CVE-2020-26267: Tensorflow Lack Of Validation Vulnerability

LOW
Advisory Identifier: GHSA-c9f3-9wfr-wgh7
Share
CVSS 4.4
CWE-125
Published
Advisory Summary

CVE-2020-26267 is a LOW (CVSS 4.4) lack of validation vulnerability (CWE-125: Out-of-bounds Read) affecting Tensorflow libraries in the PyPI ecosystem. The issue involves lack of validation in data format attributes in tensorflow. Remediation is available by upgrading affected packages to version 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2.

CVE-2020-26267 at a Glance

CVE Identifier CVE-2020-26267
GHSA Identifier GHSA-c9f3-9wfr-wgh7
Severity LOW
CVSS v3.1 Base Score 4.4
CWE Classification CWE-125: Out-of-bounds Read
Affected Product Tensorflow
Package Ecosystem PyPI
Published Date
Last Updated

Security Impact

CVSS: 4.4

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:N/I:L/A:L).

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 None (C:N)
Integrity Low (I:L)
Availability Low (A:L)

How to Fix CVE-2020-26267

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: 1.15.5 
pip install tensorflow>=1.15.5
tensorflow fixed: 2.0.4 
pip install tensorflow>=2.0.4
tensorflow fixed: 2.1.3 
pip install tensorflow>=2.1.3
tensorflow fixed: 2.2.2 
pip install tensorflow>=2.2.2
tensorflow fixed: 2.3.2 
pip install tensorflow>=2.3.2
tensorflow-cpu fixed: 1.15.5 
pip install tensorflow-cpu>=1.15.5
tensorflow-cpu fixed: 2.0.4 
pip install tensorflow-cpu>=2.0.4
tensorflow-cpu fixed: 2.1.3 
pip install tensorflow-cpu>=2.1.3
tensorflow-cpu fixed: 2.2.2 
pip install tensorflow-cpu>=2.2.2
tensorflow-cpu fixed: 2.3.2 
pip install tensorflow-cpu>=2.3.2
tensorflow-gpu fixed: 1.15.5 
pip install tensorflow-gpu>=1.15.5
tensorflow-gpu fixed: 2.0.4 
pip install tensorflow-gpu>=2.0.4
tensorflow-gpu fixed: 2.1.3 
pip install tensorflow-gpu>=2.1.3
tensorflow-gpu fixed: 2.2.2 
pip install tensorflow-gpu>=2.2.2
tensorflow-gpu fixed: 2.3.2 
pip install tensorflow-gpu>=2.3.2
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-2020-26267, 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 1.15.5), 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-125: Out-of-bounds Read, where the product reads data past the end, or before the beginning, of the intended 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 The `tf.raw_ops.DataFormatVecPermute` API does not validate the `src_format` and `dst_format` attributes. [The code](https://github.com/tensorflow/tensorflow/blob/304b96815324e6a73d046df10df6626d63ac12ad/tensorflow/core/kernels/data_format_ops.cc) assumes that these two arguments define a permutation of `NHWC`. However, these assumptions are not checked and this can result in uninitialized memory accesses, read outside of bounds and even crashes. ```python >>> import tensorflow as tf >>> tf.raw_ops.DataFormatVecPermute(x=[1,4], src_format='1234', dst_format='1234') <tf.Tensor: shape=(2,), dtype=int32, numpy=array([4, 757100143], dtype=int32)> ... >>> tf.raw_ops.DataFormatVecPermute(x=[1,4], src_format='HHHH', dst_format='WWWW') <tf.Tensor: shape=(2,), dtype=int32, numpy=array([4, 32701], dtype=int32)> ... >>> tf.raw_ops.DataFormatVecPermute(x=[1,4], src_format='H', dst_format='W') <tf.Tensor: shape=(2,), dtype=int32, numpy=array([4, 32701], dtype=int32)> >>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4], src_format='1234', dst_format='1253') <tf.Tensor: shape=(4,), dtype=int32, numpy=array([4, 2, 939037184, 3], dtype=int32)> ... >>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4], src_format='1234', dst_format='1223') <tf.Tensor: shape=(4,), dtype=int32, numpy=array([4, 32701, 2, 3], dtype=int32)> ... >>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4], src_format='1224', dst_format='1423') <tf.Tensor: shape=(4,), dtype=int32, numpy=array([1, 4, 3, 32701], dtype=int32)> ... >>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4], src_format='1234', dst_format='432') <tf.Tensor: shape=(4,), dtype=int32, numpy=array([4, 3, 2, 32701], dtype=int32)> ... >>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4], src_format='12345678', dst_format='87654321') munmap_chunk(): invalid pointer Aborted ... >>> tf.raw_ops.DataFormatVecPermute(x=[[1,5],[2,6],[3,7],[4,8]], src_format='12345678', dst_format='87654321') <tf.Tensor: shape=(4, 2), dtype=int32, numpy= array([[71364624, 0], [71365824, 0], [ 560, 0], [ 48, 0]], dtype=int32)> ... >>> tf.raw_ops.DataFormatVecPermute(x=[[1,5],[2,6],[3,7],[4,8]], src_format='12345678', dst_format='87654321') free(): invalid next size (fast) Aborted ``` A similar issue occurs in `tf.raw_ops.DataFormatDimMap`, for the same reasons: ```python >>> tf.raw_ops.DataFormatDimMap(x=[[1,5],[2,6],[3,7],[4,8]], src_format='1234', >>> dst_format='8765') <tf.Tensor: shape=(4, 2), dtype=int32, numpy= array([[1954047348, 1954047348], [1852793646, 1852793646], [1954047348, 1954047348], [1852793632, 1852793632]], dtype=int32)> ``` ### Patches We have patched the issue in GitHub commit [ebc70b7a592420d3d2f359e4b1694c236b82c7ae](https://github.com/tensorflow/tensorflow/commit/ebc70b7a592420d3d2f359e4b1694c236b82c7ae) and will release TensorFlow 2.4.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved. Since this issue also impacts TF versions before 2.4, we will patch all releases between 1.15 and 2.3 inclusive. ### 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-2020-26267 Affected Tensorflow Versions

