Headline
CVE-2022-29194: tensorflow/session_ops.cc at f3b9bf4c3c0597563b289c0512e98d4ce81f886e · tensorflow/tensorflow
TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, the implementation of tf.raw_ops.DeleteSessionTensor
does not fully validate the input arguments. This results in a CHECK
-failure which can be used to trigger a denial of service attack. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.
/* Copyright 2015 The TensorFlow Authors. All Rights Reserved. Licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ // See docs in …/ops/data_flow_ops.cc. #include <limits.h> #include <vector> #include “tensorflow/core/common_runtime/device.h” #include “tensorflow/core/framework/device_base.h” #include “tensorflow/core/framework/op_kernel.h” #include “tensorflow/core/framework/register_types.h” #include “tensorflow/core/framework/tensor.h” #include “tensorflow/core/framework/tensor_shape.h” #include “tensorflow/core/framework/types.h” #include “tensorflow/core/lib/core/errors.h” #include “tensorflow/core/lib/gtl/map_util.h” #include “tensorflow/core/platform/errors.h” #include “tensorflow/core/platform/logging.h” #include “tensorflow/core/platform/macros.h” #include “tensorflow/core/platform/mutex.h” #include “tensorflow/core/platform/thread_annotations.h” #include “tensorflow/core/platform/types.h” namespace tensorflow { class GetSessionHandleOp : public OpKernel { public: explicit GetSessionHandleOp(OpKernelConstruction* context) : OpKernel(context) {} void Compute(OpKernelContext* ctx) override { const Tensor& val = ctx->input(0); auto session_state = ctx->session_state(); OP_REQUIRES(ctx, session_state != nullptr, errors::FailedPrecondition( “GetSessionHandle called on null session state”)); int64_t id = session_state->GetNewId(); TensorStore::TensorAndKey tk{val, id, requested_device()}; OP_REQUIRES_OK(ctx, ctx->tensor_store()->AddTensor(name(), tk)); Tensor* handle = nullptr; OP_REQUIRES_OK(ctx, ctx->allocate_output(0, TensorShape({}), &handle)); if (ctx->expected_output_dtype(0) == DT_RESOURCE) { ResourceHandle resource_handle = MakeResourceHandle<Tensor>( ctx, SessionState::kTensorHandleResourceTypeName, tk.GetHandle(name())); resource_handle.set_maybe_type_name( SessionState::kTensorHandleResourceTypeName); handle->scalar<ResourceHandle>()() = resource_handle; } else { // Legacy behavior in V1. handle->flat<tstring>().setConstant(tk.GetHandle(name())); } } TF_DISALLOW_COPY_AND_ASSIGN(GetSessionHandleOp); }; REGISTER_KERNEL_BUILDER(Name(“GetSessionHandle”).Device(DEVICE_CPU), GetSessionHandleOp); REGISTER_KERNEL_BUILDER(Name(“GetSessionHandleV2”).Device(DEVICE_CPU), GetSessionHandleOp); #define REGISTER_DEFAULT_KERNEL(type) \ REGISTER_KERNEL_BUILDER(Name(“GetSessionHandle”) \ .Device(DEVICE_DEFAULT) \ .HostMemory(“handle”) \ .TypeConstraint<type>(“T”), \ GetSessionHandleOp) \ REGISTER_KERNEL_BUILDER(Name(“GetSessionHandleV2”) \ .Device(DEVICE_DEFAULT) \ .HostMemory(“handle”) \ .TypeConstraint<type>(“T”), \ GetSessionHandleOp) TF_CALL_NUMBER_TYPES(REGISTER_DEFAULT_KERNEL); REGISTER_DEFAULT_KERNEL(bool); #undef REGISTER_DEFAULT_KERNEL class GetSessionTensorOp : public OpKernel { public: explicit GetSessionTensorOp(OpKernelConstruction* context) : OpKernel(context) {} void Compute(OpKernelContext* ctx) override { const Tensor& handle = ctx->input(0); const string& name = handle.scalar<tstring>()(); Tensor val; auto session_state = ctx->session_state(); OP_REQUIRES(ctx, session_state != nullptr, errors::FailedPrecondition( “GetSessionTensor called on null session state”)); OP_REQUIRES_OK(ctx, session_state->GetTensor(name, &val)); ctx->set_output(0, val); } TF_DISALLOW_COPY_AND_ASSIGN(GetSessionTensorOp); }; REGISTER_KERNEL_BUILDER(Name(“GetSessionTensor”).Device(DEVICE_CPU), GetSessionTensorOp); #define REGISTER_DEFAULT_KERNEL(type) \ REGISTER_KERNEL_BUILDER(Name(“GetSessionTensor”) \ .Device(DEVICE_DEFAULT) \ .HostMemory(“handle”) \ .TypeConstraint<type>(“dtype”), \ GetSessionTensorOp) TF_CALL_NUMBER_TYPES(REGISTER_DEFAULT_KERNEL); REGISTER_DEFAULT_KERNEL(bool); #undef REGISTER_DEFAULT_KERNEL class DeleteSessionTensorOp : public OpKernel { public: explicit DeleteSessionTensorOp(OpKernelConstruction* context) : OpKernel(context) {} void Compute(OpKernelContext* ctx) override { const Tensor& handle = ctx->input(0); const string& name = handle.scalar<tstring>()(); auto session_state = ctx->session_state(); OP_REQUIRES(ctx, session_state != nullptr, errors::FailedPrecondition( “DeleteSessionTensor called on null session state”)); OP_REQUIRES_OK(ctx, session_state->DeleteTensor(name)); } TF_DISALLOW_COPY_AND_ASSIGN(DeleteSessionTensorOp); }; REGISTER_KERNEL_BUILDER(Name(“DeleteSessionTensor”).Device(DEVICE_CPU), DeleteSessionTensorOp); REGISTER_KERNEL_BUILDER( Name(“DeleteSessionTensor”).Device(DEVICE_DEFAULT).HostMemory(“handle”), DeleteSessionTensorOp); } // namespace tensorflow
Related news
### Impact The implementation of [`tf.raw_ops.DeleteSessionTensor`](https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/core/kernels/session_ops.cc#L128-L144) does not fully validate the input arguments. This results in a `CHECK`-failure which can be used to trigger a denial of service attack: ```python import tensorflow as tf handle = tf.constant("[]", shape=[0], dtype=tf.string) tf.raw_ops.DeleteSessionTensor(handle=handle) ``` The code assumes `handle` is a scalar but there is no validation for this: ```cc const Tensor& handle = ctx->input(0); const string& name = handle.scalar<tstring>()(); ``` ### Patches We have patched the issue in GitHub commit [cff267650c6a1b266e4b4500f69fbc49cdd773c5](https://github.com/tensorflow/tensorflow/commit/cff267650c6a1b266e4b4500f69fbc49cdd773c5). The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4...
TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, multiple TensorFlow operations misbehave in eager mode when the resource handle provided to them is invalid. In graph mode, it would have been impossible to perform these API calls, but migration to TF 2.x eager mode opened up this vulnerability. If the resource handle is empty, then a reference is bound to a null pointer inside TensorFlow codebase (various codepaths). This is undefined behavior. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.