Vulnerability-Lookup

CVE-2020-15212 (GCVE-0-2020-15212)

Vulnerability from cvelistv5 – Published: 2020-09-25 18:50 – Updated: 2024-08-04 13:08

Title

Out of bounds access in tensorflow-lite

Summary

In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_index` and then write to outside of `output_data` buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.

Severity ?

8.1 (High)


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

CWE

CWE-787 - {"CWE-787":"Out-of-bounds Write"}

Assigner

GitHub_M

References

URL

	Vendor	Product	Version
	tensorflow	tensorflow	Affected: >= 2.2.0, < 2.2.1 Affected: >= 2.3.0, < 2.3.1

PYSEC-2020-327

Vulnerability from pysec - Published: 2020-09-25 19:15 - Updated: 2021-12-09 06:35

Details

In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to segment_ids_data can alter output_index and then write to outside of output_data buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom Verifier to the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.

Impacted products

Name	purl
tensorflow-gpu	pkg:pypi/tensorflow-gpu

Aliases

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu",
        "purl": "pkg:pypi/tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "204945b19e44b57906c9344c0d00120eeeae178a"
            }
          ],
          "repo": "https://github.com/tensorflow/tensorflow",
          "type": "GIT"
        },
        {
          "events": [
            {
              "introduced": "2.3.0"
            },
            {
              "fixed": "2.3.1"
            },
            {
              "introduced": "2.2.0"
            },
            {
              "fixed": "2.2.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "2.2.0",
        "2.3.0"
      ]
    }
  ],
  "aliases": [
    "CVE-2020-15212",
    "GHSA-hx2x-85gr-wrpq"
  ],
  "details": "In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_index` and then write to outside of `output_data` buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.",
  "id": "PYSEC-2020-327",
  "modified": "2021-12-09T06:35:15.513160Z",
  "published": "2020-09-25T19:15:00Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hx2x-85gr-wrpq"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1"
    },
    {
      "type": "FIX",
      "url": "https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a"
    }
  ]
}

PYSEC-2020-135

Vulnerability from pysec - Published: 2020-09-25 19:15 - Updated: 2020-10-01 18:20

Details

In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to segment_ids_data can alter output_index and then write to outside of output_data buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom Verifier to the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.

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": "204945b19e44b57906c9344c0d00120eeeae178a"
            }
          ],
          "repo": "https://github.com/tensorflow/tensorflow",
          "type": "GIT"
        },
        {
          "events": [
            {
              "introduced": "2.3.0"
            },
            {
              "fixed": "2.3.1"
            },
            {
              "introduced": "2.2.0"
            },
            {
              "fixed": "2.2.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "2.2.0",
        "2.3.0"
      ]
    }
  ],
  "aliases": [
    "CVE-2020-15212",
    "GHSA-hx2x-85gr-wrpq"
  ],
  "details": "In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_index` and then write to outside of `output_data` buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.",
  "id": "PYSEC-2020-135",
  "modified": "2020-10-01T18:20:00Z",
  "published": "2020-09-25T19:15:00Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hx2x-85gr-wrpq"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1"
    },
    {
      "type": "FIX",
      "url": "https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a"
    }
  ]
}

PYSEC-2020-292

Vulnerability from pysec - Published: 2020-09-25 19:15 - Updated: 2021-12-09 06:34

Details

In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to segment_ids_data can alter output_index and then write to outside of output_data buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom Verifier to the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.

