BREW-ANSIBLE@12-CVE-2016-8647 (GHSA-X4CM-M36H-C6QJ)
Vulnerability from osv_homebrew – Published: 2026-08-13 16:35 – Updated: 2026-09-09 23:41 – Source website
VLAI
Summary
Improper Input Validation in ansible
Details
An input validation vulnerability was found in Ansible's mysql_user module before 2.2.1.0, which may fail to correctly change a password in certain circumstances. Thus the previous password would still be active when it should have been changed.
Severity
4.9 (Medium)
References
{
"affected": [
{
"ecosystem_specific": {
"fix": "bump",
"range_state": "fixed",
"upstream_fixed_in": "2.2.1.0"
},
"package": {
"ecosystem": "Homebrew",
"name": "ansible@12",
"purl": "pkg:brew/ansible%4012"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "12.3.0"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"database_specific": {
"confidence": "high",
"source": "matched",
"strategy": "registry",
"upstream_evidence": [
{
"ecosystem": "PyPI",
"key": "pkg:pypi/ansible@12.3.0",
"name": "ansible",
"strategy": "registry",
"subject_version": "12.3.0"
},
{
"ecosystem": "Debian",
"key": "Debian/ansible",
"name": "ansible",
"strategy": "distro"
},
{
"ecosystem": "PyPI",
"key": "upstream:pkg:pypi/ansible@12.3.0",
"name": "ansible",
"strategy": "distro",
"subject_version": "12.3.0"
}
]
},
"details": "An input validation vulnerability was found in Ansible\u0027s mysql_user module before 2.2.1.0, which may fail to correctly change a password in certain circumstances. Thus the previous password would still be active when it should have been changed.",
"id": "BREW-ansible@12-CVE-2016-8647",
"modified": "2026-09-09T23:41:06Z",
"published": "2026-08-13T16:35:22Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2016-8647"
},
{
"type": "WEB",
"url": "https://github.com/ansible/ansible-modules-core/pull/5388"
},
{
"type": "WEB",
"url": "https://github.com/ansible/ansible-modules-core/commit/30fb384e7fb9a94ac3929e4a650877e45d8834c9"
},
{
"type": "WEB",
"url": "https://access.redhat.com/errata/RHSA-2017:1685"
},
{
"type": "WEB",
"url": "https://access.redhat.com/security/cve/CVE-2016-8647"
},
{
"type": "WEB",
"url": "https://bugzilla.redhat.com/show_bug.cgi?id=1396174"
},
{
"type": "WEB",
"url": "https://bugzilla.redhat.com/show_bug.cgi?id=CVE-2016-8647"
},
{
"type": "ADVISORY",
"url": "https://github.com/advisories/GHSA-x4cm-m36h-c6qj"
},
{
"type": "PACKAGE",
"url": "https://github.com/ansible/ansible"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/ansible/PYSEC-2018-58.yaml"
}
],
"schema_version": "1.7.3",
"severity": [
{
"score": "CVSS:3.0/AV:N/AC:L/PR:H/UI:N/S:U/C:N/I:H/A:N",
"type": "CVSS_V3"
},
{
"score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:H/UI:N/VC:N/VI:H/VA:N/SC:N/SI:N/SA:N",
"type": "CVSS_V4"
}
],
"summary": "Improper Input Validation in ansible",
"upstream": [
"GHSA-x4cm-m36h-c6qj",
"CVE-2016-8647",
"PYSEC-2018-58"
]
}
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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.
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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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