CVE-2020-15203: Tensorflow Denial of Service Vulnerability

Q: What is CVE-2020-15203?

CVE-2020-15203 is a security vulnerability in Tensorflow involving Denial of Service in Tensorflow under CWE-134: Use of Externally-Controlled Format String.

Q: Which Tensorflow versions are affected?

Affected versions include releases prior to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 across affected packages: tensorflow, tensorflow-cpu, tensorflow-gpu.

Q: What version fixes CVE-2020-15203?

Upgrading to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 (or the latest patched release for your respective branch) resolves the vulnerability.

Q: Is tensorflow affected?

Yes, tensorflow is affected in releases prior to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1. Upgrade your dependency to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 or later.

Q: Is tensorflow-cpu affected?

Yes, tensorflow-cpu is affected in releases prior to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1. Upgrade your dependency to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 or later.

Q: What is the CVSS score of CVE-2020-15203?

CVE-2020-15203 has a CVSS v3.1 base score of 7.5 (HIGH) with vector string CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H.

Q: What CWE is associated with CVE-2020-15203?

CVE-2020-15203 is classified under CWE-134: Use of Externally-Controlled Format String (The product uses a function that accepts a format string as an argument, but the format string originates from an external source.).

Q: How can I check PyPI dependencies for CVE-2020-15203?

Run the dependency tree command: 'pip show tensorflow'. This command lists direct and transitive dependencies in your build configuration so you can verify if a vulnerable version is resolved.

HIGH

Advisory Identifier: GHSA-xmq7-7fxm-rr79

CVSS 7.5

CWE-134 CWE-20

Published 2020-09-25

Advisory Summary

CVE-2020-15203 is a HIGH (CVSS 7.5) denial of service vulnerability (CWE-134: Use of Externally-Controlled Format String) affecting Tensorflow libraries in the PyPI ecosystem. The issue involves denial of service in tensorflow. Remediation is available by upgrading affected packages to version 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1.

CVE-2020-15203 at a Glance

CVE Identifier CVE-2020-15203

GHSA Identifier GHSA-xmq7-7fxm-rr79

Severity HIGH

CVSS v3.1 Base Score 7.5

CWE Classification CWE-134: Use of Externally-Controlled Format String

Affected Product Tensorflow

Package Ecosystem PyPI

Published Date 2020-09-25

Last Updated 2024-10-28

Security Impact

CVSS: 7.5

The security impact metrics below are parsed directly from the CVSS v3.1 vector string (CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H).

Attack Vector Network (AV:N)

Attack Complexity Low (AC:L)

Privileges Required None (PR:N)

User Interaction None (UI:N)

Scope Unchanged (S:U)

Confidentiality None (C:N)

Integrity None (I:N)

Availability High (A:H)

How to Fix CVE-2020-15203

To remediate this vulnerability, upgrade all affected Tensorflow dependencies to a patched release. Both direct and transitive dependencies should be checked, as frameworks or security libraries may pull in vulnerable versions transitively.

tensorflow fixed: 1.15.4

pip install tensorflow>=1.15.4

tensorflow fixed: 2.0.3

pip install tensorflow>=2.0.3

tensorflow fixed: 2.1.2

pip install tensorflow>=2.1.2

tensorflow fixed: 2.2.1

pip install tensorflow>=2.2.1

tensorflow fixed: 2.3.1

pip install tensorflow>=2.3.1

tensorflow-cpu fixed: 1.15.4

pip install tensorflow-cpu>=1.15.4

tensorflow-cpu fixed: 2.0.3

pip install tensorflow-cpu>=2.0.3

tensorflow-cpu fixed: 2.1.2

pip install tensorflow-cpu>=2.1.2

tensorflow-cpu fixed: 2.2.1

pip install tensorflow-cpu>=2.2.1

tensorflow-cpu fixed: 2.3.1

pip install tensorflow-cpu>=2.3.1

tensorflow-gpu fixed: 1.15.4

pip install tensorflow-gpu>=1.15.4

tensorflow-gpu fixed: 2.0.3

pip install tensorflow-gpu>=2.0.3

tensorflow-gpu fixed: 2.1.2

pip install tensorflow-gpu>=2.1.2

tensorflow-gpu fixed: 2.2.1

pip install tensorflow-gpu>=2.2.1

tensorflow-gpu fixed: 2.3.1

pip install tensorflow-gpu>=2.3.1

Verify Direct and Transitive Dependencies

Run the following command in your project directory to verify that all resolved dependencies use the patched release:

pip show tensorflow

How to Check if You Are Affected

To determine whether your Python environment is affected by CVE-2020-15203, inspect your installed packages using pip:

pip show tensorflow

Analyze the terminal output to locate any version of Tensorflow in your dependency tree. If any resolved version matches the vulnerable ranges listed in the table below (for example, versions earlier than 1.15.4), your application is affected.