Impacted products

Name	purl
tensorflow-cpu	pkg:pypi/tensorflow-cpu

Aliases

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu",
        "purl": "pkg:pypi/tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "204945b19e44b57906c9344c0d00120eeeae178a"
            }
          ],
          "repo": "https://github.com/tensorflow/tensorflow",
          "type": "GIT"
        },
        {
          "events": [
            {
              "introduced": "2.3.0"
            },
            {
              "fixed": "2.3.1"
            },
            {
              "introduced": "2.2.0"
            },
            {
              "fixed": "2.2.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "2.2.0",
        "2.3.0"
      ]
    }
  ],
  "aliases": [
    "CVE-2020-15212",
    "GHSA-hx2x-85gr-wrpq"
  ],
  "details": "In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_index` and then write to outside of `output_data` buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.",
  "id": "PYSEC-2020-292",
  "modified": "2021-12-09T06:34:43.741009Z",
  "published": "2020-09-25T19:15:00Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hx2x-85gr-wrpq"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1"
    },
    {
      "type": "FIX",
      "url": "https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a"
    }
  ]
}

GHSA-HX2X-85GR-WRPQ

Vulnerability from github – Published: 2020-09-25 18:29 – Updated: 2024-10-30 21:18

Summary

Out of bounds access in tensorflow-lite

Details

Impact

In TensorFlow Lite models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/internal/reference/reference_ops.h#L2625-L2631

Users having access to segment_ids_data can alter output_index and then write to outside of output_data buffer.

This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits.

Patches

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

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

Workarounds

A potential workaround would be to add a custom Verifier to the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model.

A similar validation could be done if the segment ids are generated at runtime between inference steps.

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

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been discovered from a variant analysis of GHSA-p2cq-cprg-frvm.

Severity ?

8.1 (High)


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

9.1 (Critical)


                  
                    CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:N/VC:L/VI:L/VA:H/SC:L/SI:L/SA:H

Show details on source website

JSON

To clipboard

{
  "affected": [
    {
      "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": "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": "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-2020-15212"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-787"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2020-09-25T18:27:17Z",
    "nvd_published_at": "2020-09-25T19:15:00Z",
    "severity": "CRITICAL"
  },
  "details": "### Impact\nIn TensorFlow Lite models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor:\nhttps://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/internal/reference/reference_ops.h#L2625-L2631\n\nUsers having access to `segment_ids_data` can alter `output_index` and then write to outside of `output_data` buffer.\n\nThis might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits.\n\n### Patches\nWe have patched the issue in 204945b and will release patch releases for all affected versions.\n\nWe recommend users to upgrade to TensorFlow 2.2.1, or 2.3.1.\n\n### Workarounds\nA potential workaround would be to add a custom `Verifier` to the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model.\n\nA similar validation could be done if the segment ids are generated at runtime between inference steps.\n\nIf the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.\n\n### For more information\nPlease consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.\n\n### Attribution\nThis vulnerability has been discovered from a variant analysis of [GHSA-p2cq-cprg-frvm](https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p2cq-cprg-frvm).",
  "id": "GHSA-hx2x-85gr-wrpq",
  "modified": "2024-10-30T21:18:36Z",
  "published": "2020-09-25T18:29:00Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hx2x-85gr-wrpq"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2020-15212"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/commit/00c7ed7ce81c2126ebc17dfe7073b5c0efd5ec0a"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/commit/a4030d8ba3692c438997c27be2dd95f3d5f54827"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-292.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-327.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-135.yaml"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/tensorflow/tensorflow"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/lite/kernels/internal/reference/reference_ops.h#L2625-L2631"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:L/I:L/A:H",
      "type": "CVSS_V3"
    },
    {
      "score": "CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:N/VC:L/VI:L/VA:H/SC:L/SI:L/SA:H",
      "type": "CVSS_V4"
    }
  ],
  "summary": "Out of bounds access in tensorflow-lite"
}

