Michael Franz

Norman Zerbe, Lars Ole Schwen, Christian Geißler et al.

Over the past decade, artificial intelligence (AI) methods in pathology have advanced substantially. However, integration into routine clinical practice has been slow due to numerous challenges, including technical and regulatory hurdles in translating research results into clinical diagnostic products and the lack of standardized interfaces. The open and vendor-neutral EMPAIA initiative addresses these challenges. Here, we provide an overview of EMPAIA's achievements and lessons learned. EMPAIA integrates various stakeholders of the pathology AI ecosystem, i.e., pathologists, computer scientists, and industry. In close collaboration, we developed technical interoperability standards, recommendations for AI testing and product development, and explainability methods. We implemented the modular and open-source EMPAIA platform and successfully integrated 14 AI-based image analysis apps from 8 different vendors, demonstrating how different apps can use a single standardized interface. We prioritized requirements and evaluated the use of AI in real clinical settings with 14 different pathology laboratories in Europe and Asia. In addition to technical developments, we created a forum for all stakeholders to share information and experiences on digital pathology and AI. Commercial, clinical, and academic stakeholders can now adopt EMPAIA's common open-source interfaces, providing a unique opportunity for large-scale standardization and streamlining of processes. Further efforts are needed to effectively and broadly establish AI assistance in routine laboratory use. To this end, a sustainable infrastructure, the non-profit association EMPAIA International, has been established to continue standardization and support broad implementation and advocacy for an AI-assisted digital pathology future.

Alexios Voulimeneas, Dokyung Song, Fabian Parzefall et al.

N-Variant Execution (NVX) systems utilize software diversity techniques for enhancing software security. The general idea is to run multiple different variants of the same program alongside each other while monitoring their run-time behavior. If the internal disparity between the running variants causes observable differences in response to malicious inputs, the monitor can detect such divergences in execution and then raise an alert and/or terminate execution. Existing NVX systems execute multiple, artificially diversified program variants on a single host. This paper presents a novel, distributed NVX design that executes program variants across multiple heterogeneous host computers; our prototype implementation combines an x86-64 host with an ARMv8 host. Our approach greatly increases the level of "internal different-ness" between the simultaneously running variants that can be supported, encompassing different instruction sets, endianness, calling conventions, system call interfaces, and potentially also differences in hardware security features. A major challenge to building such a heterogeneous distributed NVX system is performance. We present solutions to some of the main performance challenges. We evaluate our prototype system implementing these ideas to show that it can provide reasonable performance on a wide range of realistic workloads.

Julian Lettner, Dokyung Song, Taemin Park et al.

Sanitizers can detect security vulnerabilities in C/C++ code that elude static analysis. Current practice is to continuously fuzz and sanitize internal pre-release builds. Sanitization-enabled builds are rarely released publicly. This is in large part due to the high memory and processing requirements of sanitizers. We present PartiSan, a run-time partitioning technique that speeds up sanitizers and allows them to be used in a more flexible manner. Our core idea is to partition the execution into sanitized slices that incur a run-time overhead, and unsanitized slices running at full speed. With PartiSan, sanitization is no longer an all-or-nothing proposition. A single build can be distributed to every user regardless of their willingness to enable sanitization and the capabilities of their host system. PartiSan can automatically adjust the amount of sanitization to fit within a performance budget or disable sanitization if the host lacks sufficient resources. The flexibility afforded by run-time partitioning also means that we can alternate between different types of sanitizers dynamically; today, developers have to pick a single type of sanitizer ahead of time. Finally, we show that run-time partitioning can speed up fuzzing by running the sanitized partition only when the fuzzer discovers an input that causes a crash or uncovers new execution paths.

Mathias Payer, Stephen Crane, Per Larsen et al.

Similarity metrics, e.g., signatures as used by anti-virus products, are the dominant technique to detect if a given binary is malware. The underlying assumption of this approach is that all instances of a malware (or even malware family) will be similar to each other. Software diversification is a probabilistic technique that uses code and data randomization and expressiveness in the target instruction set to generate large amounts of functionally equivalent but different binaries. Malware diversity builds on software diversity and ensures that any two diversified instances of the same malware have low similarity (according to a set of similarity metrics). An LLVM-based prototype implementation diversifies both code and data of binaries and our evaluation shows that signatures based on similarity only match one or few instances in a pool of diversified binaries generated from the same source code.