Vulnerability-Lookup

CNVD-2021-37611

Vulnerability from cnvd - Published: 2021-05-28

Title

Google TensorFlow tf.raw_ops.FractionalMaxPoolGraddenial拒绝服务漏洞

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。 Google TensorFlow tf.raw_ops.FractionalMaxPoolGraddenial存在安全漏洞。本地攻击者可利用此漏洞造成拒绝服务。

Severity

低

Patch Name

Google TensorFlow tf.raw_ops.FractionalMaxPoolGraddenial拒绝服务漏洞的补丁

Patch 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。 Google TensorFlow tf.raw_ops.FractionalMaxPoolGraddenial存在安全漏洞。本地攻击者可利用此漏洞造成拒绝服务。目前，供应商发布了安全公告及相关补丁信息，修复了此漏洞。

Formal description

目前厂商已发布升级补丁以修复漏洞，补丁获取链接： https://github.com/tensorflow/tensorflow/commit/32fdcbff9d06d010d908fcc4bd4b36eb3ce15925

Reference

https://github.com/tensorflow/tensorflow/commit/32fdcbff9d06d010d908fcc4bd4b36eb3ce15925

Impacted products

Name
['Google TensorFlow 2.4.2', 'Google TensorFlow 2.3.3', 'Google TensorFlow 2.2.3', 'Google TensorFlow 2.1.4']

Show details on source website

JSON

To clipboard

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2021-29580",
      "cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2021-29580"
    }
  },
  "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\nGoogle TensorFlow tf.raw_ops.FractionalMaxPoolGraddenial\u5b58\u5728\u5b89\u5168\u6f0f\u6d1e\u3002\u672c\u5730\u653b\u51fb\u8005\u53ef\u5229\u7528\u6b64\u6f0f\u6d1e\u9020\u6210\u62d2\u7edd\u670d\u52a1\u3002",
  "formalWay": "\u76ee\u524d\u5382\u5546\u5df2\u53d1\u5e03\u5347\u7ea7\u8865\u4e01\u4ee5\u4fee\u590d\u6f0f\u6d1e\uff0c\u8865\u4e01\u83b7\u53d6\u94fe\u63a5\uff1a\r\nhttps://github.com/tensorflow/tensorflow/commit/32fdcbff9d06d010d908fcc4bd4b36eb3ce15925",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2021-37611",
  "openTime": "2021-05-28",
  "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\nGoogle TensorFlow tf.raw_ops.FractionalMaxPoolGraddenial\u5b58\u5728\u5b89\u5168\u6f0f\u6d1e\u3002\u672c\u5730\u653b\u51fb\u8005\u53ef\u5229\u7528\u6b64\u6f0f\u6d1e\u9020\u6210\u62d2\u7edd\u670d\u52a1\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": "Google TensorFlow tf.raw_ops.FractionalMaxPoolGraddenial\u62d2\u7edd\u670d\u52a1\u6f0f\u6d1e\u7684\u8865\u4e01",
  "products": {
    "product": [
      "Google TensorFlow 2.4.2",
      "Google TensorFlow 2.3.3",
      "Google TensorFlow 2.2.3",
      "Google TensorFlow 2.1.4"
    ]
  },
  "referenceLink": "https://github.com/tensorflow/tensorflow/commit/32fdcbff9d06d010d908fcc4bd4b36eb3ce15925",
  "serverity": "\u4f4e",
  "submitTime": "2021-05-19",
  "title": "Google TensorFlow tf.raw_ops.FractionalMaxPoolGraddenial\u62d2\u7edd\u670d\u52a1\u6f0f\u6d1e"
}

CVE-2021-29580 (GCVE-0-2021-29580)

Vulnerability from cvelistv5 – Published: 2021-05-14 19:15 – Updated: 2024-08-03 22:11

Title

Undefined behavior and `CHECK`-fail in `FractionalMaxPoolGrad`

Summary

TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FractionalMaxPoolGrad` triggers an undefined behavior if one of the input tensors is empty. The code is also vulnerable to a denial of service attack as a `CHECK` condition becomes false and aborts the process. The implementation(https://github.com/tensorflow/tensorflow/blob/169054888d50ce488dfde9ca55d91d6325efbd5b/tensorflow/core/kernels/fractional_max_pool_op.cc#L215) fails to validate that input and output tensors are not empty and are of the same rank. Each of these unchecked assumptions is responsible for the above issues. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.