15 component(s)
Fixed Versions:
1.15.52.0.42.1.32.2.22.3.2
Product / Package Ecosystem Affected Versions Fixed Version
tensorflow
pypi/tensorflow
PyPI
< 1.15.5
1.15.5
tensorflow
pypi/tensorflow
PyPI
≥ 2.0.0
< 2.0.4
2.0.4
tensorflow
pypi/tensorflow
PyPI
≥ 2.1.0
< 2.1.3
2.1.3
tensorflow
pypi/tensorflow
PyPI
≥ 2.2.0
< 2.2.2
2.2.2
tensorflow
pypi/tensorflow
PyPI
≥ 2.3.0
< 2.3.2
2.3.2
tensorflow-cpu
pypi/tensorflow-cpu
PyPI
< 1.15.5
1.15.5
tensorflow-cpu
pypi/tensorflow-cpu
PyPI
≥ 2.0.0
< 2.0.4
2.0.4
tensorflow-cpu
pypi/tensorflow-cpu
PyPI
≥ 2.1.0
< 2.1.3
2.1.3
tensorflow-cpu
pypi/tensorflow-cpu
PyPI
≥ 2.2.0
< 2.2.2
2.2.2
tensorflow-cpu
pypi/tensorflow-cpu
PyPI
≥ 2.3.0
< 2.3.2
2.3.2
tensorflow-gpu
pypi/tensorflow-gpu
PyPI
< 1.15.5
1.15.5
tensorflow-gpu
pypi/tensorflow-gpu
PyPI
≥ 2.0.0
< 2.0.4
2.0.4
tensorflow-gpu
pypi/tensorflow-gpu
PyPI
≥ 2.1.0
< 2.1.3
2.1.3
tensorflow-gpu
pypi/tensorflow-gpu
PyPI
≥ 2.2.0
< 2.2.2
2.2.2
tensorflow-gpu
pypi/tensorflow-gpu
PyPI
≥ 2.3.0
< 2.3.2
2.3.2

