CVE-2021-29612: Tensorflow Heap Buffer Overflow Vulnerability

Q: What is CVE-2021-29612?

CVE-2021-29612 is a security vulnerability in Tensorflow involving Heap buffer overflow in `BandedTriangularSolve` under CWE-120: Buffer Copy without Checking Size of Input ('Classic Buffer Overflow').

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-2021-29612?

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-2021-29612?

CVE-2021-29612 has a CVSS v3.1 base score of 3.6 (LOW) with vector string CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:L/A:L.

Q: What CWE is associated with CVE-2021-29612?

CVE-2021-29612 is classified under CWE-120: Buffer Copy without Checking Size of Input ('Classic Buffer Overflow') (The product copies an input buffer to an output buffer without verifying that the size of the input buffer is less than the size of the output buffer.).

Q: How can I check PyPI dependencies for CVE-2021-29612?

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-2xgj-xhgf-ggjv

CVSS 3.6

CWE-120 CWE-787

Published 2021-05-21

Advisory Summary

CVE-2021-29612 is a LOW (CVSS 3.6) heap buffer overflow vulnerability (CWE-120: Buffer Copy without Checking Size of Input ('Classic Buffer Overflow')) affecting Tensorflow libraries in the PyPI ecosystem. The issue involves heap buffer overflow in `bandedtriangularsolve`. Remediation is available by upgrading affected packages to version 2.1.4, 2.2.3, 2.3.3, 2.4.2.

CVE-2021-29612 at a Glance

CVE Identifier CVE-2021-29612

GHSA Identifier GHSA-2xgj-xhgf-ggjv

Severity LOW

CVSS v3.1 Base Score 3.6

CWE Classification CWE-120: Buffer Copy without Checking Size of Input ('Classic Buffer Overflow')

Affected Product Tensorflow

Package Ecosystem PyPI

Published Date 2021-05-21

Last Updated 2024-11-13

Security Impact

CVSS: 3.6

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: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 Low (I:L)

Availability Low (A:L)

How to Fix CVE-2021-29612

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-2021-29612, 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-120: Buffer Copy without Checking Size of Input ('Classic Buffer Overflow'), where the product copies an input buffer to an output buffer without verifying that the size of the input buffer is less than the size of the output buffer. 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 heap buffer overflow in Eigen implementation of `tf.raw_ops.BandedTriangularSolve`: ```python import tensorflow as tf import numpy as np matrix_array = np.array([]) matrix_tensor = tf.convert_to_tensor(np.reshape(matrix_array,(0,1)),dtype=tf.float32) rhs_array = np.array([1,1]) rhs_tensor = tf.convert_to_tensor(np.reshape(rhs_array,(1,2)),dtype=tf.float32) tf.raw_ops.BandedTriangularSolve(matrix=matrix_tensor,rhs=rhs_tensor) ``` The [implementation](https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/linalg/banded_triangular_solve_op.cc#L269-L278) calls `ValidateInputTensors` for input validation but fails to validate that the two tensors are not empty: ```cc void ValidateInputTensors(OpKernelContext* ctx, const Tensor& in0, const Tensor& in1) { OP_REQUIRES( ctx, in0.dims() >= 2, errors::InvalidArgument("In[0] ndims must be >= 2: ", in0.dims())); OP_REQUIRES( ctx, in1.dims() >= 2, errors::InvalidArgument("In[1] ndims must be >= 2: ", in1.dims())); } ``` Furthermore, since `OP_REQUIRES` macro only stops execution of current function after setting `ctx->status()` to a non-OK value, callers of helper functions that use `OP_REQUIRES` must check value of `ctx->status()` before continuing. This doesn't happen [in this op's implementation](https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/linalg/banded_triangular_solve_op.cc#L219), hence the validation that is present is also not effective. ### Patches We have patched the issue in GitHub commit [ba6822bd7b7324ba201a28b2f278c29a98edbef2](https://github.com/tensorflow/tensorflow/commit/ba6822bd7b7324ba201a28b2f278c29a98edbef2) followed by GitHub commit [0ab290774f91a23bebe30a358fde4e53ab4876a0](https://github.com/tensorflow/tensorflow/commit/0ab290774f91a23bebe30a358fde4e53ab4876a0). 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 Ye Zhang and Yakun Zhang of Baidu X-Team.

