Vulnerability-Lookup

PYSEC-2021-181

Vulnerability from pysec - Published: 2021-05-14 20:15 - Updated: 2021-08-27 03:22

Details

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a CHECK-fail in tf.raw_ops.QuantizeAndDequantizeV4Grad. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L162-L163) does not validate the rank of the input_* tensors. In turn, this results in the tensors being passes as they are to QuantizeAndDequantizePerChannelGradientImpl(https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.h#L295-L306). However, the vec<T> method, requires the rank to 1 and triggers a CHECK failure otherwise. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 as this is the only other affected version.

Impacted products

Name	purl
tensorflow	pkg:pypi/tensorflow

Aliases

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow",
        "purl": "pkg:pypi/tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "20431e9044cf2ad3c0323c34888b192f3289af6b"
            }
          ],
          "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",
        "1.1.0rc0",
        "1.1.0rc1",
        "1.1.0rc2",
        "1.10.0",
        "1.10.0rc0",
        "1.10.0rc1",
        "1.10.1",
        "1.11.0",
        "1.11.0rc0",
        "1.11.0rc1",
        "1.11.0rc2",
        "1.12.0",
        "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",
        "1.3.0",
        "1.3.0rc0",
        "1.3.0rc1",
        "1.3.0rc2",
        "1.4.0",
        "1.4.0rc0",
        "1.4.0rc1",
        "1.4.1",
        "1.5.0",
        "1.5.0rc0",
        "1.5.0rc1",
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        "2.4.0",
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      ]
    }
  ],
  "aliases": [
    "CVE-2021-29544",
    "GHSA-6g85-3hm8-83f9"
  ],
  "details": "TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.QuantizeAndDequantizeV4Grad`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L162-L163) does not validate the rank of the `input_*` tensors. In turn, this results in the tensors being passes as they are to `QuantizeAndDequantizePerChannelGradientImpl`(https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.h#L295-L306). However, the `vec\u003cT\u003e` method, requires the rank to 1 and triggers a `CHECK` failure otherwise. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 as this is the only other affected version.",
  "id": "PYSEC-2021-181",
  "modified": "2021-08-27T03:22:29.285990Z",
  "published": "2021-05-14T20:15:00Z",
  "references": [
    {
      "type": "FIX",
      "url": "https://github.com/tensorflow/tensorflow/commit/20431e9044cf2ad3c0323c34888b192f3289af6b"
    },
    {
      "type": "ADVISORY",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6g85-3hm8-83f9"
    }
  ]
}

GHSA-6G85-3HM8-83F9

Vulnerability from github – Published: 2021-05-21 14:23 – Updated: 2024-11-19 19:33

Summary

CHECK-fail in `QuantizeAndDequantizeV4Grad`

Details

Impact

An attacker can trigger a denial of service via a CHECK-fail in tf.raw_ops.QuantizeAndDequantizeV4Grad:

import tensorflow as tf

gradient_tensor = tf.constant([0.0], shape=[1])
input_tensor = tf.constant([0.0], shape=[1])
input_min = tf.constant([[0.0]], shape=[1, 1])
input_max = tf.constant([[0.0]], shape=[1, 1])

tf.raw_ops.QuantizeAndDequantizeV4Grad(
  gradients=gradient_tensor, input=input_tensor,
  input_min=input_min, input_max=input_max, axis=0)

This is because the implementation does not validate the rank of the input_* tensors. In turn, this results in the tensors being passes as they are to QuantizeAndDequantizePerChannelGradientImpl:

template <typename Device, typename T>
struct QuantizeAndDequantizePerChannelGradientImpl {
  static void Compute(const Device& d,
                      typename TTypes<T, 3>::ConstTensor gradient,
                      typename TTypes<T, 3>::ConstTensor input,
                      const Tensor* input_min_tensor,
                      const Tensor* input_max_tensor,
                      typename TTypes<T, 3>::Tensor input_backprop,
                      typename TTypes<T>::Flat input_min_backprop,
                      typename TTypes<T>::Flat input_max_backprop) {
    ...
    auto input_min = input_min_tensor->vec<T>();
    auto input_max = input_max_tensor->vec<T>();
    ...
}

However, the vec<T> method, requires the rank to 1 and triggers a CHECK failure otherwise.

Patches

We have patched the issue in GitHub commit 20431e9044cf2ad3c0323c34888b192f3289af6b.

The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 as this is the only other affected version.

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 Yakun Zhang and Ying Wang of Baidu X-Team.

