PYSEC-2026-3381
Vulnerability from pysec - Published: 2026-07-13 14:19 - Updated: 2026-07-13 16:06
VLAI
Details
Impact
The RaggedRangOp function takes an argument limits that is eventually used to construct a TensorShape as an int64. If limits is a very large float, it can overflow when converted to an int64. This triggers an InvalidArgument but also throws an abort signal that crashes the program.
import tensorflow as tf
tf.raw_ops.RaggedRange(starts=[1.1,0.1],limits=[10.0,1e20],deltas=[1,1])
Patches
We have patched the issue in GitHub commit 37cefa91bee4eace55715eeef43720b958a01192.
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 Jingyi Shi.
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-2022-35940",
"GHSA-x989-q2pq-4q5x"
],
"details": "### Impact\nThe [`RaggedRangOp`](https://github.com/tensorflow/tensorflow/blob/0b6b491d21d6a4eb5fbab1cca565bc1e94ca9543/tensorflow/core/kernels/ragged_range_op.cc#L74-L88) function takes an argument `limits` that is eventually used to construct a `TensorShape` as an `int64`. If `limits` is a very large float, it can overflow when converted to an `int64`. This triggers an `InvalidArgument` but also throws an abort signal that crashes the program.\n```python\nimport tensorflow as tf\ntf.raw_ops.RaggedRange(starts=[1.1,0.1],limits=[10.0,1e20],deltas=[1,1])\n```\n\n### Patches\nWe have patched the issue in GitHub commit [37cefa91bee4eace55715eeef43720b958a01192](https://github.com/tensorflow/tensorflow/commit/37cefa91bee4eace55715eeef43720b958a01192).\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 Jingyi Shi.",
"id": "PYSEC-2026-3381",
"modified": "2026-07-13T16:06:37.855200Z",
"published": "2026-07-13T14:19:45.507685Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x989-q2pq-4q5x"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2022-35940"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/37cefa91bee4eace55715eeef43720b958a01192"
},
{
"type": "PACKAGE",
"url": "https://github.com/tensorflow/tensorflow"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/blob/0b6b491d21d6a4eb5fbab1cca565bc1e94ca9543/tensorflow/core/kernels/ragged_range_op.cc#L74-L88"
},
{
"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-x989-q2pq-4q5x"
}
],
"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 Int overflow in `RaggedRangeOp`"
}
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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
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