gsd-2021-29569
Vulnerability from gsd
Modified
2023-12-13 01:23
Details
TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/ac328eaa3870491ababc147822cd04e91a790643/tensorflow/core/kernels/requantization_range_op.cc#L49-L50) assumes that the `input_min` and `input_max` tensors have at least one element, as it accesses the first element in two arrays. If the tensors are empty, `.flat<T>()` is an empty object, backed by an empty array. Hence, accesing even the 0th element is a read outside the bounds. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
Aliases
Aliases
CVE-2021-29569


To clipboard 

{
  "GSD": {
    "alias": "CVE-2021-29569",
    "description": "TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/ac328eaa3870491ababc147822cd04e91a790643/tensorflow/core/kernels/requantization_range_op.cc#L49-L50) assumes that the `input_min` and `input_max` tensors have at least one element, as it accesses the first element in two arrays. If the tensors are empty, `.flat\u003cT\u003e()` is an empty object, backed by an empty array. Hence, accesing even the 0th element is a read outside the bounds. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.",
    "id": "GSD-2021-29569",
    "references": [
      "https://www.suse.com/security/cve/CVE-2021-29569.html",
      "https://security.archlinux.org/CVE-2021-29569"
    ]
  },
  "gsd": {
    "metadata": {
      "exploitCode": "unknown",
      "remediation": "unknown",
      "reportConfidence": "confirmed",
      "type": "vulnerability"
    },
    "osvSchema": {
      "aliases": [
        "CVE-2021-29569"
      ],
      "details": "TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/ac328eaa3870491ababc147822cd04e91a790643/tensorflow/core/kernels/requantization_range_op.cc#L49-L50) assumes that the `input_min` and `input_max` tensors have at least one element, as it accesses the first element in two arrays. If the tensors are empty, `.flat\u003cT\u003e()` is an empty object, backed by an empty array. Hence, accesing even the 0th element is a read outside the bounds. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.",
      "id": "GSD-2021-29569",
      "modified": "2023-12-13T01:23:36.809431Z",
      "schema_version": "1.4.0"
    }
  },
  "namespaces": {
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        "ASSIGNER": "security-advisories@github.com",
        "ID": "CVE-2021-29569",
        "STATE": "PUBLIC",
        "TITLE": "Heap out of bounds read in `RequantizationRange`"
      },
      "affects": {
        "vendor": {
          "vendor_data": [
            {
              "product": {
                "product_data": [
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                    "product_name": "tensorflow",
                    "version": {
                      "version_data": [
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                          "version_value": "\u003c 2.1.4"
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                          "version_value": "\u003e= 2.2.0, \u003c 2.2.3"
                        },
                        {
                          "version_value": "\u003e= 2.3.0, \u003c 2.3.3"
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                          "version_value": "\u003e= 2.4.0, \u003c 2.4.2"
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      "data_type": "CVE",
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      "description": {
        "description_data": [
          {
            "lang": "eng",
            "value": "TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/ac328eaa3870491ababc147822cd04e91a790643/tensorflow/core/kernels/requantization_range_op.cc#L49-L50) assumes that the `input_min` and `input_max` tensors have at least one element, as it accesses the first element in two arrays. If the tensors are empty, `.flat\u003cT\u003e()` is an empty object, backed by an empty array. Hence, accesing even the 0th element is a read outside the bounds. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range."
          }
        ]
      },
      "impact": {
        "cvss": {
          "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"
        }
      },
      "problemtype": {
        "problemtype_data": [
          {
            "description": [
              {
                "lang": "eng",
                "value": "CWE-125: Out-of-bounds Read"
              }
            ]
          }
        ]
      },
      "references": {
        "reference_data": [
          {
            "name": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3h8m-483j-7xxm",
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            "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3h8m-483j-7xxm"
          },
          {
            "name": "https://github.com/tensorflow/tensorflow/commit/ef0c008ee84bad91ec6725ddc42091e19a30cf0e",
            "refsource": "MISC",
            "url": "https://github.com/tensorflow/tensorflow/commit/ef0c008ee84bad91ec6725ddc42091e19a30cf0e"
          }
        ]
      },
      "source": {
        "advisory": "GHSA-3h8m-483j-7xxm",
        "discovery": "UNKNOWN"
      }
    },
    "gitlab.com": {
      "advisories": [
        {
          "affected_range": "\u003c2.1.4||\u003e=2.2.0,\u003c2.2.3||\u003e=2.3.0,\u003c2.3.3||\u003e=2.4.0,\u003c2.4.2",
          "affected_versions": "All versions before 2.1.4, all versions starting from 2.2.0 before 2.2.3, all versions starting from 2.3.0 before 2.3.3, all versions starting from 2.4.0 before 2.4.2",
          "cvss_v2": "AV:L/AC:L/Au:N/C:P/I:N/A:P",
          "cvss_v3": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:H",
          "cwe_ids": [
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            "CWE-125",
            "CWE-937"
          ],
          "date": "2021-05-21",
          "description": "TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation assumes that the `input_min` and `input_max` tensors have at least one element, as it accesses the first element in two arrays. If the tensors are empty, `.flat\u003cT\u003e` is an empty object, backed by an empty array. Hence, accesing even the 0th element is a read outside the bounds. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.",
          "fixed_versions": [
            "2.1.4",
            "2.2.3",
            "2.3.3",
            "2.4.2"
          ],
          "identifier": "CVE-2021-29569",
          "identifiers": [
            "GHSA-3h8m-483j-7xxm",
            "CVE-2021-29569"
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          "not_impacted": "All versions starting from 2.1.4 before 2.2.0, all versions starting from 2.2.3 before 2.3.0, all versions starting from 2.3.3 before 2.4.0, all versions starting from 2.4.2",
