pysec - pysec-2020-126

pysec-2020-126

Vulnerability from pysec

Published

2020-09-25 19:15

Modified

2020-10-29 16:15

Details

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, by controlling the fill argument of tf.strings.as_string, a malicious attacker is able to trigger a format string vulnerability due to the way the internal format use in a printf call is constructed. This may result in segmentation fault. The issue is patched in commit 33be22c65d86256e6826666662e40dbdfe70ee83, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

Aliases

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow",
        "purl": "pkg:pypi/tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "33be22c65d86256e6826666662e40dbdfe70ee83"
            }
          ],
          "repo": "https://github.com/tensorflow/tensorflow",
          "type": "GIT"
        },
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "1.15.4"
            },
            {
              "introduced": "2.0.0"
            },
            {
              "fixed": "2.0.3"
            },
            {
              "introduced": "2.1.0"
            },
            {
              "fixed": "2.1.2"
            },
            {
              "introduced": "2.2.0"
            },
            {
              "fixed": "2.2.1"
            },
            {
              "introduced": "2.3.0"
            },
            {
              "fixed": "2.3.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "0.12.0rc0",
        "0.12.0rc1",
        "0.12.0",
        "0.12.1",
        "1.0.0",
        "1.0.1",
        "1.1.0rc0",
        "1.1.0rc1",
        "1.1.0rc2",
        "1.1.0",
        "1.2.0rc0",
        "1.2.0rc1",
        "1.2.0rc2",
        "1.2.0",
        "1.2.1",
        "1.3.0rc0",
        "1.3.0rc1",
        "1.3.0rc2",
        "1.3.0",
        "1.4.0rc0",
        "1.4.0rc1",
        "1.4.0",
        "1.4.1",
        "1.5.0rc0",
        "1.5.0rc1",
        "1.5.0",
        "1.5.1",
        "1.6.0rc0",
        "1.6.0rc1",
        "1.6.0",
        "1.7.0rc0",
        "1.7.0rc1",
        "1.7.0",
        "1.7.1",
        "1.8.0rc0",
        "1.8.0rc1",
        "1.8.0",
        "1.9.0rc0",
        "1.9.0rc1",
        "1.9.0rc2",
        "1.9.0",
        "1.10.0rc0",
        "1.10.0rc1",
        "1.10.0",
        "1.10.1",
        "1.11.0rc0",
        "1.11.0rc1",
        "1.11.0rc2",
        "1.11.0",
        "1.12.0rc0",
        "1.12.0rc1",
        "1.12.0rc2",
        "1.12.0",
        "1.12.2",
        "1.12.3",
        "1.13.0rc0",
        "1.13.0rc1",
        "1.13.0rc2",
        "1.13.1",
        "1.13.2",
        "1.14.0rc0",
        "1.14.0rc1",
        "1.14.0",
        "1.15.0rc0",
        "1.15.0rc1",
        "1.15.0rc2",
        "1.15.0rc3",
        "1.15.0",
        "1.15.2",
        "1.15.3",
        "2.0.0",
        "2.0.1",
        "2.0.2",
        "2.1.0",
        "2.1.1",
        "2.2.0",
        "2.3.0"
      ]
    }
  ],
  "aliases": [
    "CVE-2020-15203",
    "GHSA-xmq7-7fxm-rr79"
  ],
  "details": "In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, by controlling the `fill` argument of tf.strings.as_string, a malicious attacker is able to trigger a format string vulnerability due to the way the internal format use in a `printf` call is constructed. This may result in segmentation fault. The issue is patched in commit 33be22c65d86256e6826666662e40dbdfe70ee83, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.",
  "id": "PYSEC-2020-126",
  "modified": "2020-10-29T16:15:00Z",
  "published": "2020-09-25T19:15:00Z",
  "references": [
    {
      "type": "FIX",
      "url": "https://github.com/tensorflow/tensorflow/commit/33be22c65d86256e6826666662e40dbdfe70ee83"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1"
    },
    {
      "type": "ADVISORY",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xmq7-7fxm-rr79"
    },
    {
      "type": "WEB",
      "url": "http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html"
    }
  ]
}

ghsa-xmq7-7fxm-rr79

Vulnerability from github

Published

2020-09-25 18:28

Modified

2024-10-28 21:23

Severity ?

