CVE-2021-29523: Tensorflow CHECK-fail Vulnerability

Q: What is CVE-2021-29523?

CVE-2021-29523 is a security vulnerability in Tensorflow involving CHECK-fail in AddManySparseToTensorsMap under CWE-190: Integer Overflow or Wraparound.

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-2021-29523?

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-2021-29523?

CVE-2021-29523 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-2021-29523?

CVE-2021-29523 is classified under CWE-190: Integer Overflow or Wraparound (The product performs a calculation that can produce an integer overflow or wraparound when the logic assumes that the resulting value will always be larger than the original value. This occurs when an integer value is incremented to a value that is too large to store in the associated representation. When this occurs, the value may become a very small or negative number.).

Q: How can I check PyPI dependencies for CVE-2021-29523?

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-2cpx-427x-q2c6

CVSS 2.5

CWE-190

Published 2021-05-21

Advisory Summary

CVE-2021-29523 is a LOW (CVSS 2.5) check-fail vulnerability (CWE-190: Integer Overflow or Wraparound) affecting Tensorflow libraries in the PyPI ecosystem. The issue involves check-fail in addmanysparsetotensorsmap. Remediation is available by upgrading affected packages to version 2.1.4, 2.2.3, 2.3.3, 2.4.2.

CVE-2021-29523 at a Glance

CVE Identifier CVE-2021-29523

GHSA Identifier GHSA-2cpx-427x-q2c6

Severity LOW

CVSS v3.1 Base Score 2.5

CWE Classification CWE-190: Integer Overflow or Wraparound

Affected Product Tensorflow

Package Ecosystem PyPI

Published Date 2021-05-21

Last Updated 2024-10-30

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-2021-29523

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-2021-29523, 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-190: Integer Overflow or Wraparound, where the product performs a calculation that can produce an integer overflow or wraparound when the logic assumes that the resulting value will always be larger than the original value. this occurs when an integer value is incremented to a value that is too large to store in the associated representation. when this occurs, the value may become a very small or negative number. 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 An attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.AddManySparseToTensorsMap`: ```python import tensorflow as tf import numpy as np sparse_indices = tf.constant(530, shape=[1, 1], dtype=tf.int64) sparse_values = tf.ones([1], dtype=tf.int64) shape = tf.Variable(tf.ones([55], dtype=tf.int64)) shape[:8].assign(np.array([855, 901, 429, 892, 892, 852, 93, 96], dtype=np.int64)) tf.raw_ops.AddManySparseToTensorsMap(sparse_indices=sparse_indices, sparse_values=sparse_values, sparse_shape=shape) ``` This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/kernels/sparse_tensors_map_ops.cc#L257) takes the values specified in `sparse_shape` as dimensions for the output shape: ```cc TensorShape tensor_input_shape(input_shape->vec<int64>()); ``` The [`TensorShape` constructor](https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L183-L188) uses a `CHECK` operation which triggers when [`InitDims`](https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L212-L296) returns a non-OK status. ```cc template <class Shape> TensorShapeBase<Shape>::TensorShapeBase(gtl::ArraySlice<int64> dim_sizes) { set_tag(REP16); set_data_type(DT_INVALID); TF_CHECK_OK(InitDims(dim_sizes)); } ``` In our scenario, this occurs when adding a dimension from the argument results in overflow: ```cc template <class Shape> Status TensorShapeBase<Shape>::InitDims(gtl::ArraySlice<int64> dim_sizes) { ... Status status = Status::OK(); for (int64 s : dim_sizes) { status.Update(AddDimWithStatus(internal::SubtleMustCopy(s))); if (!status.ok()) { return status; } } } template <class Shape> Status TensorShapeBase<Shape>::AddDimWithStatus(int64 size) { ... int64 new_num_elements; if (kIsPartial && (num_elements() < 0 || size < 0)) { new_num_elements = -1; } else { new_num_elements = MultiplyWithoutOverflow(num_elements(), size); if (TF_PREDICT_FALSE(new_num_elements < 0)) { return errors::Internal("Encountered overflow when multiplying ", num_elements(), " with ", size, ", result: ", new_num_elements); } } ... } ``` This is a legacy implementation of the constructor and operations should use `BuildTensorShapeBase` or `AddDimWithStatus` to prevent `CHECK`-failures in the presence of overflows. ### Patches We have patched the issue in GitHub commit [69c68ecbb24dff3fa0e46da0d16c821a2dd22d7c](https://github.com/tensorflow/tensorflow/commit/69c68ecbb24dff3fa0e46da0d16c821a2dd22d7c). 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-2021-29523 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-2cpx-427x-q2c6

NVD — CVE-2021-29523 NVD

https://nvd.nist.gov/vuln/detail/CVE-2021-29523

Security Fix Commit FIX COMMIT

https://github.com/tensorflow/tensorflow/commit/69c68ecbb24dff3fa0e46da0d16c821a2dd22d7c

Tensorflow Source Repository REPOSITORY

https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-451.yaml

Tensorflow Source Repository REPOSITORY

https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-649.yaml

Tensorflow Source Repository REPOSITORY

https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-160.yaml

Tensorflow Source Repository REPOSITORY

https://github.com/tensorflow/tensorflow

Frequently Asked Questions

What is CVE-2021-29523?

