pysec - pysec-2021-206

pysec-2021-206

Vulnerability from pysec

Published

2021-05-14 20:15

Modified

2021-08-27 03:22

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

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow",
        "purl": "pkg:pypi/tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
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            },
            {
              "fixed": "ef0c008ee84bad91ec6725ddc42091e19a30cf0e"
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          "repo": "https://github.com/tensorflow/tensorflow",
          "type": "GIT"
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            {
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            {
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            },
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              "introduced": "2.4.0"
            },
            {
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            }
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          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
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        "0.12.0rc0",
        "0.12.0rc1",
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        "1.13.0rc2",
        "1.13.1",
        "1.13.2",
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        "1.14.0rc0",
        "1.14.0rc1",
        "1.15.0",
        "1.15.0rc0",
        "1.15.0rc1",
        "1.15.0rc2",
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        "1.15.2",
        "1.15.3",
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        "1.15.5",
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        "1.2.0rc0",
        "1.2.0rc1",
        "1.2.0rc2",
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        "1.3.0rc1",
        "1.3.0rc2",
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      ]
    }
  ],
  "aliases": [
    "CVE-2021-29569",
    "GHSA-3h8m-483j-7xxm"
  ],
  "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": "PYSEC-2021-206",
  "modified": "2021-08-27T03:22:33.683964Z",
  "published": "2021-05-14T20:15:00Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3h8m-483j-7xxm"
    },
    {
      "type": "FIX",
      "url": "https://github.com/tensorflow/tensorflow/commit/ef0c008ee84bad91ec6725ddc42091e19a30cf0e"
    }
  ]
}

ghsa-3h8m-483j-7xxm

Vulnerability from github

Published

2021-05-21 14:25

Modified

2024-11-01 17:05

Severity ?

2.5 (Low) - CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
2.0 (Low) - CVSS:4.0/AV:L/AC:L/AT:P/PR:L/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N