Severity

2.5 (Low)


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

CWE

CWE-908 - Use of Uninitialized Resource

Assigner

GitHub_M mitre root

References

2 references

URL	Tags
https://github.com/tensorflow/tensorflow/security…	x_refsource_CONFIRM
https://github.com/tensorflow/tensorflow/commit/3…	x_refsource_MISC

Impacted products

1 product

Vendor	Product	Version
tensorflow	tensorflow	Affected: < 2.1.4 Affected: >= 2.2.0, < 2.2.3 Affected: >= 2.3.0, < 2.3.3 Affected: >= 2.4.0, < 2.4.2

Show details on NVD website

JSON

To clipboard

{
  "containers": {
    "adp": [
      {
        "providerMetadata": {
          "dateUpdated": "2024-08-03T22:11:06.079Z",
          "orgId": "af854a3a-2127-422b-91ae-364da2661108",
          "shortName": "CVE"
        },
        "references": [
          {
            "tags": [
              "x_refsource_CONFIRM",
              "x_transferred"
            ],
            "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x8h6-xgqx-jqgp"
          },
          {
            "tags": [
              "x_refsource_MISC",
              "x_transferred"
            ],
            "url": "https://github.com/tensorflow/tensorflow/commit/32fdcbff9d06d010d908fcc4bd4b36eb3ce15925"
          }
        ],
        "title": "CVE Program Container"
      }
    ],
    "cna": {
      "affected": [
        {
          "product": "tensorflow",
          "vendor": "tensorflow",
          "versions": [
            {
              "status": "affected",
              "version": "\u003c 2.1.4"
            },
            {
              "status": "affected",
              "version": "\u003e= 2.2.0, \u003c 2.2.3"
            },
            {
              "status": "affected",
              "version": "\u003e= 2.3.0, \u003c 2.3.3"
            },
            {
              "status": "affected",
              "version": "\u003e= 2.4.0, \u003c 2.4.2"
            }
          ]
        }
      ],
      "descriptions": [
        {
          "lang": "en",
          "value": "TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FractionalMaxPoolGrad` triggers an undefined behavior if one of the input tensors is empty. The code is also vulnerable to a denial of service attack as a `CHECK` condition becomes false and aborts the process. The implementation(https://github.com/tensorflow/tensorflow/blob/169054888d50ce488dfde9ca55d91d6325efbd5b/tensorflow/core/kernels/fractional_max_pool_op.cc#L215) fails to validate that input and output tensors are not empty and are of the same rank. Each of these unchecked assumptions is responsible for the above issues. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range."
        }
      ],
      "metrics": [
        {
          "cvssV3_1": {
            "attackComplexity": "HIGH",
            "attackVector": "LOCAL",
            "availabilityImpact": "LOW",
            "baseScore": 2.5,
            "baseSeverity": "LOW",
            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "privilegesRequired": "LOW",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L",
            "version": "3.1"
          }
        }
      ],
      "problemTypes": [
        {
          "descriptions": [
            {
              "cweId": "CWE-908",
              "description": "CWE-908: Use of Uninitialized Resource",
              "lang": "en",
              "type": "CWE"
            }
          ]
        }
      ],
      "providerMetadata": {
        "dateUpdated": "2021-05-14T19:15:43.000Z",
        "orgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
        "shortName": "GitHub_M"
      },
      "references": [
        {
          "tags": [
            "x_refsource_CONFIRM"
          ],
          "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x8h6-xgqx-jqgp"
        },
        {
          "tags": [
            "x_refsource_MISC"
          ],
          "url": "https://github.com/tensorflow/tensorflow/commit/32fdcbff9d06d010d908fcc4bd4b36eb3ce15925"
        }
      ],
      "source": {
        "advisory": "GHSA-x8h6-xgqx-jqgp",
        "discovery": "UNKNOWN"
      },
      "title": "Undefined behavior and `CHECK`-fail in `FractionalMaxPoolGrad`",
      "x_legacyV4Record": {
        "CVE_data_meta": {
          "ASSIGNER": "security-advisories@github.com",
