CVE-2020-15190: Tensorflow Segfault Vulnerability

Q: What is CVE-2020-15190?

CVE-2020-15190 is a security vulnerability in Tensorflow involving Segfault in Tensorflow under CWE-20: Improper Input Validation.

Q: Which Tensorflow versions are affected?

Affected versions include releases prior to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 across affected packages: tensorflow, tensorflow-cpu, tensorflow-gpu.

Q: What version fixes CVE-2020-15190?

Upgrading to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 (or the latest patched release for your respective branch) resolves the vulnerability.

Q: Is tensorflow affected?

Yes, tensorflow is affected in releases prior to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1. Upgrade your dependency to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 or later.

Q: Is tensorflow-cpu affected?

Yes, tensorflow-cpu is affected in releases prior to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1. Upgrade your dependency to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 or later.

Q: What is the CVSS score of CVE-2020-15190?

CVE-2020-15190 has a CVSS v3.1 base score of 5.3 (MODERATE) with vector string CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:L.

Q: What CWE is associated with CVE-2020-15190?

CVE-2020-15190 is classified under CWE-20: Improper Input Validation (The product receives input or data, but it does not validate or incorrectly validates that the input has the properties that are required to process the data safely and correctly.).

Q: How can I check PyPI dependencies for CVE-2020-15190?

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.

MODERATE

Advisory Identifier: GHSA-4g9f-63rx-5cw4

CVSS 5.3

CWE-20 CWE-476

Published 2020-09-25

Advisory Summary

CVE-2020-15190 is a MODERATE (CVSS 5.3) segfault vulnerability (CWE-20: Improper Input Validation) affecting Tensorflow libraries in the PyPI ecosystem. The issue involves segfault in tensorflow. Remediation is available by upgrading affected packages to version 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1.

CVE-2020-15190 at a Glance

CVE Identifier CVE-2020-15190

GHSA Identifier GHSA-4g9f-63rx-5cw4

Severity MODERATE

CVSS v3.1 Base Score 5.3

CWE Classification CWE-20: Improper Input Validation

Affected Product Tensorflow

Package Ecosystem PyPI

Published Date 2020-09-25

Last Updated 2024-10-28

Security Impact

CVSS: 5.3

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

Attack Vector Network (AV:N)

Attack Complexity Low (AC:L)

Privileges Required None (PR:N)

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-2020-15190

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: 1.15.4

pip install tensorflow>=1.15.4

tensorflow fixed: 2.0.3

pip install tensorflow>=2.0.3

tensorflow fixed: 2.1.2

pip install tensorflow>=2.1.2

tensorflow fixed: 2.2.1

pip install tensorflow>=2.2.1

tensorflow fixed: 2.3.1

pip install tensorflow>=2.3.1

tensorflow-cpu fixed: 1.15.4

pip install tensorflow-cpu>=1.15.4

tensorflow-cpu fixed: 2.0.3

pip install tensorflow-cpu>=2.0.3

tensorflow-cpu fixed: 2.1.2

pip install tensorflow-cpu>=2.1.2

tensorflow-cpu fixed: 2.2.1

pip install tensorflow-cpu>=2.2.1

tensorflow-cpu fixed: 2.3.1

pip install tensorflow-cpu>=2.3.1

tensorflow-gpu fixed: 1.15.4

pip install tensorflow-gpu>=1.15.4

tensorflow-gpu fixed: 2.0.3

pip install tensorflow-gpu>=2.0.3

tensorflow-gpu fixed: 2.1.2

pip install tensorflow-gpu>=2.1.2

tensorflow-gpu fixed: 2.2.1

pip install tensorflow-gpu>=2.2.1

tensorflow-gpu fixed: 2.3.1

pip install tensorflow-gpu>=2.3.1

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-2020-15190, 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 1.15.4), 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-20: Improper Input Validation, where the product receives input or data, but it does not validate or incorrectly validates that the input has the properties that are required to process the data safely and correctly. 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.Switch`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/Switch) operation takes as input a tensor and a boolean and outputs two tensors. Depending on the boolean value, one of the tensors is exactly the input tensor whereas the other one should be an empty tensor. However, the eager runtime traverses all tensors in the output: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/common_runtime/eager/kernel_and_device.cc#L308-L313 Since only one of the tensors is defined, the other one is `nullptr`, hence we are binding a reference to `nullptr`. This is undefined behavior and reported as an error if compiling with `-fsanitize=null`. In this case, this results in a segmentation fault ### Patches We have patched the issue in da8558533d925694483d2c136a9220d6d49d843c and will release a patch release for all affected versions. We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. ### 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-2020-15190 Affected Tensorflow Versions

15 component(s)

Fixed Versions:

