github - GHSA-hjmq-236j-8m87

GHSA-hjmq-236j-8m87

Vulnerability from github

Published

2020-09-25 18:28

Modified

2024-10-28 15:09

Severity ?

4.0 (Medium) - CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:N/I:N/A:L
6.3 (Medium) - CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:L

Summary

Denial of service in tensorflow-lite

Details

Impact

In TensorFlow Lite models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/segment_sum.cc#L39-L44

Patches

We have patched the issue in 204945b and will release patch releases for all affected versions.

We recommend users to upgrade to TensorFlow 2.2.1, or 2.3.1.

Workarounds

A potential workaround would be to add a custom Verifier to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps.

However, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.

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 discovered from a variant analysis of GHSA-p2cq-cprg-frvm.

Show details on source website

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.2.0"
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      "versions": [
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      ],
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    },
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      },
      "ranges": [
        {
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            {
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            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
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    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.2.0"
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      ],
      "versions": [
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      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
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            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "2.3.0"
      ]
    }
  ],
  "aliases": [
    "CVE-2020-15213"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-119",
      "CWE-770"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2020-09-25T18:22:48Z",
    "nvd_published_at": "2020-09-25T19:15:00Z",
    "severity": "MODERATE"
  },
  "details": "### Impact\nIn TensorFlow Lite models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation:\nhttps://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/segment_sum.cc#L39-L44\n\n### Patches\nWe have patched the issue in 204945b and will release patch releases for all affected versions.\n\nWe recommend users to upgrade to TensorFlow 2.2.1, or 2.3.1.\n\n### Workarounds\nA potential workaround would be to add a custom `Verifier` to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps.\n\nHowever, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.\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 discovered from a variant analysis of [GHSA-p2cq-cprg-frvm](https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p2cq-cprg-frvm).",
  "id": "GHSA-hjmq-236j-8m87",
  "modified": "2024-10-28T15:09:38Z",
  "published": "2020-09-25T18:28:53Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hjmq-236j-8m87"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2020-15213"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/commit/00c7ed7ce81c2126ebc17dfe7073b5c0efd5ec0a"
    },
    {
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      "url": "https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/commit/a4030d8ba3692c438997c27be2dd95f3d5f54827"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-293.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-328.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-136.yaml"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/tensorflow/tensorflow"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/segment_sum.cc#L39-L44"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:N/I:N/A:L",
      "type": "CVSS_V3"
    },
    {
      "score": "CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:L",
      "type": "CVSS_V4"
    }
  ],
  "summary": "Denial of service in tensorflow-lite"
}

cve-2020-15213

Vulnerability from cvelistv5

Published

2020-09-25 18:50

Modified

2024-08-04 13:08

Severity ?

4.0 (Medium) - CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:N/I:N/A:L

Summary

In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.

References

▼	URL	Tags
	https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1	x_refsource_MISC
	https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a	x_refsource_MISC
	https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hjmq-236j-8m87	x_refsource_CONFIRM

Impacted products

Vendor

Product

Version

▼

tensorflow

Version: = 2.2.0
Version: = 2.3.0

Show details on NVD website

JSON

To clipboard

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pysec-2020-293

Vulnerability from pysec

Published

2020-09-25 19:15

Modified

2021-12-09 06:34

Details

In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom Verifier to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.

Aliases

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu",
        "purl": "pkg:pypi/tensorflow-cpu"
      },
      "ranges": [
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            {
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            },
            {
              "fixed": "204945b19e44b57906c9344c0d00120eeeae178a"
            }
          ],
          "repo": "https://github.com/tensorflow/tensorflow",
          "type": "GIT"
        },
        {
          "events": [
            {
              "introduced": "2.2.0"
            },
            {
              "fixed": "2.2.1"
            },
            {
              "introduced": "2.3.0"
            },
            {
              "fixed": "2.3.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "2.2.0",
        "2.3.0"
      ]
    }
  ],
  "aliases": [
    "CVE-2020-15213",
    "GHSA-hjmq-236j-8m87"
  ],
  "details": "In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.",
  "id": "PYSEC-2020-293",
  "modified": "2021-12-09T06:34:43.849481Z",
  "published": "2020-09-25T19:15:00Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hjmq-236j-8m87"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1"
    },
    {
      "type": "FIX",
      "url": "https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a"
    }
  ]
}

pysec-2020-328

Vulnerability from pysec

Published

2020-09-25 19:15

Modified

2021-12-09 06:35

Details

In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom Verifier to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.

Aliases

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu",
        "purl": "pkg:pypi/tensorflow-gpu"
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        "2.2.0",
        "2.3.0"
      ]
    }
  ],
  "aliases": [
    "CVE-2020-15213",
    "GHSA-hjmq-236j-8m87"
  ],
  "details": "In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.",
  "id": "PYSEC-2020-328",
  "modified": "2021-12-09T06:35:15.598802Z",
  "published": "2020-09-25T19:15:00Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hjmq-236j-8m87"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1"
    },
    {
      "type": "FIX",
      "url": "https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a"
    }
  ]
}

pysec-2020-136

Vulnerability from pysec

Published

2020-09-25 19:15

Modified

2020-10-01 23:15

Details

In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom Verifier to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.

Aliases

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow",
        "purl": "pkg:pypi/tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "204945b19e44b57906c9344c0d00120eeeae178a"
            }
          ],
          "repo": "https://github.com/tensorflow/tensorflow",
          "type": "GIT"
        },
        {
          "events": [
            {
              "introduced": "2.2.0"
            },
            {
              "fixed": "2.2.1"
            },
            {
              "introduced": "2.3.0"
            },
            {
              "fixed": "2.3.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "2.2.0",
        "2.3.0"
      ]
    }
  ],
  "aliases": [
    "CVE-2020-15213",
    "GHSA-hjmq-236j-8m87"
  ],
  "details": "In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.",
  "id": "PYSEC-2020-136",
  "modified": "2020-10-01T23:15:00Z",
  "published": "2020-09-25T19:15:00Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hjmq-236j-8m87"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1"
    },
    {
      "type": "FIX",
      "url": "https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a"
    }
  ]
}

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

GHSA-hjmq-236j-8m87

Vulnerability from github

Impact

Patches

Workarounds

For more information

Attribution

cve-2020-15213

Vulnerability from cvelistv5

pysec-2020-293

Vulnerability from pysec

pysec-2020-328

Vulnerability from pysec

pysec-2020-136

Vulnerability from pysec

Tags

Sightings

Nomenclature