GHSA-3h8m-483j-7xxm
Vulnerability from github
Published
2021-05-21 14:25
Modified
2024-11-01 17:05
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 out of bounds read in `RequantizationRange`
Details

Impact

The implementation of tf.raw_ops.MaxPoolGradWithArgmax can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs:

```python import tensorflow as tf

input = tf.constant([1], shape=[1], dtype=tf.qint32) input_max = tf.constant([], dtype=tf.float32) input_min = tf.constant([], dtype=tf.float32)

tf.raw_ops.RequantizationRange(input=input, input_min=input_min, input_max=input_max) ```

The implementation assumes that the input_min and input_max tensors have at least one element, as it accesses the first element in two arrays:

cc const float input_min_float = ctx->input(1).flat<float>()(0); const float input_max_float = ctx->input(2).flat<float>()(0);

If the tensors are empty, .flat<T>() is an empty object, backed by an empty array. Hence, accesing even the 0th element is a read outside the bounds.

Patches

We have patched the issue in GitHub commit ef0c008ee84bad91ec6725ddc42091e19a30cf0e.

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"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
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              "fixed": "2.1.4"
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          "type": "ECOSYSTEM"
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    {
      "package": {
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        "name": "tensorflow"
      },
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            }
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        }
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      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
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              "introduced": "2.3.0"
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          ],
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      },
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      ]
    },
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      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
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              "introduced": "2.4.0"
            },
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          "type": "ECOSYSTEM"
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    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
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    },
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        "name": "tensorflow-gpu"
      },
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              "introduced": "2.2.0"
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            {
              "fixed": "2.2.3"
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          ],
          "type": "ECOSYSTEM"
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    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.3.0"
            },
            {
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          "type": "ECOSYSTEM"
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    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
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            {
              "introduced": "2.4.0"
            },
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            }
          ],
          "type": "ECOSYSTEM"
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      ]
    }
  ],
  "aliases": [
    "CVE-2021-29569"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-125"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2021-05-18T19:00:06Z",
    "nvd_published_at": "2021-05-14T20:15:00Z",
    "severity": "LOW"
  },
  "details": "### Impact\nThe implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs:\n\n```python\nimport tensorflow as tf\n\ninput = tf.constant([1], shape=[1], dtype=tf.qint32) \ninput_max = tf.constant([], dtype=tf.float32)\ninput_min = tf.constant([], dtype=tf.float32)\n\ntf.raw_ops.RequantizationRange(input=input, input_min=input_min, input_max=input_max)\n```\n\nThe [implementation](https://github.com/tensorflow/tensorflow/blob/ac328eaa3870491ababc147822cd04e91a790643/tensorflow/core/kernels/requantization_range_op.cc#L49-L50) assumes that the `input_min` and `input_max` tensors have at least one element, as it accesses the first element in two arrays:\n\n```cc\nconst float input_min_float = ctx-\u003einput(1).flat\u003cfloat\u003e()(0);\nconst float input_max_float = ctx-\u003einput(2).flat\u003cfloat\u003e()(0);\n```\n\nIf the tensors are empty, `.flat\u003cT\u003e()` is an empty object, backed by an empty array. Hence, accesing even the 0th element is a read outside the bounds.\n\n### Patches\nWe have patched the issue in GitHub commit [ef0c008ee84bad91ec6725ddc42091e19a30cf0e](https://github.com/tensorflow/tensorflow/commit/ef0c008ee84bad91ec6725ddc42091e19a30cf0e).\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-3h8m-483j-7xxm",
  "modified": "2024-11-01T17:05:44Z",
  "published": "2021-05-21T14:25:22Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3h8m-483j-7xxm"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2021-29569"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/commit/ef0c008ee84bad91ec6725ddc42091e19a30cf0e"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-497.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-695.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-206.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 out of bounds read in `RequantizationRange`"
}


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