GHSA-CVGX-3V3Q-M36C

Vulnerability from github – Published: 2021-11-10 19:01 – Updated: 2024-11-07 22:18
VLAI?
Summary
Heap OOB in shape inference for `QuantizeV2`
Details

Impact

The shape inference code for QuantizeV2 can trigger a read outside of bounds of heap allocated array:

import tensorflow as tf

@tf.function
def test():
  data=tf.raw_ops.QuantizeV2(
    input=[1.0,1.0],
    min_range=[1.0,10.0],
    max_range=[1.0,10.0],
    T=tf.qint32,
    mode='MIN_COMBINED',
    round_mode='HALF_TO_EVEN',
    narrow_range=False,
    axis=-100,
    ensure_minimum_range=10)
  return data

test()

This occurs whenever axis is a negative value less than -1. In this case, we are accessing data before the start of a heap buffer:

int axis = -1;
Status s = c->GetAttr("axis", &axis);
if (!s.ok() && s.code() != error::NOT_FOUND) {
  return s;
}   
... 
if (axis != -1) {
  ...
  TF_RETURN_IF_ERROR(
      c->Merge(c->Dim(minmax, 0), c->Dim(input, axis), &depth));
}

The code allows axis to be an optional argument (s would contain an error::NOT_FOUND error code). Otherwise, it assumes that axis is a valid index into the dimensions of the input tensor. If axis is less than -1 then this results in a heap OOB read.

Patches

We have patched the issue in GitHub commit a0d64445116c43cf46a5666bd4eee28e7a82f244.

The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, as this version is the only one that is also affected.

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
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"
        }
      ],
      "versions": [
        "2.6.0"
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.6.0"
            },
            {
              "fixed": "2.6.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "2.6.0"
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.6.0"
            },
            {
              "fixed": "2.6.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "2.6.0"
      ]
    }
  ],
  "aliases": [
    "CVE-2021-41211"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-125"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2021-11-08T22:32:45Z",
    "nvd_published_at": "2021-11-05T21:15:00Z",
    "severity": "HIGH"
  },
  "details": "### Impact\nThe [shape inference code for `QuantizeV2`](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/framework/common_shape_fns.cc#L2509-L2530) can trigger a read outside of bounds of heap allocated array:\n\n```python\nimport tensorflow as tf\n\n@tf.function\ndef test():\n  data=tf.raw_ops.QuantizeV2(\n    input=[1.0,1.0],\n    min_range=[1.0,10.0],\n    max_range=[1.0,10.0],\n    T=tf.qint32,\n    mode=\u0027MIN_COMBINED\u0027,\n    round_mode=\u0027HALF_TO_EVEN\u0027,\n    narrow_range=False,\n    axis=-100,\n    ensure_minimum_range=10)\n  return data\n\ntest()\n```\n\nThis occurs whenever `axis` is a negative value less than `-1`. In this case, we are accessing data before the start of a heap buffer:\n    \n```cc\nint axis = -1;\nStatus s = c-\u003eGetAttr(\"axis\", \u0026axis);\nif (!s.ok() \u0026\u0026 s.code() != error::NOT_FOUND) {\n  return s;\n}   \n... \nif (axis != -1) {\n  ...\n  TF_RETURN_IF_ERROR(\n      c-\u003eMerge(c-\u003eDim(minmax, 0), c-\u003eDim(input, axis), \u0026depth));\n}\n```\n\nThe code allows `axis` to be an optional argument (`s` would contain an `error::NOT_FOUND` error code). Otherwise, it assumes that `axis` is a valid index into the dimensions of the `input` tensor. If `axis` is less than `-1` then this results in a heap OOB read.\n    \n### Patches\nWe have patched the issue in GitHub commit [a0d64445116c43cf46a5666bd4eee28e7a82f244](https://github.com/tensorflow/tensorflow/commit/a0d64445116c43cf46a5666bd4eee28e7a82f244).\n    \nThe fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, as this version is the only one that is also affected.\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.",
  "id": "GHSA-cvgx-3v3q-m36c",
  "modified": "2024-11-07T22:18:59Z",
  "published": "2021-11-10T19:01:03Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvgx-3v3q-m36c"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2021-41211"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/commit/a0d64445116c43cf46a5666bd4eee28e7a82f244"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-620.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-818.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-403.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"
    }
  ],
  "summary": "Heap OOB in shape inference for `QuantizeV2`"
}


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.