CVE-2022-23594
Out of bounds read in Tensorflow
Tensorflow is an Open Source Machine Learning Framework. The TFG dialect of TensorFlow (MLIR) makes several assumptions about the incoming GraphDef before converting it to the MLIR-based dialect. If an attacker changes the SavedModel format on disk to invalidate these assumptions and the GraphDef is then converted to MLIR-based IR then they can cause a crash in the Python interpreter. Under certain scenarios, heap OOB read/writes are possible. These issues have been discovered via fuzzing and it is possible that more weaknesses exist. We will patch them as they are discovered.
Attack profile
How easy it is to exploit
- Attack vector
- Local
- Complexity
- Low
- Privileges needed
- Low
- User interaction
- None
What an attacker can affect
- Confidentiality
- High
- Integrity
- High
- Availability
- High
Exploit likelihood
What each source reports
CVE List Score used
Out of bounds read in Tensorflow
Tensorflow is an Open Source Machine Learning Framework. The TFG dialect of TensorFlow (MLIR) makes several assumptions about the incoming GraphDef before converting it to the MLIR-based dialect. If an attacker changes the SavedModel format on disk to invalidate these assumptions and the GraphDef is then converted to MLIR-based IR then they can cause a crash in the Python interpreter. Under certain scenarios, heap OOB read/writes are possible. These issues have been discovered via fuzzing and it is possible that more weaknesses exist. We will patch them as they are discovered.
- Record
CVE-2022-23594- CVSS 3.1
- 8.8 High
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H - Weakness
- CWE-125
- Updated
- 2026-11-02
EUVDEUVD-2022-0947
Tensorflow is an Open Source Machine Learning Framework. The TFG dialect of TensorFlow (MLIR) makes several assumptions about the incoming GraphDef before converting it to the MLIR-based dialect. If an attacker changes the SavedModel format on disk to invalidate these assumptions and the GraphDef is then converted to MLIR-based IR then they can cause a crash in the Python interpreter. Under certain scenarios, heap OOB read/writes are possible. These issues have been discovered via fuzzing and it is possible that more weaknesses exist. We will patch them as they are discovered.
- Record
EUVD-2022-0947- CVSS 3.1
- 8.8 High
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H - Updated
- 2026-11-02
GitHub AdvisoriesGHSA-9x52-887g-fhc2
Out of bounds read in Tensorflow
Impact
The TFG dialect of TensorFlow (MLIR) makes several assumptions about the incoming GraphDef before converting it to the MLIR-based dialect.
If an attacker changes the SavedModel format on disk to invalidate these assumptions and the GraphDef is then converted to MLIR-based IR then they can cause a crash in the Python interpreter. Under certain scenarios, heap OOB read/writes are possible.
These issues have been discovered via fuzzing and it is possible that more weaknesses exist. We will patch them as they are discovered.
Patches
We have patched the issue in multiple GitHub commits and these will be included in TensorFlow 2.8.0 and TensorFlow 2.7.1, as both are affected.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
- Record
GHSA-9x52-887g-fhc2- CVSS 3.1
- 8.8 High
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H - Weakness
- CWE-125
- Updated
- 2026-11-02
NVD
Tensorflow is an Open Source Machine Learning Framework. The TFG dialect of TensorFlow (MLIR) makes several assumptions about the incoming GraphDef before converting it to the MLIR-based dialect. If an attacker changes the SavedModel format on disk to invalidate these assumptions and the GraphDef is then converted to MLIR-based IR then they can cause a crash in the Python interpreter. Under certain scenarios, heap OOB read/writes are possible. These issues have been discovered via fuzzing and it is possible that more weaknesses exist. We will patch them as they are discovered.
- Record
CVE-2022-23594- CVSS 3.1
- 8.8 High
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H - CVSS 3.1
- 5.5 Medium
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H - CVSS 2.0
- 2.1 Low
AV:L/AC:L/Au:N/C:N/I:N/A:P - Weakness
- CWE-125, CWE-787
- Updated
- 2026-11-02
OSV
Out of bounds read in Tensorflow
Tensorflow is an Open Source Machine Learning Framework. The TFG dialect of TensorFlow (MLIR) makes several assumptions about the incoming GraphDef before converting it to the MLIR-based dialect. If an attacker changes the SavedModel format on disk to invalidate these assumptions and the GraphDef is then converted to MLIR-based IR then they can cause a crash in the Python interpreter. Under certain scenarios, heap OOB read/writes are possible. These issues have been discovered via fuzzing and it is possible that more weaknesses exist. We will patch them as they are discovered.
- Record
CVE-2022-23594- CVSS 3.1
- 8.8 High
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H - Weakness
- CWE-125
- Updated
- 2026-11-02
OSVGHSA-9x52-887g-fhc2
Out of bounds read in Tensorflow
Impact
The TFG dialect of TensorFlow (MLIR) makes several assumptions about the incoming GraphDef before converting it to the MLIR-based dialect.
If an attacker changes the SavedModel format on disk to invalidate these assumptions and the GraphDef is then converted to MLIR-based IR then they can cause a crash in the Python interpreter. Under certain scenarios, heap OOB read/writes are possible.
These issues have been discovered via fuzzing and it is possible that more weaknesses exist. We will patch them as they are discovered.
