GHSA-4fg4-p75j-w5xj
Vulnerability from github
Published
2021-05-21 14:23
Modified
2024-10-31 20:42
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 in `QuantizedBatchNormWithGlobalNormalization`
Details

Impact

An attacker can cause a segfault and denial of service via accessing data outside of bounds in tf.raw_ops.QuantizedBatchNormWithGlobalNormalization:

```python import tensorflow as tf

t = tf.constant([1], shape=[1, 1, 1, 1], dtype=tf.quint8) t_min = tf.constant([], shape=[0], dtype=tf.float32) t_max = tf.constant([], shape=[0], dtype=tf.float32) m = tf.constant([1], shape=[1], dtype=tf.quint8) m_min = tf.constant([], shape=[0], dtype=tf.float32) m_max = tf.constant([], shape=[0], dtype=tf.float32) v = tf.constant([1], shape=[1], dtype=tf.quint8) v_min = tf.constant([], shape=[0], dtype=tf.float32) v_max = tf.constant([], shape=[0], dtype=tf.float32) beta = tf.constant([1], shape=[1], dtype=tf.quint8) beta_min = tf.constant([], shape=[0], dtype=tf.float32) beta_max = tf.constant([], shape=[0], dtype=tf.float32) gamma = tf.constant([1], shape=[1], dtype=tf.quint8) gamma_min = tf.constant([], shape=[0], dtype=tf.float32) gamma_max = tf.constant([], shape=[0], dtype=tf.float32)

tf.raw_ops.QuantizedBatchNormWithGlobalNormalization( t=t, t_min=t_min, t_max=t_max, m=m, m_min=m_min, m_max=m_max, v=v, v_min=v_min, v_max=v_max, beta=beta, beta_min=beta_min, beta_max=beta_max, gamma=gamma, gamma_min=gamma_min, gamma_max=gamma_max, out_type=tf.qint32, variance_epsilon=0.1, scale_after_normalization=True) ```

This is because the implementation assumes the inputs are not empty:

cc const float input_min = context->input(1).flat<float>()(0); const float input_max = context->input(2).flat<float>()(0); ... const float mean_min = context->input(4).flat<float>()(0); const float mean_max = context->input(5).flat<float>()(0); ... const float var_min = context->input(7).flat<float>()(0); const float var_max = context->input(8).flat<float>()(0); ... const float beta_min = context->input(10).flat<float>()(0); const float beta_max = context->input(11).flat<float>()(0); ... const float gamma_min = context->input(13).flat<float>()(0); const float gamma_max = context->input(14).flat<float>()(0);

If any of these inputs is empty, .flat<T>() is an empty buffer, so accessing the element at index 0 is accessing data outside of bounds.

Patches

We have patched the issue in GitHub commit d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b.

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"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "2.1.4"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.2.0"
            },
            {
              "fixed": "2.2.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.3.0"
            },
            {
              "fixed": "2.3.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "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": "0"
            },
            {
              "fixed": "2.1.4"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.2.0"
            },
            {
              "fixed": "2.2.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.3.0"
            },
            {
              "fixed": "2.3.3"
            }
          ],
          "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": "0"
            },
            {
              "fixed": "2.1.4"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "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"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.4.0"
            },
            {
              "fixed": "2.4.2"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2021-29547"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-125"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2021-05-18T21:42:27Z",
    "nvd_published_at": "2021-05-14T20:15:00Z",
    "severity": "LOW"
  },
  "details": "### Impact\nAn attacker can cause a segfault and denial of service via accessing data outside of bounds in `tf.raw_ops.QuantizedBatchNormWithGlobalNormalization`:\n\n```python\nimport tensorflow as tf\n\nt = tf.constant([1], shape=[1, 1, 1, 1], dtype=tf.quint8)\nt_min = tf.constant([], shape=[0], dtype=tf.float32)\nt_max = tf.constant([], shape=[0], dtype=tf.float32)\nm = tf.constant([1], shape=[1], dtype=tf.quint8)\nm_min = tf.constant([], shape=[0], dtype=tf.float32)\nm_max = tf.constant([], shape=[0], dtype=tf.float32)\nv = tf.constant([1], shape=[1], dtype=tf.quint8)\nv_min = tf.constant([], shape=[0], dtype=tf.float32)\nv_max = tf.constant([], shape=[0], dtype=tf.float32)\nbeta = tf.constant([1], shape=[1], dtype=tf.quint8)\nbeta_min = tf.constant([], shape=[0], dtype=tf.float32)\nbeta_max = tf.constant([], shape=[0], dtype=tf.float32)\ngamma = tf.constant([1], shape=[1], dtype=tf.quint8)\ngamma_min = tf.constant([], shape=[0], dtype=tf.float32)\ngamma_max = tf.constant([], shape=[0], dtype=tf.float32) \n\ntf.raw_ops.QuantizedBatchNormWithGlobalNormalization(\n  t=t, t_min=t_min, t_max=t_max, m=m, m_min=m_min, m_max=m_max,\n  v=v, v_min=v_min, v_max=v_max, beta=beta, beta_min=beta_min,\n  beta_max=beta_max, gamma=gamma, gamma_min=gamma_min,\n  gamma_max=gamma_max, out_type=tf.qint32,\n  variance_epsilon=0.1, scale_after_normalization=True)\n```                         \n                            \nThis is because the [implementation](https://github.com/tensorflow/tensorflow/blob/55a97caa9e99c7f37a0bbbeb414dc55553d3ae7f/tensorflow/core/kernels/quantized_batch_norm_op.cc#L176-L189) assumes the inputs are not empty: \n  \n```cc\nconst float input_min = context-\u003einput(1).flat\u003cfloat\u003e()(0);\nconst float input_max = context-\u003einput(2).flat\u003cfloat\u003e()(0);\n...\nconst float mean_min = context-\u003einput(4).flat\u003cfloat\u003e()(0);\nconst float mean_max = context-\u003einput(5).flat\u003cfloat\u003e()(0);\n...\nconst float var_min = context-\u003einput(7).flat\u003cfloat\u003e()(0);\nconst float var_max = context-\u003einput(8).flat\u003cfloat\u003e()(0);\n...\nconst float beta_min = context-\u003einput(10).flat\u003cfloat\u003e()(0);\nconst float beta_max = context-\u003einput(11).flat\u003cfloat\u003e()(0);\n...\nconst float gamma_min = context-\u003einput(13).flat\u003cfloat\u003e()(0);\nconst float gamma_max = context-\u003einput(14).flat\u003cfloat\u003e()(0);\n```\n\nIf any of these inputs is empty, `.flat\u003cT\u003e()` is an empty buffer, so accessing the element at index 0 is accessing data outside of bounds.\n\n### Patches\nWe have patched the issue in GitHub commit [d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b](https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b).\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-4fg4-p75j-w5xj",
  "modified": "2024-10-31T20:42:12Z",
  "published": "2021-05-21T14:23:31Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4fg4-p75j-w5xj"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2021-29547"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-475.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-673.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-184.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 in `QuantizedBatchNormWithGlobalNormalization`"
}


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