Vulnerability from bitnami_vulndb
TensorFlow is an open source platform for machine learning. tf.keras.losses.poisson receives a y_pred and y_true that are passed through functor::mul in BinaryOp. If the resulting dimensions overflow an int32, TensorFlow will crash due to a size mismatch during broadcast assignment. We have patched the issue in GitHub commit c5b30379ba87cbe774b08ac50c1f6d36df4ebb7c. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1 and 2.9.3, as these are also affected and still in supported range. However, we will not cherrypick this commit into TensorFlow 2.8.x, as it depends on Eigen behavior that changed between 2.8 and 2.9.
{
"affected": [
{
"package": {
"ecosystem": "Bitnami",
"name": "tensorflow",
"purl": "pkg:bitnami/tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "2.9.0"
},
{
"fixed": "2.9.3"
},
{
"introduced": "2.10.0"
},
{
"fixed": "2.10.1"
}
],
"type": "SEMVER"
}
],
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
"type": "CVSS_V3"
}
]
}
],
"aliases": [
"CVE-2022-41887"
],
"database_specific": {
"cpes": [
"cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*"
],
"severity": "High"
},
"details": "TensorFlow is an open source platform for machine learning. `tf.keras.losses.poisson` receives a `y_pred` and `y_true` that are passed through `functor::mul` in `BinaryOp`. If the resulting dimensions overflow an `int32`, TensorFlow will crash due to a size mismatch during broadcast assignment. We have patched the issue in GitHub commit c5b30379ba87cbe774b08ac50c1f6d36df4ebb7c. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1 and 2.9.3, as these are also affected and still in supported range. However, we will not cherrypick this commit into TensorFlow 2.8.x, as it depends on Eigen behavior that changed between 2.8 and 2.9.",
"id": "BIT-tensorflow-2022-41887",
"modified": "2026-09-08T08:34:36.952Z",
"published": "2024-03-06T11:11:21.716Z",
"references": [
{
"type": "ADVISORY",
"url": "https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/cwise_ops_common.h"
},
{
"type": "ADVISORY",
"url": "https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/losses.py"
},
{
"type": "ADVISORY",
"url": "https://github.com/tensorflow/tensorflow/commit/c5b30379ba87cbe774b08ac50c1f6d36df4ebb7c"
},
{
"type": "ADVISORY",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8fvv-46hw-vpg3"
},
{
"type": "WEB",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2022-41887"
}
],
"schema_version": "1.5.0",
"summary": "Overflow in `tf.keras.losses.poisson` in Tensorflow"
}
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.