pysec - pysec-2022-68

pysec-2022-68

Vulnerability from pysec

Published

2022-02-04 23:15

Modified

2022-03-09 00:17

Details

Tensorflow is an Open Source Machine Learning Framework. An attacker can craft a TFLite model that would cause an integer overflow in embedding lookup operations. Both embedding_size and lookup_size are products of values provided by the user. Hence, a malicious user could trigger overflows in the multiplication. In certain scenarios, this can then result in heap OOB read/write. Users are advised to upgrade to a patched version.

Aliases

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu",
        "purl": "pkg:pypi/tensorflow-cpu"
      },
      "ranges": [
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            {
              "fixed": "a4e401da71458d253b05e41f28637b65baf64be4"
            },
            {
              "fixed": "1de49725a5fc4e48f1a3b902ec3599ee99283043"
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            {
              "fixed": "f19be71717c497723ba0cea0379e84f061a75e01"
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          "repo": "https://github.com/tensorflow/tensorflow",
          "type": "GIT"
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            },
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              "fixed": "2.6.3"
            }
          ],
          "type": "ECOSYSTEM"
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      "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",
        "2.3.4",
        "2.4.0",
        "2.4.1",
        "2.4.2",
        "2.4.3",
        "2.4.4",
        "2.5.0",
        "2.5.1",
        "2.5.2",
        "2.6.0",
        "2.6.1",
        "2.6.2"
      ]
    }
  ],
  "aliases": [
    "CVE-2022-23559",
    "GHSA-98p5-x8x4-c9m5"
  ],
  "details": "Tensorflow is an Open Source Machine Learning Framework. An attacker can craft a TFLite model that would cause an integer overflow in embedding lookup operations. Both `embedding_size` and `lookup_size` are products of values provided by the user. Hence, a malicious user could trigger overflows in the multiplication. In certain scenarios, this can then result in heap OOB read/write. Users are advised to upgrade to a patched version.",
  "id": "PYSEC-2022-68",
  "modified": "2022-03-09T00:17:32.298949Z",
  "published": "2022-02-04T23:15:00Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/kernels/embedding_lookup_sparse.cc#L179-L189"
    },
    {
      "type": "ADVISORY",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-98p5-x8x4-c9m5"
    },
    {
      "type": "FIX",
      "url": "https://github.com/tensorflow/tensorflow/commit/a4e401da71458d253b05e41f28637b65baf64be4"
    },
    {
      "type": "FIX",
      "url": "https://github.com/tensorflow/tensorflow/commit/1de49725a5fc4e48f1a3b902ec3599ee99283043"
    },
    {
      "type": "FIX",
      "url": "https://github.com/tensorflow/tensorflow/commit/f19be71717c497723ba0cea0379e84f061a75e01"
    }
  ]
}

cve-2022-23559

Vulnerability from cvelistv5

Published

2022-02-04 22:32

Modified

2024-08-03 03:43

Severity ?

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

Summary

Tensorflow is an Open Source Machine Learning Framework. An attacker can craft a TFLite model that would cause an integer overflow in embedding lookup operations. Both `embedding_size` and `lookup_size` are products of values provided by the user. Hence, a malicious user could trigger overflows in the multiplication. In certain scenarios, this can then result in heap OOB read/write. Users are advised to upgrade to a patched version.

References

▼	URL	Tags
	https://github.com/tensorflow/tensorflow/security/advisories/GHSA-98p5-x8x4-c9m5	x_refsource_CONFIRM
	https://github.com/tensorflow/tensorflow/commit/1de49725a5fc4e48f1a3b902ec3599ee99283043	x_refsource_MISC
	https://github.com/tensorflow/tensorflow/commit/a4e401da71458d253b05e41f28637b65baf64be4	x_refsource_MISC
	https://github.com/tensorflow/tensorflow/commit/f19be71717c497723ba0cea0379e84f061a75e01	x_refsource_MISC
	https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/kernels/embedding_lookup_sparse.cc#L179-L189	x_refsource_MISC

Impacted products

Vendor

Product

Version

▼

tensorflow

Version: >= 2.7.0, < 2.7.1
Version: >= 2.6.0, < 2.6.3
Version: < 2.5.3

Show details on NVD website

JSON

To clipboard

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ghsa-98p5-x8x4-c9m5

Vulnerability from github

Published

2022-02-09 23:52

Modified

2024-11-13 22:37

Severity ?