          "package_slug": "pypi/tensorflow-cpu",
          "pubdate": "2021-05-21",
          "solution": "Upgrade to versions 2.1.4, 2.2.3, 2.3.3, 2.4.2 or above.",
          "title": "Out-of-bounds Read",
          "urls": [
            "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3h8m-483j-7xxm",
            "https://nvd.nist.gov/vuln/detail/CVE-2021-29569",
            "https://github.com/tensorflow/tensorflow/commit/ef0c008ee84bad91ec6725ddc42091e19a30cf0e",
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          "affected_versions": "All versions before 2.1.4, all versions starting from 2.2.0 before 2.2.3, all versions starting from 2.3.0 before 2.3.3, all versions starting from 2.4.0 before 2.4.2",
          "cvss_v2": "AV:L/AC:L/Au:N/C:P/I:N/A:P",
          "cvss_v3": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:H",
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            "CWE-937"
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          "date": "2021-05-21",
          "description": "TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation assumes that the `input_min` and `input_max` tensors have at least one element, as it accesses the first element in two arrays. If the tensors are empty, `.flat\u003cT\u003e` is an empty object, backed by an empty array. Hence, accesing even the 0th element is a read outside the bounds. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.",
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            "2.2.3",
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            "CVE-2021-29569"
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          "package_slug": "pypi/tensorflow-gpu",
          "pubdate": "2021-05-21",
          "solution": "Upgrade to versions 2.1.4, 2.2.3, 2.3.3, 2.4.2 or above.",
          "title": "Out-of-bounds Read",
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          "uuid": "533438e2-811f-450c-bdec-57541b2a2870"
        },
        {
          "affected_range": "\u003c2.1.4||\u003e=2.2.0,\u003c2.2.3||\u003e=2.3.0,\u003c2.3.3||\u003e=2.4.0,\u003c2.4.2",
          "affected_versions": "All versions before 2.1.4, all versions starting from 2.2.0 before 2.2.3, all versions starting from 2.3.0 before 2.3.3, all versions starting from 2.4.0 before 2.4.2",
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          "cwe_ids": [
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            "CWE-937"
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          "date": "2021-05-20",
          "description": "TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation assumes that the `input_min` and `input_max` tensors have at least one element, as it accesses the first element in two arrays. If the tensors are empty, `.flat\u003cT\u003e()` is an empty object, backed by an empty array. Hence, accesing even the 0th element is a read outside the bounds.",
          "fixed_versions": [
            "2.5.0"
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          "identifier": "CVE-2021-29569",
          "identifiers": [
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          "package_slug": "pypi/tensorflow",
          "pubdate": "2021-05-14",
          "solution": "Upgrade to version 2.5.0 or above.",
          "title": "Out-of-bounds Read",
          "urls": [
            "https://nvd.nist.gov/vuln/detail/CVE-2021-29569"
          ],
          "uuid": "f7cfa439-ebaf-403e-8cdb-6176b1e473ea"
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    },
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        "nodes": [
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        "description": {
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            {
              "lang": "en",
              "value": "TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs. The implementation(https://github.com/tensorflow/tensorflow/blob/ac328eaa3870491ababc147822cd04e91a790643/tensorflow/core/kernels/requantization_range_op.cc#L49-L50) assumes that the `input_min` and `input_max` tensors have at least one element, as it accesses the first element in two arrays. If the tensors are empty, `.flat\u003cT\u003e()` is an empty object, backed by an empty array. Hence, accesing even the 0th element is a read outside the bounds. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range."
            }
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        "references": {
          "reference_data": [
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                "Third Party Advisory"
              ],
              "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3h8m-483j-7xxm"
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              "refsource": "MISC",
              "tags": [
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                "Third Party Advisory"
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              "url": "https://github.com/tensorflow/tensorflow/commit/ef0c008ee84bad91ec6725ddc42091e19a30cf0e"
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      },
      "impact": {
        "baseMetricV2": {
          "acInsufInfo": false,
          "cvssV2": {
            "accessComplexity": "LOW",
            "accessVector": "LOCAL",
            "authentication": "NONE",
            "availabilityImpact": "PARTIAL",
            "baseScore": 3.6,
            "confidentialityImpact": "PARTIAL",
            "integrityImpact": "NONE",
            "vectorString": "AV:L/AC:L/Au:N/C:P/I:N/A:P",
            "version": "2.0"
          },
          "exploitabilityScore": 3.9,
          "impactScore": 4.9,
          "obtainAllPrivilege": false,
          "obtainOtherPrivilege": false,
          "obtainUserPrivilege": false,
          "severity": "LOW",
          "userInteractionRequired": false
        },
        "baseMetricV3": {
          "cvssV3": {
            "attackComplexity": "LOW",
            "attackVector": "LOCAL",
            "availabilityImpact": "HIGH",
            "baseScore": 7.1,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "HIGH",
            "integrityImpact": "NONE",
            "privilegesRequired": "LOW",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:H",
            "version": "3.1"
          },
          "exploitabilityScore": 1.8,
          "impactScore": 5.2
        }
      },
      "lastModifiedDate": "2021-05-20T14:56Z",
      "publishedDate": "2021-05-14T20:15Z"
    }
  }
}


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