Patches
We have patched the issue in multiple GitHub commits and these will be included in TensorFlow 2.8.0 and TensorFlow 2.7.1, as both are affected.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
- Record
GHSA-9x52-887g-fhc2- CVSS 3.1
- 8.8 High
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H - Weakness
- CWE-125
- Updated
- 2026-11-02
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OSVPYSEC-2026-3157
Out of bounds read in Tensorflow
Impact
The TFG dialect of TensorFlow (MLIR) makes several assumptions about the incoming GraphDef before converting it to the MLIR-based dialect.
If an attacker changes the SavedModel format on disk to invalidate these assumptions and the GraphDef is then converted to MLIR-based IR then they can cause a crash in the Python interpreter. Under certain scenarios, heap OOB read/writes are possible.
These issues have been discovered via fuzzing and it is possible that more weaknesses exist. We will patch them as they are discovered.
Patches
We have patched the issue in multiple GitHub commits and these will be included in TensorFlow 2.8.0 and TensorFlow 2.7.1, as both are affected.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
- Record
PYSEC-2026-3157- CVSS 3.1
- 8.8 High
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H - Updated
- 2026-11-02
OSVPYSEC-2026-3309
Out of bounds read in Tensorflow
Impact
The TFG dialect of TensorFlow (MLIR) makes several assumptions about the incoming GraphDef before converting it to the MLIR-based dialect.
If an attacker changes the SavedModel format on disk to invalidate these assumptions and the GraphDef is then converted to MLIR-based IR then they can cause a crash in the Python interpreter. Under certain scenarios, heap OOB read/writes are possible.
These issues have been discovered via fuzzing and it is possible that more weaknesses exist. We will patch them as they are discovered.
Patches
We have patched the issue in multiple GitHub commits and these will be included in TensorFlow 2.8.0 and TensorFlow 2.7.1, as both are affected.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
- Record
PYSEC-2026-3309- CVSS 3.1
- 8.8 High
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H - Updated
- 2026-11-02
OSVPYSEC-2026-752
Out of bounds read in Tensorflow
Impact
The TFG dialect of TensorFlow (MLIR) makes several assumptions about the incoming GraphDef before converting it to the MLIR-based dialect.
If an attacker changes the SavedModel format on disk to invalidate these assumptions and the GraphDef is then converted to MLIR-based IR then they can cause a crash in the Python interpreter. Under certain scenarios, heap OOB read/writes are possible.
These issues have been discovered via fuzzing and it is possible that more weaknesses exist. We will patch them as they are discovered.
Patches
We have patched the issue in multiple GitHub commits and these will be included in TensorFlow 2.8.0 and TensorFlow 2.7.1, as both are affected.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
- Record
PYSEC-2026-752- CVSS 3.1
- 8.8 High
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:C/C:H/I:H/A:H - Updated
- 2026-11-02
OSVBIT-tensorflow-2022-23594 No score
Out of bounds read in Tensorflow
Tensorflow is an Open Source Machine Learning Framework. The TFG dialect of TensorFlow (MLIR) makes several assumptions about the incoming GraphDef before converting it to the MLIR-based dialect. If an attacker changes the SavedModel format on disk to invalidate these assumptions and the GraphDef is then converted to MLIR-based IR then they can cause a crash in the Python interpreter. Under certain scenarios, heap OOB read/writes are possible. These issues have been discovered via fuzzing and it is possible that more weaknesses exist. We will patch them as they are discovered.
- Record
BIT-tensorflow-2022-23594- Updated
- 2026-11-02
OSVopenSUSE-SU-2024:12116-1 No score
tensorflow-lite-2.9.1-1.1 on GA media
These are all security issues fixed in the tensorflow-lite-2.9.1-1.1 package on the GA media of openSUSE Tumbleweed.
- Record
openSUSE-SU-2024:12116-1- Updated
- 2026-11-02
Affected products
Affected packages
| Package | Affected | Fixed |
|---|---|---|
tensorflowPyPI |
2.7.0 to 2.7.0 | 2.7.1 |
tensorflow-cpuPyPI |
2.7.0 to 2.7.0 | 2.7.1 |
tensorflow-gpuPyPI |
2.7.0 to 2.7.0 | 2.7.1 |
tensorflowBitnami |
2.7.0 up to 2.7.1 | 2.7.1 |
tensorflowPyPI |
2.7.0 up to 2.7.1 | 2.7.1 |
tensorflow-cpuPyPI |
2.7.0 up to 2.7.1 | 2.7.1 |
tensorflow-gpuPyPI |
2.7.0 up to 2.7.1 | 2.7.1 |
History
| Recorded | Changed | Sources |
|---|---|---|
| facts | ||
| first indexed | CVE List, EUVD, GitHub Advisories, NVD, OSV |
Dates are when Every CVE recorded each change. Upstream dates may differ.
In short
CVE-2022-23594 is a high vulnerability (CVSS 8.8) in Google Tensorflow, published February 4, 2022. No source lists public exploit code. EPSS puts the chance of exploitation in the next 30 days at 0.14%, the 3rd percentile. Fixed in tensorflow 2.7.1.
References
github.com/tensorflow/tensorflow/security/advisories/GHSA-9x52-887g-fhc2
nvd.nist.gov/vuln/detail/CVE-2022-23594
Show 166 moreShow less
pypi.org/project/tensorflow
suse.com/security/cve/CVE-2020-10531