Note: The pip show tensorflow command checks currently installed packages in your active Python environment.

Technical Details

Vulnerability Mechanism & Technical Analysis

This vulnerability is classified under CWE-134: Use of Externally-Controlled Format String, where the product uses a function that accepts a format string as an argument, but the format string originates from an external source. In Tensorflow, software state validation or input boundaries are circumvented during processing. Review the official source advisory statement below for specific affected components, classes, and reproduction details.

Source Advisory Statement

### Impact By 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 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](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. ### Attribution This vulnerability has been reported by members of the Aivul Team from Qihoo 360.

CVE-2020-15203 Affected Tensorflow Versions

15 component(s)

Fixed Versions:

1.15.42.0.32.1.22.2.12.3.1

Product / Package	Ecosystem	Affected Versions	Fixed Version
tensorflow pypi/tensorflow	PyPI	< 1.15.4	1.15.4
tensorflow pypi/tensorflow	PyPI	≥ 2.0.0 < 2.0.3	2.0.3
tensorflow pypi/tensorflow	PyPI	≥ 2.1.0 < 2.1.2	2.1.2
tensorflow pypi/tensorflow	PyPI	≥ 2.2.0 < 2.2.1 All 1 versions 2.2.0	2.2.1
tensorflow pypi/tensorflow	PyPI	≥ 2.3.0 < 2.3.1 All 1 versions 2.3.0	2.3.1
tensorflow-cpu pypi/tensorflow-cpu	PyPI	< 1.15.4	1.15.4
tensorflow-cpu pypi/tensorflow-cpu	PyPI	≥ 2.0.0 < 2.0.3	2.0.3
tensorflow-cpu pypi/tensorflow-cpu	PyPI	≥ 2.1.0 < 2.1.2	2.1.2
tensorflow-cpu pypi/tensorflow-cpu	PyPI	≥ 2.2.0 < 2.2.1 All 1 versions 2.2.0	2.2.1
tensorflow-cpu pypi/tensorflow-cpu	PyPI	≥ 2.3.0 < 2.3.1 All 1 versions 2.3.0	2.3.1
tensorflow-gpu pypi/tensorflow-gpu	PyPI	< 1.15.4	1.15.4
tensorflow-gpu pypi/tensorflow-gpu	PyPI	≥ 2.0.0 < 2.0.3	2.0.3
tensorflow-gpu pypi/tensorflow-gpu	PyPI	≥ 2.1.0 < 2.1.2	2.1.2
tensorflow-gpu pypi/tensorflow-gpu	PyPI	≥ 2.2.0 < 2.2.1 All 1 versions 2.2.0	2.2.1
tensorflow-gpu pypi/tensorflow-gpu	PyPI	≥ 2.3.0 < 2.3.1 All 1 versions 2.3.0	2.3.1

References

GitHub Advisory / Wiki ADVISORY

https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xmq7-7fxm-rr79

NVD — CVE-2020-15203 NVD

https://nvd.nist.gov/vuln/detail/CVE-2020-15203

Security Fix Commit FIX COMMIT

https://github.com/tensorflow/tensorflow/commit/33be22c65d86256e6826666662e40dbdfe70ee83

Tensorflow Source Repository REPOSITORY

https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-283.yaml

Tensorflow Source Repository REPOSITORY

https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-318.yaml

Tensorflow Source Repository REPOSITORY

https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-126.yaml

Tensorflow Source Repository REPOSITORY

https://github.com/tensorflow/tensorflow

Tensorflow Release RELEASE

https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1

Tensorflow Security Information SECURITY

http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html

Frequently Asked Questions

What is CVE-2020-15203?

CVE-2020-15203 is a security vulnerability in Tensorflow involving Denial of Service in Tensorflow under CWE-134: Use of Externally-Controlled Format String.

Which Tensorflow versions are affected?

Affected versions include releases prior to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 across affected packages: tensorflow, tensorflow-cpu, tensorflow-gpu.

What version fixes CVE-2020-15203?

Upgrading to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 (or the latest patched release for your respective branch) resolves the vulnerability.

Is tensorflow affected?

Yes, tensorflow is affected in releases prior to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1. Upgrade your dependency to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 or later.

Is tensorflow-cpu affected?

Yes, tensorflow-cpu is affected in releases prior to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1. Upgrade your dependency to 1.15.4, 2.0.3, 2.1.2, 2.2.1, 2.3.1 or later.

