GHSA-xcwj-wfcm-m23c
Vulnerability from github
Published
2021-05-21 14:22
Modified
2024-10-30 23:19
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
Summary
Invalid validation in `SparseMatrixSparseCholesky`
Details

Impact

An attacker can trigger a null pointer dereference by providing an invalid permutation to tf.raw_ops.SparseMatrixSparseCholesky:

```python import tensorflow as tf import numpy as np from tensorflow.python.ops.linalg.sparse import sparse_csr_matrix_ops

indices_array = np.array([[0, 0]]) value_array = np.array([-10.0], dtype=np.float32) dense_shape = [1, 1] st = tf.SparseTensor(indices_array, value_array, dense_shape)

input = sparse_csr_matrix_ops.sparse_tensor_to_csr_sparse_matrix( st.indices, st.values, st.dense_shape)

permutation = tf.constant([], shape=[1, 0], dtype=tf.int32)

tf.raw_ops.SparseMatrixSparseCholesky(input=input, permutation=permutation, type=tf.float32) ```

This is because the implementation fails to properly validate the input arguments:

```cc void Compute(OpKernelContext ctx) final { ... const Tensor& input_permutation_indices = ctx->input(1); ... ValidateInputs(ctx, input_matrix, input_permutation_indices, &batch_size, &num_rows); ... }

void ValidateInputs(OpKernelContext ctx, const CSRSparseMatrix& sparse_matrix, const Tensor& permutation_indices, int batch_size, int64* num_rows) { OP_REQUIRES(ctx, sparse_matrix.dtype() == DataTypeToEnum::value, ...) ... } `` AlthoughValidateInputsis called and there are checks in the body of this function, the code proceeds to the next line inValidateInputssince [OP_REQUIRES`](https://github.com/tensorflow/tensorflow/blob/080f1d9e257589f78b3ffb75debf584168aa6062/tensorflow/core/framework/op_requires.h#L41-L48) is a macro that only exits the current function.

cc #define OP_REQUIRES(CTX, EXP, STATUS) \ do { \ if (!TF_PREDICT_TRUE(EXP)) { \ CheckNotInComputeAsync((CTX), "OP_REQUIRES_ASYNC"); \ (CTX)->CtxFailure(__FILE__, __LINE__, (STATUS)); \ return; \ } \ } while (0)

Thus, the first validation condition that fails in ValidateInputs will cause an early return from that function. However, the caller will continue execution from the next line. The fix is to either explicitly check context->status() or to convert ValidateInputs to return a Status.

Patches

We have patched the issue in GitHub commit e6a7c7cc18c3aaad1ae0872cb0a959f5c923d2bd.

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.

Show details on source website

To clipboard 

{
  "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"
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          ],
          "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-2021-29530"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-476"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2021-05-18T23:03:26Z",
    "nvd_published_at": "2021-05-14T20:15:00Z",
    "severity": "LOW"
  },
  "details": "### Impact\nAn attacker can trigger a null pointer dereference by providing an invalid `permutation` to `tf.raw_ops.SparseMatrixSparseCholesky`:\n\n```python\nimport tensorflow as tf\nimport numpy as np\nfrom tensorflow.python.ops.linalg.sparse import sparse_csr_matrix_ops\n\nindices_array = np.array([[0, 0]])\nvalue_array = np.array([-10.0], dtype=np.float32)\ndense_shape = [1, 1]\nst = tf.SparseTensor(indices_array, value_array, dense_shape)\n\ninput = sparse_csr_matrix_ops.sparse_tensor_to_csr_sparse_matrix(\n       st.indices, st.values, st.dense_shape)\n\npermutation = tf.constant([], shape=[1, 0], dtype=tf.int32)\n \ntf.raw_ops.SparseMatrixSparseCholesky(input=input, permutation=permutation, type=tf.float32)\n```\n\nThis is because the [implementation](https://github.com/tensorflow/tensorflow/blob/080f1d9e257589f78b3ffb75debf584168aa6062/tensorflow/core/kernels/sparse/sparse_cholesky_op.cc#L85-L86) fails to properly validate the input arguments: \n                          \n```cc \nvoid Compute(OpKernelContext* ctx) final {\n  ...\n  const Tensor\u0026 input_permutation_indices = ctx-\u003einput(1);\n  ...\n  ValidateInputs(ctx, *input_matrix, input_permutation_indices, \u0026batch_size, \u0026num_rows);\n  ...\n}\n\nvoid ValidateInputs(OpKernelContext* ctx,\n    const CSRSparseMatrix\u0026 sparse_matrix,\n    const Tensor\u0026 permutation_indices, int* batch_size,\n    int64* num_rows) {\n  OP_REQUIRES(ctx, sparse_matrix.dtype() == DataTypeToEnum\u003cT\u003e::value, ...)\n  ...\n}\n```\nAlthough `ValidateInputs` is called and there are checks in the body of this function, the code proceeds to the next line in `ValidateInputs` since [`OP_REQUIRES`](https://github.com/tensorflow/tensorflow/blob/080f1d9e257589f78b3ffb75debf584168aa6062/tensorflow/core/framework/op_requires.h#L41-L48) is a macro that only exits the current function.\n\n```cc\n#define OP_REQUIRES(CTX, EXP, STATUS)                     \\\n  do {                                                    \\\n    if (!TF_PREDICT_TRUE(EXP)) {                          \\\n      CheckNotInComputeAsync((CTX), \"OP_REQUIRES_ASYNC\"); \\\n      (CTX)-\u003eCtxFailure(__FILE__, __LINE__, (STATUS));    \\\n      return;                                             \\\n    }                                                     \\\n  } while (0)\n```\n\nThus, the first validation condition that fails in `ValidateInputs` will cause an early return from that function. However, the caller will continue execution from the next line. The fix is to either explicitly check `context-\u003estatus()` or to convert `ValidateInputs` to return a `Status`.\n\n### Patches\nWe have patched the issue in GitHub commit [e6a7c7cc18c3aaad1ae0872cb0a959f5c923d2bd](https://github.com/tensorflow/tensorflow/commit/e6a7c7cc18c3aaad1ae0872cb0a959f5c923d2bd).\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-xcwj-wfcm-m23c",
  "modified": "2024-10-30T23:19:46Z",
  "published": "2021-05-21T14:22:09Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xcwj-wfcm-m23c"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2021-29530"
    },
    {
      "type": "WEB",
      "url": "https://github.com/tensorflow/tensorflow/commit/e6a7c7cc18c3aaad1ae0872cb0a959f5c923d2bd"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-458.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-656.yaml"
    },
    {
      "type": "WEB",
      "url": "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-167.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": "Invalid validation in `SparseMatrixSparseCholesky`"
}


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