FKIE_CVE-2020-15201

Vulnerability from fkie_nvd - Published: 2020-09-25 19:15 - Updated: 2026-06-17 02:56
Summary
In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the `splits` tensor generate a valid partitioning of the `values` tensor. Hence, the code is prone to heap buffer overflow. If `split_values` does not end with a value at least `num_values` then the `while` loop condition will trigger a read outside of the bounds of `split_values` once `batch_idx` grows too large. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.
Impacted products
Vendor Product Version
google tensorflow 2.3.0

{
  "affected": [
    {
      "affectedData": [
        {
          "product": "tensorflow",
          "vendor": "tensorflow",
          "versions": [
            {
              "status": "affected",
              "version": "= 2.3.0"
            }
          ]
        }
      ],
      "source": "security-advisories@github.com"
    }
  ],
  "configurations": [
    {
      "nodes": [
        {
          "cpeMatch": [
            {
              "criteria": "cpe:2.3:a:google:tensorflow:2.3.0:*:*:*:-:*:*:*",
              "matchCriteriaId": "D0A7B69E-9388-48F0-B744-49453EBAF5D5",
              "vulnerable": true
            }
          ],
          "negate": false,
          "operator": "OR"
        }
      ]
    }
  ],
  "cveTags": [],
  "descriptions": [
    {
      "lang": "en",
      "value": "In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the `splits` tensor generate a valid partitioning of the `values` tensor. Hence, the code is prone to heap buffer overflow. If `split_values` does not end with a value at least `num_values` then the `while` loop condition will trigger a read outside of the bounds of `split_values` once `batch_idx` grows too large. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1."
    },
    {
      "lang": "es",
      "value": "En Tensorflow anteriores a la versi\u00f3n 2.3.1, la implementaci\u00f3n de \"RaggedCountSparseOutput\" no comprueba que los argumentos de entrada formen un tensor irregular v\u00e1lido.\u0026#xa0;En particular, no existe comprobaci\u00f3n de que los valores en el tensor \"splits\" generen una partici\u00f3n v\u00e1lida del tensor \"values\".\u0026#xa0;Por lo tanto, el c\u00f3digo es propenso a un desbordamiento del b\u00fafer de la pila.\u0026#xa0;Si \"split_values\" no termina con un valor de al menos \"num_values\", entonces la condici\u00f3n de bucle \"while\" activar\u00e1 una lectura fuera de los l\u00edmites de \"split_values\" una vez que \"batch_idx\" se incremente demasiado.\u0026#xa0;El problema es parcheado en el commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 y es publicado en TensorFlow versi\u00f3n 2.3.1"
    }
  ],
  "id": "CVE-2020-15201",
  "lastModified": "2026-06-17T02:56:15.237",
  "metrics": {
    "cvssMetricV2": [
      {
        "acInsufInfo": false,
        "baseSeverity": "MEDIUM",
        "cvssData": {
          "accessComplexity": "MEDIUM",
          "accessVector": "NETWORK",
          "authentication": "NONE",
          "availabilityImpact": "PARTIAL",
          "baseScore": 6.8,
          "confidentialityImpact": "PARTIAL",
          "integrityImpact": "PARTIAL",
          "vectorString": "AV:N/AC:M/Au:N/C:P/I:P/A:P",
          "version": "2.0"
        },
        "exploitabilityScore": 8.6,
        "impactScore": 6.4,
        "obtainAllPrivilege": false,
        "obtainOtherPrivilege": false,
        "obtainUserPrivilege": false,
        "source": "nvd@nist.gov",
        "type": "Primary",
        "userInteractionRequired": false
      }
    ],
    "cvssMetricV31": [
      {
        "cvssData": {
          "attackComplexity": "HIGH",
          "attackVector": "NETWORK",
          "availabilityImpact": "NONE",
          "baseScore": 4.8,
          "baseSeverity": "MEDIUM",
          "confidentialityImpact": "LOW",
          "integrityImpact": "LOW",
          "privilegesRequired": "NONE",
          "scope": "UNCHANGED",
          "userInteraction": "NONE",
          "vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N",
          "version": "3.1"
        },
        "exploitabilityScore": 2.2,
        "impactScore": 2.5,
        "source": "security-advisories@github.com",
        "type": "Secondary"
      },
      {
        "cvssData": {
          "attackComplexity": "HIGH",
          "attackVector": "NETWORK",
          "availabilityImpact": "NONE",
          "baseScore": 4.8,
          "baseSeverity": "MEDIUM",
          "confidentialityImpact": "LOW",
          "integrityImpact": "LOW",
          "privilegesRequired": "NONE",
          "scope": "UNCHANGED",
          "userInteraction": "NONE",
          "vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N",
          "version": "3.1"
        },
        "exploitabilityScore": 2.2,
        "impactScore": 2.5,
        "source": "nvd@nist.gov",
        "type": "Primary"
      }
    ]
  },
  "published": "2020-09-25T19:15:15.353",
  "references": [
    {
      "source": "security-advisories@github.com",
      "tags": [
        "Patch",
        "Third Party Advisory"
      ],
      "url": "https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02"
    },
    {
      "source": "security-advisories@github.com",
      "tags": [
        "Third Party Advisory"
      ],
      "url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1"
    },
    {
      "source": "security-advisories@github.com",
      "tags": [
        "Exploit",
        "Third Party Advisory"
      ],
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p5f8-gfw5-33w4"
    },
    {
      "source": "af854a3a-2127-422b-91ae-364da2661108",
      "tags": [
        "Patch",
        "Third Party Advisory"
      ],
      "url": "https://github.com/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02"
    },
    {
      "source": "af854a3a-2127-422b-91ae-364da2661108",
      "tags": [
        "Third Party Advisory"
      ],
      "url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1"
    },
    {
      "source": "af854a3a-2127-422b-91ae-364da2661108",
      "tags": [
        "Exploit",
        "Third Party Advisory"
      ],
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p5f8-gfw5-33w4"
    }
  ],
  "sourceIdentifier": "security-advisories@github.com",
  "vulnStatus": "Modified",
  "weaknesses": [
    {
      "description": [
        {
          "lang": "en",
          "value": "CWE-20"
        },
        {
          "lang": "en",
          "value": "CWE-122"
        }
      ],
      "source": "security-advisories@github.com",
      "type": "Secondary"
    },
    {
      "description": [
        {
          "lang": "en",
          "value": "CWE-787"
        }
      ],
      "source": "nvd@nist.gov",
      "type": "Primary"
    }
  ]
}



Log in or create an account to share your comment.




Tags
Taxonomy of the tags.


Loading…

Loading…

Loading…

Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.

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.

Loading…

Detection rules are retrieved from Rulezet.

Loading…

Loading…

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.


Loading…