CVE-2021-37686: Tensorflow Infinite Loop Vulnerability

MODERATE
Advisory Identifier: GHSA-mhhc-q96p-mfm9
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CVSS 5.5
CWE-835
Published
Advisory Summary

CVE-2021-37686 is a MODERATE (CVSS 5.5) infinite loop vulnerability (CWE-835: Loop with Unreachable Exit Condition ('Infinite Loop')) affecting Tensorflow libraries in the PyPI ecosystem. The issue involves infinite loop in tflite. Remediation is available by upgrading affected packages to version 2.6.0rc2.

CVE-2021-37686 at a Glance

CVE Identifier CVE-2021-37686
GHSA Identifier GHSA-mhhc-q96p-mfm9
Severity MODERATE
CVSS v3.1 Base Score 5.5
CWE Classification CWE-835: Loop with Unreachable Exit Condition ('Infinite Loop')
Affected Product Tensorflow
Package Ecosystem PyPI
Published Date
Last Updated

Security Impact

CVSS: 5.5

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:N/A:H).

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 None (I:N)
Availability High (A:H)

How to Fix CVE-2021-37686

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: 2.6.0rc2 
pip install tensorflow>=2.6.0rc2
tensorflow-cpu fixed: 2.6.0rc2 
pip install tensorflow-cpu>=2.6.0rc2
tensorflow-gpu fixed: 2.6.0rc2 
pip install tensorflow-gpu>=2.6.0rc2
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-2021-37686, 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 2.6.0rc2), 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-835: Loop with Unreachable Exit Condition ('Infinite Loop'), where the product contains an iteration or loop with an exit condition that cannot be reached, i.e., an infinite loop. 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 strided slice implementation in TFLite has a logic bug which can allow an attacker to trigger an infinite loop. This arises from newly introduced support for [ellipsis in axis definition](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/strided_slice.cc#L103-L122): ```cc for (int i = 0; i < effective_dims;) { if ((1 << i) & op_context->params->ellipsis_mask) { // ... int ellipsis_end_idx = std::min(i + 1 + num_add_axis + op_context->input_dims - begin_count, effective_dims); // ... for (; i < ellipsis_end_idx; ++i) { // ... } continue; } // ... ++i; } ``` An attacker can craft a model such that `ellipsis_end_idx` is smaller than `i` (e.g., always negative). In this case, the inner loop does not increase `i` and the `continue` statement causes execution to skip over the preincrement at the end of the outer loop. ### Patches We have patched the issue in GitHub commit [dfa22b348b70bb89d6d6ec0ff53973bacb4f4695](https://github.com/tensorflow/tensorflow/commit/dfa22b348b70bb89d6d6ec0ff53973bacb4f4695). The fix will be included in TensorFlow 2.6.0. This is the only affected version. ### 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-2021-37686 Affected Tensorflow Versions

3 component(s)
Fixed Versions:
2.6.0rc2
Product / Package Ecosystem Affected Versions Fixed Version
tensorflow
pypi/tensorflow
PyPI
≥ 2.6.0rc0
< 2.6.0rc2
2.6.0rc2
tensorflow-cpu
pypi/tensorflow-cpu
PyPI
≥ 2.6.0rc0
< 2.6.0rc2
2.6.0rc2
tensorflow-gpu
pypi/tensorflow-gpu
PyPI
≥ 2.6.0rc0
< 2.6.0rc2
2.6.0rc2

References

GitHub Advisory / Wiki ADVISORY
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-mhhc-q96p-mfm9
NVD — CVE-2021-37686 NVD
https://nvd.nist.gov/vuln/detail/CVE-2021-37686
Security Fix Commit FIX COMMIT
https://github.com/tensorflow/tensorflow/commit/dfa22b348b70bb89d6d6ec0ff53973bacb4f4695
Tensorflow Source Repository REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-599.yaml
Tensorflow Source Repository REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-797.yaml
Tensorflow Source Repository REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-308.yaml
Tensorflow Source Repository REPOSITORY
https://github.com/tensorflow/tensorflow
Tensorflow Release RELEASE
https://github.com/tensorflow/tensorflow/releases/tag/v2.3.4
Tensorflow Release RELEASE
https://github.com/tensorflow/tensorflow/releases/tag/v2.4.3
Tensorflow Release RELEASE
https://github.com/tensorflow/tensorflow/releases/tag/v2.5.1
Tensorflow Release RELEASE
https://github.com/tensorflow/tensorflow/releases/tag/v2.6.0

Frequently Asked Questions

What is CVE-2021-37686?
CVE-2021-37686 is a security vulnerability in Tensorflow involving Infinite loop in TFLite under CWE-835: Loop with Unreachable Exit Condition ('Infinite Loop').
Which Tensorflow versions are affected?
Affected versions include releases prior to 2.6.0rc2 across affected packages: tensorflow, tensorflow-cpu, tensorflow-gpu.
What version fixes CVE-2021-37686?
Upgrading to 2.6.0rc2 (or the latest patched release for your respective branch) resolves the vulnerability.
Is tensorflow affected?
Yes, tensorflow is affected in releases prior to 2.6.0rc2. Upgrade your dependency to 2.6.0rc2 or later.
Is tensorflow-cpu affected?
Yes, tensorflow-cpu is affected in releases prior to 2.6.0rc2. Upgrade your dependency to 2.6.0rc2 or later.
What is the CVSS score of CVE-2021-37686?
CVE-2021-37686 has a CVSS v3.1 base score of 5.5 (MODERATE) with vector string CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H.
What CWE is associated with CVE-2021-37686?
CVE-2021-37686 is classified under CWE-835: Loop with Unreachable Exit Condition ('Infinite Loop') (The product contains an iteration or loop with an exit condition that cannot be reached, i.e., an infinite loop.).
How can I check PyPI dependencies for CVE-2021-37686?
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-mhhc-q96p-mfm9",
  "modified" : "2024-11-13T21:20:55Z",
  "published" : "2021-08-25T14:39:58Z",
  "aliases" : [ "CVE-2021-37686" ],
  "summary" : "Infinite loop in TFLite",
  "details" : "### Impact\nThe strided slice implementation in TFLite has a logic bug which can allow an attacker to trigger an infinite loop. This arises from newly introduced support for [ellipsis in axis definition](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/strided_slice.cc#L103-L122):\n\n```cc\n  for (int i = 0; i < effective_dims;) {\n    if ((1 << i) & op_context->params->ellipsis_mask) {\n      // ...\n      int ellipsis_end_idx =\n          std::min(i + 1 + num_add_axis + op_context->input_dims - begin_count,\n                   effective_dims);\n      // ...\n      for (; i < ellipsis_end_idx; ++i) {\n        // ...\n      }\n      continue;\n    }\n    // ...\n    ++i;\n  }\n```\n\nAn attacker can craft a model such that `ellipsis_end_idx` is smaller than `i` (e.g., always negative). In this case, the inner loop does not increase `i` and the `continue` statement causes execution to skip over the preincrement at the end of the outer loop.\n\n### Patches\nWe have patched the issue in GitHub commit [dfa22b348b70bb89d6d6ec0ff53973bacb4f4695](https://github.com/tensorflow/tensorflow/commit/dfa22b348b70bb89d6d6ec0ff53973bacb4f4695).\n\nThe fix will be included in TensorFlow 2.6.0. This is the only affected version.\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:N/A:H"
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  "database_specific" : {
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}