PYSEC-2021-189
Vulnerability from pysec - Published: 2021-05-14 20:15 - Updated: 2021-08-27 03:22TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service by controlling the values of num_segments tensor argument for UnsortedSegmentJoin. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a2a607db15c7cd01d754d37e5448d72a13491bdb/tensorflow/core/kernels/unsorted_segment_join_op.cc#L92-L93) assumes that the num_segments tensor is a valid scalar. Since the tensor is empty the CHECK involved in .scalar<T>()() that checks that the number of elements is exactly 1 will be invalidated and this would result in process termination. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
| Name | purl | tensorflow | pkg:pypi/tensorflow |
|---|
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow",
"purl": "pkg:pypi/tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "704866eabe03a9aeda044ec91a8d0c83fc1ebdbe"
}
],
"repo": "https://github.com/tensorflow/tensorflow",
"type": "GIT"
},
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.2.0rc0"
},
{
"introduced": "2.2.0"
},
{
"fixed": "2.3.0rc0"
},
{
"introduced": "2.3.0"
},
{
"fixed": "2.3.4"
},
{
"introduced": "2.4.0"
},
{
"fixed": "2.4.3"
}
],
"type": "ECOSYSTEM"
}
],
"versions": [
"0.12.0",
"0.12.0rc0",
"0.12.0rc1",
"0.12.1",
"1.0.0",
"1.0.1",
"1.1.0",
"1.1.0rc0",
"1.1.0rc1",
"1.1.0rc2",
"1.10.0",
"1.10.0rc0",
"1.10.0rc1",
"1.10.1",
"1.11.0",
"1.11.0rc0",
"1.11.0rc1",
"1.11.0rc2",
"1.12.0",
"1.12.0rc0",
"1.12.0rc1",
"1.12.0rc2",
"1.12.2",
"1.12.3",
"1.13.0rc0",
"1.13.0rc1",
"1.13.0rc2",
"1.13.1",
"1.13.2",
"1.14.0",
"1.14.0rc0",
"1.14.0rc1",
"1.15.0",
"1.15.0rc0",
"1.15.0rc1",
"1.15.0rc2",
"1.15.0rc3",
"1.15.2",
"1.15.3",
"1.15.4",
"1.15.5",
"1.2.0",
"1.2.0rc0",
"1.2.0rc1",
"1.2.0rc2",
"1.2.1",
"1.3.0",
"1.3.0rc0",
"1.3.0rc1",
"1.3.0rc2",
"1.4.0",
"1.4.0rc0",
"1.4.0rc1",
"1.4.1",
"1.5.0",
"1.5.0rc0",
"1.5.0rc1",
"1.5.1",
"1.6.0",
"1.6.0rc0",
"1.6.0rc1",
"1.7.0",
"1.7.0rc0",
"1.7.0rc1",
"1.7.1",
"1.8.0",
"1.8.0rc0",
"1.8.0rc1",
"1.9.0",
"1.9.0rc0",
"1.9.0rc1",
"1.9.0rc2",
"2.0.0",
"2.0.0a0",
"2.0.0b0",
"2.0.0b1",
"2.0.0rc0",
"2.0.0rc1",
"2.0.0rc2",
"2.0.1",
"2.0.2",
"2.0.3",
"2.0.4",
"2.1.0",
"2.1.0rc0",
"2.1.0rc1",
"2.1.0rc2",
"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",
"2.4.0",
"2.4.1",
"2.4.2"
]
}
],
"aliases": [
"CVE-2021-29552",
"GHSA-jhq9-wm9m-cf89"
],
"details": "TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service by controlling the values of `num_segments` tensor argument for `UnsortedSegmentJoin`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a2a607db15c7cd01d754d37e5448d72a13491bdb/tensorflow/core/kernels/unsorted_segment_join_op.cc#L92-L93) assumes that the `num_segments` tensor is a valid scalar. Since the tensor is empty the `CHECK` involved in `.scalar\u003cT\u003e()()` that checks that the number of elements is exactly 1 will be invalidated and this would result in process termination. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.",
"id": "PYSEC-2021-189",
"modified": "2021-08-27T03:22:30.663551Z",
"published": "2021-05-14T20:15:00Z",
"references": [
{
"type": "ADVISORY",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jhq9-wm9m-cf89"
},
{
"type": "FIX",
"url": "https://github.com/tensorflow/tensorflow/commit/704866eabe03a9aeda044ec91a8d0c83fc1ebdbe"
}
]
}
Sightings
| Author | Source | Type | Date |
|---|
Nomenclature
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- 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.