Vulnerability-Lookup

PYSEC-2021-511

Vulnerability from pysec - Published: 2021-05-14 20:15 - Updated: 2021-12-09 06:34

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-cpu	pkg:pypi/tensorflow-cpu

Aliases

JSON

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{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu",
        "purl": "pkg:pypi/tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "6972f9dfe325636b3db4e0bc517ee22a159365c0"
            }
          ],
          "repo": "https://github.com/tensorflow/tensorflow",
          "type": "GIT"
        },
        {
          "events": [
            {
              "introduced": "0"
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            {
              "fixed": "2.2.0rc0"
            },
            {
              "introduced": "2.2.0"
            },
            {
              "fixed": "2.3.0rc0"
            },
            {
              "introduced": "2.3.0"
            },
            {
              "fixed": "2.3.4"
            },
            {
              "introduced": "2.4.0"
            },
            {
              "fixed": "2.4.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "1.15.0",
        "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",
        "2.4.2"
      ]
    }
  ],
  "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-2021-511",
  "modified": "2021-12-09T06:34:56.228145Z",
  "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 – Updated: 2024-11-13 15:59

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:

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:

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.

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

Show details on source website

JSON

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"
            },
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              "fixed": "2.2.3"
            }
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          "type": "ECOSYSTEM"
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    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.3.0"
            },
            {
              "fixed": "2.3.3"
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          ],
          "type": "ECOSYSTEM"
        }
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    },
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      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
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      "ranges": [
        {
          "events": [
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              "introduced": "2.4.0"
            },
            {
              "fixed": "2.4.2"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
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        }
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    },
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        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
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              "fixed": "2.2.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
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        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
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              "introduced": "2.3.0"
            },
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            }
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      ]
    },
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        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.4.0"
            },
            {
              "fixed": "2.4.2"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "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`"
}

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

Vulnerability from cvelistv5 – Published: 2021-05-14 19:15 – Updated: 2024-08-03 22:11

Title

Heap buffer overflow and undefined behavior in `FusedBatchNorm`

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.

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

Assigner

GitHub_M

References

URL

Tags

	https://github.com/tensorflow/tensorflow/security…	x_refsource_CONFIRM
	https://github.com/tensorflow/tensorflow/commit/6…	x_refsource_MISC

Impacted products

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

Show details on NVD website

JSON

To clipboard

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            {
              "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 observed by the user.
Confirmed: The vulnerability has been validated from an analyst's perspective.
Published Proof of Concept: A public proof of concept is available for this vulnerability.
Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
Not confirmed: The user expressed doubt about the validity of the vulnerability.
Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.

Detection rules are retrieved from Rulezet.

Action not permitted

PYSEC-2021-511

GHSA-9XH4-23Q4-V6WR

Impact

Patches

For more information

Attribution

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

Tags

Sightings

Nomenclature