CVE-2020-15199: Tensorflow Denial of Service Vulnerability

HIGH
Advisory Identifier: GHSA-x5cp-9pcf-pp3h
CVSS 5.9
CWE-20
Published
Advisory Summary

CVE-2020-15199 is a HIGH (CVSS 5.9) denial of service vulnerability (CWE-20: Improper Input Validation) affecting Tensorflow libraries in the PyPI ecosystem. The issue involves denial of service in tensorflow. Remediation is available by upgrading affected packages to version 2.3.1.

CVE-2020-15199 at a Glance

CVE Identifier CVE-2020-15199
GHSA Identifier GHSA-x5cp-9pcf-pp3h
Severity HIGH
CVSS v3.1 Base Score 5.9
CWE Classification CWE-20: Improper Input Validation
Affected Product Tensorflow
Package Ecosystem PyPI
Published Date
Last Updated

Security Impact

CVSS: 5.9

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

Attack Vector Network (AV:N)
Attack Complexity High (AC:H)
Privileges Required None (PR:N)
User Interaction None (UI:N)
Scope Unchanged (S:U)
Confidentiality None (C:N)
Integrity None (I:N)
Availability High (A:H)

How to Fix CVE-2020-15199

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: 2.3.1
pip install tensorflow>=2.3.1
tensorflow-cpu fixed: 2.3.1
pip install tensorflow-cpu>=2.3.1
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-2020-15199, 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 2.3.1), 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 `RaggedCountSparseOutput` does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the `splits` tensor has the minimum required number of elements. Code uses this quantity to initialize a different data structure: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/count_ops.cc#L241-L244 Since `BatchedMap` is equivalent to a vector, it needs to have at least one element to not be `nullptr`. If user passes a `splits` tensor that is empty or has exactly one element, we get a `SIGABRT` signal raised by the operating system. ### Patches We have patched the issue in 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and will release a patch release. We recommend users to upgrade to TensorFlow 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 is a variant of [GHSA-p5f8-gfw5-33w4](https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p5f8-gfw5-33w4)

CVE-2020-15199 Affected Tensorflow Versions

3 component(s)
Fixed Versions:
2.3.1
Product / Package Ecosystem Affected Versions Fixed Version
tensorflow
PyPI
≥ 2.3.0
< 2.3.1
All 1 versions
2.3.0
2.3.1
tensorflow-cpu
PyPI
≥ 2.3.0
< 2.3.1
All 1 versions
2.3.0
2.3.1
tensorflow-gpu
PyPI
≥ 2.3.0
< 2.3.1
All 1 versions
2.3.0
2.3.1

References

Frequently Asked Questions

What is CVE-2020-15199?
CVE-2020-15199 is a security vulnerability in Tensorflow involving Denial of Service in Tensorflow under CWE-20: Improper Input Validation.
Which Tensorflow versions are affected?
Affected versions include releases prior to 2.3.1 across affected packages: tensorflow, tensorflow-cpu, tensorflow-gpu.
What version fixes CVE-2020-15199?
Upgrading to 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 2.3.1. Upgrade your dependency to 2.3.1 or later.
Is tensorflow-cpu affected?
Yes, tensorflow-cpu is affected in releases prior to 2.3.1. Upgrade your dependency to 2.3.1 or later.
What is the CVSS score of CVE-2020-15199?
CVE-2020-15199 has a CVSS v3.1 base score of 5.9 (HIGH) with vector string CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:H.
What CWE is associated with CVE-2020-15199?
CVE-2020-15199 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-2020-15199?
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
{
  "schema_version" : "1.4.0",
  "id" : "GHSA-x5cp-9pcf-pp3h",
  "modified" : "2024-10-28T14:51:46Z",
  "published" : "2020-09-25T18:28:24Z",
  "aliases" : [ "CVE-2020-15199" ],
  "summary" : "Denial of Service in Tensorflow",
  "details" : "### Impact\nThe `RaggedCountSparseOutput` does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the `splits` tensor has the minimum required number of elements. Code uses this quantity to initialize a different data structure:\nhttps://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/count_ops.cc#L241-L244\n\nSince `BatchedMap` is equivalent to a vector, it needs to have at least one element to not be `nullptr`. If user passes a `splits` tensor that is empty or has exactly one element, we get a `SIGABRT` signal raised by the operating system.\n\n### Patches\nWe have patched the issue in 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and will release a patch release.\n\nWe recommend users to upgrade to TensorFlow 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 is a variant of [GHSA-p5f8-gfw5-33w4](https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p5f8-gfw5-33w4)",
  "severity" : [ {
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  "references" : [ {
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    "url" : "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x5cp-9pcf-pp3h"
  }, {
    "type" : "ADVISORY",
    "url" : "https://nvd.nist.gov/vuln/detail/CVE-2020-15199"
  }, {
    "type" : "WEB",
    "url" : "https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02"
  }, {
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    "url" : "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-279.yaml"
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  }, {
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  "database_specific" : {
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    "severity" : "HIGH",
    "github_reviewed" : true,
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    "nvd_published_at" : "2020-09-25T19:15:00Z"
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}