medium
5.5
CWE
369
Advisory Published
Advisory Published
Updated

CVE-2021-37691: Division by zero in LSH in TensorFlow Lite

First published: Thu Aug 12 2021(Updated: )

### Impact An attacker can craft a TFLite model that would trigger a division by zero error in LSH [implementation](https://github.com/tensorflow/tensorflow/blob/149562d49faa709ea80df1d99fc41d005b81082a/tensorflow/lite/kernels/lsh_projection.cc#L118). ```cc int RunningSignBit(const TfLiteTensor* input, const TfLiteTensor* weight, float seed) { int input_item_bytes = input->bytes / SizeOfDimension(input, 0); // ... } ``` There is no check that the first dimension of the input is non zero. ### Patches We have patched the issue in GitHub commit [0575b640091680cfb70f4dd93e70658de43b94f9](https://github.com/tensorflow/tensorflow/commit/0575b640091680cfb70f4dd93e70658de43b94f9). The fix will be included in TensorFlow 2.6.0. We will also cherrypick thiscommit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, 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 Yakun Zhang of Baidu Security.

Credit: security-advisories@github.com security-advisories@github.com

Affected SoftwareAffected VersionHow to fix
Google TensorFlow>=2.3.0<2.3.4
Google TensorFlow>=2.4.0<2.4.3
Google TensorFlow=2.5.0
Google TensorFlow=2.6.0-rc0
Google TensorFlow=2.6.0-rc1
Google TensorFlow=2.6.0-rc2
pip/tensorflow-gpu=2.5.0
2.5.1
pip/tensorflow-gpu>=2.4.0<2.4.3
2.4.3
pip/tensorflow-gpu<2.3.4
2.3.4
pip/tensorflow-cpu=2.5.0
2.5.1
pip/tensorflow-cpu>=2.4.0<2.4.3
2.4.3
pip/tensorflow-cpu<2.3.4
2.3.4
pip/tensorflow=2.5.0
2.5.1
pip/tensorflow>=2.4.0<2.4.3
2.4.3
pip/tensorflow<2.3.4
2.3.4

Remedy

https://github.com/tensorflow/tensorflow/commit/0575b640091680cfb70f4dd93e70658de43b94f9

Never miss a vulnerability like this again

Sign up to SecAlerts for real-time vulnerability data matched to your software, aggregated from hundreds of sources.

Read MoreStart Free Trial

Reference Links

Frequently Asked Questions

  • What is the severity of CVE-2021-37691?

    CVE-2021-37691 is considered a high severity vulnerability due to potential denial-of-service conditions.

  • How do I fix CVE-2021-37691?

    To fix CVE-2021-37691, you should update your Google TensorFlow installation to version 2.5.1 or later.

  • Which versions of TensorFlow are affected by CVE-2021-37691?

    CVE-2021-37691 affects TensorFlow versions 2.3.0 to 2.3.4, 2.4.0 to 2.4.3, and 2.5.0.

  • What type of attack can exploit CVE-2021-37691?

    An attacker can exploit CVE-2021-37691 by crafting a TFLite model that triggers a division by zero error.

  • Is CVE-2021-37691 relevant for TensorFlow Lite users?

    Yes, CVE-2021-37691 is particularly relevant for TensorFlow Lite users due to its impact on model execution.

Company

Resources

Contact

SecAlerts Pty Ltd.
132 Wickham Terrace
Fortitude Valley,
QLD 4006, Australia
info@secalerts.co
By using SecAlerts services, you agree to our services end-user license agreement. This website is safeguarded by reCAPTCHA and governed by the Google Privacy Policy and Terms of Service. All names, logos, and brands of products are owned by their respective owners, and any usage of these names, logos, and brands for identification purposes only does not imply endorsement. If you possess any content that requires removal, please get in touch with us.
© 2025 SecAlerts Pty Ltd.
ABN: 70 645 966 203, ACN: 645 966 203