PYSEC-2020-328
Vulnerability from pysec - Published: 2020-09-25 19:15 - Updated: 2021-12-09 06:35In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. 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 limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, 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.
| Name | purl | tensorflow-gpu | pkg:pypi/tensorflow-gpu |
|---|
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu",
"purl": "pkg:pypi/tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "204945b19e44b57906c9344c0d00120eeeae178a"
}
],
"repo": "https://github.com/tensorflow/tensorflow",
"type": "GIT"
},
{
"events": [
{
"introduced": "2.2.0"
},
{
"fixed": "2.2.1"
},
{
"introduced": "2.3.0"
},
{
"fixed": "2.3.1"
}
],
"type": "ECOSYSTEM"
}
],
"versions": [
"2.2.0",
"2.3.0"
]
}
],
"aliases": [
"CVE-2020-15213",
"GHSA-hjmq-236j-8m87"
],
"details": "In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. 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 limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, 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": "PYSEC-2020-328",
"modified": "2021-12-09T06:35:15.598802Z",
"published": "2020-09-25T19:15:00Z",
"references": [
{
"type": "ADVISORY",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hjmq-236j-8m87"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1"
},
{
"type": "FIX",
"url": "https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a"
}
]
}
Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or observed by the user.
- Confirmed: The vulnerability has been validated from an analyst's perspective.
- Published Proof of Concept: A public proof of concept is available for this vulnerability.
- Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
- Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
- Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
- Not confirmed: The user expressed doubt about the validity of the vulnerability.
- Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.