gsd-2020-15213
Vulnerability from gsd
Modified
2023-12-13 01:21
Details
In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.
Aliases
Aliases
CVE-2020-15213


To clipboard 

{
  "GSD": {
    "alias": "CVE-2020-15213",
    "description": "In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.",
    "id": "GSD-2020-15213"
  },
  "gsd": {
    "metadata": {
      "exploitCode": "unknown",
      "remediation": "unknown",
      "reportConfidence": "confirmed",
      "type": "vulnerability"
    },
    "osvSchema": {
      "aliases": [
        "CVE-2020-15213"
      ],
      "details": "In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code.",
      "id": "GSD-2020-15213",
      "modified": "2023-12-13T01:21:43.509676Z",
      "schema_version": "1.4.0"
    }
  },
  "namespaces": {
    "cve.org": {
      "CVE_data_meta": {
        "ASSIGNER": "security-advisories@github.com",
        "ID": "CVE-2020-15213",
        "STATE": "PUBLIC",
        "TITLE": "Denial of service in tensorflow-lite"
      },
      "affects": {
        "vendor": {
          "vendor_data": [
            {
              "product": {
                "product_data": [
                  {
                    "product_name": "tensorflow",
                    "version": {
                      "version_data": [
                        {
                          "version_value": "= 2.2.0"
                        },
                        {
                          "version_value": "= 2.3.0"
                        }
                      ]
                    }
                  }
                ]
              },
              "vendor_name": "tensorflow"
            }
          ]
        }
      },
      "data_format": "MITRE",
      "data_type": "CVE",
      "data_version": "4.0",
      "description": {
        "description_data": [
          {
            "lang": "eng",
            "value": "In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code."
          }
        ]
      },
      "impact": {
        "cvss": {
          "attackComplexity": "HIGH",
          "attackVector": "NETWORK",
          "availabilityImpact": "LOW",
          "baseScore": 4,
          "baseSeverity": "MEDIUM",
          "confidentialityImpact": "NONE",
          "integrityImpact": "NONE",
          "privilegesRequired": "NONE",
          "scope": "CHANGED",
          "userInteraction": "NONE",
          "vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:N/I:N/A:L",
          "version": "3.1"
        }
      },
      "problemtype": {
        "problemtype_data": [
          {
            "description": [
              {
                "lang": "eng",
                "value": "{\"CWE-119\":\"Improper Restriction of Operations within the Bounds of a Memory Buffer\"}"
              }
            ]
          },
          {
            "description": [
              {
                "lang": "eng",
                "value": "{\"CWE-770\":\"Allocation of Resources Without Limits or Throttling\"}"
              }
            ]
          }
        ]
      },
      "references": {
        "reference_data": [
          {
            "name": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1",
            "refsource": "MISC",
            "url": "https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1"
          },
          {
            "name": "https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a",
            "refsource": "MISC",
            "url": "https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a"
          },
          {
            "name": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hjmq-236j-8m87",
            "refsource": "CONFIRM",
            "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hjmq-236j-8m87"
          }
        ]
      },
      "source": {
        "advisory": "GHSA-hjmq-236j-8m87",
        "discovery": "UNKNOWN"
      }
    },
    "gitlab.com": {
      "advisories": [
        {
          "affected_range": "\u003e=2.2.0,\u003c2.2.1||\u003e=2.3.0,\u003c2.3.1",
          "affected_versions": "All versions starting from 2.2.0 before 2.2.1, all versions starting from 2.3.0 before 2.3.1",
          "cvss_v2": "AV:N/AC:M/Au:N/C:N/I:N/A:P",
          "cvss_v3": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:N/I:N/A:L",
          "cwe_ids": [
            "CWE-1035",
            "CWE-119",
            "CWE-770",
            "CWE-937"
          ],
          "date": "2020-10-01",
          "description": "In TensorFlow Lite, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation.",
          "fixed_versions": [
            "2.2.1",
            "2.3.1"
          ],
          "identifier": "CVE-2020-15213",
          "identifiers": [
            "CVE-2020-15213",
            "GHSA-hjmq-236j-8m87"
          ],
          "not_impacted": "All versions before 2.2.0, all versions starting from 2.2.1 before 2.3.0, all versions starting from 2.3.1",
          "package_slug": "pypi/tensorflow-cpu",
          "pubdate": "2020-09-25",
          "solution": "Upgrade to versions 2.2.1, 2.3.1 or above.",
          "title": "Improper Restriction of Operations within the Bounds of a Memory Buffer",
          "urls": [
            "https://nvd.nist.gov/vuln/detail/CVE-2020-15213"
          ],
          "uuid": "be622bbf-6902-46eb-b34b-74ff660f0974"
        },
        {
          "affected_range": "\u003e=2.2.0,\u003c2.2.1||\u003e=2.3.0,\u003c2.3.1",
          "affected_versions": "All versions starting from 2.2.0 before 2.2.1, all versions starting from 2.3.0 before 2.3.1",
          "cvss_v2": "AV:N/AC:M/Au:N/C:N/I:N/A:P",
          "cvss_v3": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:N/I:N/A:L",
          "cwe_ids": [
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          ],
          "date": "2020-10-01",
          "description": "In TensorFlow Lite, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation.",
          "fixed_versions": [
            "2.2.1",
            "2.3.1"
          ],
          "identifier": "CVE-2020-15213",
