medium
5.5
CWE
835
Advisory Published
Advisory Published
Updated

CVE-2021-37686: Infinite loop in TensorFlow Lite

First published: Thu Aug 12 2021(Updated: )

### 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.

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>=2.6.0rc0<2.6.0rc2
2.6.0rc2
pip/tensorflow-cpu>=2.6.0rc0<2.6.0rc2
2.6.0rc2
pip/tensorflow>=2.6.0rc0<2.6.0rc2
2.6.0rc2

Remedy

https://github.com/tensorflow/tensorflow/commit/dfa22b348b70bb89d6d6ec0ff53973bacb4f4695

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-2021-37686?

    CVE-2021-37686 is classified as a high-severity vulnerability due to its potential to cause an infinite loop.

  • How do I fix CVE-2021-37686?

    To fix CVE-2021-37686, update TensorFlow to version 2.6.0rc2 or later.

  • Which versions of TensorFlow are affected by CVE-2021-37686?

    CVE-2021-37686 affects TensorFlow versions between 2.3.0 and 2.4.3, as well as specific 2.6.0 release candidates.

  • What is the impact of CVE-2021-37686?

    CVE-2021-37686 can lead to denial of service by causing applications to hang in an infinite loop.

  • Is CVE-2021-37686 present in TensorFlow GPU installations?

    Yes, CVE-2021-37686 impacts both TensorFlow CPU and GPU installations that are running affected versions.

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