Security in High-Performance Computing (HPC) has traditionally been an "operational" challenge (i.e., restrict access and usage to certified users). However, as HPC gradually permeates more areas of public interest, the traditional focus of HPC only on performance might expose attack surfaces to an ever growing body of users. Paired with HPC traditional role of early technology adoption, a new set of early target-worthwhile vulnerabilities are emerging that are not necessarily found in other computing scenarios that operate with more established technologies.

In addition to early adoption vulnerabilities, potential vulnerabilities specific to the HPC community arise from acute hardware heterogeneity, novel networks technologies/topologies, massive resource management orchestration -- including power consumption, heavy reliance on open software, brittle experimental software not hardened by numerous deployments and dusty deck software with a lack of maintenance. In combination with the commercial, single-node exploits, these vulnerabilities open fertile new attack surfaces.

This workshop focuses on threats and solutions across the HPC hardware/software stack. These threats include weaknesses in current and future architectural designs, escalation of privileges through data extraction or computation manipulation and intentional misuse of resources across scientific instruments feeding HPC machines.

This workshop will focus in the topics listed below. However, this list is due to grow thanks to the interactions and discoveries presented in the workshop.

• Trade-offs between system functionality (i.e., performance, energy, etc.), cybersecurity and confidentiality.
• Privacy/ confidentiality preserving workflows and their implication on High Performance Computing.
• Approaches to evaluate data and system trustworthiness for HPC workflows.
• Approaches to embedding security features across the computing sensor platforms, network elements, and other components part of the HPC ecosystem fabrics.
• Techniques to find Attack Surfaces in HPC systems, data feeding instruments, and scientific sensors.
• Artificial Intelligence in cybersecurity and privacy: methodologies to evaluate the impact of AI and identifying potential risks.
• Machine Learning techniques applied to cybersecurity and privacy in terms of HPC workloads and HPC co-design.
• Security Enhanced prescriptive programming of HPC system.
• New attacks targeting HPC systems or components.