GHSA-X4G7-FVJJ-PRG8
Vulnerability from github – Published: 2021-05-21 14:21 – Updated: 2024-10-30 23:17
VLAI?
Summary
Division by 0 in `QuantizedConv2D`
Details
Impact
An attacker can trigger a division by 0 in tf.raw_ops.QuantizedConv2D:
import tensorflow as tf
input = tf.zeros([1, 1, 1, 1], dtype=tf.quint8)
filter = tf.constant([], shape=[1, 0, 1, 1], dtype=tf.quint8)
min_input = tf.constant(0.0)
max_input = tf.constant(0.0001)
min_filter = tf.constant(0.0)
max_filter = tf.constant(0.0001)
strides = [1, 1, 1, 1]
padding = "SAME"
tf.raw_ops.QuantizedConv2D(input=input, filter=filter, min_input=min_input, max_input=max_input, min_filter=min_filter, max_filter=max_filter, strides=strides, padding=padding)
This is because the implementation does a division by a quantity that is controlled by the caller:
const int filter_value_count = filter_width * filter_height * input_depth;
const int64 patches_per_chunk = kMaxChunkSize / (filter_value_count * sizeof(T1));
Patches
We have patched the issue in GitHub commit cfa91be9863a91d5105a3b4941096044ab32036b.
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.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
Attribution
This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.
Severity ?
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "0"
},
{
"fixed": "2.1.4"
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"type": "ECOSYSTEM"
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{
"package": {
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"name": "tensorflow"
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"introduced": "2.2.0"
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{
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"ecosystem": "PyPI",
"name": "tensorflow"
},
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"fixed": "2.3.3"
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"name": "tensorflow"
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"fixed": "2.4.2"
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"ecosystem": "PyPI",
"name": "tensorflow-cpu"
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"fixed": "2.1.4"
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"name": "tensorflow-cpu"
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"fixed": "2.2.3"
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"name": "tensorflow-cpu"
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"ranges": [
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{
"fixed": "2.3.3"
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{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-cpu"
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"ranges": [
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"events": [
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"introduced": "2.4.0"
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{
"fixed": "2.4.2"
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"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
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"events": [
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"introduced": "0"
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{
"fixed": "2.1.4"
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],
"type": "ECOSYSTEM"
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},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.2.0"
},
{
"fixed": "2.2.3"
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"type": "ECOSYSTEM"
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},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
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"ranges": [
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"introduced": "2.3.0"
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{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
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{
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],
"type": "ECOSYSTEM"
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]
}
],
"aliases": [
"CVE-2021-29527"
],
"database_specific": {
"cwe_ids": [
"CWE-369"
],
"github_reviewed": true,
"github_reviewed_at": "2021-05-18T23:10:37Z",
"nvd_published_at": "2021-05-14T20:15:00Z",
"severity": "LOW"
},
"details": "### Impact\nAn attacker can trigger a division by 0 in `tf.raw_ops.QuantizedConv2D`:\n\n```python\nimport tensorflow as tf\n\ninput = tf.zeros([1, 1, 1, 1], dtype=tf.quint8)\nfilter = tf.constant([], shape=[1, 0, 1, 1], dtype=tf.quint8)\nmin_input = tf.constant(0.0)\nmax_input = tf.constant(0.0001)\nmin_filter = tf.constant(0.0)\nmax_filter = tf.constant(0.0001)\nstrides = [1, 1, 1, 1]\npadding = \"SAME\" \n \n\ntf.raw_ops.QuantizedConv2D(input=input, filter=filter, min_input=min_input, max_input=max_input, min_filter=min_filter, max_filter=max_filter, strides=strides, padding=padding)\n```\nThis is because the [implementation](https://github.com/tensorflow/tensorflow/blob/00e9a4d67d76703fa1aee33dac582acf317e0e81/tensorflow/core/kernels/quantized_conv_ops.cc#L257-L259) does a division by a quantity that is controlled by the caller: \n\n```cc\nconst int filter_value_count = filter_width * filter_height * input_depth;\nconst int64 patches_per_chunk = kMaxChunkSize / (filter_value_count * sizeof(T1));\n```\n \n### Patches\nWe have patched the issue in GitHub commit [cfa91be9863a91d5105a3b4941096044ab32036b](https://github.com/tensorflow/tensorflow/commit/cfa91be9863a91d5105a3b4941096044ab32036b).\n\nThe 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.\n\n### For more information\nPlease consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.\n\n### Attribution\nThis vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.",
"id": "GHSA-x4g7-fvjj-prg8",
"modified": "2024-10-30T23:17:38Z",
"published": "2021-05-21T14:21:59Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x4g7-fvjj-prg8"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2021-29527"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/cfa91be9863a91d5105a3b4941096044ab32036b"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-455.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-653.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-164.yaml"
},
{
"type": "PACKAGE",
"url": "https://github.com/tensorflow/tensorflow"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L",
"type": "CVSS_V3"
},
{
"score": "CVSS:4.0/AV:L/AC:L/AT:P/PR:L/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N",
"type": "CVSS_V4"
}
],
"summary": "Division by 0 in `QuantizedConv2D`"
}
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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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