Vulnerability-Lookup

Vulnerability from bitnami_vulndb

Published

2024-03-06 11:11

Modified

2026-09-08 08:34

Summary

Seg fault in `ndarray_tensor_bridge` due to zero and large inputs in Tensorflow

Details

TensorFlow is an open source platform for machine learning. If a numpy array is created with a shape such that one element is zero and the others sum to a large number, an error will be raised. We have patched the issue in GitHub commit 2b56169c16e375c521a3bc8ea658811cc0793784. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

JSON

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{
  "affected": [
    {
      "package": {
        "ecosystem": "Bitnami",
        "name": "tensorflow",
        "purl": "pkg:bitnami/tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "2.8.4"
            },
            {
              "introduced": "2.9.0"
            },
            {
              "fixed": "2.9.3"
            },
            {
              "introduced": "2.10.0"
            },
            {
              "fixed": "2.10.1"
            }
          ],
          "type": "SEMVER"
        }
      ],
      "severity": [
        {
          "score": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H",
          "type": "CVSS_V3"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2022-41884"
  ],
  "database_specific": {
    "cpes": [
      "cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*"
    ],
    "severity": "High"
  },
  "details": "TensorFlow is an open source platform for machine learning. If a numpy array is created with a shape such that one element is zero and the others sum to a large number, an error will be raised. We have patched the issue in GitHub commit 2b56169c16e375c521a3bc8ea658811cc0793784. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.",
  "id": "BIT-tensorflow-2022-41884",
  "modified": "2026-09-08T08:34:36.952Z",
  "published": "2024-03-06T11:11:31.794Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://github.com/tensorflow/tensorflow/commit/2b56169c16e375c521a3bc8ea658811cc0793784"
    },
    {
      "type": "ADVISORY",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jq6x-99hj-q636"
    },
    {
      "type": "WEB",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2022-41884"
    }
  ],
  "schema_version": "1.5.0",
  "summary": "Seg fault in `ndarray_tensor_bridge` due to zero and large inputs in Tensorflow"
}

CVE-2022-41884 (GCVE-0-2022-41884)

Vulnerability from cvelistv5 – Published: 2022-11-18 00:00 – Updated: 2025-04-22 16:06

Title

Seg fault in `ndarray_tensor_bridge` due to zero and large inputs in Tensorflow

Summary

Severity

4.8 (Medium)


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

SSVC

Exploitation: poc Automatable: no Technical Impact: partial

CISA Coordinator · CISA-ADP (v2.0.3)

Decision recorded 2025-04-22 15:42 UTC

CWE

CWE-670 - Always-Incorrect Control Flow Implementation

Assigner

GitHub_M CNA under the mitre root

CNA scorecard C 72/100 over 7566 records in the last 180 days details

References

2 references

URL	Tags
https://github.com/tensorflow/tensorflow/security…
https://github.com/tensorflow/tensorflow/commit/2…

Impacted products

1 product

Vendor	Product	Version
tensorflow	tensorflow	Affected: >= 2.10.0, < 2.10.1 Affected: >= 2.9.0, < 2.9.3 Affected: < 2.8.4

Show details on NVD website

JSON

To clipboard

{
  "containers": {
    "adp": [
      {
        "providerMetadata": {
          "dateUpdated": "2024-08-03T12:56:38.281Z",
          "orgId": "af854a3a-2127-422b-91ae-364da2661108",
          "shortName": "CVE"
        },
        "references": [
          {
            "tags": [
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            ],
            "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jq6x-99hj-q636"
          },
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            "tags": [
              "x_transferred"
            ],
            "url": "https://github.com/tensorflow/tensorflow/commit/2b56169c16e375c521a3bc8ea658811cc0793784"
          }
        ],
        "title": "CVE Program Container"
      },
      {
        "metrics": [
          {
            "other": {
              "content": {
                "id": "CVE-2022-41884",
                "options": [
                  {
                    "Exploitation": "poc"
                  },
                  {
                    "Automatable": "no"
                  },
                  {
                    "Technical Impact": "partial"
                  }
                ],
                "role": "CISA Coordinator",
                "timestamp": "2025-04-22T15:42:39.496021Z",
                "version": "2.0.3"
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          "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": "\u003e= 2.9.0, \u003c 2.9.3"
            },
            {
              "status": "affected",
              "version": "\u003c 2.8.4"
            }
          ]
        }
      ],
      "descriptions": [
        {
          "lang": "en",
          "value": "TensorFlow is an open source platform for machine learning. If a numpy array is created with a shape such that one element is zero and the others sum to a large number, an error will be raised. We have patched the issue in GitHub commit 2b56169c16e375c521a3bc8ea658811cc0793784. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range."
        }
      ],
      "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"
          }
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      ],
      "problemTypes": [
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          "descriptions": [
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        },
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          "url": "https://github.com/tensorflow/tensorflow/commit/2b56169c16e375c521a3bc8ea658811cc0793784"
        }
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      "source": {
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        "discovery": "UNKNOWN"
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      "title": "Seg fault in `ndarray_tensor_bridge` due to zero and large inputs in Tensorflow"
    }
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  "cveMetadata": {
    "assignerOrgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
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    "cveId": "CVE-2022-41884",
    "datePublished": "2022-11-18T00:00:00.000Z",
    "dateReserved": "2022-09-30T00:00:00.000Z",
    "dateUpdated": "2025-04-22T16:06:54.684Z",
    "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.

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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.
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