CHECK-fail in SparseConcat

### Impact An attacker can trigger a denial of service via a `CHECK`-fail in `tf.raw_ops.SparseConcat`: ```python import tensorflow as tf import numpy as np indices_1 = tf.constant([[514, 514], [514, 514]], dtype=tf.int64) indices_2 = tf.constant([[514, 530], [599, 877]], dtype=tf.int64) indices = [indices_1, indices_2] values_1 = tf.zeros([0], dtype=tf.int64) values_2 = tf.zeros([0], dtype=tf.int64) values = [values_1, values_2] shape_1 = tf.constant([442, 514, 514, 515, 606, 347, 943, 61, 2], dtype=tf.int64) shape_2 = tf.zeros([9], dtype=tf.int64) shapes = [shape_1, shape_2] tf.raw_ops.SparseConcat(indices=indices, values=values, shapes=shapes, concat_dim=2) ``` This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/b432a38fe0e1b4b904a6c222cbce794c39703e87/tensorflow/core/kernels/sparse_concat_op.cc#L76) takes the values specified in `shapes[0]` as dimensions for the output shape: ```cc TensorShape input_shape(shapes[0].vec<int64>()); ``` The [`TensorShape` constructor](https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L183-L188) uses a `CHECK` operation which triggers when [`InitDims`](https://github.com/tensorflow/tensorflow/blob/6f9896890c4c703ae0a0845394086e2e1e523299/tensorflow/core/framework/tensor_shape.cc#L212-L296) returns a non-OK status. ```cc template <class Shape> TensorShapeBase<Shape>::TensorShapeBase(gtl::ArraySlice<int64> dim_sizes) { set_tag(REP16); set_data_type(DT_INVALID); TF_CHECK_OK(InitDims(dim_sizes)); } ``` In our scenario, this occurs when adding a dimension from the argument results in overflow: ```cc template <class Shape> Status TensorShapeBase<Shape>::InitDims(gtl::ArraySlice<int64> dim_sizes) { ... Status status = Status::OK(); for (int64 s : dim_sizes) { status.Update(AddDimWithStatus(internal::SubtleMustCopy(s))); if (!status.ok()) { return status; } } } template <class Shape> Status TensorShapeBase<Shape>::AddDimWithStatus(int64 size) { ... int64 new_num_elements; if (kIsPartial && (num_elements() < 0 || size < 0)) { new_num_elements = -1; } else { new_num_elements = MultiplyWithoutOverflow(num_elements(), size); if (TF_PREDICT_FALSE(new_num_elements < 0)) { return errors::Internal("Encountered overflow when multiplying ", num_elements(), " with ", size, ", result: ", new_num_elements); } } ... } ``` This is a legacy implementation of the constructor and operations should use `BuildTensorShapeBase` or `AddDimWithStatus` to prevent `CHECK`-failures in the presence of overflows. ### Patches We have patched the issue in GitHub commit [69c68ecbb24dff3fa0e46da0d16c821a2dd22d7c](https://github.com/tensorflow/tensorflow/commit/69c68ecbb24dff3fa0e46da0d16c821a2dd22d7c). 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 Yakun Zhang and Ying Wang 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-754

Also known as

BIT-tensorflow-2021-29534, CVE-2021-29534, PYSEC-2021-171, PYSEC-2021-462, PYSEC-2021-660

• github.com/tensorflow/tensorflow/security/advisories/GHSA-6j9c-grc6-5m6g
• nvd.nist.gov/vuln/detail/CVE-2021-29534
• github.com/tensorflow/tensorflow/commit/69c68ecbb24dff3fa0e46da0d16c821a2dd22d7c
• github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-462.yaml
• github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-660.yaml
• github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-171.yaml
• github.com/tensorflow/tensorflow