Vulnerability-Lookup

GSD-2022-29211

Vulnerability from gsd - Updated: 2023-12-13 01:19

Details

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.histogram_fixed_width` is vulnerable to a crash when the values array contain `Not a Number` (`NaN`) elements. The implementation assumes that all floating point operations are defined and then converts a floating point result to an integer index. If `values` contains `NaN` then the result of the division is still `NaN` and the cast to `int32` would result in a crash. This only occurs on the CPU implementation. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

Aliases

CVE-2022-29211

Aliases

CVE-2022-29211

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{
  "GSD": {
    "alias": "CVE-2022-29211",
    "description": "TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.histogram_fixed_width` is vulnerable to a crash when the values array contain `Not a Number` (`NaN`) elements. The implementation assumes that all floating point operations are defined and then converts a floating point result to an integer index. If `values` contains `NaN` then the result of the division is still `NaN` and the cast to `int32` would result in a crash. This only occurs on the CPU implementation. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.",
    "id": "GSD-2022-29211",
    "references": [
      "https://www.suse.com/security/cve/CVE-2022-29211.html"
    ]
  },
  "gsd": {
    "metadata": {
      "exploitCode": "unknown",
      "remediation": "unknown",
      "reportConfidence": "confirmed",
      "type": "vulnerability"
    },
    "osvSchema": {
      "aliases": [
        "CVE-2022-29211"
      ],
      "details": "TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.histogram_fixed_width` is vulnerable to a crash when the values array contain `Not a Number` (`NaN`) elements. The implementation assumes that all floating point operations are defined and then converts a floating point result to an integer index. If `values` contains `NaN` then the result of the division is still `NaN` and the cast to `int32` would result in a crash. This only occurs on the CPU implementation. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.",
      "id": "GSD-2022-29211",
      "modified": "2023-12-13T01:19:42.389302Z",
      "schema_version": "1.4.0"
    }
  },
  "namespaces": {
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        "ID": "CVE-2022-29211",
        "STATE": "PUBLIC",
        "TITLE": "Segfault in TensorFlow if `tf.histogram_fixed_width` is called with NaN values"
      },
      "affects": {
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          "availabilityImpact": "HIGH",
          "baseScore": 5.5,
          "baseSeverity": "MEDIUM",
          "confidentialityImpact": "NONE",
          "integrityImpact": "NONE",
          "privilegesRequired": "LOW",
          "scope": "UNCHANGED",
          "userInteraction": "NONE",
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          "package_slug": "pypi/tensorflow-cpu",
          "pubdate": "2022-05-24",
          "solution": "Upgrade to versions 2.6.4, 2.7.2, 2.8.1 or above.",
          "title": "Improper Input Validation",
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          "package_slug": "pypi/tensorflow-gpu",
          "pubdate": "2022-05-24",
          "solution": "Upgrade to versions 2.6.4, 2.7.2, 2.8.1 or above.",
          "title": "Improper Input Validation",
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          "date": "2022-06-03",
          "description": "TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of `tf.histogram_fixed_width` is vulnerable to a crash when the values array contain `Not a Number` (`NaN`) elements. The implementation assumes that all floating point operations are defined and then converts a floating point result to an integer index. If `values` contains `NaN` then the result of the division is still `NaN` and the cast to `int32` would result in a crash. This only occurs on the CPU implementation. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.",
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            "2.8.1"
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          "package_slug": "pypi/tensorflow",
          "pubdate": "2022-05-21",
          "solution": "Upgrade to versions 2.6.4, 2.7.2, 2.8.1 or above.",
          "title": "Improper Input Validation",
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            "authentication": "NONE",
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            "vectorString": "AV:L/AC:L/Au:N/C:N/I:N/A:P",
            "version": "2.0"
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          "exploitabilityScore": 3.9,
          "impactScore": 2.9,
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        }
      },
      "lastModifiedDate": "2022-06-03T15:02Z",
      "publishedDate": "2022-05-21T00:15Z"
    }
  }
}

CVE-2022-29211 (GCVE-0-2022-29211)

Vulnerability from cvelistv5 – Published: 2022-05-20 23:20 – Updated: 2025-04-22 17:57

Title

Segfault in TensorFlow if `tf.histogram_fixed_width` is called with NaN values

Summary

Severity

5.5 (Medium)


                        
                          CVSS:3.1/AV:L/AC:L/PR:L/UI:N/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 15:46 UTC

CWE

CWE-20 - Improper Input Validation

Assigner

GitHub_M mitre root

References

9 references

URL	Tags
https://github.com/tensorflow/tensorflow/releases…	x_refsource_MISC
https://github.com/tensorflow/tensorflow/releases…	x_refsource_MISC
https://github.com/tensorflow/tensorflow/releases…	x_refsource_MISC
https://github.com/tensorflow/tensorflow/releases…	x_refsource_MISC
https://github.com/tensorflow/tensorflow/security…	x_refsource_CONFIRM
https://github.com/tensorflow/tensorflow/issues/45770	x_refsource_MISC
https://github.com/tensorflow/tensorflow/commit/e…	x_refsource_MISC
https://github.com/tensorflow/tensorflow/blob/f3b…	x_refsource_MISC
https://github.com/tensorflow/tensorflow/blob/f3b…	x_refsource_MISC

Impacted products

1 product

Vendor	Product	Version
tensorflow	tensorflow	Affected: < 2.6.4 Affected: >= 2.7.0rc0, < 2.7.2 Affected: >= 2.8.0rc0, < 2.8.1 Affected: >= 2.9.0rc0, < 2.9.0

Show details on NVD website

JSON

To clipboard

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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.