GHSA-3QXP-QJQ7-W4HF
Vulnerability from github – Published: 2021-05-21 14:22 – Updated: 2024-10-30 23:20Impact
An attacker can trigger a CHECK fail in PNG encoding by providing an empty input tensor as the pixel data:
import tensorflow as tf
image = tf.zeros([0, 0, 3])
image = tf.cast(image, dtype=tf.uint8)
tf.raw_ops.EncodePng(image=image)
This is because the implementation only validates that the total number of pixels in the image does not overflow. Thus, an attacker can send an empty matrix for encoding. However, if the tensor is empty, then the associated buffer is nullptr. Hence, when calling png::WriteImageToBuffer, the first argument (i.e., image.flat<T>().data()) is NULL. This then triggers the CHECK_NOTNULL in the first line of png::WriteImageToBuffer.
template <typename T>
bool WriteImageToBuffer(
const void* image, int width, int height, int row_bytes, int num_channels,
int channel_bits, int compression, T* png_string,
const std::vector<std::pair<std::string, std::string> >* metadata) {
CHECK_NOTNULL(image);
...
}
Since image is null, this results in abort being called after printing the stacktrace. Effectively, this allows an attacker to mount a denial of service attack.
Patches
We have patched the issue in GitHub commit 26eb323554ffccd173e8a79a8c05c15b685ae4d1.
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-2021-29531"
],
"database_specific": {
"cwe_ids": [
"CWE-754"
],
"github_reviewed": true,
"github_reviewed_at": "2021-05-18T23:00:53Z",
"nvd_published_at": "2021-05-14T20:15:00Z",
"severity": "LOW"
},
"details": "### Impact\nAn attacker can trigger a `CHECK` fail in PNG encoding by providing an empty input tensor as the pixel data:\n\n```python\nimport tensorflow as tf\n\nimage = tf.zeros([0, 0, 3])\nimage = tf.cast(image, dtype=tf.uint8) \ntf.raw_ops.EncodePng(image=image) \n```\n\nThis is because the [implementation](https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L57-L60) only validates that the total number of pixels in the image does not overflow. Thus, an attacker can send an empty matrix for encoding. However, if the tensor is empty, then the associated buffer is `nullptr`. Hence, when [calling `png::WriteImageToBuffer`](https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L79-L93), the first argument (i.e., `image.flat\u003cT\u003e().data()`) is `NULL`. This then triggers the `CHECK_NOTNULL` in the [first line of `png::WriteImageToBuffer`](https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/lib/png/png_io.cc#L345-L349).\n\n```cc\ntemplate \u003ctypename T\u003e\nbool WriteImageToBuffer(\n const void* image, int width, int height, int row_bytes, int num_channels,\n int channel_bits, int compression, T* png_string,\n const std::vector\u003cstd::pair\u003cstd::string, std::string\u003e \u003e* metadata) {\n CHECK_NOTNULL(image);\n ...\n}\n```\n\nSince `image` is null, this results in `abort` being called after printing the stacktrace. Effectively, this allows an attacker to mount a denial of service attack.\n\n### Patches\nWe have patched the issue in GitHub commit [26eb323554ffccd173e8a79a8c05c15b685ae4d1](https://github.com/tensorflow/tensorflow/commit/26eb323554ffccd173e8a79a8c05c15b685ae4d1).\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-3qxp-qjq7-w4hf",
"modified": "2024-10-30T23:20:19Z",
"published": "2021-05-21T14:22:13Z",
"references": [
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3qxp-qjq7-w4hf"
},
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2021-29531"
},
{
"type": "WEB",
"url": "https://github.com/tensorflow/tensorflow/commit/26eb323554ffccd173e8a79a8c05c15b685ae4d1"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-459.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-657.yaml"
},
{
"type": "WEB",
"url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-168.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": "CHECK-fail in tf.raw_ops.EncodePng"
}
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.