GHSA-6g85-3hm8-83f9
Vulnerability from github
Impact
An attacker can trigger a denial of service via a CHECK-fail in tf.raw_ops.QuantizeAndDequantizeV4Grad:
```python import tensorflow as tf
gradient_tensor = tf.constant([0.0], shape=[1]) input_tensor = tf.constant([0.0], shape=[1]) input_min = tf.constant([[0.0]], shape=[1, 1]) input_max = tf.constant([[0.0]], shape=[1, 1])
tf.raw_ops.QuantizeAndDequantizeV4Grad( gradients=gradient_tensor, input=input_tensor, input_min=input_min, input_max=input_max, axis=0) ```
This is because the implementation does not validate the rank of the input_* tensors. In turn, this results in the tensors being passes as they are to QuantizeAndDequantizePerChannelGradientImpl:
cc
template <typename Device, typename T>
struct QuantizeAndDequantizePerChannelGradientImpl {
static void Compute(const Device& d,
typename TTypes<T, 3>::ConstTensor gradient,
typename TTypes<T, 3>::ConstTensor input,
const Tensor* input_min_tensor,
const Tensor* input_max_tensor,
typename TTypes<T, 3>::Tensor input_backprop,
typename TTypes<T>::Flat input_min_backprop,
typename TTypes<T>::Flat input_max_backprop) {
...
auto input_min = input_min_tensor->vec<T>();
auto input_max = input_max_tensor->vec<T>();
...
}
However, the vec<T> method, requires the rank to 1 and triggers a CHECK failure otherwise.
Patches
We have patched the issue in GitHub commit 20431e9044cf2ad3c0323c34888b192f3289af6b.
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 as this is the only other affected version.
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.
{
"affected": [
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow"
},
"ranges": [
{
"events": [
{
"introduced": "2.4.0"
},
{
"fixed": "2.4.2"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-cpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.4.0"
},
{
"fixed": "2.4.2"
}
],
"type": "ECOSYSTEM"
}
]
},
{
"package": {
"ecosystem": "PyPI",
"name": "tensorflow-gpu"
},
"ranges": [
{
"events": [
{
"introduced": "2.4.0"
},
{
"fixed": "2.4.2"
}
],
"type": "ECOSYSTEM"
}
]
}
],
"aliases": [
"CVE-2021-29544"
],
"database_specific": {
"cwe_ids": [
"CWE-754"
],
"github_reviewed": true,
"github_reviewed_at": "2021-05-18T21:50:36Z",
"nvd_published_at": "2021-05-14T20:15:00Z",
"severity": "LOW"
},
"details": "### Impact\nAn attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.QuantizeAndDequantizeV4Grad`:\n\n```python\nimport tensorflow as tf\n\ngradient_tensor = tf.constant([0.0], shape=[1])\ninput_tensor = tf.constant([0.0], shape=[1])\ninput_min = tf.constant([[0.0]], shape=[1, 1])\ninput_max = tf.constant([[0.0]], shape=[1, 1])\n\ntf.raw_ops.QuantizeAndDequantizeV4Grad(\n gradients=gradient_tensor, input=input_tensor,\n input_min=input_min, input_max=input_max, axis=0)\n``` \n \nThis is because the [implementation](https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L162-L163) does not validate the rank of the `input_*` tensors. In turn, this results in the tensors being passes as they are to [`QuantizeAndDequantizePerChannelGradientImpl`](https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.h#L295-L306):\n\n```cc \ntemplate \u003ctypename Device, typename T\u003e\nstruct QuantizeAndDequantizePerChannelGradientImpl {\n static void Compute(const Device\u0026 d,\n typename TTypes\u003cT, 3\u003e::ConstTensor gradient,\n typename TTypes\u003cT, 3\u003e::ConstTensor input,\n const Tensor* input_min_tensor,\n const Tensor* input_max_tensor,\n typename TTypes\u003cT, 3\u003e::Tensor input_backprop,\n typename TTypes\u003cT\u003e::Flat input_min_backprop,\n typename TTypes\u003cT\u003e::Flat input_max_backprop) {\n ...\n auto input_min = input_min_tensor-\u003evec\u003cT\u003e();\n auto input_max = input_max_tensor-\u003evec\u003cT\u003e();\n ...\n}\n```\n\nHowever, the `vec\u003cT\u003e` method, requires the rank to 1 and triggers a `CHECK` failure otherwise.\n\n### Patches\nWe have patched the issue in GitHub commit [20431e9044cf2ad3c0323c34888b192f3289af6b](https://github.com/tensorflow/tensorflow/commit/20431e9044cf2ad3c0323c34888b192f3289af6b).\n\nThe fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 as this is the only other affected version.\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-6g85-3hm8-83f9",
"modified": "2024-11-19T19:33:14Z",
"published": "2021-05-21T14:23:22Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6g85-3hm8-83f9"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2021-29544"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/20431e9044cf2ad3c0323c34888b192f3289af6b"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-472.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-670.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-181.yaml"
},
{
"type": "PACKAGE",
"url": "https://github.com/tensorflow/tensorflow"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L162-L163"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.h#L295-L306"
}
],
"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": "CHECK-fail in `QuantizeAndDequantizeV4Grad`"
}
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.