References

GitHub Advisory / Wiki ADVISORY
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c9f3-9wfr-wgh7
NVD — CVE-2020-26267 NVD
https://nvd.nist.gov/vuln/detail/CVE-2020-26267
Security Fix Commit FIX COMMIT
https://github.com/tensorflow/tensorflow/commit/ebc70b7a592420d3d2f359e4b1694c236b82c7ae
Tensorflow Source Repository REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-298.yaml
Tensorflow Source Repository REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-333.yaml
Tensorflow Source Repository REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-140.yaml
Tensorflow Source Repository REPOSITORY
https://github.com/tensorflow/tensorflow

Frequently Asked Questions

What is CVE-2020-26267?
CVE-2020-26267 is a security vulnerability in Tensorflow involving Lack of validation in data format attributes in TensorFlow under CWE-125: Out-of-bounds Read.
Which Tensorflow versions are affected?
Affected versions include releases prior to 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 across affected packages: tensorflow, tensorflow-cpu, tensorflow-gpu.
What version fixes CVE-2020-26267?
Upgrading to 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 (or the latest patched release for your respective branch) resolves the vulnerability.
Is tensorflow affected?
Yes, tensorflow is affected in releases prior to 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2. Upgrade your dependency to 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 or later.
Is tensorflow-cpu affected?
Yes, tensorflow-cpu is affected in releases prior to 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2. Upgrade your dependency to 1.15.5, 2.0.4, 2.1.3, 2.2.2, 2.3.2 or later.
What is the CVSS score of CVE-2020-26267?
CVE-2020-26267 has a CVSS v3.1 base score of 4.4 (LOW) with vector string CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L.
What CWE is associated with CVE-2020-26267?
CVE-2020-26267 is classified under CWE-125: Out-of-bounds Read (The product reads data past the end, or before the beginning, of the intended buffer.).
How can I check PyPI dependencies for CVE-2020-26267?
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 
{
  "schema_version" : "1.4.0",
  "id" : "GHSA-c9f3-9wfr-wgh7",
  "modified" : "2024-10-28T20:02:35Z",
  "published" : "2020-12-10T19:07:26Z",
  "aliases" : [ "CVE-2020-26267" ],
  "summary" : "Lack of validation in data format attributes in TensorFlow",
  "details" : "### Impact\nThe `tf.raw_ops.DataFormatVecPermute` API does not validate the `src_format` and `dst_format` attributes. [The code](https://github.com/tensorflow/tensorflow/blob/304b96815324e6a73d046df10df6626d63ac12ad/tensorflow/core/kernels/data_format_ops.cc) assumes that these two arguments define a permutation of `NHWC`.\n\nHowever, these assumptions are not checked and this can result in uninitialized memory accesses, read outside of bounds and even crashes.\n\n```python\n>>> import tensorflow as tf\n>>> tf.raw_ops.DataFormatVecPermute(x=[1,4], src_format='1234', dst_format='1234')\n<tf.Tensor: shape=(2,), dtype=int32, numpy=array([4, 757100143], dtype=int32)>\n...\n>>> tf.raw_ops.DataFormatVecPermute(x=[1,4], src_format='HHHH', dst_format='WWWW')\n<tf.Tensor: shape=(2,), dtype=int32, numpy=array([4, 32701], dtype=int32)>\n...\n>>> tf.raw_ops.DataFormatVecPermute(x=[1,4], src_format='H', dst_format='W')\n<tf.Tensor: shape=(2,), dtype=int32, numpy=array([4, 32701], dtype=int32)>\n>>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4], \n                                    src_format='1234', dst_format='1253')\n<tf.Tensor: shape=(4,), dtype=int32, numpy=array([4, 2, 939037184, 3], dtype=int32)>\n...