GSD-2020-15212

Vulnerability from gsd - Updated: 2023-12-13 01:21

Details

Aliases

CVE-2020-15212

Aliases

CVE-2020-15212

JSON

To clipboard

{
  "GSD": {
    "alias": "CVE-2020-15212",
    "description": "In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_index` and then write to outside of `output_data` buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.",
    "id": "GSD-2020-15212"
  },
  "gsd": {
    "metadata": {
      "exploitCode": "unknown",
      "remediation": "unknown",
      "reportConfidence": "confirmed",
      "type": "vulnerability"
    },
    "osvSchema": {
      "aliases": [
        "CVE-2020-15212"
      ],
      "details": "In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_index` and then write to outside of `output_data` buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.",
      "id": "GSD-2020-15212",
      "modified": "2023-12-13T01:21:43.431939Z",
      "schema_version": "1.4.0"
    }
  },
  "namespaces": {
    "cve.org": {
      "CVE_data_meta": {
        "ASSIGNER": "security-advisories@github.com",
        "ID": "CVE-2020-15212",
        "STATE": "PUBLIC",
        "TITLE": "Out of bounds access in tensorflow-lite"
      },
      "affects": {
        "vendor": {
          "vendor_data": [
            {
              "product": {
                "product_data": [
                  {
                    "product_name": "tensorflow",
                    "version": {
                      "version_data": [
                        {
                          "version_value": "\u003e= 2.2.0, \u003c 2.2.1"
                        },
                        {
                          "version_value": "\u003e= 2.3.0, \u003c 2.3.1"
                        }
                      ]
                    }
                  }
                ]
              },
              "vendor_name": "tensorflow"
            }
          ]
        }
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            "value": "In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_index` and then write to outside of `output_data` buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code."
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          "baseScore": 8.1,
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          "confidentialityImpact": "LOW",
          "integrityImpact": "LOW",
          "privilegesRequired": "NONE",
          "scope": "CHANGED",
          "userInteraction": "NONE",
          "vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:L/I:L/A:H",
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      "source": {
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    },
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        {
          "affected_range": "\u003e=2.2.0,\u003c2.2.1||\u003e=2.3.0,\u003c2.3.1",
          "affected_versions": "All versions starting from 2.2.0 before 2.2.1, all versions starting from 2.3.0 before 2.3.1",
          "cvss_v2": "AV:N/AC:L/Au:N/C:P/I:P/A:P",
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          "description": "In TensorFlow Lite, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_index` and then write to outside `output_data` buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits.",
          "fixed_versions": [
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          ],
          "identifier": "CVE-2020-15212",
          "identifiers": [
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          "package_slug": "pypi/tensorflow-cpu",
          "pubdate": "2020-09-25",
          "solution": "Upgrade to versions 2.2.1, 2.3.1 or above.",
          "title": "Out-of-bounds Write",
          "urls": [
            "https://nvd.nist.gov/vuln/detail/CVE-2020-15212"
          ],
          "uuid": "a683bcb2-dfb1-464d-a2b8-40f91a9303a0"
        },
        {
          "affected_range": "\u003e=2.2.0,\u003c2.2.1||\u003e=2.3.0,\u003c2.3.1",
          "affected_versions": "All versions starting from 2.2.0 before 2.2.1, all versions starting from 2.3.0 before 2.3.1",
          "cvss_v2": "AV:N/AC:L/Au:N/C:P/I:P/A:P",
          "cvss_v3": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:H",
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          "package_slug": "pypi/tensorflow-gpu",
          "pubdate": "2020-09-25",
          "solution": "Upgrade to versions 2.2.1, 2.3.1 or above.",
          "title": "Out-of-bounds Write",
          "urls": [
            "https://nvd.nist.gov/vuln/detail/CVE-2020-15212"
          ],
          "uuid": "2ffc9208-51b6-4ca3-9381-9d4381d76455"
        },
        {
          "affected_range": "\u003e=2.2.0,\u003c2.2.1||\u003e=2.3.0,\u003c2.3.1",
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          "cwe_ids": [
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          "date": "2021-08-17",
          "description": "In TensorFlow Lite, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_index` and then write to outside `output_data` buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits.",
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          "package_slug": "pypi/tensorflow",
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          "solution": "Upgrade to versions 2.2.1, 2.3.1 or above.",
          "title": "Out-of-bounds Write",
          "urls": [
            "https://nvd.nist.gov/vuln/detail/CVE-2020-15212"
          ],
          "uuid": "0f195132-7fc5-4b52-bf65-2b9da43353c2"
        }
      ]
    },
    "nvd.nist.gov": {
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              "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hx2x-85gr-wrpq"
            },
            {
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                "Third Party Advisory"
              ],
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            },
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              "name": "https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a",
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              "tags": [
                "Patch",
                "Third Party Advisory"
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      },
      "impact": {
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          "acInsufInfo": false,
          "cvssV2": {
            "accessComplexity": "LOW",
            "accessVector": "NETWORK",
            "authentication": "NONE",
            "availabilityImpact": "PARTIAL",
            "baseScore": 7.5,
            "confidentialityImpact": "PARTIAL",
            "integrityImpact": "PARTIAL",
            "vectorString": "AV:N/AC:L/Au:N/C:P/I:P/A:P",
            "version": "2.0"
          },
          "exploitabilityScore": 10.0,
          "impactScore": 6.4,
          "obtainAllPrivilege": false,
          "obtainOtherPrivilege": false,
          "obtainUserPrivilege": false,
          "severity": "HIGH",
          "userInteractionRequired": false
        },
        "baseMetricV3": {
          "cvssV3": {
            "attackComplexity": "LOW",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 8.6,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "LOW",
            "integrityImpact": "LOW",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:H",
            "version": "3.1"
          },
          "exploitabilityScore": 3.9,
          "impactScore": 4.7
        }
      },
      "lastModifiedDate": "2021-08-17T13:21Z",
      "publishedDate": "2020-09-25T19:15Z"
    }
  }
}

