gsd-2022-23594
Vulnerability from gsd
Modified
2023-12-13 01:19
Details
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.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2022-23594",
"description": "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.",
"id": "GSD-2022-23594",
"references": [
"https://www.suse.com/security/cve/CVE-2022-23594.html"
]
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
"aliases": [
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],
"details": "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.",
"id": "GSD-2022-23594",
"modified": "2023-12-13T01:19:34.924682Z",
"schema_version": "1.4.0"
}
},
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"TITLE": "Out of bounds read in Tensorflow"
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}
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"scope": "CHANGED",
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"CWE-787",
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"date": "2022-02-11",
"description": "Tensorflow is an Open Source Machine Learning Framework. The TFG dialect of TensorFlow 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.",
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"not_impacted": "All versions before 2.7.0, all versions after 2.7.0",
"package_slug": "pypi/tensorflow-cpu",
"pubdate": "2022-02-09",
"solution": "Upgrade to version 2.7.1 or above.",
"title": "Out-of-bounds Write",
"urls": [
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"CWE-787",
"CWE-937"
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"description": "Tensorflow is an Open Source Machine Learning Framework. The TFG dialect of TensorFlow 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.",
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],
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"package_slug": "pypi/tensorflow-gpu",
"pubdate": "2022-02-09",
"solution": "Upgrade to version 2.7.1 or above.",
"title": "Out-of-bounds Write",
"urls": [
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"description": "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.",
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],
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"package_slug": "pypi/tensorflow",
"pubdate": "2022-02-04",
"solution": "Upgrade to version 2.7.1 or above.",
"title": "Out-of-bounds Read",
"urls": [
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"https://github.com/tensorflow/tensorflow/tree/274df9b02330b790aa8de1cee164b70f72b9b244/tensorflow/core/ir/importexport",
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"impact": {
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},
"lastModifiedDate": "2022-02-10T18:15Z",
"publishedDate": "2022-02-04T23:15Z"
}
}
}
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Sightings
| Author | Source | Type | Date |
|---|
Nomenclature
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- Not confirmed: The user expresses doubt about the veracity of the vulnerability.
- Not patched: This vulnerability was not successfully patched by the user reporting the sighting.