ghsa-9xh4-23q4-v6wr
Vulnerability from github
Published
2021-05-21 14:26
Modified
2024-11-13 15:59
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 and undefined behavior in `FusedBatchNorm`
Details

Impact

The implementation of tf.raw_ops.FusedBatchNorm is vulnerable to a heap buffer overflow:

```python import tensorflow as tf

x = tf.zeros([10, 10, 10, 6], dtype=tf.float32) scale = tf.constant([0.0], shape=[1], dtype=tf.float32) offset = tf.constant([0.0], shape=[1], dtype=tf.float32) mean = tf.constant([0.0], shape=[1], dtype=tf.float32) variance = tf.constant([0.0], shape=[1], dtype=tf.float32) epsilon = 0.0 exponential_avg_factor = 0.0 data_format = "NHWC" is_training = False

tf.raw_ops.FusedBatchNorm( x=x, scale=scale, offset=offset, mean=mean, variance=variance, epsilon=epsilon, exponential_avg_factor=exponential_avg_factor, data_format=data_format, is_training=is_training) ```

If the tensors are empty, the same implementation can trigger undefined behavior by dereferencing null pointers:

```python import tensorflow as tf import numpy as np

x = tf.zeros([10, 10, 10, 1], dtype=tf.float32) scale = tf.constant([], shape=[0], dtype=tf.float32) offset = tf.constant([], shape=[0], dtype=tf.float32) mean = tf.constant([], shape=[0], dtype=tf.float32) variance = tf.constant([], shape=[0], dtype=tf.float32) epsilon = 0.0 exponential_avg_factor = 0.0 data_format = "NHWC" is_training = False

tf.raw_ops.FusedBatchNorm( x=x, scale=scale, offset=offset, mean=mean, variance=variance, epsilon=epsilon, exponential_avg_factor=exponential_avg_factor, data_format=data_format, is_training=is_training) ```

The implementation fails to validate that scale, offset, mean and variance (the last two only when required) all have the same number of elements as the number of channels of x. This results in heap out of bounds reads when the buffers backing these tensors are indexed past their boundary.

If the tensors are empty, the validation mentioned in the above paragraph would also trigger and prevent the undefined behavior.

Patches

We have patched the issue in GitHub commit 6972f9dfe325636b3db4e0bc517ee22a159365c0.

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.

Show details on source website

To clipboard 

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
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        {
          "events": [
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        "name": "tensorflow-gpu"
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        "name": "tensorflow-gpu"
      },
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              "introduced": "2.2.0"
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              "fixed": "2.2.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
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              "introduced": "2.3.0"
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        "ecosystem": "PyPI",
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          ],
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  ],
  "aliases": [
    "CVE-2021-29583"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-125",
      "CWE-476",
      "CWE-787"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2021-05-18T17:28:58Z",
    "nvd_published_at": "2021-05-14T20:15:00Z",
    "severity": "LOW"
  },
  "details": "### Impact\nThe implementation of `tf.raw_ops.FusedBatchNorm` is vulnerable to a heap buffer overflow:\n      \n```python\nimport tensorflow as tf\n\nx = tf.zeros([10, 10, 10, 6], dtype=tf.float32)\nscale = tf.constant([0.0], shape=[1], dtype=tf.float32)\noffset = tf.constant([0.0], shape=[1], dtype=tf.float32)\nmean = tf.constant([0.0], shape=[1], dtype=tf.float32)\nvariance = tf.constant([0.0], shape=[1], dtype=tf.float32)\nepsilon = 0.0\nexponential_avg_factor = 0.0\ndata_format = \"NHWC\"\nis_training = False\n    \ntf.raw_ops.FusedBatchNorm(\n  x=x, scale=scale, offset=offset, mean=mean, variance=variance,\n  epsilon=epsilon, exponential_avg_factor=exponential_avg_factor,\n  data_format=data_format, is_training=is_training)\n```\n  \nIf the tensors are empty, the same implementation can trigger undefined behavior by dereferencing null pointers:\n\n```python \nimport tensorflow as tf\nimport numpy as np\n\nx = tf.zeros([10, 10, 10, 1], dtype=tf.float32)\nscale = tf.constant([], shape=[0], dtype=tf.float32)\noffset = tf.constant([], shape=[0], dtype=tf.float32)\nmean = tf.constant([], shape=[0], dtype=tf.float32)\nvariance = tf.constant([], shape=[0], dtype=tf.float32)\nepsilon = 0.0\nexponential_avg_factor = 0.0\ndata_format = \"NHWC\"\nis_training = False\n\ntf.raw_ops.FusedBatchNorm(\n  x=x, scale=scale, offset=offset, mean=mean, variance=variance, \n  epsilon=epsilon, exponential_avg_factor=exponential_avg_factor,\n  data_format=data_format, is_training=is_training)\n``` \n\nThe  [implementation](https://github.com/tensorflow/tensorflow/blob/57d86e0db5d1365f19adcce848dfc1bf89fdd4c7/tensorflow/core/kernels/fused_batch_norm_op.cc) fails to validate that `scale`, `offset`, `mean` and `variance` (the last two only when required) all have the same number of elements as the number of channels of `x`. This results in heap out of bounds reads when the buffers backing these tensors are indexed past their boundary.\n\nIf the tensors are empty, the validation mentioned in the above paragraph would also trigger and prevent the undefined behavior.\n\n### Patches\nWe have patched the issue in GitHub commit [6972f9dfe325636b3db4e0bc517ee22a159365c0](https://github.com/tensorflow/tensorflow/commit/6972f9dfe325636b3db4e0bc517ee22a159365c0).\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-9xh4-23q4-v6wr",
  "modified": "2024-11-13T15:59:06Z",
  "published": "2021-05-21T14:26:35Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9xh4-23q4-v6wr"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2021-29583"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/commit/6972f9dfe325636b3db4e0bc517ee22a159365c0"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-511.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-709.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-220.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 and undefined behavior in `FusedBatchNorm`"
}


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