CVE-2021-41213: Tensorflow Deadlock Vulnerability
MODERATECVE-2021-41213 is a MODERATE (CVSS 5.5) deadlock vulnerability (CWE-662: Improper Synchronization) affecting Tensorflow libraries in the PyPI ecosystem. The issue involves deadlock in mutually recursive `tf.function` objects. Remediation is available by upgrading affected packages to version 2.6.1, 2.5.2, 2.4.4.
CVE-2021-41213 at a Glance
Security Impact
CVSS: 5.5
The security impact metrics below are parsed directly from the CVSS v3.1 vector string (CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H).
How to Fix CVE-2021-41213
To remediate this vulnerability, upgrade all affected Tensorflow dependencies to a patched release. Both direct and transitive dependencies should be checked, as frameworks or security libraries may pull in vulnerable versions transitively.
pip install tensorflow>=2.6.1
pip install tensorflow>=2.5.2
pip install tensorflow>=2.4.4
pip install tensorflow-cpu>=2.6.1
pip install tensorflow-cpu>=2.5.2
pip install tensorflow-cpu>=2.4.4
pip install tensorflow-gpu>=2.6.1
pip install tensorflow-gpu>=2.5.2
pip install tensorflow-gpu>=2.4.4
Run the following command in your project directory to verify that all resolved dependencies use the patched release:
pip show tensorflow
How to Check if You Are Affected
To determine whether your Python environment is affected by CVE-2021-41213, inspect your installed packages using pip:
pip show tensorflow
Analyze the terminal output to locate any version of Tensorflow in your dependency tree. If any resolved version matches the vulnerable ranges listed in the table below (for example, versions earlier than 2.6.1), your application is affected.
Note: The pip show tensorflow command checks currently installed packages in your active Python environment.
Technical Details
Vulnerability Mechanism & Technical Analysis
This vulnerability is classified under CWE-662: Improper Synchronization, where the product utilizes multiple threads, processes, components, or systems to allow temporary access to a shared resource that can only be exclusive to one process at a time, but it does not properly synchronize these actions, which might cause simultaneous accesses of this resource by multiple threads or processes. In Tensorflow, software state validation or input boundaries are circumvented during processing. Review the official source advisory statement below for specific affected components, classes, and reproduction details.
Source Advisory Statement
CVE-2021-41213 Affected Tensorflow Versions
9 component(s)| Product / Package | Ecosystem | Affected Versions | Fixed Version |
|---|---|---|---|
|
tensorflow
|
PyPI |
≥ 2.6.0
< 2.6.1
|
2.6.1
|
|
tensorflow
|
PyPI |
≥ 2.5.0
< 2.5.2
|
2.5.2
|
|
tensorflow
|
PyPI |
< 2.4.4
|
2.4.4
|
|
tensorflow-cpu
|
PyPI |
≥ 2.6.0
< 2.6.1
|
2.6.1
|
|
tensorflow-cpu
|
PyPI |
≥ 2.5.0
< 2.5.2
|
2.5.2
|
|
tensorflow-cpu
|
PyPI |
< 2.4.4
|
2.4.4
|
|
tensorflow-gpu
|
PyPI |
≥ 2.6.0
< 2.6.1
|
2.6.1
|
|
tensorflow-gpu
|
PyPI |
≥ 2.5.0
< 2.5.2
|
2.5.2
|
|
tensorflow-gpu
|
PyPI |
< 2.4.4
|
2.4.4
|
References
Frequently Asked Questions
What is CVE-2021-41213?
Which Tensorflow versions are affected?
What version fixes CVE-2021-41213?
Is tensorflow affected?
Is tensorflow-cpu affected?
What is the CVSS score of CVE-2021-41213?
What CWE is associated with CVE-2021-41213?
How can I check PyPI dependencies for CVE-2021-41213?
Machine-readable OSV JSON
OSV v1.5 Schema
{
"schema_version" : "1.4.0",
"id" : "GHSA-h67m-xg8f-fxcf",
"modified" : "2024-11-07T22:17:51Z",
"published" : "2021-11-10T18:59:32Z",
"aliases" : [ "CVE-2021-41213" ],
"summary" : "Deadlock in mutually recursive `tf.function` objects",
"details" : "### Impact\nThe [code behind `tf.function` API](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/python/eager/def_function.py#L542) can be made to deadlock when two `tf.function` decorated Python functions are mutually recursive:\n\n```python \nimport tensorflow as tf\n\n@tf.function() \ndef fun1(num):\n if num == 1:\n return\n print(num)\n fun2(num-1)\n\n@tf.function()\ndef fun2(num):\n if num == 0:\n return\n print(num)\n fun1(num-1)\n\nfun1(9)\n```\n\nThis occurs due to using a non-reentrant `Lock` Python object.\n\nLoading any model which contains mutually recursive functions is vulnerable. An attacker can cause denial of service by causing users to load such models and calling a recursive `tf.function`, although this is not a frequent scenario.\n\n### Patches\nWe have patched the issue in GitHub commit [afac8158d43691661ad083f6dd9e56f327c1dcb7](https://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7).\n\nThe fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.\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### Attribution\nThis vulnerability has been reported by members of the Aivul Team from Qihoo 360.",
"severity" : [ {
"type" : "CVSS_V3",
"score" : "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H"
} ],
"affected" : [ {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "2.6.0"
}, {
"fixed" : "2.6.1"
} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
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"introduced" : "2.5.0"
}, {
"fixed" : "2.5.2"
} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow"
},
"ranges" : [ {
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}, {
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} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow-cpu"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "2.6.0"
}, {
"fixed" : "2.6.1"
} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow-cpu"
},
"ranges" : [ {
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"introduced" : "2.5.0"
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} ]
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}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow-cpu"
},
"ranges" : [ {
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"events" : [ {
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}, {
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} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow-gpu"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "2.6.0"
}, {
"fixed" : "2.6.1"
} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow-gpu"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "2.5.0"
}, {
"fixed" : "2.5.2"
} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow-gpu"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "0"
}, {
"fixed" : "2.4.4"
} ]
} ]
} ],
"references" : [ {
"type" : "WEB",
"url" : "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h67m-xg8f-fxcf"
}, {
"type" : "ADVISORY",
"url" : "https://nvd.nist.gov/vuln/detail/CVE-2021-41213"
}, {
"type" : "WEB",
"url" : "https://github.com/tensorflow/tensorflow/commit/afac8158d43691661ad083f6dd9e56f327c1dcb7"
}, {
"type" : "WEB",
"url" : "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-622.yaml"
}, {
"type" : "WEB",
"url" : "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-820.yaml"
}, {
"type" : "WEB",
"url" : "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-405.yaml"
}, {
"type" : "PACKAGE",
"url" : "https://github.com/tensorflow/tensorflow"
} ],
"database_specific" : {
"cwe_ids" : [ "CWE-662", "CWE-667" ],
"severity" : "MODERATE",
"github_reviewed" : true,
"github_reviewed_at" : "2021-11-08T22:25:35Z",
"nvd_published_at" : "2021-11-05T23:15:00Z"
}
}