SoK: Towards Security and Safety of Edge AI
Advanced AI applications have become increasingly available to a broad audience, e.g., as centrally managed large language models (LLMs). Such centralization is both a risk and a performance bottleneck - Edge AI promises to be a solution to these problems. However, its decentralized approach raises additional challenges regarding security and safety. In this paper, we argue that both of these aspects are critical for Edge AI, and even more so, their integration. Concretely, we survey security and safety threats, summarize existing countermeasures, and collect open challenges as a call for more research in this area.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Inference-Time Safety For Code LLMs Via Retrieval-Augmented Revision
Large Language Models (LLMs) are increasingly deployed for code generation in high-stakes software development, yet their limited transparency in security reasoning and brittleness to evolving vulnerability patterns rais…
Code GenerationA Survey on the Safety and Security Threats of Computer-Using Agents: JARVIS or Ultron?
Recently, AI-driven interactions with computing devices have advanced from basic prototype tools to sophisticated, LLM-based systems that emulate human-like operations in graphical user interfaces. We are now witnessing …
Open Problems in Machine Unlearning for AI Safety
As AI systems become more capable, widely deployed, and increasingly autonomous in critical areas such as cybersecurity, biological research, and healthcare, ensuring their safety and alignment with human values is param…
Machine UnlearninghealthAIChain: Improving security and safety using Blockchain Technology applications in AI-based healthcare systems
Blockchain as a digital ledger for keeping records of digital transactions and other information, it is secure and decentralized technology. The globally growing number of digital population every day possesses a signifi…
Building Trust: Foundations of Security, Safety and Transparency in AI
This paper explores the rapidly evolving ecosystem of publicly available AI models, and their potential implications on the security and safety landscape. As AI models become increasingly prevalent, understanding their p…