PYSEC-2022-94
Vulnerability from pysec - Published: 2022-02-04 23:15 - Updated: 2022-03-09 00:17Tensorflow is an Open Source Machine Learning Framework. When decoding PNG images TensorFlow can produce a memory leak if the image is invalid. After calling png::CommonInitDecode(..., &decode), the decode value contains allocated buffers which can only be freed by calling png::CommonFreeDecode(&decode). However, several error case in the function implementation invoke the OP_REQUIRES macro which immediately terminates the execution of the function, without allowing for the memory free to occur. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
| Name | purl | tensorflow-cpu | pkg:pypi/tensorflow-cpu |
|---|
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-cpu",
"purl": "pkg:pypi/tensorflow-cpu"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "ab51e5b813573dc9f51efa335aebcf2994125ee9"
}
],
"repo": "https://github.com/tensorflow/tensorflow",
"type": "GIT"
},
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.5.3"
},
{
"introduced": "2.6.0"
},
{
"fixed": "2.6.3"
}
],
"type": "ECOSYSTEM"
}
],
"versions": [
"1.15.0",
"2.1.0",
"2.1.1",
"2.1.2",
"2.1.3",
"2.1.4",
"2.2.0",
"2.2.1",
"2.2.2",
"2.2.3",
"2.3.0",
"2.3.1",
"2.3.2",
"2.3.3",
"2.3.4",
"2.4.0",
"2.4.1",
"2.4.2",
"2.4.3",
"2.4.4",
"2.5.0",
"2.5.1",
"2.5.2",
"2.6.0",
"2.6.1",
"2.6.2"
]
}
],
"aliases": [
"CVE-2022-23585",
"GHSA-fq6p-6334-8gr4"
],
"details": "Tensorflow is an Open Source Machine Learning Framework. When decoding PNG images TensorFlow can produce a memory leak if the image is invalid. After calling `png::CommonInitDecode(..., \u0026decode)`, the `decode` value contains allocated buffers which can only be freed by calling `png::CommonFreeDecode(\u0026decode)`. However, several error case in the function implementation invoke the `OP_REQUIRES` macro which immediately terminates the execution of the function, without allowing for the memory free to occur. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.",
"id": "PYSEC-2022-94",
"modified": "2022-03-09T00:17:35.562344Z",
"published": "2022-02-04T23:15:00Z",
"references": [
{
"type": "ADVISORY",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-fq6p-6334-8gr4"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/blob/a1320ec1eac186da1d03f033109191f715b2b130/tensorflow/core/kernels/image/decode_image_op.cc#L322-L416"
},
{
"type": "FIX",
"url": "https://github.com/tensorflow/tensorflow/commit/ab51e5b813573dc9f51efa335aebcf2994125ee9"
}
]
}
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.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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.