What is the CVSS score of CVE-2020-15203?

CVE-2020-15203 has a CVSS v3.1 base score of 7.5 (HIGH) with vector string CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H.

What CWE is associated with CVE-2020-15203?

CVE-2020-15203 is classified under CWE-134: Use of Externally-Controlled Format String (The product uses a function that accepts a format string as an argument, but the format string originates from an external source.).

How can I check PyPI dependencies for CVE-2020-15203?

Run the dependency tree command: 'pip show tensorflow'. This command lists direct and transitive dependencies in your build configuration so you can verify if a vulnerable version is resolved.

Machine-readable OSV JSON OSV v1.5 Schema

Normalized schema output

View Raw JSON

{
  "schema_version" : "1.4.0",
  "id" : "GHSA-xmq7-7fxm-rr79",
  "modified" : "2024-10-28T21:23:19Z",
  "published" : "2020-09-25T18:28:37Z",
  "aliases" : [ "CVE-2020-15203" ],
  "summary" : "Denial of Service in Tensorflow",
  "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='-')                                                                     \nOut[1]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['1234  '], dtype=object)>                                              \nIn [2]: tf.strings.as_string(input=[1234], width=6, fill='+')                                                                     \nOut[2]: <tf.Tensor: shape=(1,), dtype=string, numpy=array([' +1234'], dtype=object)> \nIn [3]: tf.strings.as_string(input=[1234], width=6, fill=\"h\")                                                                     \nOut[3]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['%6d'], dtype=object)> \nIn [4]: tf.strings.as_string(input=[1234], width=6, fill=\"d\")                                                                     \nOut[4]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['12346d'], dtype=object)> \nIn [5]: tf.strings.as_string(input=[1234], width=6, fill=\"o\")\nOut[5]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['23226d'], dtype=object)>\nIn [6]: tf.strings.as_string(input=[1234], width=6, fill=\"x\")\nOut[6]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['4d26d'], dtype=object)>\nIn [7]: tf.strings.as_string(input=[1234], width=6, fill=\"g\")\nOut[7]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['8.67458e-3116d'], dtype=object)>\nIn [8]: tf.strings.as_string(input=[1234], width=6, fill=\"a\")\nOut[8]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['0x0.00ff7eebb4d4p-10226d'], dtype=object)>\nIn [9]: tf.strings.as_string(input=[1234], width=6, fill=\"c\")\nOut[9]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['\\xd26d'], dtype=object)>\nIn [10]: tf.strings.as_string(input=[1234], width=6, fill=\"p\")\nOut[10]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['0x4d26d'], dtype=object)>\nIn [11]: tf.strings.as_string(input=[1234], width=6, fill='m') \nOut[11]: <tf.Tensor: shape=(1,), dtype=string, numpy=array(['Success6d'], dtype=object)>\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.",
  "severity" : [ {
    "type" : "CVSS_V3",
    "score" : "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H"
  }, {
    "type" : "CVSS_V4",
    "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"
  } ],
  "affected" : [ {
    "package" : {
      "ecosystem" : "PyPI",
      "name" : "tensorflow"
    },
    "ranges" : [ {
      "type" : "ECOSYSTEM",
      "events" : [ {
        "introduced" : "0"
      }, {
        "fixed" : "1.15.4"
      } ]
    } ]
  }, {
    "package" : {
      "ecosystem" : "PyPI",
      "name" : "tensorflow"
    },
    "ranges" : [ {
      "type" : "ECOSYSTEM",
      "events" : [ {
        "introduced" : "2.0.0"
      }, {
        "fixed" : "2.0.3"
      } ]
    } ]
  }, {
    "package" : {
      "ecosystem" : "PyPI",
      "name" : "tensorflow"
    },
    "ranges" : [ {
      "type" : "ECOSYSTEM",
      "events" : [ {
        "introduced" : "2.1.0"
      }, {
