gsd-2020-15212
Vulnerability from gsd
Modified
2023-12-13 01:21
Details
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_index` and then write to outside of `output_data` buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2020-15212",
"description": "In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_index` and then write to outside of `output_data` buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.",
"id": "GSD-2020-15212"
},
"gsd": {
"metadata": {
"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
"osvSchema": {
"aliases": [
"CVE-2020-15212"
],
"details": "In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_index` and then write to outside of `output_data` buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.",
"id": "GSD-2020-15212",
"modified": "2023-12-13T01:21:43.431939Z",
"schema_version": "1.4.0"
}
},
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"ID": "CVE-2020-15212",
"STATE": "PUBLIC",
"TITLE": "Out of bounds access in tensorflow-lite"
},
"affects": {
"vendor": {
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"baseScore": 8.1,
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"scope": "CHANGED",
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"description": "In TensorFlow Lite, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_index` and then write to outside `output_data` buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits.",
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"package_slug": "pypi/tensorflow-cpu",
"pubdate": "2020-09-25",
"solution": "Upgrade to versions 2.2.1, 2.3.1 or above.",
"title": "Out-of-bounds Write",
"urls": [
"https://nvd.nist.gov/vuln/detail/CVE-2020-15212"
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"uuid": "a683bcb2-dfb1-464d-a2b8-40f91a9303a0"
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"solution": "Upgrade to versions 2.2.1, 2.3.1 or above.",
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"date": "2021-08-17",
"description": "In TensorFlow Lite, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_index` and then write to outside `output_data` buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits.",
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},
"lastModifiedDate": "2021-08-17T13:21Z",
"publishedDate": "2020-09-25T19:15Z"
}
}
}
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Sightings
| Author | Source | Type | Date |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or seen somewhere by the user.
- Confirmed: The vulnerability is confirmed from an analyst perspective.
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- Patched: This vulnerability was successfully patched by the user reporting the sighting.
- Not exploited: This vulnerability was not exploited or seen by the user reporting the sighting.
- 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.