PYSEC-2026-3308
Vulnerability from pysec - Published: 2026-07-13 14:19 - Updated: 2026-07-13 16:06
VLAI
Details
Impact
When tf.linalg.matrix_rank receives an empty input a, the GPU kernel gives a CHECK fail that can be used to trigger a denial of service attack.
import tensorflow as tf
a = tf.constant([], shape=[0, 1, 1], dtype=tf.float32)
tf.linalg.matrix_rank(a=a)
Patches
We have patched the issue in GitHub commit c55b476aa0e0bd4ee99d0f3ad18d9d706cd1260a.
The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
Attribution
This vulnerability has been reported by Kang Hong Jin.
Severity
5.9 (Medium)
Impacted products
| Name | purl | tensorflow-cpu | pkg:pypi/tensorflow-cpu |
|---|
Aliases
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-cpu",
"purl": "pkg:pypi/tensorflow-cpu"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.7.2"
},
{
"introduced": "2.8.0"
},
{
"fixed": "2.8.1"
},
{
"introduced": "2.9.0"
},
{
"fixed": "2.9.1"
}
],
"type": "ECOSYSTEM"
}
],
"versions": [
"1.15.0",
"2.1.0",
"2.1.1",
"2.1.2",
"2.1.3",
"2.1.4",
"2.2.0",
"2.2.1",
"2.2.2",
"2.2.3",
"2.3.0",
"2.3.1",
"2.3.2",
"2.3.3",
"2.3.4",
"2.4.0",
"2.4.1",
"2.4.2",
"2.4.3",
"2.4.4",
"2.5.0",
"2.5.1",
"2.5.2",
"2.5.3",
"2.6.0",
"2.6.1",
"2.6.2",
"2.6.3",
"2.6.4",
"2.6.5",
"2.7.0",
"2.7.1",
"2.8.0",
"2.9.0"
]
}
],
"aliases": [
"CVE-2022-35988",
"GHSA-9vqj-64pv-w55c"
],
"details": "### Impact\nWhen `tf.linalg.matrix_rank` receives an empty input `a`, the GPU kernel gives a `CHECK` fail that can be used to trigger a denial of service attack.\n```python\nimport tensorflow as tf\n\na = tf.constant([], shape=[0, 1, 1], dtype=tf.float32)\ntf.linalg.matrix_rank(a=a)\n```\n\n### Patches\nWe have patched the issue in GitHub commit [c55b476aa0e0bd4ee99d0f3ad18d9d706cd1260a](https://github.com/tensorflow/tensorflow/commit/c55b476aa0e0bd4ee99d0f3ad18d9d706cd1260a).\n\nThe fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, as these are also affected and still in supported range.\n\n\n### For more information\nPlease consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.\n\n\n### Attribution\nThis vulnerability has been reported by Kang Hong Jin.\n",
"id": "PYSEC-2026-3308",
"modified": "2026-07-13T16:06:29.000540Z",
"published": "2026-07-13T14:19:50.441234Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9vqj-64pv-w55c"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2022-35988"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/c55b476aa0e0bd4ee99d0f3ad18d9d706cd1260a"
},
{
"type": "PACKAGE",
"url": "https://github.com/tensorflow/tensorflow"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.10.0"
},
{
"type": "PACKAGE",
"url": "https://pypi.org/project/tensorflow-cpu"
},
{
"type": "ADVISORY",
"url": "https://github.com/advisories/GHSA-9vqj-64pv-w55c"
}
],
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
}
],
"summary": "TensorFlow vulnerable to `CHECK` fail in `tf.linalg.matrix_rank`"
}
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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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