CVE-2021-29523 is a security vulnerability in Tensorflow involving CHECK-fail in AddManySparseToTensorsMap under CWE-190: Integer Overflow or Wraparound.

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-2021-29523?

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-2021-29523?

CVE-2021-29523 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-2021-29523?

CVE-2021-29523 is classified under CWE-190: Integer Overflow or Wraparound (The product performs a calculation that can produce an integer overflow or wraparound when the logic assumes that the resulting value will always be larger than the original value. This occurs when an integer value is incremented to a value that is too large to store in the associated representation. When this occurs, the value may become a very small or negative number.).

How can I check PyPI dependencies for CVE-2021-29523?

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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  "details" : "### Impact\nAn attacker can trigger a denial of service via a `CHECK`-fail in  `tf.raw_ops.AddManySparseToTensorsMap`:\n\n```python\nimport tensorflow as tf\nimport numpy as np\n\nsparse_indices = tf.constant(530, shape=[1, 1], dtype=tf.int64)\nsparse_values = tf.ones([1], dtype=tf.int64)\n\nshape = tf.Variable(tf.ones([55], dtype=tf.int64))\nshape[:8].assign(np.array([855, 901, 429, 892, 892, 852, 93, 96], dtype=np.int64))\n\ntf.raw_ops.AddManySparseToTensorsMap(sparse_indices=sparse_indices,\n                    sparse_values=sparse_values,\n                    sparse_shape=shape)\n```\n\nThis is because the [implementation](https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/kernels/sparse_tensors_map_ops.cc#L257) takes the values specified in `sparse_shape` as dimensions for the output shape: \n\n```cc\n    TensorShape tensor_input_shape(input_shape->vec<int64>());\n```\n\nThe [`TensorShape` constructor](https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L183-L188) uses a `CHECK` operation which triggers when [`InitDims`](https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L212-L296) returns a non-OK status.\n  \n```cc\ntemplate <class Shape>\nTensorShapeBase<Shape>::TensorShapeBase(gtl::ArraySlice<int64> dim_sizes) {\n  set_tag(REP16);\n  set_data_type(DT_INVALID);\n  TF_CHECK_OK(InitDims(dim_sizes));\n}\n```\n\nIn our scenario, this occurs when adding a dimension from the argument results in overflow:\n\n```cc\ntemplate <class Shape>\nStatus TensorShapeBase<Shape>::InitDims(gtl::ArraySlice<int64> dim_sizes) {\n  ...\n  Status status = Status::OK();\n  for (int64 s : dim_sizes) {\n    status.Update(AddDimWithStatus(internal::SubtleMustCopy(s)));\n    if (!status.ok()) {\n      return status;\n    }\n  }\n}\n\ntemplate <class Shape>\nStatus TensorShapeBase<Shape>::AddDimWithStatus(int64 size) {\n  ...\n  int64 new_num_elements;\n  if (kIsPartial && (num_elements() < 0 || size < 0)) {\n    new_num_elements = -1;\n  } else {\n    new_num_elements = MultiplyWithoutOverflow(num_elements(), size);\n    if (TF_PREDICT_FALSE(new_num_elements < 0)) {\n        return errors::Internal(\"Encountered overflow when multiplying \",\n                                num_elements(), \" with \", size,\n                                \", result: \", new_num_elements);\n      }\n  }\n  ...\n}\n```\n\nThis is a legacy implementation of the constructor and operations should use `BuildTensorShapeBase` or `AddDimWithStatus` to prevent `CHECK`-failures in the presence of overflows.\n\n### Patches\nWe have patched the issue in GitHub commit [69c68ecbb24dff3fa0e46da0d16c821a2dd22d7c](https://github.com/tensorflow/tensorflow/commit/69c68ecbb24dff3fa0e46da0d16c821a2dd22d7c).\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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  "database_specific" : {
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    "severity" : "LOW",
    "github_reviewed" : true,
    "github_reviewed_at" : "2021-05-18T23:20:56Z",
    "nvd_published_at" : "2021-05-14T20:15:00Z"
  }
}

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-2cpx-427x-q2c6
Schema Version: 1.4.0
Published: 2021-05-21 14:21
Last Modified: 2024-10-30 23:14
Database Record File: advisories/github-reviewed/2021/05/GHSA-2cpx-427x-q2c6/GHSA-2cpx-427x-q2c6.json