cvelistv5 - cve-2020-15203

URL	Tags
security-advisories@github.com	http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html	Mailing List, Third Party Advisory
security-advisories@github.com	https://github.com/tensorflow/tensorflow/commit/33be22c65d86256e6826666662e40dbdfe70ee83	Patch, Third Party Advisory
security-advisories@github.com	https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1	Third Party Advisory
security-advisories@github.com	https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xmq7-7fxm-rr79	Exploit, Third Party Advisory
af854a3a-2127-422b-91ae-364da2661108	http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html	Mailing List, Third Party Advisory
af854a3a-2127-422b-91ae-364da2661108	https://github.com/tensorflow/tensorflow/commit/33be22c65d86256e6826666662e40dbdfe70ee83	Patch, Third Party Advisory
af854a3a-2127-422b-91ae-364da2661108	https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1	Third Party Advisory
af854a3a-2127-422b-91ae-364da2661108	https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xmq7-7fxm-rr79	Exploit, Third Party Advisory

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": [
        {
          "events": [
            {
              "introduced": "2.0.0"
            },
            {
              "fixed": "2.0.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.1.0"
            },
            {
              "fixed": "2.1.2"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.2.0"
            },
            {
              "fixed": "2.2.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "2.2.0"
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.3.0"
            },
            {
              "fixed": "2.3.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "2.3.0"
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "1.15.4"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.0.0"
            },
            {
              "fixed": "2.0.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.1.0"
            },
            {
              "fixed": "2.1.2"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "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"
}

pysec-2020-283

Vulnerability from pysec

Published

2020-09-25 19:15

Modified

2021-12-09 06:34

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-cpu",
        "purl": "pkg:pypi/tensorflow-cpu"
      },
      "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": [
        "1.15.0",
        "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-2020-283",
  "modified": "2021-12-09T06:34:42.062393Z",
  "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"
    }
  ]
}

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",
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        "1.1.0rc0",
        "1.1.0rc1",
        "1.1.0rc2",
        "1.1.0",
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        "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"
    }
  ]
}

pysec-2020-318

Vulnerability from pysec

Published

2020-09-25 19:15

Modified

2021-12-09 06:35

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-gpu",
        "purl": "pkg:pypi/tensorflow-gpu"
      },
      "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": [
        "0.12.0",
        "0.12.1",
        "1.0.0",
        "1.0.1",
        "1.1.0",
        "1.10.0",
        "1.10.1",
        "1.11.0",
        "1.12.0",
        "1.12.2",
        "1.12.3",
        "1.13.1",
        "1.13.2",
        "1.14.0",
        "1.15.0",
        "1.15.2",
        "1.15.3",
        "1.2.0",
        "1.2.1",
        "1.3.0",
        "1.4.0",
        "1.4.1",
        "1.5.0",
        "1.5.1",
        "1.6.0",
        "1.7.0",
        "1.7.1",
        "1.8.0",
        "1.9.0",
        "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-2020-318",
  "modified": "2021-12-09T06:35:13.671465Z",
  "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"
    }
  ]
}

