CVE-2021-29608: Tensorflow Heap OOB And Null Pointer Vulnerability

MODERATE
Advisory Identifier: GHSA-rgvq-pcvf-hx75
Share
CVSS 5.3
CWE-131
Published
Advisory Summary

CVE-2021-29608 is a MODERATE (CVSS 5.3) heap oob and null pointer vulnerability (CWE-131: Incorrect Calculation of Buffer Size) affecting Tensorflow libraries in the PyPI ecosystem. The issue involves heap oob and null pointer dereference in `raggedtensortotensor`. Remediation is available by upgrading affected packages to version 2.1.4, 2.2.3, 2.3.3, 2.4.2.

CVE-2021-29608 at a Glance

CVE Identifier CVE-2021-29608
GHSA Identifier GHSA-rgvq-pcvf-hx75
Severity MODERATE
CVSS v3.1 Base Score 5.3
CWE Classification CWE-131: Incorrect Calculation of Buffer Size
Affected Product Tensorflow
Package Ecosystem PyPI
Published Date
Last Updated

Security Impact

CVSS: 5.3

The security impact metrics below are parsed directly from the CVSS v3.1 vector string (CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:L/A:H).

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

How to Fix CVE-2021-29608

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: 2.1.4 
pip install tensorflow>=2.1.4
tensorflow fixed: 2.2.3 
pip install tensorflow>=2.2.3
tensorflow fixed: 2.3.3 
pip install tensorflow>=2.3.3
tensorflow fixed: 2.4.2 
pip install tensorflow>=2.4.2
tensorflow-cpu fixed: 2.1.4 
pip install tensorflow-cpu>=2.1.4
tensorflow-cpu fixed: 2.2.3 
pip install tensorflow-cpu>=2.2.3
tensorflow-cpu fixed: 2.3.3 
pip install tensorflow-cpu>=2.3.3
tensorflow-cpu fixed: 2.4.2 
pip install tensorflow-cpu>=2.4.2
tensorflow-gpu fixed: 2.1.4 
pip install tensorflow-gpu>=2.1.4
tensorflow-gpu fixed: 2.2.3 
pip install tensorflow-gpu>=2.2.3
tensorflow-gpu fixed: 2.3.3 
pip install tensorflow-gpu>=2.3.3
tensorflow-gpu fixed: 2.4.2 
pip install tensorflow-gpu>=2.4.2
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-2021-29608, 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 2.1.4), 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-131: Incorrect Calculation of Buffer Size, where the product does not correctly calculate the size to be used when allocating a buffer, which could lead to a buffer overflow. 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 Due to lack of validation in `tf.raw_ops.RaggedTensorToTensor`, an attacker can exploit an undefined behavior if input arguments are empty: ```python import tensorflow as tf shape = tf.constant([-1, -1], shape=[2], dtype=tf.int64) values = tf.constant([], shape=[0], dtype=tf.int64) default_value = tf.constant(404, dtype=tf.int64) row = tf.constant([269, 404, 0, 0, 0, 0, 0], shape=[7], dtype=tf.int64) rows = [row] types = ['ROW_SPLITS'] tf.raw_ops.RaggedTensorToTensor( shape=shape, values=values, default_value=default_value, row_partition_tensors=rows, row_partition_types=types) ``` The [implementation](https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L356-L360) only checks that one of the tensors is not empty, but does not check for the other ones. There are multiple `DCHECK` validations to prevent heap OOB, but these are no-op in release builds, hence they don't prevent anything. ### Patches We have patched the issue in GitHub commit [b761c9b652af2107cfbc33efd19be0ce41daa33e](https://github.com/tensorflow/tensorflow/commit/b761c9b652af2107cfbc33efd19be0ce41daa33e) followed by GitHub commit [f94ef358bb3e91d517446454edff6535bcfe8e4a](https://github.com/tensorflow/tensorflow/commit/f94ef358bb3e91d517446454edff6535bcfe8e4a) and GitHub commit [c4d7afb6a5986b04505aca4466ae1951686c80f6](https://github.com/tensorflow/tensorflow/commit/c4d7afb6a5986b04505aca4466ae1951686c80f6). The fix will be included in TensorFlow 2.5.0. We will also cherrypick these commits on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, 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 Yakun Zhang and Ying Wang of Baidu X-Team.

CVE-2021-29608 Affected Tensorflow Versions

12 component(s)
Fixed Versions:
2.1.42.2.32.3.32.4.2
Product / Package Ecosystem Affected Versions Fixed Version
tensorflow
pypi/tensorflow
PyPI
< 2.1.4
2.1.4
tensorflow
pypi/tensorflow
PyPI
≥ 2.2.0
< 2.2.3
2.2.3
tensorflow
pypi/tensorflow
PyPI
≥ 2.3.0
< 2.3.3
2.3.3
tensorflow
pypi/tensorflow
PyPI
≥ 2.4.0
< 2.4.2
2.4.2
tensorflow-cpu
pypi/tensorflow-cpu
PyPI
< 2.1.4
2.1.4
tensorflow-cpu
pypi/tensorflow-cpu
PyPI
≥ 2.2.0
< 2.2.3
2.2.3
tensorflow-cpu
pypi/tensorflow-cpu
PyPI
≥ 2.3.0
< 2.3.3
2.3.3
tensorflow-cpu
pypi/tensorflow-cpu
PyPI
≥ 2.4.0
< 2.4.2
2.4.2
tensorflow-gpu
pypi/tensorflow-gpu
PyPI
< 2.1.4
2.1.4
tensorflow-gpu
pypi/tensorflow-gpu
PyPI
≥ 2.2.0
< 2.2.3
2.2.3
tensorflow-gpu
pypi/tensorflow-gpu
PyPI
≥ 2.3.0
< 2.3.3
2.3.3
tensorflow-gpu
pypi/tensorflow-gpu
PyPI
≥ 2.4.0
< 2.4.2
2.4.2

