PYSEC-2021-184
Vulnerability from pysec - Published: 2021-05-14 20:15 - Updated: 2021-08-27 03:22
VLAI?
Details
TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a segfault and denial of service via accessing data outside of bounds in tf.raw_ops.QuantizedBatchNormWithGlobalNormalization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc#L176-L189) assumes the inputs are not empty. If any of these inputs is empty, .flat<T>() is an empty buffer, so accessing the element at index 0 is accessing data outside of bounds. 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.
Impacted products
| Name | purl | tensorflow | pkg:pypi/tensorflow |
|---|
Aliases
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow",
"purl": "pkg:pypi/tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b"
}
],
"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-2021-29547",
"GHSA-4fg4-p75j-w5xj"
],
"details": "TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a segfault and denial of service via accessing data outside of bounds in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc#L176-L189) assumes the inputs are not empty. If any of these inputs is empty, `.flat\u003cT\u003e()` is an empty buffer, so accessing the element at index 0 is accessing data outside of bounds. 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-2021-184",
"modified": "2021-08-27T03:22:29.791310Z",
"published": "2021-05-14T20:15:00Z",
"references": [
{
"type": "ADVISORY",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4fg4-p75j-w5xj"
},
{
"type": "FIX",
"url": "https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b"
}
]
}
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Sightings
| Author | Source | Type | Date |
|---|
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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