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

Show details on source website

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.4.0"
            },
            {
              "fixed": "2.4.2"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.4.0"
            },
            {
              "fixed": "2.4.2"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.4.0"
            },
            {
              "fixed": "2.4.2"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2021-29544"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-754"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2021-05-18T21:50:36Z",
    "nvd_published_at": "2021-05-14T20:15:00Z",
    "severity": "LOW"
  },
  "details": "### Impact\nAn attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.QuantizeAndDequantizeV4Grad`:\n\n```python\nimport tensorflow as tf\n\ngradient_tensor = tf.constant([0.0], shape=[1])\ninput_tensor = tf.constant([0.0], shape=[1])\ninput_min = tf.constant([[0.0]], shape=[1, 1])\ninput_max = tf.constant([[0.0]], shape=[1, 1])\n\ntf.raw_ops.QuantizeAndDequantizeV4Grad(\n  gradients=gradient_tensor, input=input_tensor,\n  input_min=input_min, input_max=input_max, axis=0)\n```                     \n                        \nThis is because the [implementation](https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L162-L163) does not validate the rank of the `input_*` tensors. In turn, this results in the tensors being passes as they are to [`QuantizeAndDequantizePerChannelGradientImpl`](https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.h#L295-L306):\n\n```cc \ntemplate \u003ctypename Device, typename T\u003e\nstruct QuantizeAndDequantizePerChannelGradientImpl {\n  static void Compute(const Device\u0026 d,\n                      typename TTypes\u003cT, 3\u003e::ConstTensor gradient,\n                      typename TTypes\u003cT, 3\u003e::ConstTensor input,\n                      const Tensor* input_min_tensor,\n                      const Tensor* input_max_tensor,\n                      typename TTypes\u003cT, 3\u003e::Tensor input_backprop,\n                      typename TTypes\u003cT\u003e::Flat input_min_backprop,\n                      typename TTypes\u003cT\u003e::Flat input_max_backprop) {\n    ...\n    auto input_min = input_min_tensor-\u003evec\u003cT\u003e();\n    auto input_max = input_max_tensor-\u003evec\u003cT\u003e();\n    ...\n}\n```\n\nHowever, the `vec\u003cT\u003e` method, requires the rank to 1 and triggers a `CHECK` failure otherwise.\n\n### Patches\nWe have patched the issue in GitHub commit [20431e9044cf2ad3c0323c34888b192f3289af6b](https://github.com/tensorflow/tensorflow/commit/20431e9044cf2ad3c0323c34888b192f3289af6b).\n\nThe fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 as this is the only other affected version.\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 Yakun Zhang and Ying Wang of Baidu X-Team.",
  "id": "GHSA-6g85-3hm8-83f9",
  "modified": "2024-11-19T19:33:14Z",
  "published": "2021-05-21T14:23:22Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-6g85-3hm8-83f9"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2021-29544"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/commit/20431e9044cf2ad3c0323c34888b192f3289af6b"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-472.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-670.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-181.yaml"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/tensorflow/tensorflow"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.cc#L162-L163"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/blob/95078c145b5a7a43ee046144005f733092756ab5/tensorflow/core/kernels/quantize_and_dequantize_op.h#L295-L306"
    }
  ],
  "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": "CHECK-fail in `QuantizeAndDequantizeV4Grad`"
}

CVE-2021-29544 (GCVE-0-2021-29544)

Vulnerability from cvelistv5 – Published: 2021-05-14 19:11 – Updated: 2024-10-31 20:41

Title

CHECK-fail in `QuantizeAndDequantizeV4Grad`

Summary

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.QuantizeAndDequantizeV4Grad`. This is because the implementation does not validate the rank of the `input_*` tensors. In turn, this results in the tensors being passes as they are to `QuantizeAndDequantizePerChannelGradientImpl`. However, the `vec<T>` method, requires the rank to 1 and triggers a `CHECK` failure otherwise. The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 as this is the only other affected version.

Severity ?

2.5 (Low)


                        
                          CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L

CWE

CWE-754 - Improper Check for Unusual or Exceptional Conditions

Assigner

GitHub_M

References

URL

Tags

	https://github.com/tensorflow/tensorflow/security…	x_refsource_CONFIRM
	https://github.com/tensorflow/tensorflow/commit/2…	x_refsource_MISC
	https://github.com/tensorflow/tensorflow/blob/950…	x_refsource_MISC
	https://github.com/tensorflow/tensorflow/blob/950…	x_refsource_MISC

Impacted products

	Vendor	Product	Version
	tensorflow	tensorflow	Affected: >= 2.4.0, < 2.4.2

Show details on NVD website

JSON

To clipboard

{
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      {
        "providerMetadata": {
          "dateUpdated": "2024-08-03T22:11:05.634Z",
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        "title": "CVE Program Container"
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          "vendor": "tensorflow",
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        }
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      "metrics": [
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          "cvssV3_1": {
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            "attackVector": "LOCAL",
            "availabilityImpact": "LOW",
            "baseScore": 2.5,
            "baseSeverity": "LOW",
            "confidentialityImpact": "NONE",
            "integrityImpact": "NONE",
            "privilegesRequired": "LOW",
            "scope": "UNCHANGED",
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        },
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        }
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      "source": {
        "advisory": "GHSA-6g85-3hm8-83f9",
        "discovery": "UNKNOWN"
      },
      "title": "CHECK-fail in `QuantizeAndDequantizeV4Grad`"
    }
  },
  "cveMetadata": {
    "assignerOrgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
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}

Sightings

Author	Source	Type	Date

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.

Detection rules are retrieved from Rulezet.

Action not permitted

PYSEC-2021-181

GHSA-6G85-3HM8-83F9

Impact

Patches

For more information

Attribution

CVE-2021-29544 (GCVE-0-2021-29544)

Tags

Sightings

Nomenclature