GSD-2022-21737
Vulnerability from gsd - Updated: 2023-12-13 01:19Details
Tensorflow is an Open Source Machine Learning Framework. The implementation of `*Bincount` operations allows malicious users to cause denial of service by passing in arguments which would trigger a `CHECK`-fail. There are several conditions that the input arguments must satisfy. Some are not caught during shape inference and others are not caught during kernel implementation. This results in `CHECK` failures later when the output tensors get allocated. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2022-21737",
"description": "Tensorflow is an Open Source Machine Learning Framework. The implementation of `*Bincount` operations allows malicious users to cause denial of service by passing in arguments which would trigger a `CHECK`-fail. There are several conditions that the input arguments must satisfy. Some are not caught during shape inference and others are not caught during kernel implementation. This results in `CHECK` failures later when the output tensors get allocated. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.",
"id": "GSD-2022-21737",
"references": [
"https://www.suse.com/security/cve/CVE-2022-21737.html"
]
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
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"aliases": [
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],
"details": "Tensorflow is an Open Source Machine Learning Framework. The implementation of `*Bincount` operations allows malicious users to cause denial of service by passing in arguments which would trigger a `CHECK`-fail. There are several conditions that the input arguments must satisfy. Some are not caught during shape inference and others are not caught during kernel implementation. This results in `CHECK` failures later when the output tensors get allocated. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.",
"id": "GSD-2022-21737",
"modified": "2023-12-13T01:19:14.877388Z",
"schema_version": "1.4.0"
}
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"TITLE": "Assertion failure based denial of service in Tensorflow"
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"affects": {
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}
]
},
"impact": {
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"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "HIGH",
"baseScore": 6.5,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
"version": "3.1"
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{
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{
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"url": "https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/bincount_op.cc"
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"source": {
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"discovery": "UNKNOWN"
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"cwe_ids": [
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"date": "2022-02-11",
"description": "Tensorflow is an Open Source Machine Learning Framework. The implementation of `*Bincount` operations allows malicious users to cause denial of service by passing in arguments which would trigger a `CHECK`-fail. There are several conditions that the input arguments must satisfy. Some are not caught during shape inference and others are not caught during kernel implementation. This results in `CHECK` failures later when the output tensors get allocated. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.",
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"package_slug": "pypi/tensorflow-cpu",
"pubdate": "2022-02-09",
"solution": "Upgrade to versions 2.5.3, 2.6.3, 2.7.1 or above.",
"title": "Improper Check for Unusual or Exceptional Conditions",
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"https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/bincount_op.cc",
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"uuid": "82ea2b97-72dc-4204-8976-d6710e0df066"
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"date": "2022-02-11",
"description": "Tensorflow is an Open Source Machine Learning Framework. The implementation of `*Bincount` operations allows malicious users to cause denial of service by passing in arguments which would trigger a `CHECK`-fail. There are several conditions that the input arguments must satisfy. Some are not caught during shape inference and others are not caught during kernel implementation. This results in `CHECK` failures later when the output tensors get allocated. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.",
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"package_slug": "pypi/tensorflow-gpu",
"pubdate": "2022-02-09",
"solution": "Upgrade to versions 2.5.3, 2.6.3, 2.7.1 or above.",
"title": "Improper Check for Unusual or Exceptional Conditions",
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"date": "2022-02-09",
"description": "Tensorflow is an Open Source Machine Learning Framework. The implementation of `*Bincount` operations allows malicious users to cause denial of service by passing in arguments which would trigger a `CHECK`-fail. There are several conditions that the input arguments must satisfy. Some are not caught during shape inference and others are not caught during kernel implementation. This results in `CHECK` failures later when the output tensors get allocated. The fix will be included in TensorFlow We will also cherrypick this commit on TensorFlow, TensorFlow, and TensorFlow, as these are also affected and still in supported range.",
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"package_slug": "pypi/tensorflow",
"pubdate": "2022-02-03",
"solution": "Upgrade to versions 2.5.3, 2.6.3, 2.7.1 or above.",
"title": "Improper Check for Unusual or Exceptional Conditions",
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"https://github.com/tensorflow/tensorflow/commit/7019ce4f68925fd01cdafde26f8d8c938f47e6f9",
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"impact": {
"baseMetricV2": {
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"cvssV2": {
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"accessVector": "NETWORK",
"authentication": "SINGLE",
"availabilityImpact": "PARTIAL",
"baseScore": 4.0,
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"integrityImpact": "NONE",
"vectorString": "AV:N/AC:L/Au:S/C:N/I:N/A:P",
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"exploitabilityScore": 8.0,
"impactScore": 2.9,
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"obtainOtherPrivilege": false,
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"severity": "MEDIUM",
"userInteractionRequired": false
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"baseScore": 6.5,
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"confidentialityImpact": "NONE",
"integrityImpact": "NONE",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
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"exploitabilityScore": 2.8,
"impactScore": 3.6
}
},
"lastModifiedDate": "2022-02-09T05:08Z",
"publishedDate": "2022-02-03T14:15Z"
}
}
}
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Sightings
| Author | Source | Type | Date |
|---|
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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