Vulnerability-Lookup

PYSEC-2021-620

Vulnerability from pysec - Published: 2021-11-05 21:15 - Updated: 2021-12-09 06:35

Details

TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for QuantizeV2 can trigger a read outside of bounds of heap allocated array. 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. 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. 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.

Impacted products

Name	purl
tensorflow-cpu	pkg:pypi/tensorflow-cpu

Aliases

JSON

To clipboard

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu",
        "purl": "pkg:pypi/tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "a0d64445116c43cf46a5666bd4eee28e7a82f244"
            }
          ],
          "repo": "https://github.com/tensorflow/tensorflow",
          "type": "GIT"
        },
        {
          "events": [
            {
              "introduced": "2.6.0"
            },
            {
              "fixed": "2.6.1"
            },
            {
              "introduced": "2.7.0rc0"
            },
            {
              "fixed": "2.7.0"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "2.6.0",
        "2.7.0rc0",
        "2.7.0rc1"
      ]
    }
  ],
  "aliases": [
    "CVE-2021-41211",
    "GHSA-cvgx-3v3q-m36c"
  ],
  "details": "TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `QuantizeV2` can trigger a read outside of bounds of heap allocated array. 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. 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. 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.",
  "id": "PYSEC-2021-620",
  "modified": "2021-12-09T06:35:09.057312Z",
  "published": "2021-11-05T21:15:00Z",
  "references": [
    {
      "type": "ADVISORY",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-cvgx-3v3q-m36c"
    },
    {
      "type": "FIX",
      "url": "https://github.com/tensorflow/tensorflow/commit/a0d64445116c43cf46a5666bd4eee28e7a82f244"
    }
  ]
}

CVE-2021-41211 (GCVE-0-2021-41211)

Vulnerability from cvelistv5 – Published: 2021-11-05 20:15 – Updated: 2024-08-04 03:08

Title

Heap OOB read in shape inference for `QuantizeV2`

Summary

TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `QuantizeV2` can trigger a read outside of bounds of heap allocated array. 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. 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. 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.

Severity

7.1 (High)


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

CWE

CWE-125 - Out-of-bounds Read

Assigner

GitHub_M mitre root Scorecard

References

2 references

URL	Tags
https://github.com/tensorflow/tensorflow/security…	x_refsource_CONFIRM
https://github.com/tensorflow/tensorflow/commit/a…	x_refsource_MISC

Impacted products

1 product

Vendor	Product	Version
tensorflow	tensorflow	Affected: >= 2.6.0, < 2.6.1

Show details on NVD website

JSON

To clipboard

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          "value": "TensorFlow is an open source platform for machine learning. In affected versions the shape inference code for `QuantizeV2` can trigger a read outside of bounds of heap allocated array. 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. 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. 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."
        }
      ],
      "metrics": [
        {
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            "attackComplexity": "LOW",
            "attackVector": "LOCAL",
            "availabilityImpact": "HIGH",
            "baseScore": 7.1,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "HIGH",
            "integrityImpact": "NONE",
            "privilegesRequired": "LOW",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
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      "problemTypes": [
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      "source": {
        "advisory": "GHSA-cvgx-3v3q-m36c",
        "discovery": "UNKNOWN"
      },
      "title": "Heap OOB read in shape inference for `QuantizeV2`",
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  "dataType": "CVE_RECORD",
  "dataVersion": "5.1"
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GHSA-CVGX-3V3Q-M36C

Vulnerability from github – Published: 2021-11-10 19:01 – Updated: 2024-11-07 22:18

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

JSON

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`"
}

Sightings

Author	Source	Type	Date	Other

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.

The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base model, served locally by ML-Gateway. They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Browse all ATT&CK techniques and the vulnerabilities related to each.

Related by attack behaviour

Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.

Action not permitted

PYSEC-2021-620

CVE-2021-41211 (GCVE-0-2021-41211)

GHSA-CVGX-3V3Q-M36C

Impact

Patches

For more information

Attribution

Tags

Sightings

Nomenclature

Related by attack behaviour