pysec - pysec-2020-126

PYSEC-2020-126

Vulnerability from pysec - Published: 2020-09-25 19:15 - Updated: 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.

Impacted products

Name	purl
tensorflow	pkg:pypi/tensorflow

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",
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        "1.15.0rc0",
        "1.15.0rc1",
        "1.15.0rc2",
        "1.15.0rc3",
        "1.15.0",
        "1.15.2",
        "1.15.3",
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      ]
    }
  ],
  "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 – Updated: 2024-10-28 21:23

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:

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.

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

Show details on source website

JSON

To clipboard

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  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
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          "events": [
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        "ecosystem": "PyPI",
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              "introduced": "2.3.0"
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      "versions": [
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    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
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    },
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        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
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          "type": "ECOSYSTEM"
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      ]
    },
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        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
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            {
              "introduced": "2.2.0"
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      },
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        }
      ],
      "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 (GCVE-0-2020-15203)

Vulnerability from cvelistv5 – Published: 2020-09-25 18:46 – Updated: 2024-08-04 13:08

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.

Severity ?

7.5 (High)


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

CWE

CWE-20 - {"CWE-20":"Improper Input Validation"}

Assigner

GitHub_M

References

URL

Tags

	https://github.com/tensorflow/tensorflow/releases…	x_refsource_MISC
	https://github.com/tensorflow/tensorflow/security…	x_refsource_CONFIRM
	https://github.com/tensorflow/tensorflow/commit/3…	x_refsource_MISC
	http://lists.opensuse.org/opensuse-security-annou…	vendor-advisoryx_refsource_SUSE

Impacted products

	Vendor	Product	Version
	tensorflow	tensorflow	Affected: < 1.15.4 Affected: >= 2.0.0, < 2.0.3 Affected: >= 2.1.0, < 2.1.2 Affected: >= 2.2.0, < 2.2.1 Affected: >= 2.3.0, < 2.3.1

Show details on NVD website

JSON

To clipboard

{
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        "providerMetadata": {
          "dateUpdated": "2024-08-04T13:08:22.972Z",
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          {
            "name": "openSUSE-SU-2020:1766",
            "tags": [
              "vendor-advisory",
              "x_refsource_SUSE",
              "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"
            },
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              "status": "affected",
              "version": "\u003e= 2.3.0, \u003c 2.3.1"
            }
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        }
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          "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\"}",
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              "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": {
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            "vendor_data": [
              {
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                  "product_data": [
                    {
                      "product_name": "tensorflow",
                      "version": {
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                          {
                            "version_value": "\u003c 1.15.4"
                          },
                          {
                            "version_value": "\u003e= 2.0.0, \u003c 2.0.3"
                          },
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                            "version_value": "\u003e= 2.1.0, \u003c 2.1.2"
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            },
            {
              "name": "https://github.com/tensorflow/tensorflow/commit/33be22c65d86256e6826666662e40dbdfe70ee83",
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            },
            {
              "name": "openSUSE-SU-2020:1766",
              "refsource": "SUSE",
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  "cveMetadata": {
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    "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 observed by the user.
Confirmed: The vulnerability has been validated from an analyst's perspective.
Published Proof of Concept: A public proof of concept is available for this vulnerability.
Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
Not confirmed: The user expressed doubt about the validity of the vulnerability.
Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.

Detection rules are retrieved from Rulezet.

Action not permitted

PYSEC-2020-126

GHSA-XMQ7-7FXM-RR79

Impact

Patches

For more information

Attribution

CVE-2020-15203 (GCVE-0-2020-15203)

Tags

Sightings

Nomenclature