Summary

Heap out of bounds read in `RequantizationRange`

Details

Impact

The implementation of tf.raw_ops.MaxPoolGradWithArgmax can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs:

```python import tensorflow as tf

input = tf.constant([1], shape=[1], dtype=tf.qint32) input_max = tf.constant([], dtype=tf.float32) input_min = tf.constant([], dtype=tf.float32)

tf.raw_ops.RequantizationRange(input=input, input_min=input_min, input_max=input_max) ```

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:

cc const float input_min_float = ctx->input(1).flat<float>()(0); const float input_max_float = ctx->input(2).flat<float>()(0);

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.

Patches

We have patched the issue in GitHub commit ef0c008ee84bad91ec6725ddc42091e19a30cf0e.

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.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.

Show details on source website

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "2.1.4"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.2.0"
            },
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              "fixed": "2.2.3"
            }
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      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
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              "introduced": "2.3.0"
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        "name": "tensorflow"
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          "type": "ECOSYSTEM"
        }
      ]
    },
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      "package": {
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        "name": "tensorflow-cpu"
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      "ranges": [
        {
          "events": [
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    },
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        "name": "tensorflow-cpu"
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          "type": "ECOSYSTEM"
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    },
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        "name": "tensorflow-cpu"
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            },
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          "type": "ECOSYSTEM"
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        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
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            },
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        "name": "tensorflow-gpu"
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            {
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          "type": "ECOSYSTEM"
        }
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    },
    {
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        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.3.0"
            },
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      ]
    },
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        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
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        {
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              "introduced": "2.4.0"
            },
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            }
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        }
      ]
    }
  ],
  "aliases": [
    "CVE-2021-29569"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-125"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2021-05-18T19:00:06Z",
    "nvd_published_at": "2021-05-14T20:15:00Z",
    "severity": "LOW"
  },
  "details": "### Impact\nThe implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs:\n\n```python\nimport tensorflow as tf\n\ninput = tf.constant([1], shape=[1], dtype=tf.qint32) \ninput_max = tf.constant([], dtype=tf.float32)\ninput_min = tf.constant([], dtype=tf.float32)\n\ntf.raw_ops.RequantizationRange(input=input, input_min=input_min, input_max=input_max)\n```\n\nThe [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:\n\n```cc\nconst float input_min_float = ctx-\u003einput(1).flat\u003cfloat\u003e()(0);\nconst float input_max_float = ctx-\u003einput(2).flat\u003cfloat\u003e()(0);\n```\n\nIf 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.\n\n### Patches\nWe have patched the issue in GitHub commit [ef0c008ee84bad91ec6725ddc42091e19a30cf0e](https://github.com/tensorflow/tensorflow/commit/ef0c008ee84bad91ec6725ddc42091e19a30cf0e).\n\nThe 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.\n\n### For more information\nPlease consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.\n\n### Attribution\nThis vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.",
  "id": "GHSA-3h8m-483j-7xxm",
  "modified": "2024-11-01T17:05:44Z",
  "published": "2021-05-21T14:25:22Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3h8m-483j-7xxm"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2021-29569"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/commit/ef0c008ee84bad91ec6725ddc42091e19a30cf0e"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-497.yaml"
    },
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      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-695.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-206.yaml"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/tensorflow/tensorflow"
    }
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  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L",
      "type": "CVSS_V3"
    },
    {
      "score": "CVSS:4.0/AV:L/AC:L/AT:P/PR:L/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N",
      "type": "CVSS_V4"
    }
  ],
  "summary": "Heap out of bounds read in `RequantizationRange`"
}

cve-2021-29569

Vulnerability from cvelistv5

Published

2021-05-14 19:16

Modified

2024-08-03 22:11

Severity ?

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

Summary

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.

References

▼	URL	Tags
	https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3h8m-483j-7xxm	x_refsource_CONFIRM
	https://github.com/tensorflow/tensorflow/commit/ef0c008ee84bad91ec6725ddc42091e19a30cf0e	x_refsource_MISC

Impacted products

Vendor

Product

Version

▼

tensorflow

Version: < 2.1.4
Version: >= 2.2.0, < 2.2.3
Version: >= 2.3.0, < 2.3.3
Version: >= 2.4.0, < 2.4.2

Show details on NVD website

JSON

To clipboard

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                "vendor_name": "tensorflow"
              }
            ]
          }
        },
        "data_format": "MITRE",
        "data_type": "CVE",
        "data_version": "4.0",
        "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",
              "refsource": "CONFIRM",
              "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"
        }
      }
    }
  },
  "cveMetadata": {
    "assignerOrgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
    "assignerShortName": "GitHub_M",
    "cveId": "CVE-2021-29569",
    "datePublished": "2021-05-14T19:16:36",
    "dateReserved": "2021-03-30T00:00:00",
    "dateUpdated": "2024-08-03T22:11:05.766Z",
    "state": "PUBLISHED"
  },
  "dataType": "CVE_RECORD",
  "dataVersion": "5.1"
}

Sightings

Author	Source	Type	Date

Nomenclature

Seen: The vulnerability was mentioned, discussed, or seen somewhere by the user.
Confirmed: The vulnerability is confirmed from an analyst perspective.
Exploited: This vulnerability was exploited and seen by the user reporting the sighting.
Patched: This vulnerability was successfully patched by the user reporting the sighting.
Not exploited: This vulnerability was not exploited or seen by the user reporting the sighting.
Not confirmed: The user expresses doubt about the veracity of the vulnerability.
Not patched: This vulnerability was not successfully patched by the user reporting the sighting.

Action not permitted

pysec-2021-206

Vulnerability from pysec

ghsa-3h8m-483j-7xxm

Vulnerability from github

Impact

Patches

For more information

Attribution

cve-2021-29569

Vulnerability from cvelistv5

Tags

Sightings

Nomenclature