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

Summary

Denial of Service in Tensorflow

Details

Impact

By controlling the fill argument of tf.strings.as_string, a malicious attacker is able to trigger a format string vulnerability due to the way the internal format use in a printf call is constructed: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/as_string_op.cc#L68-L74

This can result in unexpected output: python In [1]: tf.strings.as_string(input=[1234], width=6, fill='-') Out[1]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['1234 '], dtype=object)> In [2]: tf.strings.as_string(input=[1234], width=6, fill='+') Out[2]: <tf.Tensor: shape=(1,), dtype=string, numpy=array([' +1234'], dtype=object)> In [3]: tf.strings.as_string(input=[1234], width=6, fill="h") Out[3]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['%6d'], dtype=object)> In [4]: tf.strings.as_string(input=[1234], width=6, fill="d") Out[4]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['12346d'], dtype=object)> In [5]: tf.strings.as_string(input=[1234], width=6, fill="o") Out[5]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['23226d'], dtype=object)> In [6]: tf.strings.as_string(input=[1234], width=6, fill="x") Out[6]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['4d26d'], dtype=object)> In [7]: tf.strings.as_string(input=[1234], width=6, fill="g") Out[7]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['8.67458e-3116d'], dtype=object)> In [8]: tf.strings.as_string(input=[1234], width=6, fill="a") Out[8]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['0x0.00ff7eebb4d4p-10226d'], dtype=object)> In [9]: tf.strings.as_string(input=[1234], width=6, fill="c") Out[9]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['\xd26d'], dtype=object)> In [10]: tf.strings.as_string(input=[1234], width=6, fill="p") Out[10]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['0x4d26d'], dtype=object)> In [11]: tf.strings.as_string(input=[1234], width=6, fill='m') Out[11]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['Success6d'], dtype=object)>

However, passing in n or s results in segmentation fault.

Patches

We have patched the issue in 33be22c65d86256e6826666662e40dbdfe70ee83 and will release patch releases for all versions between 1.15 and 2.3.

We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

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 members of the Aivul Team from Qihoo 360.

Show details on source website

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "1.15.4"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
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            {
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            }
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          "type": "ECOSYSTEM"
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      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
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              "introduced": "2.1.0"
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      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
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          "events": [
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              "introduced": "2.2.0"
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            }
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          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "2.2.0"
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    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
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      "ranges": [
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          "events": [
            {
              "introduced": "2.3.0"
            },
            {
              "fixed": "2.3.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "2.3.0"
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
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              "introduced": "0"
            },
            {
              "fixed": "1.15.4"
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          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
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        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
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      "ranges": [
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          "events": [
            {
              "introduced": "2.1.0"
            },
            {
              "fixed": "2.1.2"
            }
          ],
          "type": "ECOSYSTEM"
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      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.2.0"
            },
            {
              "fixed": "2.2.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "2.2.0"
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.3.0"
            },
            {