CNVD-2020-54784

Vulnerability from cnvd - Published: 2020-09-30

Title

TensorFlow Lite输入验证错误漏洞

Description

Google TensorFlow是美国谷歌（Google）公司的一套用于机器学习的端到端开源平台。 TensorFlow Lite 2.2.1之前版本，2.3.1版本中存在安全漏洞，该漏洞源于网络系统或产品未对输入的数据进行正确的验证。攻击者可利用该漏洞通过一定手段导致分段错误，它也可以用于进一步破坏内存，并且可以与其他漏洞链接在一起以创建更高级的漏洞利用。

Severity

中

Patch Name

TensorFlow Lite输入验证错误漏洞的补丁

Patch Description

Formal description

厂商已发布了漏洞修复程序，请及时关注更新： https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1

Reference

https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hx2x-85gr-wrpq

Impacted products

Name
['Google TensorFlow Lite <2.2.1', 'Google TensorFlow Lite 2.3.1']

Show details on source website

JSON

To clipboard

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2020-15212",
      "cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2020-15212"
    }
  },
  "description": "Google TensorFlow\u662f\u7f8e\u56fd\u8c37\u6b4c\uff08Google\uff09\u516c\u53f8\u7684\u4e00\u5957\u7528\u4e8e\u673a\u5668\u5b66\u4e60\u7684\u7aef\u5230\u7aef\u5f00\u6e90\u5e73\u53f0\u3002\n\nTensorFlow Lite 2.2.1\u4e4b\u524d\u7248\u672c\uff0c2.3.1\u7248\u672c\u4e2d\u5b58\u5728\u5b89\u5168\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u7f51\u7edc\u7cfb\u7edf\u6216\u4ea7\u54c1\u672a\u5bf9\u8f93\u5165\u7684\u6570\u636e\u8fdb\u884c\u6b63\u786e\u7684\u9a8c\u8bc1\u3002\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u901a\u8fc7\u4e00\u5b9a\u624b\u6bb5\u5bfc\u81f4\u5206\u6bb5\u9519\u8bef\uff0c\u5b83\u4e5f\u53ef\u4ee5\u7528\u4e8e\u8fdb\u4e00\u6b65\u7834\u574f\u5185\u5b58\uff0c\u5e76\u4e14\u53ef\u4ee5\u4e0e\u5176\u4ed6\u6f0f\u6d1e\u94fe\u63a5\u5728\u4e00\u8d77\u4ee5\u521b\u5efa\u66f4\u9ad8\u7ea7\u7684\u6f0f\u6d1e\u5229\u7528\u3002",
  "formalWay": "\u5382\u5546\u5df2\u53d1\u5e03\u4e86\u6f0f\u6d1e\u4fee\u590d\u7a0b\u5e8f\uff0c\u8bf7\u53ca\u65f6\u5173\u6ce8\u66f4\u65b0\uff1a\r\nhttps://github.com/tensorflow/tensorflow/releases/tag/v2.3.1",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2020-54784",
  "openTime": "2020-09-30",
  "patchDescription": "Google TensorFlow\u662f\u7f8e\u56fd\u8c37\u6b4c\uff08Google\uff09\u516c\u53f8\u7684\u4e00\u5957\u7528\u4e8e\u673a\u5668\u5b66\u4e60\u7684\u7aef\u5230\u7aef\u5f00\u6e90\u5e73\u53f0\u3002\r\n\r\nTensorFlow Lite 2.2.1\u4e4b\u524d\u7248\u672c\uff0c2.3.1\u7248\u672c\u4e2d\u5b58\u5728\u5b89\u5168\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u7f51\u7edc\u7cfb\u7edf\u6216\u4ea7\u54c1\u672a\u5bf9\u8f93\u5165\u7684\u6570\u636e\u8fdb\u884c\u6b63\u786e\u7684\u9a8c\u8bc1\u3002\u653b\u51fb\u8005\u53ef\u5229\u7528\u8be5\u6f0f\u6d1e\u901a\u8fc7\u4e00\u5b9a\u624b\u6bb5\u5bfc\u81f4\u5206\u6bb5\u9519\u8bef\uff0c\u5b83\u4e5f\u53ef\u4ee5\u7528\u4e8e\u8fdb\u4e00\u6b65\u7834\u574f\u5185\u5b58\uff0c\u5e76\u4e14\u53ef\u4ee5\u4e0e\u5176\u4ed6\u6f0f\u6d1e\u94fe\u63a5\u5728\u4e00\u8d77\u4ee5\u521b\u5efa\u66f4\u9ad8\u7ea7\u7684\u6f0f\u6d1e\u5229\u7528\u3002\u76ee\u524d\uff0c\u4f9b\u5e94\u5546\u53d1\u5e03\u4e86\u5b89\u5168\u516c\u544a\u53ca\u76f8\u5173\u8865\u4e01\u4fe1\u606f\uff0c\u4fee\u590d\u4e86\u6b64\u6f0f\u6d1e\u3002",