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

Summary

Integer overflow in TFLite

Details

Impact

An attacker can craft a TFLite model that would cause an integer overflow in embedding lookup operations:

cc int embedding_size = 1; int lookup_size = 1; for (int i = 0; i < lookup_rank - 1; i++, k++) { const int dim = dense_shape->data.i32[i]; lookup_size *= dim; output_shape->data[k] = dim; } for (int i = 1; i < embedding_rank; i++, k++) { const int dim = SizeOfDimension(value, i); embedding_size *= dim; output_shape->data[k] = dim; }

Both embedding_size and lookup_size are products of values provided by the user. Hence, a malicious user could trigger overflows in the multiplication.

In certain scenarios, this can then result in heap OOB read/write.

Patches

We have patched the issue in GitHub commits f19be71717c497723ba0cea0379e84f061a75e01, 1de49725a5fc4e48f1a3b902ec3599ee99283043 and a4e401da71458d253b05e41f28637b65baf64be4.

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 Wang Xuan of Qihoo 360 AIVul Team.

Show details on source website

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
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          ],
          "type": "ECOSYSTEM"
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    },
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        "name": "tensorflow"
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        "name": "tensorflow-gpu"
      },
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        {
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            },
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            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "2.7.0"
      ]
    }
  ],
  "aliases": [
    "CVE-2022-23559"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-190"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2022-02-03T20:34:01Z",
    "nvd_published_at": "2022-02-04T23:15:00Z",
    "severity": "HIGH"
  },
  "details": "### Impact \nAn attacker can craft a TFLite model that would cause an integer overflow [in embedding lookup operations](https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/kernels/embedding_lookup_sparse.cc#L179-L189):\n\n```cc\n  int embedding_size = 1;\n  int lookup_size = 1;\n  for (int i = 0; i \u003c lookup_rank - 1; i++, k++) {\n    const int dim = dense_shape-\u003edata.i32[i];\n    lookup_size *= dim;\n    output_shape-\u003edata[k] = dim;\n  }\n  for (int i = 1; i \u003c embedding_rank; i++, k++) {\n    const int dim = SizeOfDimension(value, i);\n    embedding_size *= dim;\n    output_shape-\u003edata[k] = dim;\n  } \n```\n\nBoth `embedding_size` and `lookup_size` are products of values provided by the user. Hence, a malicious user could trigger overflows in the multiplication.\n\nIn certain scenarios, this can then result in heap OOB read/write.\n  \n### Patches\nWe have patched the issue in GitHub commits [f19be71717c497723ba0cea0379e84f061a75e01](https://github.com/tensorflow/tensorflow/commit/f19be71717c497723ba0cea0379e84f061a75e01), [1de49725a5fc4e48f1a3b902ec3599ee99283043](https://github.com/tensorflow/tensorflow/commit/1de49725a5fc4e48f1a3b902ec3599ee99283043) and [a4e401da71458d253b05e41f28637b65baf64be4](https://github.com/tensorflow/tensorflow/commit/a4e401da71458d253b05e41f28637b65baf64be4).\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 Wang Xuan of Qihoo 360 AIVul Team. ",
  "id": "GHSA-98p5-x8x4-c9m5",
  "modified": "2024-11-13T22:37:28Z",
  "published": "2022-02-09T23:52:51Z",
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    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/commit/a4e401da71458d253b05e41f28637b65baf64be4"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/commit/f19be71717c497723ba0cea0379e84f061a75e01"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2022-68.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2022-123.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/kernels/embedding_lookup_sparse.cc#L179-L189"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H",
      "type": "CVSS_V3"
    },
    {
      "score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N",
      "type": "CVSS_V4"
    }
  ],
  "summary": "Integer overflow in TFLite"
}

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-2022-68

Vulnerability from pysec

cve-2022-23559

Vulnerability from cvelistv5

ghsa-98p5-x8x4-c9m5

Vulnerability from github

Impact

Patches

For more information

Attribution

Tags

Sightings

Nomenclature