              "fixed": "2.3.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "2.3.0"
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "1.15.4"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.0.0"
            },
            {
              "fixed": "2.0.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.1.0"
            },
            {
              "fixed": "2.1.2"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.2.0"
            },
            {
              "fixed": "2.2.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "2.2.0"
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.3.0"
            },
            {
              "fixed": "2.3.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "2.3.0"
      ]
    }
  ],
  "aliases": [
    "CVE-2020-15203"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-134",
      "CWE-20"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2020-09-25T17:34:02Z",
    "nvd_published_at": "2020-09-25T19:15:00Z",
    "severity": "HIGH"
  },
  "details": "### Impact\nBy controlling the `fill` argument of [`tf.strings.as_string`](https://www.tensorflow.org/api_docs/python/tf/strings/as_string), a malicious attacker is able to trigger a format string vulnerability due to the way the internal format use in a `printf` call is constructed: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/as_string_op.cc#L68-L74\n\nThis can result in unexpected output:\n```python\nIn [1]: tf.strings.as_string(input=[1234], width=6, fill=\u0027-\u0027)                                                                     \nOut[1]: \u003ctf.Tensor: shape=(1,), dtype=string, numpy=array([\u00271234  \u0027], dtype=object)\u003e                                              \nIn [2]: tf.strings.as_string(input=[1234], width=6, fill=\u0027+\u0027)                                                                     \nOut[2]: \u003ctf.Tensor: shape=(1,), dtype=string, numpy=array([\u0027 +1234\u0027], dtype=object)\u003e \nIn [3]: tf.strings.as_string(input=[1234], width=6, fill=\"h\")                                                                     \nOut[3]: \u003ctf.Tensor: shape=(1,), dtype=string, numpy=array([\u0027%6d\u0027], dtype=object)\u003e \nIn [4]: tf.strings.as_string(input=[1234], width=6, fill=\"d\")                                                                     \nOut[4]: \u003ctf.Tensor: shape=(1,), dtype=string, numpy=array([\u002712346d\u0027], dtype=object)\u003e \nIn [5]: tf.strings.as_string(input=[1234], width=6, fill=\"o\")\nOut[5]: \u003ctf.Tensor: shape=(1,), dtype=string, numpy=array([\u002723226d\u0027], dtype=object)\u003e\nIn [6]: tf.strings.as_string(input=[1234], width=6, fill=\"x\")\nOut[6]: \u003ctf.Tensor: shape=(1,), dtype=string, numpy=array([\u00274d26d\u0027], dtype=object)\u003e\nIn [7]: tf.strings.as_string(input=[1234], width=6, fill=\"g\")\nOut[7]: \u003ctf.Tensor: shape=(1,), dtype=string, numpy=array([\u00278.67458e-3116d\u0027], dtype=object)\u003e\nIn [8]: tf.strings.as_string(input=[1234], width=6, fill=\"a\")\nOut[8]: \u003ctf.Tensor: shape=(1,), dtype=string, numpy=array([\u00270x0.00ff7eebb4d4p-10226d\u0027], dtype=object)\u003e\nIn [9]: tf.strings.as_string(input=[1234], width=6, fill=\"c\")\nOut[9]: \u003ctf.Tensor: shape=(1,), dtype=string, numpy=array([\u0027\\xd26d\u0027], dtype=object)\u003e\nIn [10]: tf.strings.as_string(input=[1234], width=6, fill=\"p\")\nOut[10]: \u003ctf.Tensor: shape=(1,), dtype=string, numpy=array([\u00270x4d26d\u0027], dtype=object)\u003e\nIn [11]: tf.strings.as_string(input=[1234], width=6, fill=\u0027m\u0027) \nOut[11]: \u003ctf.Tensor: shape=(1,), dtype=string, numpy=array([\u0027Success6d\u0027], dtype=object)\u003e\n```\n\nHowever, passing in `n` or `s` results in segmentation fault.\n\n### Patches\nWe have patched the issue in 33be22c65d86256e6826666662e40dbdfe70ee83 and will release patch releases for all versions between 1.15 and 2.3.\n\nWe recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.\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 members of the Aivul Team from Qihoo 360.",
  "id": "GHSA-xmq7-7fxm-rr79",
  "modified": "2024-10-28T21:23:19Z",
  "published": "2020-09-25T18:28:37Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xmq7-7fxm-rr79"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2020-15203"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/commit/33be22c65d86256e6826666662e40dbdfe70ee83"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-283.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-318.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-126.yaml"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/tensorflow/tensorflow"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1"
    },
    {
      "type": "WEB",
      "url": "http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
      "type": "CVSS_V3"
    },
    {
      "score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N",
      "type": "CVSS_V4"
    }
  ],
  "summary": "Denial of Service in Tensorflow"
}

