ghsa-98p5-x8x4-c9m5
Vulnerability from github
Published
2022-02-09 23:52
Modified
2024-11-13 22:37
Severity ?
8.8 (High) - CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
8.7 (High) - CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N
Summary
Integer overflow in TFLite
Details

Impact

An attacker can craft a TFLite model that would cause an integer overflow in embedding lookup operations:

cc int embedding_size = 1; int lookup_size = 1; for (int i = 0; i < lookup_rank - 1; i++, k++) { const int dim = dense_shape->data.i32[i]; lookup_size *= dim; output_shape->data[k] = dim; } for (int i = 1; i < embedding_rank; i++, k++) { const int dim = SizeOfDimension(value, i); embedding_size *= dim; output_shape->data[k] = dim; }

Both embedding_size and lookup_size are products of values provided by the user. Hence, a malicious user could trigger overflows in the multiplication.

In certain scenarios, this can then result in heap OOB read/write.

Patches

We have patched the issue in GitHub commits f19be71717c497723ba0cea0379e84f061a75e01, 1de49725a5fc4e48f1a3b902ec3599ee99283043 and a4e401da71458d253b05e41f28637b65baf64be4.

The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 Wang Xuan of Qihoo 360 AIVul Team.

Show details on source website

To clipboard 

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "2.5.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.6.0"
            },
            {
              "fixed": "2.6.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.7.0"
            },
            {
              "fixed": "2.7.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "2.7.0"
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "2.5.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.6.0"
            },
            {
              "fixed": "2.6.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.7.0"
            },
            {
              "fixed": "2.7.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "2.7.0"
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "2.5.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.6.0"
            },
            {
              "fixed": "2.6.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.7.0"
            },
            {
              "fixed": "2.7.1"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ],
      "versions": [
        "2.7.0"
      ]
    }
  ],
  "aliases": [
    "CVE-2022-23559"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-190"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2022-02-03T20:34:01Z",
    "nvd_published_at": "2022-02-04T23:15:00Z",
    "severity": "HIGH"
  },
  "details": "### Impact \nAn attacker can craft a TFLite model that would cause an integer overflow [in embedding lookup operations](https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/kernels/embedding_lookup_sparse.cc#L179-L189):\n\n```cc\n  int embedding_size = 1;\n  int lookup_size = 1;\n  for (int i = 0; i \u003c lookup_rank - 1; i++, k++) {\n    const int dim = dense_shape-\u003edata.i32[i];\n    lookup_size *= dim;\n    output_shape-\u003edata[k] = dim;\n  }\n  for (int i = 1; i \u003c embedding_rank; i++, k++) {\n    const int dim = SizeOfDimension(value, i);\n    embedding_size *= dim;\n    output_shape-\u003edata[k] = dim;\n  } \n```\n\nBoth `embedding_size` and `lookup_size` are products of values provided by the user. Hence, a malicious user could trigger overflows in the multiplication.\n\nIn certain scenarios, this can then result in heap OOB read/write.\n  \n### Patches\nWe have patched the issue in GitHub commits [f19be71717c497723ba0cea0379e84f061a75e01](https://github.com/tensorflow/tensorflow/commit/f19be71717c497723ba0cea0379e84f061a75e01), [1de49725a5fc4e48f1a3b902ec3599ee99283043](https://github.com/tensorflow/tensorflow/commit/1de49725a5fc4e48f1a3b902ec3599ee99283043) and [a4e401da71458d253b05e41f28637b65baf64be4](https://github.com/tensorflow/tensorflow/commit/a4e401da71458d253b05e41f28637b65baf64be4).\n\nThe fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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 Wang Xuan of Qihoo 360 AIVul Team. ",
  "id": "GHSA-98p5-x8x4-c9m5",
  "modified": "2024-11-13T22:37:28Z",
  "published": "2022-02-09T23:52:51Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-98p5-x8x4-c9m5"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2022-23559"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/commit/1de49725a5fc4e48f1a3b902ec3599ee99283043"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/commit/a4e401da71458d253b05e41f28637b65baf64be4"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/commit/f19be71717c497723ba0cea0379e84f061a75e01"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2022-68.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2022-123.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/blob/ca6f96b62ad84207fbec580404eaa7dd7403a550/tensorflow/lite/kernels/embedding_lookup_sparse.cc#L179-L189"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H",
      "type": "CVSS_V3"
    },
    {
      "score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:H/SC:N/SI:N/SA:N",
      "type": "CVSS_V4"
    }
  ],
  "summary": "Integer overflow in TFLite"
}


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