cnvd - cnvd-2022-87256

cnvd-2022-87256

Vulnerability from cnvd

Title: Google TensorFlow tf.keras.losses.poisson缓冲区溢出漏洞

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 2.9.0及其之后版本，2.9.3之前版本存在缓冲区溢出漏洞，该漏洞源于“tf.keras.losses.poisson”对用户提供的数据缺乏适当的验证。攻击者可利用漏洞导致拒绝服务。

Severity: 高

Patch Name: Google TensorFlow tf.keras.losses.poisson缓冲区溢出漏洞的补丁

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 2.9.0及其之后版本，2.9.3之前版本存在缓冲区溢出漏洞，该漏洞源于“tf.keras.losses.poisson”对用户提供的数据缺乏适当的验证。攻击者可利用漏洞导致拒绝服务。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/security/advisories/GHSA-8fvv-46hw-vpg3

Reference: https://github.com/tensorflow/tensorflow/commit/c5b30379ba87cbe774b08ac50c1f6d36df4ebb7c

Impacted products

Name
Google TensorFlow >=2.9.0，<2.9.3

Show details on source website

JSON

To clipboard

{
  "cves": {
    "cve": {
      "cveNumber": "CVE-2022-41887",
      "cveUrl": "https://nvd.nist.gov/vuln/detail/CVE-2022-41887"
    }
  },
  "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 2.9.0\u53ca\u5176\u4e4b\u540e\u7248\u672c\uff0c2.9.3\u4e4b\u524d\u7248\u672c\u5b58\u5728\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u201ctf.keras.losses.poisson\u201d\u5bf9\u7528\u6237\u63d0\u4f9b\u7684\u6570\u636e\u7f3a\u4e4f\u9002\u5f53\u7684\u9a8c\u8bc1\u3002\u653b\u51fb\u8005\u53ef\u5229\u7528\u6f0f\u6d1e\u5bfc\u81f4\u62d2\u7edd\u670d\u52a1\u3002",
  "discovererName": "Pattarakrit Rattankul",
  "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/security/advisories/GHSA-8fvv-46hw-vpg3",
  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2022-87256",
  "openTime": "2022-12-08",
  "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 2.9.0\u53ca\u5176\u4e4b\u540e\u7248\u672c\uff0c2.9.3\u4e4b\u524d\u7248\u672c\u5b58\u5728\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\uff0c\u8be5\u6f0f\u6d1e\u6e90\u4e8e\u201ctf.keras.losses.poisson\u201d\u5bf9\u7528\u6237\u63d0\u4f9b\u7684\u6570\u636e\u7f3a\u4e4f\u9002\u5f53\u7684\u9a8c\u8bc1\u3002\u653b\u51fb\u8005\u53ef\u5229\u7528\u6f0f\u6d1e\u5bfc\u81f4\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.keras.losses.poisson\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e\u7684\u8865\u4e01",
  "products": {
    "product": "Google TensorFlow \u003e=2.9.0\uff0c\u003c2.9.3"
  },
  "referenceLink": "https://github.com/tensorflow/tensorflow/commit/c5b30379ba87cbe774b08ac50c1f6d36df4ebb7c",
  "serverity": "\u9ad8",
  "submitTime": "2022-11-23",
  "title": "Google TensorFlow tf.keras.losses.poisson\u7f13\u51b2\u533a\u6ea2\u51fa\u6f0f\u6d1e"
}

CVE-2022-41887 (GCVE-0-2022-41887)

Vulnerability from cvelistv5

Published

2022-11-18 00:00

Modified

2025-04-22 16:06

Severity ?

4.8 (Medium) - CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:N/A:H

CWE

CWE-131 - Incorrect Calculation of Buffer Size

Summary

TensorFlow is an open source platform for machine learning. `tf.keras.losses.poisson` receives a `y_pred` and `y_true` that are passed through `functor::mul` in `BinaryOp`. If the resulting dimensions overflow an `int32`, TensorFlow will crash due to a size mismatch during broadcast assignment. We have patched the issue in GitHub commit c5b30379ba87cbe774b08ac50c1f6d36df4ebb7c. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1 and 2.9.3, as these are also affected and still in supported range. However, we will not cherrypick this commit into TensorFlow 2.8.x, as it depends on Eigen behavior that changed between 2.8 and 2.9.

