PYSEC-2026-2477
Vulnerability from pysec - Published: 2026-07-13 15:15 - Updated: 2026-07-13 16:04
VLAI
Details
The flash-attention training framework thru commit e724e2588cbe754beb97cf7c011b5e7e34119e62 (2025-13-04) contains an insecure deserialization vulnerability (CWE-502) in its checkpoint loading mechanism. The load_checkpoint() function in checkpoint.py and the checkpoint loading code in eval.py use torch.load() without enabling the security-restrictive weights_only=True parameter. This allows the deserialization of arbitrary Python objects via the pickle module. An attacker can exploit this by providing a maliciously crafted checkpoint file. When a victim loads this checkpoint during model warmstarting or evaluation, arbitrary code is executed on the victim's system.
Severity
7.3 (High)
Impacted products
| Name | purl | flash-attn | pkg:pypi/flash-attn |
|---|
Aliases
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "flash-attn",
"purl": "pkg:pypi/flash-attn"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"last_affected": "2.8.3"
}
],
"type": "ECOSYSTEM"
}
],
"versions": [
"0.2.0",
"0.2.1",
"0.2.2",
"0.2.3",
"0.2.4",
"0.2.5",
"0.2.6.post1",
"0.2.7",
"0.2.8",
"1.0.0",
"1.0.1",
"1.0.2",
"1.0.3",
"1.0.3.post0",
"1.0.4",
"1.0.5",
"1.0.6",
"1.0.7",
"1.0.8",
"1.0.9",
"2.0.0.post1",
"2.0.1",
"2.0.2",
"2.0.3",
"2.0.4",
"2.0.5",
"2.0.6",
"2.0.6.post2",
"2.0.7",
"2.0.8",
"2.0.9",
"2.1.0",
"2.1.1",
"2.1.2.post3",
"2.2.0",
"2.2.1",
"2.2.2",
"2.2.3.post2",
"2.2.4",
"2.2.4.post1",
"2.2.5",
"2.3.0",
"2.3.1.post1",
"2.3.2",
"2.3.3",
"2.3.4",
"2.3.5",
"2.3.6",
"2.4.0.post1",
"2.4.1",
"2.4.2",
"2.4.3.post1",
"2.5.0",
"2.5.1.post1",
"2.5.2",
"2.5.3",
"2.5.4",
"2.5.5",
"2.5.6",
"2.5.7",
"2.5.8",
"2.5.9.post1",
"2.6.0.post1",
"2.6.1",
"2.6.2",
"2.6.3",
"2.7.0.post2",
"2.7.1.post4",
"2.7.2.post1",
"2.7.3",
"2.7.4.post1",
"2.8.0.post2",
"2.8.1",
"2.8.2",
"2.8.3"
]
}
],
"aliases": [
"CVE-2026-31253",
"GHSA-7g5w-pq96-8c5w"
],
"details": "The flash-attention training framework thru commit e724e2588cbe754beb97cf7c011b5e7e34119e62 (2025-13-04) contains an insecure deserialization vulnerability (CWE-502) in its checkpoint loading mechanism. The load_checkpoint() function in checkpoint.py and the checkpoint loading code in eval.py use torch.load() without enabling the security-restrictive weights_only=True parameter. This allows the deserialization of arbitrary Python objects via the pickle module. An attacker can exploit this by providing a maliciously crafted checkpoint file. When a victim loads this checkpoint during model warmstarting or evaluation, arbitrary code is executed on the victim\u0027s system.",
"id": "PYSEC-2026-2477",
"modified": "2026-07-13T16:04:03.490636Z",
"published": "2026-07-13T15:15:45.233345Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-31253"
},
{
"type": "PACKAGE",
"url": "https://github.com/Dao-AILab/flash-attention"
},
{
"type": "WEB",
"url": "https://www.notion.so/CVE-2026-31253-35d1e1393188813f9e77e2038104bc49"
},
{
"type": "PACKAGE",
"url": "https://pypi.org/project/flash-attn"
},
{
"type": "ADVISORY",
"url": "https://github.com/advisories/GHSA-7g5w-pq96-8c5w"
}
],
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:L",
"type": "CVSS_V3"
}
],
"summary": "flash-attention contains an insecure deserialization vulnerability in its checkpoint loading mechanism"
}
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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.
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