,
"github_reviewed_at": "2026-05-20T15:36:43Z",
"nvd_published_at": "2026-05-14T12:16:35Z",
"severity": "MODERATE"
},
"details": "Uncontrolled Recursion vulnerability in Apache Commons.\n\nWhen processing an untrusted configuration file, Commons Configuration will throw a StackOverflowError for YAML input with cycles.\nThis issue affects Apache Commons: from 2.2 before 2.15.0.\n\nUsers are recommended to upgrade to version 2.15.0, which fixes the issue.",
"id": "GHSA-337m-mw94-2v6g",
"modified": "2026-05-20T15:36:43Z",
"published": "2026-05-14T12:30:27Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-45205"
},
{
"type": "WEB",
"url": "https://github.com/apache/commons-configuration/pull/634"
},
{
"type": "PACKAGE",
"url": "https://github.com/apache/commons-configuration"
},
{
"type": "WEB",
"url": "https://lists.apache.org/thread/q3q3j10ohcqhs6o0rg1v7kz6kk27vtkk"
},
{
"type": "WEB",
"url": "http://www.openwall.com/lists/oss-security/2026/05/14/5"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:L",
"type": "CVSS_V3"
}
],
"summary": "Apache Commons Configuration: StackOverflowError for YAML input with cycles"
}
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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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