PYSEC-2020-133
Vulnerability from pysec - Published: 2020-09-25 19:15 - Updated: 2020-10-29 16:15
VLAI
Details
In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption. We have patched the issue in d58c96946b and will release patch releases for all versions between 1.15 and 2.3. We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
Impacted products
| Name | purl | tensorflow | pkg:pypi/tensorflow |
|---|
Aliases
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow",
"purl": "pkg:pypi/tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "d58c96946b2880991d63d1dacacb32f0a4dfa453"
}
],
"repo": "https://github.com/tensorflow/tensorflow",
"type": "GIT"
},
{
"events": [
{
"introduced": "0"
},
{
"fixed": "1.15.4"
},
{
"introduced": "2.0.0"
},
{
"fixed": "2.0.3"
},
{
"introduced": "2.1.0"
},
{
"fixed": "2.1.2"
},
{
"introduced": "2.2.0"
},
{
"fixed": "2.2.1"
},
{
"introduced": "2.3.0"
},
{
"fixed": "2.3.1"
}
],
"type": "ECOSYSTEM"
}
],
"versions": [
"0.12.0rc0",
"0.12.0rc1",
"0.12.0",
"0.12.1",
"1.0.0",
"1.0.1",
"1.1.0rc0",
"1.1.0rc1",
"1.1.0rc2",
"1.1.0",
"1.2.0rc0",
"1.2.0rc1",
"1.2.0rc2",
"1.2.0",
"1.2.1",
"1.3.0rc0",
"1.3.0rc1",
"1.3.0rc2",
"1.3.0",
"1.4.0rc0",
"1.4.0rc1",
"1.4.0",
"1.4.1",
"1.5.0rc0",
"1.5.0rc1",
"1.5.0",
"1.5.1",
"1.6.0rc0",
"1.6.0rc1",
"1.6.0",
"1.7.0rc0",
"1.7.0rc1",
"1.7.0",
"1.7.1",
"1.8.0rc0",
"1.8.0rc1",
"1.8.0",
"1.9.0rc0",
"1.9.0rc1",
"1.9.0rc2",
"1.9.0",
"1.10.0rc0",
"1.10.0rc1",
"1.10.0",
"1.10.1",
"1.11.0rc0",
"1.11.0rc1",
"1.11.0rc2",
"1.11.0",
"1.12.0rc0",
"1.12.0rc1",
"1.12.0rc2",
"1.12.0",
"1.12.2",
"1.12.3",
"1.13.0rc0",
"1.13.0rc1",
"1.13.0rc2",
"1.13.1",
"1.13.2",
"1.14.0rc0",
"1.14.0rc1",
"1.14.0",
"1.15.0rc0",
"1.15.0rc1",
"1.15.0rc2",
"1.15.0rc3",
"1.15.0",
"1.15.2",
"1.15.3",
"2.0.0",
"2.0.1",
"2.0.2",
"2.1.0",
"2.1.1",
"2.2.0",
"2.3.0"
]
}
],
"aliases": [
"CVE-2020-15210",
"GHSA-x9j7-x98r-r4w2"
],
"details": "In tensorflow-lite before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, if a TFLite saved model uses the same tensor as both input and output of an operator, then, depending on the operator, we can observe a segmentation fault or just memory corruption. We have patched the issue in d58c96946b and will release patch releases for all versions between 1.15 and 2.3. We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.",
"id": "PYSEC-2020-133",
"modified": "2020-10-29T16:15:00Z",
"published": "2020-09-25T19:15:00Z",
"references": [
{
"type": "FIX",
"url": "https://github.com/tensorflow/tensorflow/commit/d58c96946b2880991d63d1dacacb32f0a4dfa453"
},
{
"type": "ADVISORY",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x9j7-x98r-r4w2"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1"
},
{
"type": "WEB",
"url": "http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html"
}
]
}
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Sightings
| Author | Source | Type | Date | Other |
|---|
Nomenclature
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The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
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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.
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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.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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