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Secure Systems

Colluding Adversaries in Machine Learning Pipelines Making Software Both Memory-Safe and Leak-Proof: Introducing BLACKOUT Unintended Interactions among ML Defenses and Risks ML Security at SSG: Past, Present and Future Better visualisation of consensus protocols Historical insight into the development of Mobile TEEs Protecting against run-time attacks with Pointer Authentication How to evade hate speech filters with "love" Common sense applications of trusted hardware Leading European cybersecurity research organizations and Intel Labs join forces to conduct research on Collaborative Autonomous & Resilient Systems Voiko älykodissa elää turvassa? Entä mitä riskejä liittyy fiksuun sähköverkkoon tai kulkuneuvoihin? Erasmus Mundus Program on Security and Cloud Computing (SECCLO) Ethics in information security Off the hook: A New Privacy-Friendly Phishing Protection Add-on HAIC OmniShare: Encrypted Cloud Storage for all your Devices Zero-effort authentication : useful but difficult to get right Practical attacks against 4G (LTE) access network protocols
Deployment Concerns for ML Systems: Unintended Interactions
Vasisht Dudd · 2026-06-16 · via Secure Systems
Existing research focuses on defenses against individual machine learning (ML) risks. However, trustworthy ML…