BREW-MLX-LM-CVE-2023-7018 (GHSA-V68G-WM8C-6X7J)
Vulnerability from osv_homebrew – Published: 2026-08-13 17:14 – Updated: 2026-09-10 00:32 – Source website
VLAI
Summary
transformers has a Deserialization of Untrusted Data vulnerability
Details
Deserialization of Untrusted Data in GitHub repository huggingface/transformers prior to 4.36.
Severity
7.8 (High)
{
"affected": [
{
"ecosystem_specific": {
"fix": "bump",
"range_state": "fixed",
"resource": "transformers",
"resource_purl": "pkg:pypi/transformers@5.16.1",
"upstream_fixed_in": "4.36.0"
},
"package": {
"ecosystem": "Homebrew",
"name": "mlx-lm",
"purl": "pkg:brew/mlx-lm"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "0.31.3_2"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"database_specific": {
"confidence": "high",
"source": "matched",
"strategy": "registry",
"upstream_evidence": [
{
"ecosystem": "PyPI",
"key": "pkg:pypi/transformers@5.16.1",
"name": "transformers",
"resource": "transformers",
"strategy": "registry",
"subject_version": "5.16.1"
}
]
},
"details": "Deserialization of Untrusted Data in GitHub repository huggingface/transformers prior to 4.36.",
"id": "BREW-mlx-lm-CVE-2023-7018",
"modified": "2026-09-10T00:32:14Z",
"published": "2026-08-13T17:14:17Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2023-7018"
},
{
"type": "WEB",
"url": "https://github.com/huggingface/transformers/commit/1d63b0ec361e7a38f1339385e8a5a855085532ce"
},
{
"type": "PACKAGE",
"url": "https://github.com/huggingface/transformers"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/transformers/PYSEC-2023-301.yaml"
},
{
"type": "WEB",
"url": "https://huntr.com/bounties/e1a3e548-e53a-48df-b708-9ee62140963c"
}
],
"schema_version": "1.7.3",
"severity": [
{
"score": "CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H",
"type": "CVSS_V3"
}
],
"summary": "transformers has a Deserialization of Untrusted Data vulnerability",
"upstream": [
"GHSA-v68g-wm8c-6x7j",
"CVE-2023-7018",
"PYSEC-2023-301"
]
}
Loading…
Loading…
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.
Loading…
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.
Loading…
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.
Loading…