  "patchName": "TensorFlow Lite\u8f93\u5165\u9a8c\u8bc1\u9519\u8bef\u6f0f\u6d1e\u7684\u8865\u4e01",
  "products": {
    "product": [
      "Google TensorFlow Lite \u003c2.2.1",
      "Google TensorFlow Lite 2.3.1"
    ]
  },
  "referenceLink": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hx2x-85gr-wrpq",
  "serverity": "\u4e2d",
  "submitTime": "2020-09-28",
  "title": "TensorFlow Lite\u8f93\u5165\u9a8c\u8bc1\u9519\u8bef\u6f0f\u6d1e"
}

bit-tensorflow-2020-15212

Vulnerability from bitnami_vulndb

Published

2024-03-06 11:20

Modified

2025-05-20 10:02

Summary

Out of bounds access in tensorflow-lite

Details

In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to segment_ids_data can alter output_index and then write to outside of output_data buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom Verifier to the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "Bitnami",
        "name": "tensorflow",
        "purl": "pkg:bitnami/tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.2.0"
            },
            {
              "fixed": "2.2.1"
            },
            {
              "introduced": "2.3.0"
            },
            {
              "fixed": "2.3.1"
            }
          ],
          "type": "SEMVER"
        }
      ],
      "severity": [
        {
          "score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:H",
          "type": "CVSS_V3"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2020-15212"
  ],
  "database_specific": {
    "cpes": [
      "cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*"
    ],
    "severity": "High"
  },
  "details": "In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_index` and then write to outside of `output_data` buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.",
  "id": "BIT-tensorflow-2020-15212",
  "modified": "2025-05-20T10:02:07.006Z",
  "published": "2024-03-06T11:20:24.948Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hx2x-85gr-wrpq"
    },
    {
      "type": "WEB",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2020-15212"
    }
  ],
  "schema_version": "1.5.0",
  "summary": "Out of bounds access in tensorflow-lite"
}

FKIE_CVE-2020-15212

Vulnerability from fkie_nvd - Published: 2020-09-25 19:15 - Updated: 2024-11-21 05:05

Severity ?

