Vulnerability-Lookup

FKIE_CVE-2021-29554

Vulnerability from fkie_nvd - Published: 2021-05-14 19:15 - Updated: 2026-06-17 03:47

Severity

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

Summary

TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service via a FPE runtime error in `tf.raw_ops.DenseCountSparseOutput`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/efff014f3b2d8ef6141da30c806faf141297eca1/tensorflow/core/kernels/count_ops.cc#L123-L127) computes a divisor value from user data but does not check that the result is 0 before doing the division. Since `data` is given by the `values` argument, `num_batch_elements` is 0. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, and TensorFlow 2.3.3, as these are also affected.

References

URL	Tags
security-advisories@github.com	https://github.com/tensorflow/tensorflow/commit/da5ff2daf618591f64b2b62d9d9803951b945e9f	Patch, Third Party Advisory
security-advisories@github.com	https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qg48-85hg-mqc5	Exploit, Patch, Third Party Advisory
af854a3a-2127-422b-91ae-364da2661108	https://github.com/tensorflow/tensorflow/commit/da5ff2daf618591f64b2b62d9d9803951b945e9f	Patch, Third Party Advisory
af854a3a-2127-422b-91ae-364da2661108	https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qg48-85hg-mqc5	Exploit, Patch, Third Party Advisory

Impacted products

Vendor	Product	Version
google	tensorflow	*
google	tensorflow	*
google	tensorflow	*
google	tensorflow	*

JSON

To clipboard

{
  "affected": [
    {
      "affectedData": [
        {
          "product": "tensorflow",
          "vendor": "tensorflow",
          "versions": [
            {
              "status": "affected",
              "version": "\u003c 2.3.3"
            },
            {
              "status": "affected",
              "version": "\u003e= 2.4.0, \u003c 2.4.2"
            }
          ]
        }
      ],
      "source": "security-advisories@github.com"
    }
  ],
  "configurations": [
    {
      "nodes": [
        {
          "cpeMatch": [
            {
              "criteria": "cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*",
              "matchCriteriaId": "323ABCCE-24EB-47CC-87F6-48C101477587",
              "versionEndExcluding": "2.1.4",
              "vulnerable": true
            },
            {
              "criteria": "cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*",
              "matchCriteriaId": "64ABA90C-0649-4BB0-89C9-83C14BBDCC0F",
              "versionEndExcluding": "2.2.3",
              "versionStartIncluding": "2.2.0",
              "vulnerable": true
            },
            {
              "criteria": "cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*",
              "matchCriteriaId": "0F83E0CF-CBF6-4C24-8683-3E7A5DC95BA9",
              "versionEndExcluding": "2.3.3",
              "versionStartIncluding": "2.3.0",
              "vulnerable": true
            },
            {
              "criteria": "cpe:2.3:a:google:tensorflow:*:*:*:*:*:*:*:*",
              "matchCriteriaId": "8259531B-A8AC-4F8B-B60F-B69DE4767C03",
              "versionEndExcluding": "2.4.2",
              "versionStartIncluding": "2.4.0",
              "vulnerable": true
            }
          ],
          "negate": false,
          "operator": "OR"
        }
      ]
    }
  ],
  "cveTags": [],
  "descriptions": [
    {
      "lang": "en",
      "value": "TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a denial of service via a FPE runtime error in `tf.raw_ops.DenseCountSparseOutput`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/efff014f3b2d8ef6141da30c806faf141297eca1/tensorflow/core/kernels/count_ops.cc#L123-L127) computes a divisor value from user data but does not check that the result is 0 before doing the division. Since `data` is given by the `values` argument, `num_batch_elements` is 0. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, and TensorFlow 2.3.3, as these are also affected."
    },
    {
      "lang": "es",
      "value": "TensorFlow es una plataforma de c\u00f3digo abierto de extremo a extremo para el aprendizaje autom\u00e1tico.\u0026#xa0;Un atacante puede causar una denegaci\u00f3n de servicio por medio de un error de tiempo de ejecuci\u00f3n de FPE en \"tf.raw_ops.DenseCountSparseOutput\".\u0026#xa0;Esto es debido a que la implementaci\u00f3n (https://github.com/tensorflow/tensorflow/blob/efff014f3b2d8ef6141da30c806faf141297eca1/tensorflow/core/kernels/count_ops.cc#L123-L127) calcula un valor divisor a partir de los datos del usuario pero no comprueba que el resultado es 0 antes de hacer la divisi\u00f3n.\u0026#xa0;Dado que \"data\" viene dado por el argumento \"values\", \"num_batch_elements\" es 0. La correcci\u00f3n ser\u00e1 inclu\u00edda en TensorFlow versi\u00f3n 2.5.0.\u0026#xa0;Tambi\u00e9n seleccionaremos este commit en TensorFlow versi\u00f3n 2.4.2 y TensorFlow versi\u00f3n 2.3.3, ya que estos tambi\u00e9n est\u00e1n afectados"
    }
  ],
  "id": "CVE-2021-29554",
  "lastModified": "2026-06-17T03:47:54.000",
  "metrics": {
    "cvssMetricV2": [
      {
        "acInsufInfo": false,
        "baseSeverity": "LOW",
        "cvssData": {
          "accessComplexity": "LOW",
          "accessVector": "LOCAL",
          "authentication": "NONE",
          "availabilityImpact": "PARTIAL",
          "baseScore": 2.1,
          "confidentialityImpact": "NONE",
          "integrityImpact": "NONE",
          "vectorString": "AV:L/AC:L/Au:N/C:N/I:N/A:P",
          "version": "2.0"
        },
        "exploitabilityScore": 3.9,
        "impactScore": 2.9,
        "obtainAllPrivilege": false,
        "obtainOtherPrivilege": false,
        "obtainUserPrivilege": false,
        "source": "nvd@nist.gov",
        "type": "Primary",
        "userInteractionRequired": false
      }
    ],
    "cvssMetricV31": [
      {
        "cvssData": {
          "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"
        },
        "exploitabilityScore": 1.0,
        "impactScore": 1.4,
        "source": "security-advisories@github.com",
        "type": "Secondary"
      },
      {
        "cvssData": {
          "attackComplexity": "LOW",
          "attackVector": "LOCAL",
          "availabilityImpact": "HIGH",
          "baseScore": 5.5,
          "baseSeverity": "MEDIUM",
          "confidentialityImpact": "NONE",
          "integrityImpact": "NONE",
          "privilegesRequired": "LOW",
          "scope": "UNCHANGED",
          "userInteraction": "NONE",
          "vectorString": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H",
          "version": "3.1"
        },
        "exploitabilityScore": 1.8,
        "impactScore": 3.6,
        "source": "nvd@nist.gov",
        "type": "Primary"
      }
    ]
  },
  "published": "2021-05-14T19:15:07.800",
  "references": [
    {
      "source": "security-advisories@github.com",
      "tags": [
        "Patch",
        "Third Party Advisory"
      ],
      "url": "https://github.com/tensorflow/tensorflow/commit/da5ff2daf618591f64b2b62d9d9803951b945e9f"
    },
    {
      "source": "security-advisories@github.com",
      "tags": [
        "Exploit",
        "Patch",
        "Third Party Advisory"
      ],
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qg48-85hg-mqc5"
    },
    {
      "source": "af854a3a-2127-422b-91ae-364da2661108",
      "tags": [
        "Patch",
        "Third Party Advisory"
      ],
      "url": "https://github.com/tensorflow/tensorflow/commit/da5ff2daf618591f64b2b62d9d9803951b945e9f"
    },
    {
      "source": "af854a3a-2127-422b-91ae-364da2661108",
      "tags": [
        "Exploit",
        "Patch",
        "Third Party Advisory"
      ],
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qg48-85hg-mqc5"
    }
  ],
  "sourceIdentifier": "security-advisories@github.com",
  "vulnStatus": "Modified",
  "weaknesses": [
    {
      "description": [
        {
          "lang": "en",
          "value": "CWE-369"
        }
      ],
      "source": "security-advisories@github.com",
      "type": "Secondary"
    }
  ]
}

