An endpoint for trusted fabrication, the endpoint including at least one secure controller configured for connection to a wide area network; and at least one untrusted controller configured for local communication, wherein the endpoint is configured for connection to a fabricator and further configured to receive digitally-signed data specifying at least one item for manufacture; verify the digitally-signed data; and direct the fabricator to manufacture the at least one item after verifying the digitally signed data. A method for trusted on-demand manufacturing, the method including receiving, at an endpoint connected to a fabricator, digitally signed data describing at least one item for manufacture; verifying, at the endpoint, the digitally signed data; and manufacturing the at least one item using the digitally signed data after verifying the digitally signed data, wherein the endpoint comprises at least one secure controller and at least one untrusted controller.

The present disclosure describes systems and methods for controlling access to secure debugging and profiling features of a computer system. Some illustrative embodiments include a system that includes a processor, and a memory coupled to the processor (the memory used to store information and an attribute associated with the stored information). At least one bit of the attribute determines a security level, selected from a plurality of security levels, of the stored information associated with the attribute. Asserting at least one other bit of the attribute enables exportation of the stored information from the computer system if the security level of the stored information is higher than at least one other security level of the plurality of security levels.

The present disclosure describes systems and methods for controlling access to secure debugging and profiling features of a computer system. Some illustrative embodiments include a system that includes a processor, and a memory coupled to the processor (the memory used to store information and an attribute associated with the stored information). At least one bit of the attribute determines a security level, selected from a plurality of security levels, of the stored information associated with the attribute. Asserting at least one other bit of the attribute enables exportation of the stored information from the computer system if the security level of the stored information is higher than at least one other security level of the plurality of security levels.

In an example, a method includes providing a computing device with an instruction to cause the computing device to execute the instruction. The method further includes monitoring a side channel of a microarchitectural component of the computing device to obtain an indication of whether or not a state of the microarchitectural component changes as a result of the computing device executing the instruction. The method further includes determining whether or not the indication corresponds to an expected state of the microarchitectural component for the instruction.

A secure guest of a computing environment requests confidential data. The confidential data is included in metadata of the secure guest, which is stored in a trusted execution environment of the computing environment. Based on the request, the confidential data is obtained from the metadata of the secure guest that is stored in the trusted execution environment.

Methods, apparatus, systems and articles of manufacture for distributed use of a machine learning model are disclosed. An example edge device includes a model partitioner to partition a machine learning model received from an aggregator into private layers and public layers. A public model data store is implemented outside of a trusted execution environment of the edge device. The model partitioner is to store the public layers in the public model data store. A private model data store is implemented within the trusted execution environment. The model partitioner is to store the private layers in the private model data store.

Methods, apparatus, systems and articles of manufacture for distributed use of a machine learning model are disclosed. An example edge device includes a model partitioner to partition a machine learning model received from an aggregator into private layers and public layers. A public model data store is implemented outside of a trusted execution environment of the edge device. The model partitioner is to store the public layers in the public model data store. A private model data store is implemented within the trusted execution environment. The model partitioner is to store the private layers in the private model data store.

An apparatus to facilitate scalable runtime validation for on-device design rule checks is disclosed. The apparatus includes a memory to store a contention set, one or more multiplexors, and a validator communicably coupled to the memory. In one implementation, the validator is to: receive design rule information for the one or more multiplexers, the design rule information referencing the contention set; analyze, using the design rule information, a user bitstream against the contention set at a programming time of the apparatus, the user bitstream for programming the one or more multiplexors; and provide an error indication responsive to identifying a match between the user bitstream and the contention set.

Systems, apparatuses and methods may provide for technology that assumes, by a root of trust located in a trusted region of a system on chip (SOC), control over a reset of the SOC and conducting, by the root of trust, an authentication of an update package in response to an update condition. The root of trust technology may also apply the update package to firmware located in non-volatile memory (NVM) associated with a microcontroller of the SOC if the authentication is successful.

An Information Handling System (IHS) includes at least one hardware device in communication with a Baseboard Management Controller (BMC). The hardware device includes executable instructions for establishing a secure communication channel with the BMC, and subsequently receiving a list of allowed commands from the BMC. When a command is received by the hardware device, it determines whether the command is included in the list such that when the command is in the list and the command is received within the secure communication channel, the hardware device performs the command. However, when the command is in the list and the command is received outside of the secure communication channel, the hardware device ignores the command.