CVE-2021-29612 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-2xgj-xhgf-ggjv

NVD — CVE-2021-29612 NVD

https://nvd.nist.gov/vuln/detail/CVE-2021-29612

Security Fix Commit FIX COMMIT

https://github.com/tensorflow/tensorflow/commit/0ab290774f91a23bebe30a358fde4e53ab4876a0

Security Fix Commit FIX COMMIT

https://github.com/tensorflow/tensorflow/commit/ba6822bd7b7324ba201a28b2f278c29a98edbef2

Tensorflow Source Repository REPOSITORY

https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-540.yaml

Tensorflow Source Repository REPOSITORY

https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-738.yaml

Tensorflow Source Repository REPOSITORY

https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-249.yaml

Frequently Asked Questions

What is CVE-2021-29612?

CVE-2021-29612 is a security vulnerability in Tensorflow involving Heap buffer overflow in `BandedTriangularSolve` under CWE-120: Buffer Copy without Checking Size of Input ('Classic Buffer Overflow').

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-2021-29612?

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-2021-29612?

CVE-2021-29612 has a CVSS v3.1 base score of 3.6 (LOW) with vector string CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:L/A:L.

What CWE is associated with CVE-2021-29612?

CVE-2021-29612 is classified under CWE-120: Buffer Copy without Checking Size of Input ('Classic Buffer Overflow') (The product copies an input buffer to an output buffer without verifying that the size of the input buffer is less than the size of the output buffer.).

How can I check PyPI dependencies for CVE-2021-29612?

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-2xgj-xhgf-ggjv",
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  "published" : "2021-05-21T14:28:37Z",
  "aliases" : [ "CVE-2021-29612" ],
  "summary" : "Heap buffer overflow in `BandedTriangularSolve`",
  "details" : "### Impact\nAn attacker can trigger a heap buffer overflow in Eigen implementation of `tf.raw_ops.BandedTriangularSolve`:\n\n```python\nimport tensorflow as tf\nimport numpy as np\n  \nmatrix_array = np.array([])\nmatrix_tensor = tf.convert_to_tensor(np.reshape(matrix_array,(0,1)),dtype=tf.float32)\nrhs_array = np.array([1,1])\nrhs_tensor = tf.convert_to_tensor(np.reshape(rhs_array,(1,2)),dtype=tf.float32)\ntf.raw_ops.BandedTriangularSolve(matrix=matrix_tensor,rhs=rhs_tensor)\n```\n\nThe [implementation](https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/linalg/banded_triangular_solve_op.cc#L269-L278) calls `ValidateInputTensors` for input validation but fails to validate that the two tensors are not empty:\n  \n```cc\nvoid ValidateInputTensors(OpKernelContext* ctx, const Tensor& in0, const Tensor& in1) {\n  OP_REQUIRES(\n      ctx, in0.dims() >= 2, \n      errors::InvalidArgument(\"In[0] ndims must be >= 2: \", in0.dims()));\n\n  OP_REQUIRES(\n      ctx, in1.dims() >= 2,\n      errors::InvalidArgument(\"In[1] ndims must be >= 2: \", in1.dims()));\n}\n``` \n\nFurthermore, since `OP_REQUIRES` macro only stops execution of current function after setting `ctx->status()` to a non-OK value, callers of helper functions that use `OP_REQUIRES` must check value of `ctx->status()` before continuing. This doesn't happen [in this op's implementation](https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/linalg/banded_triangular_solve_op.cc#L219), hence the validation that is present is also not effective.\n\n### Patches\nWe have patched the issue in GitHub commit [ba6822bd7b7324ba201a28b2f278c29a98edbef2](https://github.com/tensorflow/tensorflow/commit/ba6822bd7b7324ba201a28b2f278c29a98edbef2) followed by GitHub commit [0ab290774f91a23bebe30a358fde4e53ab4876a0](https://github.com/tensorflow/tensorflow/commit/0ab290774f91a23bebe30a358fde4e53ab4876a0).\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 Ye Zhang and Yakun Zhang of Baidu X-Team.",
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    "type" : "ADVISORY",
    "url" : "https://nvd.nist.gov/vuln/detail/CVE-2021-29612"
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  "database_specific" : {
    "cwe_ids" : [ "CWE-120", "CWE-787" ],
    "severity" : "LOW",
    "github_reviewed" : true,
    "github_reviewed_at" : "2021-05-17T21:56:29Z",
    "nvd_published_at" : "2021-05-14T20:15:00Z"
  }
}

CVSS V3

3.6 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 Low (I:L)

Availability Low (A:L)

Vector String

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

Advisory Metadata

Source ID: GHSA-2xgj-xhgf-ggjv
Schema Version: 1.4.0
Published: 2021-05-21 14:28
Last Modified: 2024-11-13 16:02
Database Record File: advisories/github-reviewed/2021/05/GHSA-2xgj-xhgf-ggjv/GHSA-2xgj-xhgf-ggjv.json