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

Summary

References

URL	Tags
security-advisories@github.com	https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a	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-hx2x-85gr-wrpq	Exploit, Patch, Third Party Advisory
af854a3a-2127-422b-91ae-364da2661108	https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a	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-hx2x-85gr-wrpq	Exploit, Patch, Third Party Advisory

Impacted products

	Vendor	Product	Version
	google	tensorflow	*
	google	tensorflow	*

JSON

To clipboard

{
  "configurations": [
    {
      "nodes": [
        {
          "cpeMatch": [
            {
              "criteria": "cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*",
              "matchCriteriaId": "323B716A-E8F7-4CDA-B8FD-A56977D59C02",
              "versionEndExcluding": "2.2.1",
              "versionStartIncluding": "2.2.0",
              "vulnerable": true
            },
            {
              "criteria": "cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*",
              "matchCriteriaId": "C09502A8-B667-4867-BEBD-40333E98A601",
              "versionEndExcluding": "2.3.1",
              "versionStartIncluding": "2.3.0",
              "vulnerable": true
            }
          ],
          "negate": false,
          "operator": "OR"
        }
      ]
    }
  ],
  "cveTags": [],
  "descriptions": [
    {
      "lang": "en",
      "value": "In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger writes outside of bounds of heap allocated buffers by inserting negative elements in the segment ids tensor. Users having access to `segment_ids_data` can alter `output_index` and then write to outside of `output_data` buffer. This might result in a segmentation fault but it can also be used to further corrupt the memory and can be chained with other vulnerabilities to create more advanced exploits. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to the model loading code to ensure that the segment ids are all positive, although this only handles the case when the segment ids are stored statically in the model. A similar validation could be done if the segment ids are generated at runtime between inference steps. If the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code."
    },
    {
      "lang": "es",
      "value": "En TensorFlow Lite versiones anteriores a 2.2.1 y 2.3.1, los modelos que utilizan la suma de segmentos pueden activar escrituras fuera de l\u00edmites de los b\u00faferes asignados de la pila insertando elementos negativos en el tensor de los ids de segmento. Los usuarios que tienen acceso a \"segment_ids_data\" pueden alterar \"output_index\" y luego escribir fuera del b\u00fafer de \"output_data\". Esto podr\u00eda resultar en un fallo de segmentaci\u00f3n, pero tambi\u00e9n se puede usar para corromper a\u00fan m\u00e1s la memoria y se puede encadenar con otras vulnerabilidades para crear explotaciones m\u00e1s avanzadas. El problema es parcheado en el commit 204945b19e44b57906c9344c0d00120eeeae178a y es publicado en TensorFlow versiones 2.2.1 o 2.3.1. Una soluci\u00f3n alternativa potencial ser\u00eda agregar un \"Verifier\" personalizado al c\u00f3digo de carga del modelo para asegurar que los ids de segmento sean todos positivos, aunque esto solo maneja el caso cuando los ids de segmento son almacenados est\u00e1ticamente en el modelo. Una comprobaci\u00f3n similar podr\u00eda ser realizada si los ids de segmento se generan en el tiempo de ejecuci\u00f3n entre los pasos de inferencia. Si los ids de segmento son generados como salidas de un tensor durante los pasos de inferencia, entonces no existe una posible soluci\u00f3n alternativa y se recomienda a los usuarios actualizar al c\u00f3digo parcheado"
    }
  ],
  "id": "CVE-2020-15212",
  "lastModified": "2024-11-21T05:05:06.047",
  "metrics": {
    "cvssMetricV2": [
      {
        "acInsufInfo": false,
        "baseSeverity": "HIGH",
        "cvssData": {