          "ID": "CVE-2021-29580",
          "STATE": "PUBLIC",
          "TITLE": "Undefined behavior and `CHECK`-fail in `FractionalMaxPoolGrad`"
        },
        "affects": {
          "vendor": {
            "vendor_data": [
              {
                "product": {
                  "product_data": [
                    {
                      "product_name": "tensorflow",
                      "version": {
                        "version_data": [
                          {
                            "version_value": "\u003c 2.1.4"
                          },
                          {
                            "version_value": "\u003e= 2.2.0, \u003c 2.2.3"
                          },
                          {
                            "version_value": "\u003e= 2.3.0, \u003c 2.3.3"
                          },
                          {
                            "version_value": "\u003e= 2.4.0, \u003c 2.4.2"
                          }
                        ]
                      }
                    }
                  ]
                },
                "vendor_name": "tensorflow"
              }
            ]
          }
        },
        "data_format": "MITRE",
        "data_type": "CVE",
        "data_version": "4.0",
        "description": {
          "description_data": [
            {
              "lang": "eng",
              "value": "TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.FractionalMaxPoolGrad` triggers an undefined behavior if one of the input tensors is empty. The code is also vulnerable to a denial of service attack as a `CHECK` condition becomes false and aborts the process. The implementation(https://github.com/tensorflow/tensorflow/blob/169054888d50ce488dfde9ca55d91d6325efbd5b/tensorflow/core/kernels/fractional_max_pool_op.cc#L215) fails to validate that input and output tensors are not empty and are of the same rank. Each of these unchecked assumptions is responsible for the above issues. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range."
            }
          ]
        },
        "impact": {
          "cvss": {
            "attackComplexity": "HIGH",
            "attackVector": "LOCAL",
            "availabilityImpact": "LOW",
            "baseScore": 2.5,
            "baseSeverity": "LOW",
            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "privilegesRequired": "LOW",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L",
            "version": "3.1"
          }
        },
        "problemtype": {
          "problemtype_data": [
            {
              "description": [
                {
                  "lang": "eng",
                  "value": "CWE-908: Use of Uninitialized Resource"
                }
              ]
            }
          ]
        },
        "references": {
          "reference_data": [
            {
              "name": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x8h6-xgqx-jqgp",
              "refsource": "CONFIRM",
              "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-x8h6-xgqx-jqgp"
            },
            {
              "name": "https://github.com/tensorflow/tensorflow/commit/32fdcbff9d06d010d908fcc4bd4b36eb3ce15925",
              "refsource": "MISC",
              "url": "https://github.com/tensorflow/tensorflow/commit/32fdcbff9d06d010d908fcc4bd4b36eb3ce15925"
            }
          ]
        },
        "source": {
          "advisory": "GHSA-x8h6-xgqx-jqgp",
          "discovery": "UNKNOWN"
        }
      }
    }
  },
  "cveMetadata": {
    "assignerOrgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
    "assignerShortName": "GitHub_M",
    "cveId": "CVE-2021-29580",
    "datePublished": "2021-05-14T19:15:43.000Z",
    "dateReserved": "2021-03-30T00:00:00.000Z",
    "dateUpdated": "2024-08-03T22:11:06.079Z",
    "state": "PUBLISHED"
  },
  "dataType": "CVE_RECORD",
  "dataVersion": "5.1"
}

Sightings

Author	Source	Type	Date	Other

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.

The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base model, served locally by ML-Gateway. They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.

Related by attack behaviour

Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.