gsd-2020-15203

Vulnerability from gsd

Modified

2023-12-13 01:21

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

CVE-2020-15203

Aliases

CVE-2020-15203

JSON

To clipboard

{
  "GSD": {
    "alias": "CVE-2020-15203",
    "description": "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": "GSD-2020-15203",
    "references": [
      "https://www.suse.com/security/cve/CVE-2020-15203.html"
    ]
  },
  "gsd": {
    "metadata": {
      "exploitCode": "unknown",
      "remediation": "unknown",
      "reportConfidence": "confirmed",
      "type": "vulnerability"
    },
    "osvSchema": {
      "aliases": [
        "CVE-2020-15203"
      ],
      "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": "GSD-2020-15203",
      "modified": "2023-12-13T01:21:43.757311Z",
      "schema_version": "1.4.0"
    }
  },
  "namespaces": {
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        "ID": "CVE-2020-15203",
        "STATE": "PUBLIC",
        "TITLE": "Denial of Service in Tensorflow"
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        "vendor": {
          "vendor_data": [
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              "product": {
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      "impact": {
        "cvss": {
          "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",
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      "problemtype": {
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      },
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    },
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          "affected_range": "\u003c1.15.4||\u003e=2.0.0,\u003c2.0.3||\u003e=2.1.0,\u003c2.1.2||\u003e=2.2.0,\u003c2.2.1||\u003e=2.3.0,\u003c2.3.1",
          "affected_versions": "All versions before 1.15.4, all versions starting from 2.0.0 before 2.0.3, all versions starting from 2.1.0 before 2.1.2, all versions starting from 2.2.0 before 2.2.1, all versions starting from 2.3.0 before 2.3.1",
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          "package_slug": "pypi/tensorflow-cpu",
          "pubdate": "2020-09-25",
          "solution": "Upgrade to versions 2.1.2, 2.2.1, 2.3.1 or above.",
          "title": "Improper Input Validation",
          "urls": [
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          "affected_versions": "All versions before 1.15.4, all versions starting from 2.0.0 before 2.0.3, all versions starting from 2.1.0 before 2.1.2, all versions starting from 2.2.0 before 2.2.1, all versions starting from 2.3.0 before 2.3.1",
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            "CWE-937"
          ],
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            "2.0.3",
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          "affected_versions": "All versions before 1.15.4, all versions starting from 2.0.0 before 2.0.3, all versions starting from 2.1.0 before 2.1.2, all versions starting from 2.2.0 before 2.2.1, all versions starting from 2.3.0 before 2.3.1",
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          "cwe_ids": [
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            "CWE-134",
            "CWE-937"
          ],
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            "2.0.3",
            "2.1.2",
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          "package_slug": "pypi/tensorflow",
          "pubdate": "2020-09-25",
          "solution": "Upgrade to versions 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 or above.",
          "title": "Improper Input Validation",
          "urls": [
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          ],
          "uuid": "73924ca7-f598-4948-9f2a-c20aa1f9e7a2"
        }
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                "Patch",
                "Third Party Advisory"
              ],
              "url": "https://github.com/tensorflow/tensorflow/commit/33be22c65d86256e6826666662e40dbdfe70ee83"
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            },
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          ]
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      },
      "impact": {
        "baseMetricV2": {
          "acInsufInfo": false,
          "cvssV2": {
            "accessComplexity": "LOW",
            "accessVector": "NETWORK",
            "authentication": "NONE",
            "availabilityImpact": "PARTIAL",
            "baseScore": 5.0,
            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "vectorString": "AV:N/AC:L/Au:N/C:N/I:N/A:P",
            "version": "2.0"
          },
          "exploitabilityScore": 10.0,
          "impactScore": 2.9,
          "obtainAllPrivilege": false,
          "obtainOtherPrivilege": false,
          "obtainUserPrivilege": false,
          "severity": "MEDIUM",
          "userInteractionRequired": false
        },
        "baseMetricV3": {
          "cvssV3": {
            "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"
          },
          "exploitabilityScore": 3.9,
          "impactScore": 3.6
        }
      },
      "lastModifiedDate": "2021-11-18T17:26Z",
      "publishedDate": "2020-09-25T19:15Z"
    }
  }
}

cve-2020-15203

Vulnerability from cvelistv5

Tags

Sightings

Nomenclature

Action not permitted

cve-2020-15203

Vulnerability from cvelistv5

ghsa-xmq7-7fxm-rr79

Vulnerability from github

Impact

Patches

For more information

Attribution

pysec-2020-283

Vulnerability from pysec

pysec-2020-126

Vulnerability from pysec

pysec-2020-318

Vulnerability from pysec

gsd-2020-15203

Vulnerability from gsd

Tags

Sightings

Nomenclature