PYSEC-2020-281
Vulnerability from pysec - Published: 2020-09-25 19:15 - Updated: 2021-12-09 06:34In Tensorflow before version 2.3.1, the RaggedCountSparseOutput implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the splits tensor generate a valid partitioning of the values tensor. Hence, the code is prone to heap buffer overflow. If split_values does not end with a value at least num_values then the while loop condition will trigger a read outside of the bounds of split_values once batch_idx grows too large. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.
| 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": "3cbb917b4714766030b28eba9fb41bb97ce9ee02"
}
],
"repo": "https://github.com/tensorflow/tensorflow",
"type": "GIT"
},
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.3.1"
}
],
"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"
]
}
],
"aliases": [
"CVE-2020-15201",
"GHSA-p5f8-gfw5-33w4"
],
"details": "In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the `splits` tensor generate a valid partitioning of the `values` tensor. Hence, the code is prone to heap buffer overflow. If `split_values` does not end with a value at least `num_values` then the `while` loop condition will trigger a read outside of the bounds of `split_values` once `batch_idx` grows too large. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.",
"id": "PYSEC-2020-281",
"modified": "2021-12-09T06:34:41.679840Z",
"published": "2020-09-25T19:15:00Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1"
},
{
"type": "ADVISORY",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p5f8-gfw5-33w4"
},
{
"type": "FIX",
"url": "https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02"
}
]
}
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.