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SECURITY.md
running the `tf.train.Server`. ## Untrusted inputs during training and prediction TensorFlow supports a wide range of input data formats. For example it can process images, audio, videos, and text. There are several modules specialized in taking those formats, modifying them, and/or converting them to intermediate formats that can be processed by TensorFlow. These modifications and conversions are handled by a variety of libraries that
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tensorflow/c/eager/c_api_experimental_test.cc
client_job->mutable_tasks()->insert( {0, strings::StrCat("localhost:", client_port)}); server_def.set_job_name("localhost"); auto serialized = server_def.SerializeAsString(); TFE_ContextSetServerDef(ctx, 0, serialized.data(), serialized.size(), status); EXPECT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status); EXPECT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status);
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tensorflow/c/eager/c_api_test.cc
->mutable_rewrite_options() ->set_min_graph_nodes(-1); string serialized_config; ASSERT_TRUE(config.SerializeToString(&serialized_config)); TFE_OpSetAttrString( op, "config_proto", reinterpret_cast<const void*>(serialized_config.c_str()), serialized_config.length()); } if (use_tfrt) { // Set some test-only graph compiler options.
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tensorflow/c/c_api_test.cc
TF_ASSERT_OK(OpRegistry::Global()->LookUpOpDef("Add", &expected_op_def)); string expected_serialized; expected_op_def->SerializeToString(&expected_serialized); string actual_string(reinterpret_cast<const char*>(buffer->data), buffer->length); EXPECT_EQ(expected_serialized, actual_string); TF_GraphGetOpDef(graph, "MyFakeOp", buffer, status);
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tensorflow/c/eager/c_api_experimental.h
TF_CAPI_EXPORT extern void TFE_OpAttrsSerialize(const TFE_OpAttrs* attrs, TF_Buffer* buf, TF_Status* status); // Set an op's attribute from a serialized AttrValue protocol buffer. // // Analogous to TF_SetAttrValueProto for building graph operations. TF_CAPI_EXPORT extern void TFE_OpSetAttrValueProto(const TFE_Op* op,
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RELEASE.md
* Fixes a null-dereference when specializing tensor type ([CVE-2022-23570](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-23570)) * Fixes a crash when type cannot be specialized ([CVE-2022-23572](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-23572)) * Fixes a heap OOB read/write in `SpecializeType` ([CVE-2022-23574](https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2022-23574))
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tensorflow/c/eager/c_api_test_util.cc
TFE_Context* ctx = TFE_NewContext(opts, status); EXPECT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status); TFE_ContextSetServerDefWithTimeout(ctx, 0, serialized_server_def.data(), serialized_server_def.size(), init_timeout_in_ms, status, /*clear_existing_contexts=*/false);
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tensorflow/c/eager/immediate_execution_context.h
virtual Status AddDevices(std::vector<std::unique_ptr<Device>> devices) = 0; // Block until all pending nodes are finished. virtual Status AsyncWait() = 0; // Add a function (serialized FunctionDef protocol buffer) so that it can // be executed as an op. Return error if the function with the same name // already exists. virtual Status AddFunctionDef(const FunctionDef& fdef) = 0;
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tensorflow/c/eager/c_api.cc
} return nullptr; } void TFE_ContextAddFunctionDef(TFE_Context* ctx, const char* serialized_function_def, size_t size, TF_Status* status) { tensorflow::FunctionDef function_def; if (!function_def.ParseFromArray(serialized_function_def, size)) { status->status = tensorflow::errors::InvalidArgument("Invalid FunctionDef proto"); return; }
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tensorflow/c/eager/abstract_function.h
#include "tensorflow/core/platform/refcount.h" #include "tensorflow/core/platform/status.h" namespace tensorflow { class FunctionRecord; // A traced function: this hides the complexity of converting the serialized // representation between various supported formats e.g. FunctionDef and Mlir // function. class AbstractFunction : public core::RefCounted { protected: enum AbstractFunctionKind { kGraph, kMlir };
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