pysec - pysec-2022-51

pysec-2022-51

Vulnerability from pysec

Published

2022-02-03 11:15

Modified

2022-03-09 00:17

Details

Tensorflow is an Open Source Machine Learning Framework. The implementation of shape inference for Dequantize is vulnerable to an integer overflow weakness. The axis argument can be -1 (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked, and, since the code computes axis + 1, an attacker can trigger an integer overflow. 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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      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu",
        "purl": "pkg:pypi/tensorflow-cpu"
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          "repo": "https://github.com/tensorflow/tensorflow",
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  "aliases": [
    "CVE-2022-21727",
    "GHSA-c6fh-56w7-fvjw"
  ],
  "details": "Tensorflow is an Open Source Machine Learning Framework. The implementation of shape inference for `Dequantize` is vulnerable to an integer overflow weakness. The `axis` argument can be `-1` (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked, and, since the code computes `axis + 1`, an attacker can trigger an integer overflow. 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.",
  "id": "PYSEC-2022-51",
  "modified": "2022-03-09T00:17:30.181517Z",
  "published": "2022-02-03T11:15:00Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c6fh-56w7-fvjw"
    },
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      "type": "FIX",
      "url": "https://github.com/tensorflow/tensorflow/commit/b64638ec5ccaa77b7c1eb90958e3d85ce381f91b"
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    }
  ]
}

CVE-2022-21727 (GCVE-0-2022-21727)

Vulnerability from cvelistv5

Published

2022-02-03 11:07

Modified

2025-05-05 16:32

Severity ?

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

CWE

Summary

Tensorflow is an Open Source Machine Learning Framework. The implementation of shape inference for `Dequantize` is vulnerable to an integer overflow weakness. The `axis` argument can be `-1` (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked, and, since the code computes `axis + 1`, an attacker can trigger an integer overflow. 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.

References

►

URL

Tags

	https://github.com/tensorflow/tensorflow/security/advisories/GHSA-c6fh-56w7-fvjw	x_refsource_CONFIRM
	https://github.com/tensorflow/tensorflow/commit/b64638ec5ccaa77b7c1eb90958e3d85ce381f91b	x_refsource_MISC
	https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/ops/array_ops.cc#L3001-L3034	x_refsource_MISC

Impacted products

	Vendor	Product	Version
	n/a	n/a	Version: n/a

Show details on NVD website

JSON

To clipboard

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ghsa-c6fh-56w7-fvjw

Vulnerability from github

Published

2022-02-09 18:29

Modified

2024-11-13 22:10

Severity ?

7.6 (High) - CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:H
7.2 (High) - CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:L/VI:L/VA:H/SC:N/SI:N/SA:N

Summary

Integer overflow in Tensorflow

Details

Impact

The implementation of shape inference for Dequantize is vulnerable to an integer overflow weakness:

```python import tensorflow as tf

input = tf.constant([1,1],dtype=tf.qint32)

@tf.function def test(): y = tf.raw_ops.Dequantize( input=input, min_range=[1.0], max_range=[10.0], mode='MIN_COMBINED', narrow_range=False, axis=2**31-1, dtype=tf.bfloat16) return y

test() ```

The axis argument can be -1 (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked, and, since the code computes axis + 1, an attacker can trigger an integer overflow:

cc int axis = -1; Status s = c->GetAttr("axis", &axis); // ... if (axis < -1) { return errors::InvalidArgument("axis should be at least -1, got ", axis); } // ... if (axis != -1) { ShapeHandle input; TF_RETURN_IF_ERROR(c->WithRankAtLeast(c->input(0), axis + 1, &input)); // ... }

Patches

We have patched the issue in GitHub commit b64638ec5ccaa77b7c1eb90958e3d85ce381f91b.

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 Yu Tian of Qihoo 360 AIVul Team.

Show details on source website

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
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        "name": "tensorflow"
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    ],
    "github_reviewed": true,
    "github_reviewed_at": "2022-02-03T18:12:51Z",
    "nvd_published_at": "2022-02-03T11:15:00Z",
    "severity": "HIGH"
  },
  "details": "### Impact \nThe [implementation of shape inference for `Dequantize`](https://github.com/tensorflow/tensorflow/blob/5100e359aef5c8021f2e71c7b986420b85ce7b3d/tensorflow/core/ops/array_ops.cc#L3001-L3034) is vulnerable to an integer overflow weakness:\n\n```python\nimport tensorflow as tf\n\ninput = tf.constant([1,1],dtype=tf.qint32)\n\n@tf.function\ndef test():\n  y = tf.raw_ops.Dequantize(\n    input=input,\n    min_range=[1.0],\n    max_range=[10.0],\n    mode=\u0027MIN_COMBINED\u0027,\n    narrow_range=False,\n    axis=2**31-1,\n    dtype=tf.bfloat16)\n  return y\n\ntest()\n``` \n\nThe `axis` argument can be `-1` (the default value for the optional argument) or any other positive value at most the number of dimensions of the input. Unfortunately, the upper bound is not checked, and, since the code computes `axis + 1`, an attacker can trigger an integer overflow:\n\n```cc\n  int axis = -1; \n  Status s = c-\u003eGetAttr(\"axis\", \u0026axis);\n  // ...\n  if (axis \u003c -1) {\n    return errors::InvalidArgument(\"axis should be at least -1, got \",\n                                   axis);\n  }\n  // ...\n  if (axis != -1) {\n    ShapeHandle input;\n    TF_RETURN_IF_ERROR(c-\u003eWithRankAtLeast(c-\u003einput(0), axis + 1, \u0026input));\n    // ...\n  }\n```\n  \n### Patches\nWe have patched the issue in GitHub commit [b64638ec5ccaa77b7c1eb90958e3d85ce381f91b](https://github.com/tensorflow/tensorflow/commit/b64638ec5ccaa77b7c1eb90958e3d85ce381f91b).\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 Yu Tian of Qihoo 360 AIVul Team.",
  "id": "GHSA-c6fh-56w7-fvjw",
  "modified": "2024-11-13T22:10:03Z",
  "published": "2022-02-09T18:29:13Z",
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  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:L/I:L/A:H",
      "type": "CVSS_V3"
    },
    {
      "score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:L/VI:L/VA:H/SC:N/SI:N/SA:N",
      "type": "CVSS_V4"
    }
  ],
  "summary": "Integer overflow in Tensorflow"
}

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-2022-51

Vulnerability from pysec

CVE-2022-21727 (GCVE-0-2022-21727)

Vulnerability from cvelistv5

ghsa-c6fh-56w7-fvjw

Vulnerability from github

Impact

Patches

For more information

Attribution

Tags

Sightings

Nomenclature