CVE-2022-23578: Memory leak in Tensorflow
First published: Fri Feb 04 2022(Updated: )
### Impact If a graph node is invalid, TensorFlow can leak memory in the [implementation of `ImmutableExecutorState::Initialize`](https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/common_runtime/immutable_executor_state.cc#L84-L262): ```cc Status s = params_.create_kernel(n->properties(), &item->kernel); if (!s.ok()) { item->kernel = nullptr; s = AttachDef(s, *n); return s; } ``` Here, we set `item->kernel` to `nullptr` but it is a simple `OpKernel*` pointer so the memory that was previously allocated to it would leak. ### Patches We have patched the issue in GitHub commit [c79ccba517dbb1a0ccb9b01ee3bd2a63748b60dd](https://github.com/tensorflow/tensorflow/commit/c79ccba517dbb1a0ccb9b01ee3bd2a63748b60dd). The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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.
Credit: security-advisories@github.com security-advisories@github.com
| Affected Software | Affected Version | How to fix |
|---|---|---|
| pip/tensorflow-gpu | =2.7.0 | 2.7.1 |
| pip/tensorflow-gpu | >=2.6.0<2.6.3 | 2.6.3 |
| pip/tensorflow-gpu | <2.5.3 | 2.5.3 |
| pip/tensorflow-cpu | =2.7.0 | 2.7.1 |
| pip/tensorflow-cpu | >=2.6.0<2.6.3 | 2.6.3 |
| pip/tensorflow-cpu | <2.5.3 | 2.5.3 |
| pip/tensorflow | =2.7.0 | 2.7.1 |
| pip/tensorflow | >=2.6.0<2.6.3 | 2.6.3 |
| pip/tensorflow | <2.5.3 | 2.5.3 |
| TensorFlow Keras | <=2.5.2 | |
| TensorFlow Keras | >=2.6.0<=2.6.2 | |
| TensorFlow Keras | =2.7.0 |
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Reference Links
- https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/common_runtime/immutable_executor_state.cc#L84-L262
- https://github.com/tensorflow/tensorflow/commit/c79ccba517dbb1a0ccb9b01ee3bd2a63748b60dd
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8r7c-3cm2-3h8f
- https://nvd.nist.gov/vuln/detail/CVE-2022-23578
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2022-87.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2022-142.yaml
- https://github.com/advisories/GHSA-8r7c-3cm2-3h8f (Advisory)
Frequently Asked Questions
What is the severity of CVE-2022-23578?
CVE-2022-23578 has a severity rating categorized as medium.
How do I fix CVE-2022-23578?
To fix CVE-2022-23578, upgrade TensorFlow to version 2.7.1 or later.
Which versions of TensorFlow are affected by CVE-2022-23578?
CVE-2022-23578 affects TensorFlow versions prior to 2.7.1, including 2.5.2, 2.6.0, and 2.6.2.
What type of vulnerability is CVE-2022-23578?
CVE-2022-23578 is a memory leak vulnerability that can occur when a graph node is invalid.
Is CVE-2022-23578 relevant for both CPU and GPU versions of TensorFlow?
Yes, CVE-2022-23578 impacts both the CPU and GPU versions of TensorFlow.
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