References

▼	URL	Tags
	https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8fvv-46hw-vpg3
	https://github.com/tensorflow/tensorflow/commit/c5b30379ba87cbe774b08ac50c1f6d36df4ebb7c
	https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/cwise_ops_common.h
	https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/losses.py

Impacted products

	Vendor	Product	Version
	tensorflow	tensorflow	Version: >= 2.10.0, < 2.10.1 Version: < 2.9.3

Show details on NVD website

JSON

To clipboard

{
  "containers": {
    "adp": [
      {
        "providerMetadata": {
          "dateUpdated": "2024-08-03T12:56:38.355Z",
          "orgId": "af854a3a-2127-422b-91ae-364da2661108",
          "shortName": "CVE"
        },
        "references": [
          {
            "tags": [
              "x_transferred"
            ],
            "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8fvv-46hw-vpg3"
          },
          {
            "tags": [
              "x_transferred"
            ],
            "url": "https://github.com/tensorflow/tensorflow/commit/c5b30379ba87cbe774b08ac50c1f6d36df4ebb7c"
          },
          {
            "tags": [
              "x_transferred"
            ],
            "url": "https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/cwise_ops_common.h"
          },
          {
            "tags": [
              "x_transferred"
            ],
            "url": "https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/losses.py"
          }
        ],
        "title": "CVE Program Container"
      },
      {
        "metrics": [
          {
            "other": {
              "content": {
                "id": "CVE-2022-41887",
                "options": [
                  {
                    "Exploitation": "poc"
                  },
                  {
                    "Automatable": "no"
                  },
                  {
                    "Technical Impact": "partial"
                  }
                ],
                "role": "CISA Coordinator",
                "timestamp": "2025-04-22T15:42:29.614034Z",
                "version": "2.0.3"
              },
              "type": "ssvc"
            }
          }
        ],
        "providerMetadata": {
          "dateUpdated": "2025-04-22T16:06:20.833Z",
          "orgId": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
          "shortName": "CISA-ADP"
        },
        "title": "CISA ADP Vulnrichment"
      }
    ],
    "cna": {
      "affected": [
        {
          "product": "tensorflow",
          "vendor": "tensorflow",
          "versions": [
            {
              "status": "affected",
              "version": "\u003e= 2.10.0, \u003c 2.10.1"
            },
            {
              "status": "affected",
              "version": "\u003c 2.9.3"
            }
          ]
        }
      ],
      "descriptions": [
        {
          "lang": "en",
          "value": "TensorFlow is an open source platform for machine learning. `tf.keras.losses.poisson` receives a `y_pred` and `y_true` that are passed through `functor::mul` in `BinaryOp`. If the resulting dimensions overflow an `int32`, TensorFlow will crash due to a size mismatch during broadcast assignment. We have patched the issue in GitHub commit c5b30379ba87cbe774b08ac50c1f6d36df4ebb7c. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1 and 2.9.3, as these are also affected and still in supported range. However, we will not cherrypick this commit into TensorFlow 2.8.x, as it depends on Eigen behavior that changed between 2.8 and 2.9."
        }
      ],
      "metrics": [
        {
          "cvssV3_1": {
            "attackComplexity": "HIGH",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 4.8,
            "baseSeverity": "MEDIUM",
            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "privilegesRequired": "LOW",
            "scope": "UNCHANGED",
            "userInteraction": "REQUIRED",
            "vectorString": "CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:N/I:N/A:H",
            "version": "3.1"
          }
        }
      ],
      "problemTypes": [
        {
          "descriptions": [
            {
              "cweId": "CWE-131",
              "description": "CWE-131: Incorrect Calculation of Buffer Size",
              "lang": "en",
              "type": "CWE"
            }
          ]
        }
      ],
      "providerMetadata": {
        "dateUpdated": "2022-11-19T00:00:00.000Z",
        "orgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
        "shortName": "GitHub_M"
      },
      "references": [
        {
          "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-8fvv-46hw-vpg3"
        },
        {
          "url": "https://github.com/tensorflow/tensorflow/commit/c5b30379ba87cbe774b08ac50c1f6d36df4ebb7c"
        },
        {
          "url": "https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/cwise_ops_common.h"
        },
        {
          "url": "https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/losses.py"
        }
      ],
      "source": {
        "advisory": "GHSA-8fvv-46hw-vpg3",
        "discovery": "UNKNOWN"
      },
      "title": "Overflow in `tf.keras.losses.poisson` in Tensorflow"
    }
  },
  "cveMetadata": {
    "assignerOrgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
    "assignerShortName": "GitHub_M",
    "cveId": "CVE-2022-41887",
    "datePublished": "2022-11-18T00:00:00.000Z",
    "dateReserved": "2022-09-30T00:00:00.000Z",
    "dateUpdated": "2025-04-22T16:06:20.833Z",
    "state": "PUBLISHED"
  },
  "dataType": "CVE_RECORD",
  "dataVersion": "5.1"
}

Sightings

Author	Source	Type	Date

Nomenclature

Seen: The vulnerability was mentioned, discussed, or seen somewhere by the user.
Confirmed: The vulnerability is confirmed from an analyst perspective.
Exploited: This vulnerability was exploited and seen by the user reporting the sighting.
Patched: This vulnerability was successfully patched by the user reporting the sighting.
Not exploited: This vulnerability was not exploited or seen by the user reporting the sighting.
Not confirmed: The user expresses doubt about the veracity of the vulnerability.
Not patched: This vulnerability was not successfully patched by the user reporting the sighting.

Action not permitted