          "accessComplexity": "LOW",
          "accessVector": "NETWORK",
          "authentication": "NONE",
          "availabilityImpact": "PARTIAL",
          "baseScore": 7.5,
          "confidentialityImpact": "PARTIAL",
          "integrityImpact": "PARTIAL",
          "vectorString": "AV:N/AC:L/Au:N/C:P/I:P/A:P",
          "version": "2.0"
        },
        "exploitabilityScore": 10.0,
        "impactScore": 6.4,
        "obtainAllPrivilege": false,
        "obtainOtherPrivilege": false,
        "obtainUserPrivilege": false,
        "source": "nvd@nist.gov",
        "type": "Primary",
        "userInteractionRequired": false
      }
    ],
    "cvssMetricV31": [
      {
        "cvssData": {
          "attackComplexity": "HIGH",
          "attackVector": "NETWORK",
          "availabilityImpact": "HIGH",
          "baseScore": 8.1,
          "baseSeverity": "HIGH",
          "confidentialityImpact": "LOW",
          "integrityImpact": "LOW",
          "privilegesRequired": "NONE",
          "scope": "CHANGED",
          "userInteraction": "NONE",
          "vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:L/I:L/A:H",
          "version": "3.1"
        },
        "exploitabilityScore": 2.2,
        "impactScore": 5.3,
        "source": "security-advisories@github.com",
        "type": "Secondary"
      },
      {
        "cvssData": {
          "attackComplexity": "LOW",
          "attackVector": "NETWORK",
          "availabilityImpact": "HIGH",
          "baseScore": 8.6,
          "baseSeverity": "HIGH",
          "confidentialityImpact": "LOW",
          "integrityImpact": "LOW",
          "privilegesRequired": "NONE",
          "scope": "UNCHANGED",
          "userInteraction": "NONE",
          "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:H",
          "version": "3.1"
        },
        "exploitabilityScore": 3.9,
        "impactScore": 4.7,
        "source": "nvd@nist.gov",
        "type": "Primary"
      }
    ]
  },
  "published": "2020-09-25T19:15:16.510",
  "references": [
    {
      "source": "security-advisories@github.com",
      "tags": [
        "Patch",
        "Third Party Advisory"
      ],
      "url": "https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a"
    },
    {
      "source": "security-advisories@github.com",
      "tags": [
        "Third Party Advisory"
      ],
      "url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1"
    },
    {
      "source": "security-advisories@github.com",
      "tags": [
        "Exploit",
        "Patch",
        "Third Party Advisory"
      ],
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hx2x-85gr-wrpq"
    },
    {
      "source": "af854a3a-2127-422b-91ae-364da2661108",
      "tags": [
        "Patch",
        "Third Party Advisory"
      ],
      "url": "https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a"
    },
    {
      "source": "af854a3a-2127-422b-91ae-364da2661108",
      "tags": [
        "Third Party Advisory"
      ],
      "url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1"
    },
    {
      "source": "af854a3a-2127-422b-91ae-364da2661108",
      "tags": [
        "Exploit",
        "Patch",
        "Third Party Advisory"
      ],
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hx2x-85gr-wrpq"
    }
  ],
  "sourceIdentifier": "security-advisories@github.com",
  "vulnStatus": "Modified",
  "weaknesses": [
    {
      "description": [
        {
          "lang": "en",
          "value": "CWE-787"
        }
      ],
      "source": "security-advisories@github.com",
      "type": "Secondary"
    },
    {
      "description": [
        {
          "lang": "en",
          "value": "CWE-787"
        }
      ],
      "source": "nvd@nist.gov",
      "type": "Primary"
    }
  ]
}

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

CVE-2020-15212 (GCVE-0-2020-15212)

Impact

Patches

Workarounds

For more information

Attribution

Vulnerability from bitnami_vulndb

Tags

Sightings

Nomenclature