GHSA-37Q5-V5QM-C9V8
Vulnerability from github – Published: 2024-04-10 18:30 – Updated: 2024-04-10 22:20
VLAI
Summary
Transformers Deserialization of Untrusted Data vulnerability
Details
The huggingface/transformers library is vulnerable to arbitrary code execution through deserialization of untrusted data within the load_repo_checkpoint() function of the TFPreTrainedModel() class. Attackers can execute arbitrary code and commands by crafting a malicious serialized payload, exploiting the use of pickle.load() on data from potentially untrusted sources. This vulnerability allows for remote code execution (RCE) by deceiving victims into loading a seemingly harmless checkpoint during a normal training process, thereby enabling attackers to execute arbitrary code on the targeted machine.
Severity
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "transformers"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "4.38.0"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2024-3568"
],
"database_specific": {
"cwe_ids": [
"CWE-502"
],
"github_reviewed": true,
"github_reviewed_at": "2024-04-10T22:20:56Z",
"nvd_published_at": "2024-04-10T17:15:58Z",
"severity": "LOW"
},
"details": "The huggingface/transformers library is vulnerable to arbitrary code execution through deserialization of untrusted data within the `load_repo_checkpoint()` function of the `TFPreTrainedModel()` class. Attackers can execute arbitrary code and commands by crafting a malicious serialized payload, exploiting the use of `pickle.load()` on data from potentially untrusted sources. This vulnerability allows for remote code execution (RCE) by deceiving victims into loading a seemingly harmless checkpoint during a normal training process, thereby enabling attackers to execute arbitrary code on the targeted machine.",
"id": "GHSA-37q5-v5qm-c9v8",
"modified": "2024-04-10T22:20:56Z",
"published": "2024-04-10T18:30:48Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2024-3568"
},
{
"type": "WEB",
"url": "https://github.com/huggingface/transformers/commit/693667b8ac8138b83f8adb6522ddaf42fa07c125"
},
{
"type": "PACKAGE",
"url": "https://github.com/huggingface/transformers"
},
{
"type": "WEB",
"url": "https://huntr.com/bounties/b3c36992-5264-4d7f-9906-a996efafba8f"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.0/AV:N/AC:H/PR:N/UI:R/S:C/C:N/I:N/A:L",
"type": "CVSS_V3"
}
],
"summary": "Transformers Deserialization of Untrusted Data vulnerability"
}
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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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