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SECURITY.md
# Using TensorFlow Securely This document discusses the TensorFlow security model. It describes the security risks to consider when using models, checkpoints or input data for training or serving. We also provide guidelines on what constitutes a vulnerability in TensorFlow and how to report them. This document applies to other repositories in the TensorFlow organization, covering security practices for the entirety of the TensorFlow ecosystem.
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tensorflow/BUILD
"//learning/brain/mlir/...", "//learning/brain/tfrt/...", "//learning/lib/ami/simple_ml/...", "//learning/pathways/...", "//learning/serving/contrib/tfrt/mlir/canonical_ops/...", "//learning/serving/experimental/remote_predict/...", "//perftools/accelerators/xprof/convert/...", "//perftools/accelerators/xprof/integration_tests/...",
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RELEASE.md
* `Estimator.export_savedmodel()` now includes all valid serving signatures that can be constructed from the Serving Input Receiver and all available ExportOutputs. For instance, a classifier may provide regression- and prediction-flavored outputs, in addition to the classification-flavored one. Building signatures from these allows TF Serving to honor requests using the
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