PYSEC-2021-394
Vulnerability from pysec - Published: 2021-11-05 20:15 - Updated: 2021-11-13 06:52
VLAI
Details
TensorFlow is an open source platform for machine learning. In affeced versions during execution, EinsumHelper::ParseEquation() is supposed to set the flags in input_has_ellipsis vector and *output_has_ellipsis boolean to indicate whether there is ellipsis in the corresponding inputs and output. However, the code only changes these flags to true and never assigns false. This results in unitialized variable access if callers assume that EinsumHelper::ParseEquation() always sets these flags. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.
Impacted products
| Name | purl | tensorflow | pkg:pypi/tensorflow |
|---|
Aliases
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow",
"purl": "pkg:pypi/tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "f09caa532b6e1ac8d2aa61b7832c78c5b79300c6"
}
],
"repo": "https://github.com/tensorflow/tensorflow",
"type": "GIT"
},
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.4.4"
},
{
"introduced": "2.5.0"
},
{
"fixed": "2.5.2"
},
{
"introduced": "2.6.0"
},
{
"fixed": "2.6.1"
},
{
"introduced": "2.7.0rc0"
},
{
"fixed": "2.7.0"
}
],
"type": "ECOSYSTEM"
}
],
"versions": [
"0.12.0",
"0.12.1",
"1.0.0",
"1.0.1",
"1.1.0",
"1.10.0",
"1.10.1",
"1.11.0",
"1.12.0",
"1.12.2",
"1.12.3",
"1.13.1",
"1.13.2",
"1.14.0",
"1.15.0",
"1.15.2",
"1.15.3",
"1.15.4",
"1.15.5",
"1.2.0",
"1.2.1",
"1.3.0",
"1.4.0",
"1.4.1",
"1.5.0",
"1.5.1",
"1.6.0",
"1.7.0",
"1.7.1",
"1.8.0",
"1.9.0",
"2.0.0",
"2.0.1",
"2.0.2",
"2.0.3",
"2.0.4",
"2.1.0",
"2.1.1",
"2.1.2",
"2.1.3",
"2.1.4",
"2.2.0",
"2.2.0rc0",
"2.2.0rc1",
"2.2.0rc2",
"2.2.0rc3",
"2.2.0rc4",
"2.2.1",
"2.2.2",
"2.2.3",
"2.3.0",
"2.3.0rc0",
"2.3.0rc1",
"2.3.0rc2",
"2.3.1",
"2.3.2",
"2.3.3",
"2.3.4",
"2.4.0",
"2.4.0rc0",
"2.4.0rc1",
"2.4.0rc2",
"2.4.0rc3",
"2.4.0rc4",
"2.4.1",
"2.4.2",
"2.4.3",
"2.5.0",
"2.5.1",
"2.6.0",
"2.7.0rc0",
"2.7.0rc1"
]
}
],
"aliases": [
"CVE-2021-41201",
"GHSA-j86v-p27c-73fm"
],
"details": "TensorFlow is an open source platform for machine learning. In affeced versions during execution, `EinsumHelper::ParseEquation()` is supposed to set the flags in `input_has_ellipsis` vector and `*output_has_ellipsis` boolean to indicate whether there is ellipsis in the corresponding inputs and output. However, the code only changes these flags to `true` and never assigns `false`. This results in unitialized variable access if callers assume that `EinsumHelper::ParseEquation()` always sets these flags. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.",
"id": "PYSEC-2021-394",
"modified": "2021-11-13T06:52:42.499515Z",
"published": "2021-11-05T20:15:00Z",
"references": [
{
"type": "FIX",
"url": "https://github.com/tensorflow/tensorflow/commit/f09caa532b6e1ac8d2aa61b7832c78c5b79300c6"
},
{
"type": "ADVISORY",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j86v-p27c-73fm"
}
]
}
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Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.
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.
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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.
They have not been verified by an analyst and are provided for guidance only.
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.
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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Related by attack behaviour
Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.
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