CVE-2022-23591: Stack overflow in Tensorflow
First published: Fri Feb 04 2022(Updated: )
### Impact The `GraphDef` format in TensorFlow does not allow self recursive functions. The runtime assumes that this invariant is satisfied. However, a `GraphDef` containing a fragment such as the following can be consumed when loading a `SavedModel`: ``` library { function { signature { name: "SomeOp" description: "Self recursive op" } node_def { name: "1" op: "SomeOp" } node_def { name: "2" op: "SomeOp" } } } ``` This would result in a stack overflow during execution as resolving each `NodeDef` means resolving the function itself and its nodes. ### Patches We have patched the issue in GitHub commit [448a16182065bd08a202d9057dd8ca541e67996c](https://github.com/tensorflow/tensorflow/commit/448a16182065bd08a202d9057dd8ca541e67996c). 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 |
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.
Reference Links
- https://github.com/tensorflow/tensorflow/commit/448a16182065bd08a202d9057dd8ca541e67996c
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-247x-2f9f-5wp7
- https://nvd.nist.gov/vuln/detail/CVE-2022-23591
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2022-100.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2022-155.yaml
- https://github.com/advisories/GHSA-247x-2f9f-5wp7 (Advisory)
Frequently Asked Questions
What is the severity of CVE-2022-23591?
CVE-2022-23591 is a high severity vulnerability due to its impact on TensorFlow's GraphDef format.
How do I fix CVE-2022-23591?
To fix CVE-2022-23591, upgrade to TensorFlow version 2.7.1 or later, or apply the appropriate patch.
What platforms are affected by CVE-2022-23591?
CVE-2022-23591 affects multiple versions of TensorFlow packaged for both GPU and CPU.
What type of vulnerability is CVE-2022-23591?
CVE-2022-23591 is categorized as a code execution vulnerability due to the improper handling of self-recursive functions.
What are the recommended versions to mitigate CVE-2022-23591?
The recommended versions to mitigate CVE-2022-23591 are TensorFlow 2.7.1, 2.6.3, or 2.5.3 depending on your current version.
- agent/weakness
- agent/type
- agent/remedy
- collector/mitre-cve
- source/MITRE
- agent/title
- agent/severity
- agent/first-publish-date
- collector/github-advisory-latest
- source/GitHub
- alias/GHSA-247x-2f9f-5wp7
- alias/CVE-2022-23591
- agent/software-canonical-lookup
- agent/last-modified-date
- agent/author
- agent/references
- agent/description
- agent/event
- agent/softwarecombine
- collector/github-advisory
- agent/trending
- agent/source
- agent/tags
- collector/nvd-cve
- source/NVD
- agent/software-canonical-lookup-request
- collector/nvd-index
- package-manager/pip
- vendor/google
- canonical/tensorflow keras
- version/tensorflow keras/2.5.2
- version/tensorflow keras/2.6.0
- version/tensorflow keras/2.6.2
- version/tensorflow keras/2.7.0