pysec - pysec-2021-709

pysec-2021-709

Vulnerability from pysec

Published

2021-05-14 20:15

Modified

2021-12-09 06:35

Details

TensorFlow is an end-to-end open source platform for machine learning. The implementation of tf.raw_ops.FusedBatchNorm is vulnerable to a heap buffer overflow. If the tensors are empty, the same implementation can trigger undefined behavior by dereferencing null pointers. The 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. If the tensors are empty, the validation mentioned in the above paragraph would also trigger and prevent the undefined behavior. 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.

Impacted products

Name	purl
tensorflow-gpu	pkg:pypi/tensorflow-gpu

Aliases

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu",
        "purl": "pkg:pypi/tensorflow-gpu"
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        "1.1.0",
        "1.10.0",
        "1.10.1",
        "1.11.0",
        "1.12.0",
        "1.12.2",
        "1.12.3",
        "1.13.1",
        "1.13.2",
        "1.14.0",
        "1.15.0",
        "1.15.2",
        "1.15.3",
        "1.15.4",
        "1.15.5",
        "1.2.0",
        "1.2.1",
        "1.3.0",
        "1.4.0",
        "1.4.1",
        "1.5.0",
        "1.5.1",
        "1.6.0",
        "1.7.0",
        "1.7.1",
        "1.8.0",
        "1.9.0",
        "2.0.0",
        "2.0.1",
        "2.0.2",
        "2.0.3",
        "2.0.4",
        "2.1.0",
        "2.1.1",
        "2.1.2",
        "2.1.3",
        "2.1.4",
        "2.2.0",
        "2.2.1",
        "2.2.2",
        "2.2.3",
        "2.3.0",
        "2.3.1",
        "2.3.2",
        "2.3.3",
        "2.4.0",
        "2.4.1",
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    }
  ],
  "aliases": [
    "CVE-2021-29583",
    "GHSA-9xh4-23q4-v6wr"
  ],
  "details": "TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FusedBatchNorm` is vulnerable to a heap buffer overflow. If the tensors are empty, the same implementation can trigger undefined behavior by dereferencing null pointers. The 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. If the tensors are empty, the validation mentioned in the above paragraph would also trigger and prevent the undefined behavior. 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.",
  "id": "PYSEC-2021-709",
  "modified": "2021-12-09T06:35:29.040868Z",
  "published": "2021-05-14T20:15:00Z",
  "references": [
    {
      "type": "FIX",
      "url": "https://github.com/tensorflow/tensorflow/commit/6972f9dfe325636b3db4e0bc517ee22a159365c0"
    },
    {
      "type": "ADVISORY",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9xh4-23q4-v6wr"
    }
  ]
}

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

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

JSON

To clipboard

{
  "affected": [
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      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
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          "events": [
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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"
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    },
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    },
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    },
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      "type": "CVSS_V3"
    },
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      "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`"
}

CVE-2021-29583 (GCVE-0-2021-29583)

Vulnerability from cvelistv5

Published

2021-05-14 19:15

Modified

2024-08-03 22:11

Severity ?

2.5 (Low) - CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L

CWE

CWE-476 - NULL Pointer Dereference

Summary

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FusedBatchNorm` is vulnerable to a heap buffer overflow. If the tensors are empty, the same implementation can trigger undefined behavior by dereferencing null pointers. The 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. If the tensors are empty, the validation mentioned in the above paragraph would also trigger and prevent the undefined behavior. 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.

References

►

URL

Tags

	https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9xh4-23q4-v6wr	x_refsource_CONFIRM
	https://github.com/tensorflow/tensorflow/commit/6972f9dfe325636b3db4e0bc517ee22a159365c0	x_refsource_MISC

Impacted products

	Vendor	Product	Version
	tensorflow	tensorflow	Version: < 2.1.4 Version: >= 2.2.0, < 2.2.3 Version: >= 2.3.0, < 2.3.3 Version: >= 2.4.0, < 2.4.2

Show details on NVD website

JSON

To clipboard

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                            "version_value": "\u003c 2.1.4"
                          },
                          {
                            "version_value": "\u003e= 2.2.0, \u003c 2.2.3"
                          },
                          {
                            "version_value": "\u003e= 2.3.0, \u003c 2.3.3"
                          },
                          {
                            "version_value": "\u003e= 2.4.0, \u003c 2.4.2"
                          }
                        ]
                      }
                    }
                  ]
                },
                "vendor_name": "tensorflow"
              }
            ]
          }
        },
        "data_format": "MITRE",
        "data_type": "CVE",
        "data_version": "4.0",
        "description": {
          "description_data": [
            {
              "lang": "eng",
              "value": "TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FusedBatchNorm` is vulnerable to a heap buffer overflow. If the tensors are empty, the same implementation can trigger undefined behavior by dereferencing null pointers. The 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. If the tensors are empty, the validation mentioned in the above paragraph would also trigger and prevent the undefined behavior. 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."
            }
          ]
        },
        "impact": {
          "cvss": {
            "attackComplexity": "HIGH",
            "attackVector": "LOCAL",
            "availabilityImpact": "LOW",
            "baseScore": 2.5,
            "baseSeverity": "LOW",
            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "privilegesRequired": "LOW",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L",
            "version": "3.1"
          }
        },
        "problemtype": {
          "problemtype_data": [
            {
              "description": [
                {
                  "lang": "eng",
                  "value": "CWE-476: NULL Pointer Dereference"
                }
              ]
            }
          ]
        },
        "references": {
          "reference_data": [
            {
              "name": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9xh4-23q4-v6wr",
              "refsource": "CONFIRM",
              "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9xh4-23q4-v6wr"
            },
            {
              "name": "https://github.com/tensorflow/tensorflow/commit/6972f9dfe325636b3db4e0bc517ee22a159365c0",
              "refsource": "MISC",
              "url": "https://github.com/tensorflow/tensorflow/commit/6972f9dfe325636b3db4e0bc517ee22a159365c0"
            }
          ]
        },
        "source": {
          "advisory": "GHSA-9xh4-23q4-v6wr",
          "discovery": "UNKNOWN"
        }
      }
    }
  },
  "cveMetadata": {
    "assignerOrgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
    "assignerShortName": "GitHub_M",
    "cveId": "CVE-2021-29583",
    "datePublished": "2021-05-14T19:15:27",
    "dateReserved": "2021-03-30T00:00:00",
    "dateUpdated": "2024-08-03T22:11:06.268Z",
    "state": "PUBLISHED"
  },
  "dataType": "CVE_RECORD",
  "dataVersion": "5.1"
}

Sightings

Author	Source	Type	Date

Nomenclature

Seen: The vulnerability was mentioned, discussed, or seen somewhere by the user.
Confirmed: The vulnerability is confirmed from an analyst perspective.
Exploited: This vulnerability was exploited and seen by the user reporting the sighting.
Patched: This vulnerability was successfully patched by the user reporting the sighting.
Not exploited: This vulnerability was not exploited or seen by the user reporting the sighting.
Not confirmed: The user expresses doubt about the veracity of the vulnerability.
Not patched: This vulnerability was not successfully patched by the user reporting the sighting.

Action not permitted

pysec-2021-709

Vulnerability from pysec

ghsa-9xh4-23q4-v6wr

Vulnerability from github

Impact

Patches

For more information

Attribution

CVE-2021-29583 (GCVE-0-2021-29583)

Vulnerability from cvelistv5

Tags

Sightings

Nomenclature