CVE-2021-37652: Tensorflow Use After Free Vulnerability

HIGH
Advisory Identifier: GHSA-m7fm-4jfh-jrg6
CVSS 7.8
CWE-415 CWE-416
Published
Advisory Summary

CVE-2021-37652 is a HIGH (CVSS 7.8) use after free vulnerability (CWE-415: Double Free) affecting Tensorflow libraries in the PyPI ecosystem. The issue involves use after free in boosted trees creation. Remediation is available by upgrading affected packages to version 2.3.4, 2.4.3, 2.5.1.

CVE-2021-37652 at a Glance

CVE Identifier CVE-2021-37652
GHSA Identifier GHSA-m7fm-4jfh-jrg6
Severity HIGH
CVSS v3.1 Base Score 7.8
CWE Classification CWE-415: Double Free
Affected Product Tensorflow
Package Ecosystem PyPI
Published Date
Last Updated

Security Impact

CVSS: 7.8

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:H/I:H/A:H).

Attack Vector Local (AV:L)
Attack Complexity Low (AC:L)
Privileges Required Low (PR:L)
User Interaction None (UI:N)
Scope Unchanged (S:U)
Confidentiality High (C:H)
Integrity High (I:H)
Availability High (A:H)

How to Fix CVE-2021-37652

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.

tensorflow fixed: 2.3.4
pip install tensorflow>=2.3.4
tensorflow fixed: 2.4.3
pip install tensorflow>=2.4.3
tensorflow fixed: 2.5.1
pip install tensorflow>=2.5.1
tensorflow-cpu fixed: 2.3.4
pip install tensorflow-cpu>=2.3.4
tensorflow-cpu fixed: 2.4.3
pip install tensorflow-cpu>=2.4.3
tensorflow-cpu fixed: 2.5.1
pip install tensorflow-cpu>=2.5.1
tensorflow-gpu fixed: 2.3.4
pip install tensorflow-gpu>=2.3.4
tensorflow-gpu fixed: 2.4.3
pip install tensorflow-gpu>=2.4.3
tensorflow-gpu fixed: 2.5.1
pip install tensorflow-gpu>=2.5.1
Verify Direct and Transitive Dependencies

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-2021-37652, 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 2.3.4), 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-415: Double Free, where the product calls free() twice on the same memory address. 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

### Impact The implementation for `tf.raw_ops.BoostedTreesCreateEnsemble` can result in a use after free error if an attacker supplies specially crafted arguments: ```python import tensorflow as tf v= tf.Variable([0.0]) tf.raw_ops.BoostedTreesCreateEnsemble( tree_ensemble_handle=v.handle, stamp_token=[0], tree_ensemble_serialized=['0']) ``` The [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/boosted_trees/resource_ops.cc#L55) uses a reference counted resource and decrements the refcount if the initialization fails, as it should. However, when the code was written, the resource was represented as a naked pointer but later refactoring has changed it to be a smart pointer. Thus, when the pointer leaves the scope, a subsequent `free`-ing of the resource occurs, but this fails to take into account that the refcount has already reached 0, thus the resource has been already freed. During this double-free process, members of the resource object are accessed for cleanup but they are invalid as the entire resource has been freed. ### Patches We have patched the issue in GitHub commit [5ecec9c6fbdbc6be03295685190a45e7eee726ab](https://github.com/tensorflow/tensorflow/commit/5ecec9c6fbdbc6be03295685190a45e7eee726ab). 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. ### For more information Please 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. ### Attribution This vulnerability has been reported by members of the Aivul Team from Qihoo 360.

