CVE-2021-41213 Deadlock in mutually recursive `tf.function` objects
An attacker can cause a denial of service by forcing the loading of models with mutually recursive functions, leading to a deadlock in TensorFlow.
- Exploited in the wild
- Not known
- Login needed
- Any signed-in user
- Attackable
- Local or same network only
- Victim action needed
- Not needed
- Fix
- Available
What is CVE-2021-41213?
CVE-2021-41213 is a medium-severity denial of service vulnerability in Google Tensorflow, published on 5 Nov 2021. It can only be exploited from the same machine or local network, with no action from the victim.
Official description
TensorFlow is an open source platform for machine learning. In affected versions the code behind `tf.function` API can be made to deadlock when two `tf.function` decorated Python functions are mutually recursive. This occurs due to using a non-reentrant `Lock` Python object. Loading any model which contains mutually recursive functions is vulnerable. An attacker can cause denial of service by causing users to load such models and calling a recursive `tf.function`, although this is not a frequent scenario. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
Which versions are affected by CVE-2021-41213?
22 open source packages ship an affected version, including tensorflow, tensorflow-cpu and tensorflow-gpu. It affects 1 product.
| Package | Affected | Fixed in |
|---|---|---|
tensorflow Bitnami | From 2.4.0 | 2.4.4, 2.5.2, 2.6.1 |
tensorflow PyPI | All versions | 2.4.4, 2.5.2, 2.6.1, 2.7.0 |
tensorflow PyPI | All versions | 2.4.4 |
tensorflow PyPI | All versions | 2.4.4 |
tensorflow PyPI | From 2.5.0 | 2.5.2 |
tensorflow PyPI | From 2.5.0 | 2.5.2 |
16 more packages
tensorflow PyPI | From 2.6.0 | 2.6.1 |
tensorflow PyPI | From 2.6.0 | 2.6.1 |
tensorflow-cpu PyPI | All versions | 2.4.4, 2.5.2, 2.6.1, 2.7.0 |
tensorflow-cpu PyPI | All versions | 2.4.4 |
tensorflow-cpu PyPI | All versions | 2.4.4 |
tensorflow-cpu PyPI | From 2.5.0 | 2.5.2 |
tensorflow-cpu PyPI | From 2.5.0 | 2.5.2 |
tensorflow-cpu PyPI | From 2.6.0 | 2.6.1 |
tensorflow-cpu PyPI | From 2.6.0 | 2.6.1 |
tensorflow-gpu PyPI | All versions | 2.4.4, 2.5.2, 2.6.1, 2.7.0 |
tensorflow-gpu PyPI | All versions | 2.4.4 |
tensorflow-gpu PyPI | All versions | 2.4.4 |
tensorflow-gpu PyPI | From 2.5.0 | 2.5.2 |
tensorflow-gpu PyPI | From 2.5.0 | 2.5.2 |
tensorflow-gpu PyPI | From 2.6.0 | 2.6.1 |
tensorflow-gpu PyPI | From 2.6.0 | 2.6.1 |
Products include Google Tensorflow.
How do I fix CVE-2021-41213?
Upgrade tensorflow to a fixed version for your release line: 2.4.4, 2.5.2, 2.6.1 or 2.7.0.
Upgrade, then put limits on request size, rate and nesting depth so one client cannot exhaust the service. More about Denial of service.
Is CVE-2021-41213 being exploited?
There is no known exploitation of CVE-2021-41213 in the wild and no public exploit code.
Other vulnerabilities in Google Tensorflow
- CVE-2023-25668 TensorFlow vulnerable to heap out-of-buffer read in the QuantizeAndDequantize operation
- CVE-2018-7575 Integer Overflow or Wraparound in Google TensorFlow
- CVE-2021-37678 Arbitrary code execution due to YAML deserialization
- CVE-2021-35958 Path traversal in n/a
- CVE-2020-15202 Integer truncation in Shard API usage
Sources
Also known as BIT-tensorflow-2021-41213, GHSA-h67m-xg8f-fxcf, PYSEC-2021-405, PYSEC-2021-622, PYSEC-2021-820.