        "fixed" : "2.1.2"
      } ]
    } ]
  }, {
    "package" : {
      "ecosystem" : "PyPI",
      "name" : "tensorflow"
    },
    "ranges" : [ {
      "type" : "ECOSYSTEM",
      "events" : [ {
        "introduced" : "2.2.0"
      }, {
        "fixed" : "2.2.1"
      } ]
    } ],
    "versions" : [ "2.2.0" ]
  }, {
    "package" : {
      "ecosystem" : "PyPI",
      "name" : "tensorflow"
    },
    "ranges" : [ {
      "type" : "ECOSYSTEM",
      "events" : [ {
        "introduced" : "2.3.0"
      }, {
        "fixed" : "2.3.1"
      } ]
    } ],
    "versions" : [ "2.3.0" ]
  }, {
    "package" : {
      "ecosystem" : "PyPI",
      "name" : "tensorflow-cpu"
    },
    "ranges" : [ {
      "type" : "ECOSYSTEM",
      "events" : [ {
        "introduced" : "0"
      }, {
        "fixed" : "1.15.4"
      } ]
    } ]
  }, {
    "package" : {
      "ecosystem" : "PyPI",
      "name" : "tensorflow-cpu"
    },
    "ranges" : [ {
      "type" : "ECOSYSTEM",
      "events" : [ {
        "introduced" : "2.0.0"
      }, {
        "fixed" : "2.0.3"
      } ]
    } ]
  }, {
    "package" : {
      "ecosystem" : "PyPI",
      "name" : "tensorflow-cpu"
    },
    "ranges" : [ {
      "type" : "ECOSYSTEM",
      "events" : [ {
        "introduced" : "2.1.0"
      }, {
        "fixed" : "2.1.2"
      } ]
    } ]
  }, {
    "package" : {
      "ecosystem" : "PyPI",
      "name" : "tensorflow-cpu"
    },
    "ranges" : [ {
      "type" : "ECOSYSTEM",
      "events" : [ {
        "introduced" : "2.2.0"
      }, {
        "fixed" : "2.2.1"
      } ]
    } ],
    "versions" : [ "2.2.0" ]
  }, {
    "package" : {
      "ecosystem" : "PyPI",
      "name" : "tensorflow-cpu"
    },
    "ranges" : [ {
      "type" : "ECOSYSTEM",
      "events" : [ {
        "introduced" : "2.3.0"
      }, {
        "fixed" : "2.3.1"
      } ]
    } ],
    "versions" : [ "2.3.0" ]
  }, {
    "package" : {
      "ecosystem" : "PyPI",
      "name" : "tensorflow-gpu"
    },
    "ranges" : [ {
      "type" : "ECOSYSTEM",
      "events" : [ {
        "introduced" : "0"
      }, {
        "fixed" : "1.15.4"
      } ]
    } ]
  }, {
    "package" : {
      "ecosystem" : "PyPI",
      "name" : "tensorflow-gpu"
    },
    "ranges" : [ {
      "type" : "ECOSYSTEM",
      "events" : [ {
        "introduced" : "2.0.0"
      }, {
        "fixed" : "2.0.3"
      } ]
    } ]
  }, {
    "package" : {
      "ecosystem" : "PyPI",
      "name" : "tensorflow-gpu"
    },
    "ranges" : [ {
      "type" : "ECOSYSTEM",
      "events" : [ {
        "introduced" : "2.1.0"
      }, {
        "fixed" : "2.1.2"
      } ]
    } ]
  }, {
    "package" : {
      "ecosystem" : "PyPI",
      "name" : "tensorflow-gpu"
    },
    "ranges" : [ {
      "type" : "ECOSYSTEM",
      "events" : [ {
        "introduced" : "2.2.0"
      }, {
        "fixed" : "2.2.1"
      } ]
    } ],
    "versions" : [ "2.2.0" ]
  }, {
    "package" : {
      "ecosystem" : "PyPI",
      "name" : "tensorflow-gpu"
    },
    "ranges" : [ {
      "type" : "ECOSYSTEM",
      "events" : [ {
        "introduced" : "2.3.0"
      }, {
        "fixed" : "2.3.1"
      } ]
    } ],
    "versions" : [ "2.3.0" ]
  } ],
  "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"
  } ],
  "database_specific" : {
    "cwe_ids" : [ "CWE-134", "CWE-20" ],
    "severity" : "HIGH",
    "github_reviewed" : true,
    "github_reviewed_at" : "2020-09-25T17:34:02Z",
    "nvd_published_at" : "2020-09-25T19:15:00Z"
  }
}

CVSS V3

7.5 HIGH

Attack Vector Network (AV:N)

Attack Complexity Low (AC:L)

Privileges Required None (PR:N)

User Interaction None (UI:N)

Scope Unchanged (S:U)

Confidentiality None (C:N)

Integrity None (I:N)

Availability High (A:H)

Vector String

CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

Advisory Metadata

Source ID: GHSA-xmq7-7fxm-rr79
Schema Version: 1.4.0
Published: 2020-09-25 18:28
Last Modified: 2024-10-28 21:23
Database Record File: advisories/github-reviewed/2020/09/GHSA-xmq7-7fxm-rr79/GHSA-xmq7-7fxm-rr79.json