GHSA-4J2P-28Q2-5M79
Vulnerability from github – Published: 2026-08-10 21:32 – Updated: 2026-09-08 18:45
VLAI
Summary
Accelerate path traversal and denial of service via sharded checkpoint weight_map entries
Details
Hugging Face Accelerate through 1.14.0 contains a path traversal vulnerability in load_checkpoint_in_model and load_checkpoint_and_dispatch functions that fail to sanitize weight_map entries from sharded checkpoint indexes. Attackers can supply relative paths with ../ sequences or absolute paths to read arbitrary files, or point shard entries at named pipes to cause indefinite blocking and denial of service.
Severity
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "accelerate"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"last_affected": "1.14.0"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2026-69112"
],
"database_specific": {
"cwe_ids": [
"CWE-22"
],
"github_reviewed": true,
"github_reviewed_at": "2026-09-08T18:45:47Z",
"nvd_published_at": "2026-08-10T20:17:31Z",
"severity": "MODERATE"
},
"details": "Hugging Face Accelerate through 1.14.0 contains a path traversal vulnerability in load_checkpoint_in_model and load_checkpoint_and_dispatch functions that fail to sanitize weight_map entries from sharded checkpoint indexes. Attackers can supply relative paths with ../ sequences or absolute paths to read arbitrary files, or point shard entries at named pipes to cause indefinite blocking and denial of service.",
"id": "GHSA-4j2p-28q2-5m79",
"modified": "2026-09-08T18:45:47Z",
"published": "2026-08-10T21:32:04Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-69112"
},
{
"type": "WEB",
"url": "https://github.com/huggingface/accelerate/issues/4067"
},
{
"type": "WEB",
"url": "https://github.com/huggingface/accelerate/pull/4070"
},
{
"type": "WEB",
"url": "https://github.com/huggingface/accelerate/pull/4138"
},
{
"type": "PACKAGE",
"url": "https://github.com/huggingface/accelerate"
},
{
"type": "WEB",
"url": "https://www.vulncheck.com/advisories/hugging-face-accelerate-path-traversal-and-dos-via-weight-map"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:N/A:H",
"type": "CVSS_V3"
},
{
"score": "CVSS:4.0/AV:L/AC:L/AT:N/PR:N/UI:P/VC:H/VI:N/VA:H/SC:N/SI:N/SA:N",
"type": "CVSS_V4"
}
],
"summary": "Accelerate path traversal and denial of service via sharded checkpoint weight_map entries"
}
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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.
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.
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