cve-2020-15203

Vulnerability from cvelistv5

Published

2020-09-25 18:46

Modified

2024-08-04 13:08

Severity ?

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

Summary

In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, by controlling the `fill` argument of tf.strings.as_string, a malicious attacker is able to trigger a format string vulnerability due to the way the internal format use in a `printf` call is constructed. This may result in segmentation fault. The issue is patched in commit 33be22c65d86256e6826666662e40dbdfe70ee83, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.

References

▼	URL	Tags
	https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1	x_refsource_MISC
	https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xmq7-7fxm-rr79	x_refsource_CONFIRM
	https://github.com/tensorflow/tensorflow/commit/33be22c65d86256e6826666662e40dbdfe70ee83	x_refsource_MISC
	http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html	vendor-advisory, x_refsource_SUSE

Impacted products

Vendor

Product

Version

▼

tensorflow

Version: < 1.15.4
Version: >= 2.0.0, < 2.0.3
Version: >= 2.1.0, < 2.1.2
Version: >= 2.2.0, < 2.2.1
Version: >= 2.3.0, < 2.3.1

Show details on NVD website

JSON

To clipboard

{
  "containers": {
    "adp": [
      {
        "providerMetadata": {
          "dateUpdated": "2024-08-04T13:08:22.972Z",
          "orgId": "af854a3a-2127-422b-91ae-364da2661108",
          "shortName": "CVE"
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        "references": [
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            "url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1"
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          {
            "name": "openSUSE-SU-2020:1766",
            "tags": [
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              "x_transferred"
            ],
            "url": "http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html"
          }
        ],
        "title": "CVE Program Container"
      }
    ],
    "cna": {
      "affected": [
        {
          "product": "tensorflow",
          "vendor": "tensorflow",
          "versions": [
            {
              "status": "affected",
              "version": "\u003c 1.15.4"
            },
            {
              "status": "affected",
              "version": "\u003e= 2.0.0, \u003c 2.0.3"
            },
            {
              "status": "affected",
              "version": "\u003e= 2.1.0, \u003c 2.1.2"
            },
            {
              "status": "affected",
              "version": "\u003e= 2.2.0, \u003c 2.2.1"
            },
            {
              "status": "affected",
              "version": "\u003e= 2.3.0, \u003c 2.3.1"
            }
          ]
        }
      ],
      "descriptions": [
        {
          "lang": "en",
          "value": "In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, by controlling the `fill` argument of tf.strings.as_string, a malicious attacker is able to trigger a format string vulnerability due to the way the internal format use in a `printf` call is constructed. This may result in segmentation fault. The issue is patched in commit 33be22c65d86256e6826666662e40dbdfe70ee83, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1."
        }
      ],
      "metrics": [
        {
          "cvssV3_1": {
            "attackComplexity": "LOW",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 7.5,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
            "version": "3.1"
          }
        }
      ],
      "problemTypes": [
        {
          "descriptions": [
            {
              "cweId": "CWE-20",
              "description": "{\"CWE-20\":\"Improper Input Validation\"}",
              "lang": "en",
              "type": "CWE"
            }
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        }
      ],
      "providerMetadata": {
        "dateUpdated": "2020-10-29T15:06:13",
        "orgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
        "shortName": "GitHub_M"
      },
      "references": [
        {
          "tags": [
            "x_refsource_MISC"
          ],
          "url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1"
        },
        {
          "tags": [
            "x_refsource_CONFIRM"
          ],
          "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xmq7-7fxm-rr79"
        },
        {
          "tags": [
            "x_refsource_MISC"
          ],
          "url": "https://github.com/tensorflow/tensorflow/commit/33be22c65d86256e6826666662e40dbdfe70ee83"
        },
        {
          "name": "openSUSE-SU-2020:1766",
          "tags": [
            "vendor-advisory",
            "x_refsource_SUSE"
          ],
          "url": "http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html"
        }
      ],
      "source": {
        "advisory": "GHSA-xmq7-7fxm-rr79",
        "discovery": "UNKNOWN"
      },
      "title": "Denial of Service in Tensorflow",
      "x_legacyV4Record": {
        "CVE_data_meta": {
          "ASSIGNER": "security-advisories@github.com",
          "ID": "CVE-2020-15203",
          "STATE": "PUBLIC",
          "TITLE": "Denial of Service in Tensorflow"
        },
        "affects": {
          "vendor": {
            "vendor_data": [
              {
                "product": {
                  "product_data": [
                    {
                      "product_name": "tensorflow",
                      "version": {
                        "version_data": [
                          {
                            "version_value": "\u003c 1.15.4"
                          },
                          {
                            "version_value": "\u003e= 2.0.0, \u003c 2.0.3"
                          },
                          {
                            "version_value": "\u003e= 2.1.0, \u003c 2.1.2"
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                            "version_value": "\u003e= 2.2.0, \u003c 2.2.1"
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                      }
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        "data_format": "MITRE",
        "data_type": "CVE",
        "data_version": "4.0",
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          "description_data": [
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              "lang": "eng",
              "value": "In Tensorflow before versions 1.15.4, 2.0.3, 2.1.2, 2.2.1 and 2.3.1, by controlling the `fill` argument of tf.strings.as_string, a malicious attacker is able to trigger a format string vulnerability due to the way the internal format use in a `printf` call is constructed. This may result in segmentation fault. The issue is patched in commit 33be22c65d86256e6826666662e40dbdfe70ee83, and is released in TensorFlow versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1."
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            "attackVector": "NETWORK",
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            "baseScore": 7.5,
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            "confidentialityImpact": "NONE",
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          "reference_data": [
            {
              "name": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1",
              "refsource": "MISC",
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            },
            {
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              "refsource": "CONFIRM",
              "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xmq7-7fxm-rr79"
            },
            {
              "name": "https://github.com/tensorflow/tensorflow/commit/33be22c65d86256e6826666662e40dbdfe70ee83",
              "refsource": "MISC",
              "url": "https://github.com/tensorflow/tensorflow/commit/33be22c65d86256e6826666662e40dbdfe70ee83"
            },
            {
              "name": "openSUSE-SU-2020:1766",
              "refsource": "SUSE",
              "url": "http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html"
            }
          ]
        },
        "source": {
          "advisory": "GHSA-xmq7-7fxm-rr79",
          "discovery": "UNKNOWN"
        }
      }
    }
  },
  "cveMetadata": {
    "assignerOrgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
    "assignerShortName": "GitHub_M",
    "cveId": "CVE-2020-15203",
    "datePublished": "2020-09-25T18:46:08",
    "dateReserved": "2020-06-25T00:00:00",
    "dateUpdated": "2024-08-04T13:08:22.972Z",
    "state": "PUBLISHED"
  },
  "dataType": "CVE_RECORD",
  "dataVersion": "5.1"
}

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-2020-126

Vulnerability from pysec

ghsa-xmq7-7fxm-rr79

Vulnerability from github

Impact

Patches

For more information

Attribution

cve-2020-15203

Vulnerability from cvelistv5

Tags

Sightings

Nomenclature