BREW-BACKGROUNDREMOVER-C… (GHSA-9FQ2-X9R6-WFMF)
Vulnerability from osv_homebrew – Published: 2026-08-13 16:37 – Updated: 2026-09-09 23:43 – Source website
VLAI
Summary
Numpy Deserialization of Untrusted Data
Details
** DISPUTED ** An issue was discovered in NumPy 1.16.2 and earlier. It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call. NOTE: third parties dispute this issue because it is a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated sources.
Severity
9.8 (Critical)
References
{
"affected": [
{
"ecosystem_specific": {
"fix": "bump",
"range_state": "fixed",
"resource": "numpy",
"resource_purl": "pkg:pypi/numpy@2.4.6",
"upstream_fixed_in": "1.16.3"
},
"package": {
"ecosystem": "Homebrew",
"name": "backgroundremover",
"purl": "pkg:brew/backgroundremover"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "0.4.5"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"database_specific": {
"confidence": "high",
"source": "matched",
"strategy": "registry",
"upstream_evidence": [
{
"ecosystem": "PyPI",
"key": "pkg:pypi/numpy@2.4.6",
"name": "numpy",
"resource": "numpy",
"strategy": "registry",
"subject_version": "2.4.6"
}
]
},
"details": "** DISPUTED ** An issue was discovered in NumPy 1.16.2 and earlier. It uses the pickle Python module unsafely, which allows remote attackers to execute arbitrary code via a crafted serialized object, as demonstrated by a numpy.load call. NOTE: third parties dispute this issue because it is a behavior that might have legitimate applications in (for example) loading serialized Python object arrays from trusted and authenticated sources.",
"id": "BREW-backgroundremover-CVE-2019-6446",
"modified": "2026-09-09T23:43:06Z",
"published": "2026-08-13T16:37:03Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2019-6446"
},
{
"type": "WEB",
"url": "https://github.com/numpy/numpy/issues/12759"
},
{
"type": "WEB",
"url": "https://github.com/numpy/numpy/pull/12889"
},
{
"type": "WEB",
"url": "https://github.com/numpy/numpy/pull/13359"
},
{
"type": "WEB",
"url": "https://github.com/numpy/numpy/commit/89b688732b37616c9d26623f81aaee1703c30ffb"
},
{
"type": "WEB",
"url": "https://access.redhat.com/errata/RHSA-2019:3335"
},
{
"type": "WEB",
"url": "https://access.redhat.com/errata/RHSA-2019:3704"
},
{
"type": "WEB",
"url": "https://bugzilla.suse.com/show_bug.cgi?id=1122208"
},
{
"type": "ADVISORY",
"url": "https://github.com/advisories/GHSA-9fq2-x9r6-wfmf"
},
{
"type": "PACKAGE",
"url": "https://github.com/numpy/numpy"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/numpy/PYSEC-2019-108.yaml"
},
{
"type": "WEB",
"url": "https://lists.fedoraproject.org/archives/list/package-announce%40lists.fedoraproject.org/message/7ZZAYIQNUUYXGMKHSPEEXS4TRYFOUYE4"
},
{
"type": "WEB",
"url": "https://web.archive.org/web/20210124234613/https://www.securityfocus.com/bid/106670"
},
{
"type": "WEB",
"url": "http://lists.opensuse.org/opensuse-security-announce/2019-09/msg00091.html"
},
{
"type": "WEB",
"url": "http://lists.opensuse.org/opensuse-security-announce/2019-09/msg00092.html"
},
{
"type": "WEB",
"url": "http://lists.opensuse.org/opensuse-security-announce/2019-10/msg00015.html"
},
{
"type": "WEB",
"url": "http://www.securityfocus.com/bid/106670"
}
],
"schema_version": "1.7.3",
"severity": [
{
"score": "CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
"type": "CVSS_V3"
},
{
"score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N",
"type": "CVSS_V4"
}
],
"summary": "Numpy Deserialization of Untrusted Data",
"upstream": [
"GHSA-9fq2-x9r6-wfmf",
"CVE-2019-6446",
"PYSEC-2019-108"
]
}
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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.
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- 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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