GSD-2021-37678
Vulnerability from gsd - Updated: 2023-12-13 01:23Details
TensorFlow is an end-to-end open source platform for machine learning. In affected versions TensorFlow and Keras can be tricked to perform arbitrary code execution when deserializing a Keras model from YAML format. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/python/keras/saving/model_config.py#L66-L104) uses `yaml.unsafe_load` which can perform arbitrary code execution on the input. Given that YAML format support requires a significant amount of work, we have removed it for now. We have patched the issue in GitHub commit 23d6383eb6c14084a8fc3bdf164043b974818012. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2021-37678",
"description": "TensorFlow is an end-to-end open source platform for machine learning. In affected versions TensorFlow and Keras can be tricked to perform arbitrary code execution when deserializing a Keras model from YAML format. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/python/keras/saving/model_config.py#L66-L104) uses `yaml.unsafe_load` which can perform arbitrary code execution on the input. Given that YAML format support requires a significant amount of work, we have removed it for now. We have patched the issue in GitHub commit 23d6383eb6c14084a8fc3bdf164043b974818012. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.",
"id": "GSD-2021-37678",
"references": [
"https://www.suse.com/security/cve/CVE-2021-37678.html",
"https://security.archlinux.org/CVE-2021-37678"
]
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
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],
"details": "TensorFlow is an end-to-end open source platform for machine learning. In affected versions TensorFlow and Keras can be tricked to perform arbitrary code execution when deserializing a Keras model from YAML format. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/python/keras/saving/model_config.py#L66-L104) uses `yaml.unsafe_load` which can perform arbitrary code execution on the input. Given that YAML format support requires a significant amount of work, we have removed it for now. We have patched the issue in GitHub commit 23d6383eb6c14084a8fc3bdf164043b974818012. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.",
"id": "GSD-2021-37678",
"modified": "2023-12-13T01:23:09.532848Z",
"schema_version": "1.4.0"
}
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"TITLE": "Arbitrary code execution due to YAML deserialization"
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"affects": {
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"value": "TensorFlow is an end-to-end open source platform for machine learning. In affected versions TensorFlow and Keras can be tricked to perform arbitrary code execution when deserializing a Keras model from YAML format. The [implementation](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/python/keras/saving/model_config.py#L66-L104) uses `yaml.unsafe_load` which can perform arbitrary code execution on the input. Given that YAML format support requires a significant amount of work, we have removed it for now. We have patched the issue in GitHub commit 23d6383eb6c14084a8fc3bdf164043b974818012. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range."
}
]
},
"impact": {
"cvss": {
"attackComplexity": "LOW",
"attackVector": "LOCAL",
"availabilityImpact": "HIGH",
"baseScore": 9.3,
"baseSeverity": "CRITICAL",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "CHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:L/AC:L/PR:N/UI:N/S:C/C:H/I:H/A:H",
"version": "3.1"
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"value": "CWE-502: Deserialization of Untrusted Data"
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{
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"url": "https://github.com/tensorflow/tensorflow/commit/23d6383eb6c14084a8fc3bdf164043b974818012"
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"discovery": "UNKNOWN"
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"affected_range": "\u003c2.3.4||\u003e=2.4.0,\u003c2.4.3||==2.5.0",
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"date": "2021-08-25",
"description": "TensorFlow is an end-to-end open source platform for machine learning. In affected versions TensorFlow and Keras can be tricked to perform arbitrary code execution when deserializing a Keras model from YAML format. The implementation uses `yaml.unsafe_load` which can perform arbitrary code execution on the input. Given that YAML format support requires a significant amount of work, we have removed it for now. We have patched the issue in GitHub commit 23d6383eb6c14084a8fc3bdf164043b974818012. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.",
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"2.4.3",
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"identifier": "CVE-2021-37678",
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"not_impacted": "All versions starting from 2.3.4 before 2.4.0, all versions starting from 2.4.3 before 2.5.0, all versions after 2.5.0",
"package_slug": "pypi/tensorflow-cpu",
"pubdate": "2021-08-25",
"solution": "Upgrade to versions 2.3.4, 2.4.3, 2.5.1 or above.",
"title": "Deserialization of Untrusted Data",
"urls": [
"https://github.com/tensorflow/tensorflow/security/advisories/GHSA-r6jx-9g48-2r5r",
"https://nvd.nist.gov/vuln/detail/CVE-2021-37678",
"https://github.com/tensorflow/tensorflow/commit/23d6383eb6c14084a8fc3bdf164043b974818012",
"https://github.com/advisories/GHSA-r6jx-9g48-2r5r"
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"uuid": "e312c99f-7271-4f87-afd5-9a8432bbc0e3"
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"cwe_ids": [
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"CWE-78",
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],
"date": "2021-08-25",
"description": "TensorFlow is an end-to-end open source platform for machine learning. In affected versions TensorFlow and Keras can be tricked to perform arbitrary code execution when deserializing a Keras model from YAML format. The implementation uses `yaml.unsafe_load` which can perform arbitrary code execution on the input. Given that YAML format support requires a significant amount of work, we have removed it for now. We have patched the issue in GitHub commit 23d6383eb6c14084a8fc3bdf164043b974818012. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.",
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"2.4.3",
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"identifier": "CVE-2021-37678",
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"package_slug": "pypi/tensorflow-gpu",
"pubdate": "2021-08-25",
"solution": "Upgrade to versions 2.3.4, 2.4.3, 2.5.1 or above.",
"title": "Deserialization of Untrusted Data",
"urls": [
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"date": "2021-08-19",
"description": "TensorFlow is an end-to-end open source platform for machine learning. The implementation uses `yaml.unsafe_load` which can perform arbitrary code execution on the input. Given that YAML format support requires a significant amount of work, we have removed it for now. We have patched the issue in GitHub commit d6383eb6c14084a8fc3bdf164043b974818012. 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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"identifier": "CVE-2021-37678",
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"package_slug": "pypi/tensorflow",
"pubdate": "2021-08-12",
"solution": "Upgrade to versions 2.3.4, 2.4.3 or above.",
"title": "Deserialization of Untrusted Data",
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"integrityImpact": "PARTIAL",
"vectorString": "AV:L/AC:L/Au:N/C:P/I:P/A:P",
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"version": "3.1"
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"exploitabilityScore": 2.0,
"impactScore": 6.0
}
},
"lastModifiedDate": "2021-08-19T13:44Z",
"publishedDate": "2021-08-12T23:15Z"
}
}
}
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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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