CVE-2021-29554 (GCVE-0-2021-29554)

Vulnerability from cvelistv5 – Published: 2021-05-14 19:10 – Updated: 2024-08-03 22:11

Title

Division by 0 in `DenseCountSparseOutput`

Summary

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-369 - Divide By Zero

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/d…	x_refsource_MISC

Impacted products

1 product

Vendor	Product	Version
tensorflow	tensorflow	Affected: < 2.3.3 Affected: >= 2.4.0, < 2.4.2

Show details on NVD website

JSON

To clipboard

{
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      {
        "providerMetadata": {
          "dateUpdated": "2024-08-03T22:11:06.254Z",
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            "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qg48-85hg-mqc5"
          },
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            ],
            "url": "https://github.com/tensorflow/tensorflow/commit/da5ff2daf618591f64b2b62d9d9803951b945e9f"
          }
        ],
        "title": "CVE Program Container"
      }
    ],
    "cna": {
      "affected": [
        {
          "product": "tensorflow",
          "vendor": "tensorflow",
          "versions": [
            {
              "status": "affected",
              "version": "\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. An attacker can cause a denial of service via a FPE runtime error in `tf.raw_ops.DenseCountSparseOutput`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/efff014f3b2d8ef6141da30c806faf141297eca1/tensorflow/core/kernels/count_ops.cc#L123-L127) computes a divisor value from user data but does not check that the result is 0 before doing the division. Since `data` is given by the `values` argument, `num_batch_elements` is 0. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, and TensorFlow 2.3.3, as these are also affected."
        }
      ],
      "metrics": [
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            "attackComplexity": "HIGH",
            "attackVector": "LOCAL",
            "availabilityImpact": "LOW",
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            "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"
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          "TITLE": "Division by 0 in `DenseCountSparseOutput`"
        },
        "affects": {
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            "vendor_data": [
              {
                "product": {
                  "product_data": [
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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.