CVE-2021-37652 Affected Tensorflow Versions

9 component(s)
Fixed Versions:
2.3.42.4.32.5.1
Product / Package Ecosystem Affected Versions Fixed Version
tensorflow
PyPI
< 2.3.4
2.3.4
tensorflow
PyPI
≥ 2.4.0
< 2.4.3
2.4.3
tensorflow
PyPI
≥ 2.5.0
< 2.5.1
All 1 versions
2.5.0
2.5.1
tensorflow-cpu
PyPI
< 2.3.4
2.3.4
tensorflow-cpu
PyPI
≥ 2.4.0
< 2.4.3
2.4.3
tensorflow-cpu
PyPI
≥ 2.5.0
< 2.5.1
All 1 versions
2.5.0
2.5.1
tensorflow-gpu
PyPI
< 2.3.4
2.3.4
tensorflow-gpu
PyPI
≥ 2.4.0
< 2.4.3
2.4.3
tensorflow-gpu
PyPI
≥ 2.5.0
< 2.5.1
All 1 versions
2.5.0
2.5.1

References

Frequently Asked Questions

What is CVE-2021-37652?
CVE-2021-37652 is a security vulnerability in Tensorflow involving Use after free in boosted trees creation under CWE-415: Double Free.
Which Tensorflow versions are affected?
Affected versions include releases prior to 2.3.4, 2.4.3, 2.5.1 across affected packages: tensorflow, tensorflow-cpu, tensorflow-gpu.
What version fixes CVE-2021-37652?
Upgrading to 2.3.4, 2.4.3, 2.5.1 (or the latest patched release for your respective branch) resolves the vulnerability.
Is tensorflow affected?
Yes, tensorflow is affected in releases prior to 2.3.4, 2.4.3, 2.5.1. Upgrade your dependency to 2.3.4, 2.4.3, 2.5.1 or later.
Is tensorflow-cpu affected?
Yes, tensorflow-cpu is affected in releases prior to 2.3.4, 2.4.3, 2.5.1. Upgrade your dependency to 2.3.4, 2.4.3, 2.5.1 or later.
What is the CVSS score of CVE-2021-37652?
CVE-2021-37652 has a CVSS v3.1 base score of 7.8 (HIGH) with vector string CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H.
What CWE is associated with CVE-2021-37652?
CVE-2021-37652 is classified under CWE-415: Double Free (The product calls free() twice on the same memory address.).
How can I check PyPI dependencies for CVE-2021-37652?
Run the dependency tree command: 'pip show tensorflow'. This command lists direct and transitive dependencies in your build configuration so you can verify if a vulnerable version is resolved.
Machine-readable OSV JSON OSV v1.5 Schema
Normalized schema output
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  "published" : "2021-08-25T14:43:07Z",
  "aliases" : [ "CVE-2021-37652" ],
  "summary" : "Use after free in boosted trees creation",
  "details" : "### Impact\nThe implementation for `tf.raw_ops.BoostedTreesCreateEnsemble` can result in a use after free error if an attacker supplies specially crafted arguments:\n\n```python\nimport tensorflow as tf\n\nv= tf.Variable([0.0])\ntf.raw_ops.BoostedTreesCreateEnsemble(\n  tree_ensemble_handle=v.handle,\n  stamp_token=[0],\n  tree_ensemble_serialized=['0']) \n```\n\nThe [implementation](https://github.com/tensorflow/tensorflow/blob/f24faa153ad31a4b51578f8181d3aaab77a1ddeb/tensorflow/core/kernels/boosted_trees/resource_ops.cc#L55) uses a reference counted resource and decrements the refcount if the initialization fails, as it should. However, when the code was written, the  resource was represented as a naked pointer but later refactoring has changed it to be a smart pointer. Thus, when the pointer leaves the scope, a subsequent `free`-ing of the resource occurs, but this fails to take into account that the refcount has already reached 0, thus the resource has been already freed. During this double-free process, members of the resource object are accessed for cleanup but they are invalid as the entire resource has been freed.\n\n### Patches\nWe have patched the issue in GitHub commit [5ecec9c6fbdbc6be03295685190a45e7eee726ab](https://github.com/tensorflow/tensorflow/commit/5ecec9c6fbdbc6be03295685190a45e7eee726ab).\n\nThe 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.\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.",
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