CVE-2021-29519: Tensorflow CHECK-fail Vulnerability

Q: What is CVE-2021-29519?

CVE-2021-29519 is a security vulnerability in Tensorflow involving CHECK-fail in SparseCross due to type confusion under CWE-843: Access of Resource Using Incompatible Type ('Type Confusion').

Q: 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.

Q: What version fixes CVE-2021-29519?

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.

Q: 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.

Q: 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.

Q: What is the CVSS score of CVE-2021-29519?

CVE-2021-29519 has a CVSS v3.1 base score of 2.5 (LOW) with vector string CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L.

Q: What CWE is associated with CVE-2021-29519?

CVE-2021-29519 is classified under CWE-843: Access of Resource Using Incompatible Type ('Type Confusion') (The product allocates or initializes a resource such as a pointer, object, or variable using one type, but it later accesses that resource using a type that is incompatible with the original type.).

Q: How can I check PyPI dependencies for CVE-2021-29519?

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.

LOW

Advisory Identifier: GHSA-772j-h9xw-ffp5

CVSS 2.5

CWE-843

Published 2021-05-21

Advisory Summary

CVE-2021-29519 is a LOW (CVSS 2.5) check-fail vulnerability (CWE-843: Access of Resource Using Incompatible Type ('Type Confusion')) affecting Tensorflow libraries in the PyPI ecosystem. The issue involves check-fail in sparsecross due to type confusion. Remediation is available by upgrading affected packages to version 2.1.4, 2.2.3, 2.3.3, 2.4.2.

CVE-2021-29519 at a Glance

CVE Identifier CVE-2021-29519

GHSA Identifier GHSA-772j-h9xw-ffp5

Severity LOW

CVSS v3.1 Base Score 2.5

CWE Classification CWE-843: Access of Resource Using Incompatible Type ('Type Confusion')

Affected Product Tensorflow

Package Ecosystem PyPI

Published Date 2021-05-21

Last Updated 2024-10-28

Security Impact

CVSS: 2.5

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:N/A:L).

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 None (I:N)

Availability Low (A:L)

How to Fix CVE-2021-29519

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-2021-29519, 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-843: Access of Resource Using Incompatible Type ('Type Confusion'), where the product allocates or initializes a resource such as a pointer, object, or variable using one type, but it later accesses that resource using a type that is incompatible with the original type. 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 API of `tf.raw_ops.SparseCross` allows combinations which would result in a `CHECK`-failure and denial of service: ```python import tensorflow as tf hashed_output = False num_buckets = 1949315406 hash_key = 1869835877 out_type = tf.string internal_type = tf.string indices_1 = tf.constant([0, 6], shape=[1, 2], dtype=tf.int64) indices_2 = tf.constant([0, 0], shape=[1, 2], dtype=tf.int64) indices = [indices_1, indices_2] values_1 = tf.constant([0], dtype=tf.int64) values_2 = tf.constant([72], dtype=tf.int64) values = [values_1, values_2] batch_size = 4 shape_1 = tf.constant([4, 122], dtype=tf.int64) shape_2 = tf.constant([4, 188], dtype=tf.int64) shapes = [shape_1, shape_2] dense_1 = tf.constant([188, 127, 336, 0], shape=[4, 1], dtype=tf.int64) dense_2 = tf.constant([341, 470, 470, 470], shape=[4, 1], dtype=tf.int64) dense_3 = tf.constant([188, 188, 341, 922], shape=[4, 1], dtype=tf.int64) denses = [dense_1, dense_2, dense_3] tf.raw_ops.SparseCross(indices=indices, values=values, shapes=shapes, dense_inputs=denses, hashed_output=hashed_output, num_buckets=num_buckets, hash_key=hash_key, out_type=out_type, internal_type=internal_type) ``` The above code will result in a `CHECK` fail in [`tensor.cc`](https://github.com/tensorflow/tensorflow/blob/3d782b7d47b1bf2ed32bd4a246d6d6cadc4c903d/tensorflow/core/framework/tensor.cc#L670-L675): ```cc void Tensor::CheckTypeAndIsAligned(DataType expected_dtype) const { CHECK_EQ(dtype(), expected_dtype) << " " << DataTypeString(expected_dtype) << " expected, got " << DataTypeString(dtype()); ... } ``` This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/3d782b7d47b1bf2ed32bd4a246d6d6cadc4c903d/tensorflow/core/kernels/sparse_cross_op.cc#L114-L116) is tricked to consider a tensor of type `tstring` which in fact contains integral elements: ```cc if (DT_STRING == values_.dtype()) return Fingerprint64(values_.vec<tstring>().data()[start + n]); return values_.vec<int64>().data()[start + n]; ``` Fixing the type confusion by preventing mixing `DT_STRING` and `DT_INT64` types solves this issue. ### Patches We have patched the issue in GitHub commit [b1cc5e5a50e7cee09f2c6eb48eb40ee9c4125025](https://github.com/tensorflow/tensorflow/commit/b1cc5e5a50e7cee09f2c6eb48eb40ee9c4125025). The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit 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-2021-29519 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-772j-h9xw-ffp5

NVD — CVE-2021-29519 NVD

https://nvd.nist.gov/vuln/detail/CVE-2021-29519

Security Fix Commit FIX COMMIT

https://github.com/tensorflow/tensorflow/commit/b1cc5e5a50e7cee09f2c6eb48eb40ee9c4125025

Tensorflow Source Repository REPOSITORY

https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-447.yaml

Tensorflow Source Repository REPOSITORY

https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-645.yaml

Tensorflow Source Repository REPOSITORY

https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-156.yaml

Frequently Asked Questions

What is CVE-2021-29519?