1.15.42.0.32.1.22.2.12.3.1

Product / Package	Ecosystem	Affected Versions	Fixed Version
tensorflow pypi/tensorflow	PyPI	< 1.15.4	1.15.4
tensorflow pypi/tensorflow	PyPI	≥ 2.0.0 < 2.0.3	2.0.3
tensorflow pypi/tensorflow	PyPI	≥ 2.1.0 < 2.1.2	2.1.2
tensorflow pypi/tensorflow	PyPI	≥ 2.2.0 < 2.2.1 All 1 versions 2.2.0	2.2.1
tensorflow pypi/tensorflow	PyPI	≥ 2.3.0 < 2.3.1 All 1 versions 2.3.0	2.3.1
tensorflow-cpu pypi/tensorflow-cpu	PyPI	< 1.15.4	1.15.4
tensorflow-cpu pypi/tensorflow-cpu	PyPI	≥ 2.0.0 < 2.0.3	2.0.3
tensorflow-cpu pypi/tensorflow-cpu	PyPI	≥ 2.1.0 < 2.1.2	2.1.2
tensorflow-cpu pypi/tensorflow-cpu	PyPI	≥ 2.2.0 < 2.2.1 All 1 versions 2.2.0	2.2.1
tensorflow-cpu pypi/tensorflow-cpu	PyPI	≥ 2.3.0 < 2.3.1 All 1 versions 2.3.0	2.3.1
tensorflow-gpu pypi/tensorflow-gpu	PyPI	< 1.15.4	1.15.4
tensorflow-gpu pypi/tensorflow-gpu	PyPI	≥ 2.0.0 < 2.0.3	2.0.3
tensorflow-gpu pypi/tensorflow-gpu	PyPI	≥ 2.1.0 < 2.1.2	2.1.2
tensorflow-gpu pypi/tensorflow-gpu	PyPI	≥ 2.2.0 < 2.2.1 All 1 versions 2.2.0	2.2.1
tensorflow-gpu pypi/tensorflow-gpu	PyPI	≥ 2.3.0 < 2.3.1 All 1 versions 2.3.0	2.3.1

References

NVD — CVE-2020-15190 NVD

https://nvd.nist.gov/vuln/detail/CVE-2020-15190

GitHub Advisory / Wiki ADVISORY

https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4g9f-63rx-5cw4

Security Fix Commit FIX COMMIT

https://github.com/tensorflow/tensorflow/commit/da8558533d925694483d2c136a9220d6d49d843c

Tensorflow Source Repository REPOSITORY

https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-270.yaml

Tensorflow Source Repository REPOSITORY

https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-305.yaml

Tensorflow Source Repository REPOSITORY

https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-113.yaml

Tensorflow Source Repository REPOSITORY

https://github.com/tensorflow/tensorflow

Tensorflow Release RELEASE

https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1

Tensorflow Security Information SECURITY

http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html

Frequently Asked Questions

What is CVE-2020-15190?

CVE-2020-15190 is a security vulnerability in Tensorflow involving Segfault in Tensorflow under CWE-20: Improper Input Validation.

Which Tensorflow versions are affected?

Affected versions include releases prior to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 across affected packages: tensorflow, tensorflow-cpu, tensorflow-gpu.

What version fixes CVE-2020-15190?

Upgrading to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 (or the latest patched release for your respective branch) resolves the vulnerability.

Is tensorflow affected?

Yes, tensorflow is affected in releases prior to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1. Upgrade your dependency to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 or later.

Is tensorflow-cpu affected?

Yes, tensorflow-cpu is affected in releases prior to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1. Upgrade your dependency to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 or later.

What is the CVSS score of CVE-2020-15190?

CVE-2020-15190 has a CVSS v3.1 base score of 5.3 (MODERATE) with vector string CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:L.

What CWE is associated with CVE-2020-15190?

CVE-2020-15190 is classified under CWE-20: Improper Input Validation (The product receives input or data, but it does not validate or incorrectly validates that the input has the properties that are required to process the data safely and correctly.).

How can I check PyPI dependencies for CVE-2020-15190?

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-4g9f-63rx-5cw4",
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  "aliases" : [ "CVE-2020-15190" ],
  "summary" : "Segfault in Tensorflow",
  "details" : "### Impact\nThe [`tf.raw_ops.Switch`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/Switch) operation takes as input a tensor and a boolean and outputs two tensors. Depending on the boolean value, one of the tensors is exactly the input tensor whereas the other one should be an empty tensor.\n\nHowever, the eager runtime traverses all tensors in the output:\nhttps://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/common_runtime/eager/kernel_and_device.cc#L308-L313\n\nSince only one of the tensors is defined, the other one is `nullptr`, hence we are binding a reference to `nullptr`. This is undefined behavior and reported as an error if compiling with `-fsanitize=null`. In this case, this results in a segmentation fault\n\n### Patches\nWe have patched the issue in da8558533d925694483d2c136a9220d6d49d843c and will release a patch release for all affected versions.\n\nWe recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.\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.",
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  "database_specific" : {
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}

CVSS V3

5.3 MODERATE

Attack Vector Network (AV:N)

Attack Complexity Low (AC:L)

Privileges Required None (PR:N)

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:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:L

Advisory Metadata

Source ID: GHSA-4g9f-63rx-5cw4
Schema Version: 1.4.0
Published: 2020-09-25 18:28
Last Modified: 2024-10-28 20:05
Database Record File: advisories/github-reviewed/2020/09/GHSA-4g9f-63rx-5cw4/GHSA-4g9f-63rx-5cw4.json