pysec - pysec-2022-61

PYSEC-2022-61

Vulnerability from pysec - Published: 2022-02-03 14:15 - Updated: 2022-03-09 00:17

Details

Tensorflow is an Open Source Machine Learning Framework. The implementation of *Bincount operations allows malicious users to cause denial of service by passing in arguments which would trigger a CHECK-fail. There are several conditions that the input arguments must satisfy. Some are not caught during shape inference and others are not caught during kernel implementation. This results in CHECK failures later when the output tensors get allocated. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Impacted products

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

Aliases

JSON

To clipboard

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        "ecosystem": "PyPI",
        "name": "tensorflow-cpu",
        "purl": "pkg:pypi/tensorflow-cpu"
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  "id": "PYSEC-2022-61",
  "modified": "2022-03-09T00:17:31.433747Z",
  "published": "2022-02-03T14:15:00Z",
  "references": [
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      "type": "ADVISORY",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-f2vv-v9cg-qhh7"
    }
  ]
}

CVE-2022-21737 (GCVE-0-2022-21737)

Vulnerability from cvelistv5 – Published: 2022-02-03 13:43 – Updated: 2025-05-05 16:31

Title

Assertion failure based denial of service in Tensorflow

Summary

Tensorflow is an Open Source Machine Learning Framework. The implementation of `*Bincount` operations allows malicious users to cause denial of service by passing in arguments which would trigger a `CHECK`-fail. There are several conditions that the input arguments must satisfy. Some are not caught during shape inference and others are not caught during kernel implementation. This results in `CHECK` failures later when the output tensors get allocated. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

Severity ?

6.5 (Medium)


                        
                          CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

CWE

Assigner

GitHub_M

References

URL

Tags

	https://github.com/tensorflow/tensorflow/security…	x_refsource_CONFIRM
	https://github.com/tensorflow/tensorflow/commit/7…	x_refsource_MISC
	https://github.com/tensorflow/tensorflow/blob/510…	x_refsource_MISC

Show details on NVD website

JSON

To clipboard

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GHSA-F2VV-V9CG-QHH7

Vulnerability from github – Published: 2022-02-09 23:43 – Updated: 2024-11-13 22:32

Summary

Assertion failure based denial of service in Tensorflow

Details

Impact

The implementation of *Bincount operations allows malicious users to cause denial of service by passing in arguments which would trigger a CHECK-fail:

import tensorflow as tf

tf.raw_ops.DenseBincount(
  input=[[0], [1], [2]],
  size=[1],
  weights=[3,2,1],
  binary_output=False)

There are several conditions that the input arguments must satisfy. Some are not caught during shape inference and others are not caught during kernel implementation. This results in CHECK failures later when the output tensors get allocated.

Patches

We have patched the issue in GitHub commit 7019ce4f68925fd01cdafde26f8d8c938f47e6f9.

The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 Faysal Hossain Shezan from University of Virginia.

Severity ?

6.5 (Medium)


                  
                    CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

7.1 (High)


                  
                    CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N

Show details on source website

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
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    }
  ],
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  ],
  "database_specific": {
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      "CWE-617",
      "CWE-754"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2022-02-03T19:59:49Z",
    "nvd_published_at": "2022-02-03T14:15:00Z",
    "severity": "HIGH"
  },
  "details": "### Impact \nThe [implementation of `*Bincount` operations](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/kernels/bincount_op.cc) allows malicious users to cause denial of service by passing in arguments which would trigger a `CHECK`-fail:\n\n```python\nimport tensorflow as tf\n\ntf.raw_ops.DenseBincount(\n  input=[[0], [1], [2]],\n  size=[1],\n  weights=[3,2,1],\n  binary_output=False)\n```\n\nThere are several conditions that the input arguments must satisfy. Some are not caught during shape inference and others are not caught during kernel implementation. This results in `CHECK` failures later when the output tensors get allocated.\n\n### Patches\nWe have patched the issue in GitHub commit [7019ce4f68925fd01cdafde26f8d8c938f47e6f9](https://github.com/tensorflow/tensorflow/commit/7019ce4f68925fd01cdafde26f8d8c938f47e6f9).\n\nThe fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 Faysal Hossain Shezan from University of Virginia.",
  "id": "GHSA-f2vv-v9cg-qhh7",
  "modified": "2024-11-13T22:32:39Z",
  "published": "2022-02-09T23:43:48Z",
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    },
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}

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-2022-61

CVE-2022-21737 (GCVE-0-2022-21737)

GHSA-F2VV-V9CG-QHH7

Impact

Patches

For more information

Attribution

Tags

Sightings

Nomenclature