GHSA-CQVQ-FVHR-V6HC
Vulnerability from github – Published: 2022-11-21 23:51 – Updated: 2022-11-21 23:51
VLAI
Summary
`CHECK` failure in `SobolSample` via missing validation
Details
Impact
Another instance of CVE-2022-35935, where SobolSample is vulnerable to a denial of service via assumed scalar inputs, was found and fixed.
import tensorflow as tf
tf.raw_ops.SobolSample(dim=tf.constant([1,0]), num_results=tf.constant([1]), skip=tf.constant([1]))
Patches
We have patched the issue in GitHub commits c65c67f88ad770662e8f191269a907bf2b94b1bf and 02400ea266bd811fc016a848445de1bbff3a23a0
The fix will be included in TensorFlow 2.11. We will also cherrypick both commits on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range. TensorFlow 2.7.4 will have the first commit cherrypicked.
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 from Singapore Management University - Neophytos Christou, Secure Systems Labs, Brown University - 刘力源, Information System & Security and Countermeasures Experiments Center, Beijing Institute of Technology - Pattarakrit Rattankul
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{
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},
"ranges": [
{
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},
{
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}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [],
"database_specific": {
"cwe_ids": [],
"github_reviewed": true,
"github_reviewed_at": "2022-11-21T23:51:53Z",
"nvd_published_at": null,
"severity": "LOW"
},
"details": "### Impact\nAnother instance of CVE-2022-35935, where `SobolSample` is vulnerable to a denial of service via assumed scalar inputs, was found and fixed.\n```python\nimport tensorflow as tf\ntf.raw_ops.SobolSample(dim=tf.constant([1,0]), num_results=tf.constant([1]), skip=tf.constant([1]))\n```\n\n### Patches\nWe have patched the issue in GitHub commits [c65c67f88ad770662e8f191269a907bf2b94b1bf](https://github.com/tensorflow/tensorflow/commit/c65c67f88ad770662e8f191269a907bf2b94b1bf) and [02400ea266bd811fc016a848445de1bbff3a23a0](https://github.com/tensorflow/tensorflow/commit/02400ea266bd811fc016a848445de1bbff3a23a0)\n\nThe fix will be included in TensorFlow 2.11. We will also cherrypick both commits on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range. TensorFlow 2.7.4 will have the first commit cherrypicked.\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:\n- Kang Hong Jin from Singapore Management University\n- Neophytos Christou, Secure Systems Labs, Brown University\n- \u5218\u529b\u6e90, Information System \u0026 Security and Countermeasures Experiments Center, Beijing Institute of Technology\n- Pattarakrit Rattankul\n",
"id": "GHSA-cqvq-fvhr-v6hc",
"modified": "2022-11-21T23:51:53Z",
"published": "2022-11-21T23:51:53Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cqvq-fvhr-v6hc"
},
{
"type": "PACKAGE",
"url": "https://github.com/tensorflow/tensorflow"
}
],
"schema_version": "1.4.0",
"severity": [],
"summary": "`CHECK` failure in `SobolSample` via missing validation"
}
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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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