          "identifiers": [
            "CVE-2020-15213",
            "GHSA-hjmq-236j-8m87"
          ],
          "not_impacted": "All versions before 2.2.0, all versions starting from 2.2.1 before 2.3.0, all versions starting from 2.3.1",
          "package_slug": "pypi/tensorflow-gpu",
          "pubdate": "2020-09-25",
          "solution": "Upgrade to versions 2.2.1, 2.3.1 or above.",
          "title": "Improper Restriction of Operations within the Bounds of a Memory Buffer",
          "urls": [
            "https://nvd.nist.gov/vuln/detail/CVE-2020-15213"
          ],
          "uuid": "21063206-f680-4c67-a63a-cb63fb4b4b61"
        },
        {
          "affected_range": "\u003e=2.2.0,\u003c2.2.1||\u003e=2.3.0,\u003c2.3.1",
          "affected_versions": "All versions starting from 2.2.0 before 2.2.1, all versions starting from 2.3.0 before 2.3.1",
          "cvss_v2": "AV:N/AC:M/Au:N/C:N/I:N/A:P",
          "cvss_v3": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:N/I:N/A:L",
          "cwe_ids": [
            "CWE-1035",
            "CWE-770",
            "CWE-937"
          ],
          "date": "2021-11-18",
          "description": "In TensorFlow Lite, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation.",
          "fixed_versions": [
            "2.2.1",
            "2.3.1"
          ],
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          "identifiers": [
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            "GHSA-hjmq-236j-8m87"
          ],
          "not_impacted": "All versions before 2.2.0, all versions starting from 2.2.1 before 2.3.0, all versions starting from 2.3.1",
          "package_slug": "pypi/tensorflow",
          "pubdate": "2020-09-25",
          "solution": "Upgrade to versions 2.2.1, 2.3.1 or above.",
          "title": "Improper Restriction of Operations within the Bounds of a Memory Buffer",
          "urls": [
            "https://nvd.nist.gov/vuln/detail/CVE-2020-15213"
          ],
          "uuid": "45c3232a-8f3b-4bec-81b7-6ac6eb6cb685"
        }
      ]
    },
    "nvd.nist.gov": {
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        "nodes": [
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            "children": [],
            "cpe_match": [
              {
                "cpe23Uri": "cpe:2.3:a:google:tensorflow:*:*:*:*:lite:*:*:*",
                "cpe_name": [],
                "versionEndExcluding": "2.2.1",
                "versionStartIncluding": "2.2.0",
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              {
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                "cpe_name": [],
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                "versionStartIncluding": "2.3.0",
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          "ID": "CVE-2020-15213"
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        "data_version": "4.0",
        "description": {
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              "lang": "en",
              "value": "In TensorFlow Lite before versions 2.2.1 and 2.3.1, models using segment sum can trigger a denial of service by causing an out of memory allocation in the implementation of segment sum. Since code uses the last element of the tensor holding them to determine the dimensionality of output tensor, attackers can use a very large value to trigger a large allocation. The issue is patched in commit 204945b19e44b57906c9344c0d00120eeeae178a and is released in TensorFlow versions 2.2.1, or 2.3.1. A potential workaround would be to add a custom `Verifier` to limit the maximum value in the segment ids tensor. This only handles the case when the segment ids are stored statically in the model, but a similar validation could be done if the segment ids are generated at runtime, between inference steps. However, if the segment ids are generated as outputs of a tensor during inference steps, then there are no possible workaround and users are advised to upgrade to patched code."
            }
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              "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-hjmq-236j-8m87"
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              "refsource": "MISC",
              "tags": [
                "Patch",
                "Third Party Advisory"
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              "url": "https://github.com/tensorflow/tensorflow/commit/204945b19e44b57906c9344c0d00120eeeae178a"
            }
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      },
      "impact": {
        "baseMetricV2": {
          "acInsufInfo": false,
          "cvssV2": {
            "accessComplexity": "MEDIUM",
            "accessVector": "NETWORK",
            "authentication": "NONE",
            "availabilityImpact": "PARTIAL",
            "baseScore": 4.3,
            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "vectorString": "AV:N/AC:M/Au:N/C:N/I:N/A:P",
            "version": "2.0"
          },
          "exploitabilityScore": 8.6,
          "impactScore": 2.9,
          "obtainAllPrivilege": false,
          "obtainOtherPrivilege": false,
          "obtainUserPrivilege": false,
          "severity": "MEDIUM",
          "userInteractionRequired": false
        },
        "baseMetricV3": {
          "cvssV3": {
            "attackComplexity": "HIGH",
            "attackVector": "NETWORK",
            "availabilityImpact": "LOW",
            "baseScore": 4.0,
            "baseSeverity": "MEDIUM",
            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "privilegesRequired": "NONE",
            "scope": "CHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:N/I:N/A:L",
            "version": "3.1"
          },
          "exploitabilityScore": 2.2,
          "impactScore": 1.4
        }
      },
      "lastModifiedDate": "2021-11-18T17:28Z",
      "publishedDate": "2020-09-25T19:15Z"
    }
  }
}


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