CVE-2021-29519 is a security vulnerability in Tensorflow involving CHECK-fail in SparseCross due to type confusion under CWE-843: Access of Resource Using Incompatible Type ('Type Confusion').

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-2021-29519?

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-2021-29519?

CVE-2021-29519 has a CVSS v3.1 base score of 2.5 (LOW) with vector string CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L.

What CWE is associated with CVE-2021-29519?

CVE-2021-29519 is classified under CWE-843: Access of Resource Using Incompatible Type ('Type Confusion') (The product allocates or initializes a resource such as a pointer, object, or variable using one type, but it later accesses that resource using a type that is incompatible with the original type.).

How can I check PyPI dependencies for CVE-2021-29519?

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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  "id" : "GHSA-772j-h9xw-ffp5",
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  "published" : "2021-05-21T14:21:08Z",
  "aliases" : [ "CVE-2021-29519" ],
  "summary" : "CHECK-fail in SparseCross due to type confusion",
  "details" : "### Impact\nThe API of `tf.raw_ops.SparseCross` allows combinations which would result in a `CHECK`-failure and denial of service:\n\n```python\nimport tensorflow as tf\n\nhashed_output = False\nnum_buckets = 1949315406\nhash_key = 1869835877\nout_type = tf.string \ninternal_type = tf.string\n\nindices_1 = tf.constant([0, 6], shape=[1, 2], dtype=tf.int64)\nindices_2 = tf.constant([0, 0], shape=[1, 2], dtype=tf.int64)\nindices = [indices_1, indices_2]\n\nvalues_1 = tf.constant([0], dtype=tf.int64)\nvalues_2 = tf.constant([72], dtype=tf.int64)\nvalues = [values_1, values_2]\n\nbatch_size = 4\nshape_1 = tf.constant([4, 122], dtype=tf.int64)\nshape_2 = tf.constant([4, 188], dtype=tf.int64)\nshapes = [shape_1, shape_2]\n\ndense_1 = tf.constant([188, 127, 336, 0], shape=[4, 1], dtype=tf.int64)\ndense_2 = tf.constant([341, 470, 470, 470], shape=[4, 1], dtype=tf.int64)\ndense_3 = tf.constant([188, 188, 341, 922], shape=[4, 1], dtype=tf.int64)\ndenses = [dense_1, dense_2, dense_3]\n\ntf.raw_ops.SparseCross(indices=indices, values=values, shapes=shapes, dense_inputs=denses, hashed_output=hashed_output,\n                       num_buckets=num_buckets, hash_key=hash_key, out_type=out_type, internal_type=internal_type)\n```\n\nThe above code will result in a `CHECK` fail in [`tensor.cc`](https://github.com/tensorflow/tensorflow/blob/3d782b7d47b1bf2ed32bd4a246d6d6cadc4c903d/tensorflow/core/framework/tensor.cc#L670-L675):\n\n```cc\nvoid Tensor::CheckTypeAndIsAligned(DataType expected_dtype) const {\n  CHECK_EQ(dtype(), expected_dtype)\n      << \" \" << DataTypeString(expected_dtype) << \" expected, got \"\n      << DataTypeString(dtype());\n  ...\n}\n```\n\nThis is because the [implementation](https://github.com/tensorflow/tensorflow/blob/3d782b7d47b1bf2ed32bd4a246d6d6cadc4c903d/tensorflow/core/kernels/sparse_cross_op.cc#L114-L116) is tricked to consider a tensor of type `tstring` which in fact contains integral elements:\n\n```cc\n  if (DT_STRING == values_.dtype())\n      return Fingerprint64(values_.vec<tstring>().data()[start + n]);\n  return values_.vec<int64>().data()[start + n];\n```\n\nFixing the type confusion by preventing mixing `DT_STRING` and `DT_INT64` types solves this issue.\n\n### Patches\nWe have patched the issue in GitHub commit [b1cc5e5a50e7cee09f2c6eb48eb40ee9c4125025](https://github.com/tensorflow/tensorflow/commit/b1cc5e5a50e7cee09f2c6eb48eb40ee9c4125025).\n\nThe fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit 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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    "url" : "https://nvd.nist.gov/vuln/detail/CVE-2021-29519"
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  "database_specific" : {
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    "severity" : "LOW",
    "github_reviewed" : true,
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  }
}

CVSS V3

2.5 LOW

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 None (I:N)

Availability Low (A:L)

Vector String

CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L

Advisory Metadata

Source ID: GHSA-772j-h9xw-ffp5
Schema Version: 1.4.0
Published: 2021-05-21 14:21
Last Modified: 2024-10-28 21:22
Database Record File: advisories/github-reviewed/2021/05/GHSA-772j-h9xw-ffp5/GHSA-772j-h9xw-ffp5.json