\n>>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4],\n                                    src_format='1234', dst_format='1223')\n<tf.Tensor: shape=(4,), dtype=int32, numpy=array([4, 32701, 2, 3], dtype=int32)>\n...\n>>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4],\n                                    src_format='1224', dst_format='1423')\n<tf.Tensor: shape=(4,), dtype=int32, numpy=array([1, 4, 3, 32701], dtype=int32)>\n...\n>>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4], src_format='1234', dst_format='432')\n<tf.Tensor: shape=(4,), dtype=int32, numpy=array([4, 3, 2, 32701], dtype=int32)>\n...\n>>> tf.raw_ops.DataFormatVecPermute(x=[1,2,3,4],\n                                    src_format='12345678', dst_format='87654321')\nmunmap_chunk(): invalid pointer\nAborted\n...\n>>> tf.raw_ops.DataFormatVecPermute(x=[[1,5],[2,6],[3,7],[4,8]],           \n                                    src_format='12345678', dst_format='87654321')\n<tf.Tensor: shape=(4, 2), dtype=int32, numpy=\narray([[71364624,        0],\n       [71365824,        0],\n       [     560,        0],\n       [      48,        0]], dtype=int32)>\n...\n>>> tf.raw_ops.DataFormatVecPermute(x=[[1,5],[2,6],[3,7],[4,8]], \n                                    src_format='12345678', dst_format='87654321')\nfree(): invalid next size (fast)\nAborted\n```\n\nA similar issue occurs in `tf.raw_ops.DataFormatDimMap`, for the same reasons:\n\n```python\n>>> tf.raw_ops.DataFormatDimMap(x=[[1,5],[2,6],[3,7],[4,8]], src_format='1234',\n>>> dst_format='8765')\n<tf.Tensor: shape=(4, 2), dtype=int32, numpy=\narray([[1954047348, 1954047348],\n       [1852793646, 1852793646],\n       [1954047348, 1954047348],\n       [1852793632, 1852793632]], dtype=int32)>\n```\n\n### Patches\nWe have patched the issue in GitHub commit [ebc70b7a592420d3d2f359e4b1694c236b82c7ae](https://github.com/tensorflow/tensorflow/commit/ebc70b7a592420d3d2f359e4b1694c236b82c7ae) and will release TensorFlow 2.4.0 containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.\n\nSince this issue also impacts TF versions before 2.4, we will patch all releases between 1.15 and 2.3 inclusive.\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.",
  "severity" : [ {
    "type" : "CVSS_V3",
    "score" : "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L"
  }, {
    "type" : "CVSS_V4",
    "score" : "CVSS:4.0/AV:L/AC:L/AT:P/PR:N/UI:N/VC:N/VI:L/VA:L/SC:N/SI:N/SA:N"
  } ],
  "affected" : [ {
    "package" : {
      "ecosystem" : "PyPI",
      "name" : "tensorflow"
    },
    "ranges" : [ {
      "type" : "ECOSYSTEM",
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    } ]
  } ],
  "references" : [ {
    "type" : "WEB",
    "url" : "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c9f3-9wfr-wgh7"
  }, {
    "type" : "ADVISORY",
    "url" : "https://nvd.nist.gov/vuln/detail/CVE-2020-26267"
  }, {
    "type" : "WEB",
    "url" : "https://github.com/tensorflow/tensorflow/commit/ebc70b7a592420d3d2f359e4b1694c236b82c7ae"
  }, {
    "type" : "WEB",
    "url" : "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-298.yaml"
  }, {
    "type" : "WEB",
    "url" : "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-333.yaml"
  }, {
    "type" : "WEB",
    "url" : "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-140.yaml"
  }, {
    "type" : "PACKAGE",
    "url" : "https://github.com/tensorflow/tensorflow"
  } ],
  "database_specific" : {
    "cwe_ids" : [ "CWE-125" ],
    "severity" : "LOW",
    "github_reviewed" : true,
    "github_reviewed_at" : "2020-12-10T19:05:08Z",
    "nvd_published_at" : "2020-12-10T23:15:00Z"
  }
}