pysec - pysec-2021-173

pysec-2021-173

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. An attacker can cause a heap buffer overflow in QuantizedReshape by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a324ac84e573fba362a5e53d4e74d5de6729933e/tensorflow/core/kernels/quantized_reshape_op.cc#L38-L55) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly. However, if any of these tensors is empty, then .flat<T>() is an empty buffer and accessing the element at position 0 results in overflow. 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": "a324ac84e573fba362a5e53d4e74d5de6729933e"
            }
          ],
          "repo": "https://github.com/tensorflow/tensorflow",
          "type": "GIT"
        },
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "2.2.0rc0"
            },
            {
              "introduced": "2.2.0"
            },
            {
              "fixed": "2.3.0rc0"
            },
            {
              "introduced": "2.3.0"
            },
            {
              "fixed": "2.3.4"
            },
            {
              "introduced": "2.4.0"
            },
            {
              "fixed": "2.4.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "0.12.0",
        "0.12.0rc0",
        "0.12.0rc1",
        "0.12.1",
        "1.0.0",
        "1.0.1",
        "1.1.0",
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        "1.10.0rc0",
        "1.10.0rc1",
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        "1.11.0rc2",
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        "1.12.0rc0",
        "1.12.0rc1",
        "1.12.0rc2",
        "1.12.2",
        "1.12.3",
        "1.13.0rc0",
        "1.13.0rc1",
        "1.13.0rc2",
        "1.13.1",
        "1.13.2",
        "1.14.0",
        "1.14.0rc0",
        "1.14.0rc1",
        "1.15.0",
        "1.15.0rc0",
        "1.15.0rc1",
        "1.15.0rc2",
        "1.15.0rc3",
        "1.15.2",
        "1.15.3",
        "1.15.4",
        "1.15.5",
        "1.2.0",
        "1.2.0rc0",
        "1.2.0rc1",
        "1.2.0rc2",
        "1.2.1",
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        "1.3.0rc1",
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      ]
    }
  ],
  "aliases": [
    "CVE-2021-29536",
    "GHSA-2gfx-95x2-5v3x"
  ],
  "details": "TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow in `QuantizedReshape` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a324ac84e573fba362a5e53d4e74d5de6729933e/tensorflow/core/kernels/quantized_reshape_op.cc#L38-L55) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly. However, if any of these tensors is empty, then `.flat\u003cT\u003e()` is an empty buffer and accessing the element at position 0 results in overflow. 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-2021-173",
  "modified": "2021-08-27T03:22:27.845923Z",
  "published": "2021-05-14T20:15:00Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-2gfx-95x2-5v3x"
    },
    {
      "type": "FIX",
      "url": "https://github.com/tensorflow/tensorflow/commit/a324ac84e573fba362a5e53d4e74d5de6729933e"
    }
  ]
}

cve-2021-29536

Vulnerability from cvelistv5

Published

2021-05-14 19:11

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. An attacker can cause a heap buffer overflow in `QuantizedReshape` by passing in invalid thresholds for the quantization. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/a324ac84e573fba362a5e53d4e74d5de6729933e/tensorflow/core/kernels/quantized_reshape_op.cc#L38-L55) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly. However, if any of these tensors is empty, then `.flat<T>()` is an empty buffer and accessing the element at position 0 results in overflow. 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-2gfx-95x2-5v3x	x_refsource_CONFIRM
	https://github.com/tensorflow/tensorflow/commit/a324ac84e573fba362a5e53d4e74d5de6729933e	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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ghsa-2gfx-95x2-5v3x

Vulnerability from github

Published

2021-05-21 14:22

Modified

2024-10-30 23:24

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 buffer overflow in `QuantizedReshape`

Details

Impact

An attacker can cause a heap buffer overflow in QuantizedReshape by passing in invalid thresholds for the quantization:

```python import tensorflow as tf

tensor = tf.constant([], dtype=tf.qint32) shape = tf.constant([], dtype=tf.int32) input_min = tf.constant([], dtype=tf.float32) input_max = tf.constant([], dtype=tf.float32)

tf.raw_ops.QuantizedReshape(tensor=tensor, shape=shape, input_min=input_min, input_max=input_max) ```

This is because the implementation assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly:

cc const auto& input_min_float_tensor = ctx->input(2); ... const float input_min_float = input_min_float_tensor.flat<float>()(0); const auto& input_max_float_tensor = ctx->input(3); ... const float input_max_float = input_max_float_tensor.flat<float>()(0);

However, if any of these tensors is empty, then .flat<T>() is an empty buffer and accessing the element at position 0 results in overflow.

Patches

We have patched the issue in GitHub commit a324ac84e573fba362a5e53d4e74d5de6729933e.

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

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        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
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  "aliases": [
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  "database_specific": {
    "cwe_ids": [
      "CWE-787"
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    "github_reviewed": true,
    "github_reviewed_at": "2021-05-18T22:36:45Z",
    "nvd_published_at": "2021-05-14T20:15:00Z",
    "severity": "LOW"
  },
  "details": "### Impact\nAn attacker can cause a heap buffer overflow in `QuantizedReshape` by passing in invalid thresholds for the quantization:\n\n```python\nimport tensorflow as tf\n\ntensor = tf.constant([], dtype=tf.qint32)\nshape = tf.constant([], dtype=tf.int32)\ninput_min = tf.constant([], dtype=tf.float32)\ninput_max = tf.constant([], dtype=tf.float32)\n\ntf.raw_ops.QuantizedReshape(tensor=tensor, shape=shape, input_min=input_min, input_max=input_max)\n```\n\nThis is because the [implementation](https://github.com/tensorflow/tensorflow/blob/a324ac84e573fba362a5e53d4e74d5de6729933e/tensorflow/core/kernels/quantized_reshape_op.cc#L38-L55) assumes that the 2 arguments are always valid scalars and tries to access the numeric value directly:\n\n```cc\nconst auto\u0026 input_min_float_tensor = ctx-\u003einput(2);\n...\nconst float input_min_float = input_min_float_tensor.flat\u003cfloat\u003e()(0);\nconst auto\u0026 input_max_float_tensor = ctx-\u003einput(3);\n...\nconst float input_max_float = input_max_float_tensor.flat\u003cfloat\u003e()(0);\n```\n\nHowever, if any of these tensors is empty, then `.flat\u003cT\u003e()` is an empty buffer and accessing the element at position 0 results in overflow.\n\n### Patches\nWe have patched the issue in GitHub commit [a324ac84e573fba362a5e53d4e74d5de6729933e](https://github.com/tensorflow/tensorflow/commit/a324ac84e573fba362a5e53d4e74d5de6729933e).\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-2gfx-95x2-5v3x",
  "modified": "2024-10-30T23:24:29Z",
  "published": "2021-05-21T14:22:32Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-2gfx-95x2-5v3x"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2021-29536"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/commit/a324ac84e573fba362a5e53d4e74d5de6729933e"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-464.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-662.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-173.yaml"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/tensorflow/tensorflow"
    }
  ],
  "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 buffer overflow in `QuantizedReshape`"
}

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-2021-173

Vulnerability from pysec

cve-2021-29536

Vulnerability from cvelistv5

ghsa-2gfx-95x2-5v3x

Vulnerability from github

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Patches

For more information

Attribution

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Sightings

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