References

GitHub Advisory / Wiki ADVISORY
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-rgvq-pcvf-hx75
NVD — CVE-2021-29608 NVD
https://nvd.nist.gov/vuln/detail/CVE-2021-29608
Security Fix Commit FIX COMMIT
https://github.com/tensorflow/tensorflow/commit/b761c9b652af2107cfbc33efd19be0ce41daa33e
Security Fix Commit FIX COMMIT
https://github.com/tensorflow/tensorflow/commit/c4d7afb6a5986b04505aca4466ae1951686c80f6
Security Fix Commit FIX COMMIT
https://github.com/tensorflow/tensorflow/commit/f94ef358bb3e91d517446454edff6535bcfe8e4a
Tensorflow Source Repository REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-536.yaml
Tensorflow Source Repository REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-734.yaml
Tensorflow Source Repository REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-245.yaml
Tensorflow Source Repository REPOSITORY
https://github.com/tensorflow/tensorflow

Frequently Asked Questions

What is CVE-2021-29608?
CVE-2021-29608 is a security vulnerability in Tensorflow involving Heap OOB and null pointer dereference in `RaggedTensorToTensor` under CWE-131: Incorrect Calculation of Buffer Size.
Which Tensorflow versions are affected?
Affected versions include releases prior to 2.1.4, 2.2.3, 2.3.3, 2.4.2 across affected packages: tensorflow, tensorflow-cpu, tensorflow-gpu.
What version fixes CVE-2021-29608?
Upgrading to 2.1.4, 2.2.3, 2.3.3, 2.4.2 (or the latest patched release for your respective branch) resolves the vulnerability.
Is tensorflow affected?
Yes, tensorflow is affected in releases prior to 2.1.4, 2.2.3, 2.3.3, 2.4.2. Upgrade your dependency to 2.1.4, 2.2.3, 2.3.3, 2.4.2 or later.
Is tensorflow-cpu affected?
Yes, tensorflow-cpu is affected in releases prior to 2.1.4, 2.2.3, 2.3.3, 2.4.2. Upgrade your dependency to 2.1.4, 2.2.3, 2.3.3, 2.4.2 or later.
What is the CVSS score of CVE-2021-29608?
CVE-2021-29608 has a CVSS v3.1 base score of 5.3 (MODERATE) with vector string CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:L/A:H.
What CWE is associated with CVE-2021-29608?
CVE-2021-29608 is classified under CWE-131: Incorrect Calculation of Buffer Size (The product does not correctly calculate the size to be used when allocating a buffer, which could lead to a buffer overflow.).
How can I check PyPI dependencies for CVE-2021-29608?
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
View Raw JSON 
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  "schema_version" : "1.4.0",
  "id" : "GHSA-rgvq-pcvf-hx75",
  "modified" : "2024-11-13T16:13:53Z",
  "published" : "2021-05-21T14:28:27Z",
  "aliases" : [ "CVE-2021-29608" ],
  "summary" : "Heap OOB and null pointer dereference in `RaggedTensorToTensor`",
  "details" : "### Impact\nDue to lack of validation in `tf.raw_ops.RaggedTensorToTensor`, an attacker can exploit an undefined behavior if input arguments are empty:\n\n```python\nimport tensorflow as tf\n\nshape = tf.constant([-1, -1], shape=[2], dtype=tf.int64)\nvalues = tf.constant([], shape=[0], dtype=tf.int64)\ndefault_value = tf.constant(404, dtype=tf.int64)\nrow = tf.constant([269, 404, 0, 0, 0, 0, 0], shape=[7], dtype=tf.int64)\nrows = [row]\ntypes = ['ROW_SPLITS']\n\ntf.raw_ops.RaggedTensorToTensor(\n  shape=shape, values=values, default_value=default_value, \n  row_partition_tensors=rows, row_partition_types=types)\n```\n\nThe [implementation](https://github.com/tensorflow/tensorflow/blob/656e7673b14acd7835dc778867f84916c6d1cac2/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L356-L360) only checks that one of the tensors is not empty, but does not check for the other ones.\n\nThere are multiple `DCHECK` validations to prevent heap OOB, but these are no-op in release builds, hence they don't prevent anything.\n\n### Patches\nWe have patched the issue in GitHub commit [b761c9b652af2107cfbc33efd19be0ce41daa33e](https://github.com/tensorflow/tensorflow/commit/b761c9b652af2107cfbc33efd19be0ce41daa33e) followed by GitHub commit [f94ef358bb3e91d517446454edff6535bcfe8e4a](https://github.com/tensorflow/tensorflow/commit/f94ef358bb3e91d517446454edff6535bcfe8e4a) and GitHub commit [c4d7afb6a5986b04505aca4466ae1951686c80f6](https://github.com/tensorflow/tensorflow/commit/c4d7afb6a5986b04505aca4466ae1951686c80f6).\n\nThe fix will be included in TensorFlow 2.5.0. We will also cherrypick these commits on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, 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 Yakun Zhang and Ying Wang of Baidu X-Team.",
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}