惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

腾讯CDC
IT之家
IT之家
有赞技术团队
有赞技术团队
WordPress大学
WordPress大学
Apple Machine Learning Research
Apple Machine Learning Research
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
人人都是产品经理
人人都是产品经理
The Cloudflare Blog
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
博客园 - 【当耐特】
V
V2EX
Last Week in AI
Last Week in AI
H
Help Net Security
The GitHub Blog
The GitHub Blog
S
SegmentFault 最新的问题
F
Fortinet All Blogs
I
InfoQ
宝玉的分享
宝玉的分享
A
About on SuperTechFans
MongoDB | Blog
MongoDB | Blog
Microsoft Azure Blog
Microsoft Azure Blog
Blog — PlanetScale
Blog — PlanetScale
B
Blog

cs updates on arXiv.org

Beyond Binary Edits Robust Multimodal Knowledge Editing with Adversarial Subspace Alignment Agentic Proving for Program Verification MemAudit: Post-hoc Auditing of Poisoned Agent Memory via Causal Attribution and Structural Anomaly Detection OpenSkillEval: Automatically Auditing the Open Skill Ecosystem for LLM Agents One Policy, Infinite NPCs: Persona-Traceable Shared RL Policies for Scalable Game Agents How Human-Like Are Large Language Models? A Register-Aware Linguistic Evaluation Framework Benchmarking Google Embeddings 2 against Open-Source Models for Multilingual Dense Retrieval and RAG Systems Structure-Guided Entity Resolution: Fine-Tuning LLMs for Robust Name Matching in Complex Linguistic Contexts Solving the Aircraft Disassembly Scheduling Problem Co-ReAct: Rubrics as Step-Level Collaborators for ReAct Agents CP or DP? Why Not Both: A Case Study in the Partial Shop Scheduling Problem Asking For An Old Friend: Diagnosing and Mitigating Temporal Failure Modes in LLM-based Statutory Question Answering EDGE-OPD: Internalizing Privileged Context with Evidence Guided On-Policy Distillation ARES: Automated Rubric Synthesis for Scalable LLM Reinforcement Learning SSDAU: Structured Semantic Data Augmentation for Joint Entity and Relation Extraction Naturalistic measure of social norms alignment Articulatory strategy as a source of variation in acoustic vowel dynamics When Planning Fails Despite Correct Execution: On Epistemic Calibration for LLM-Based Multi-Agent Systems EquiSumm : A Gender Bias-Aware Framework for Inclusive Tweet Summarization Metacognition as Reward: Reinforcing LLM Reasoning via Knowledge and Regulation Signals From Correctness to Preference: A Framework for Personalized Agentic Reinforcement Learning Cultural Adaptation in Large Language Models for Political Discourse Emotion Recognition in Sign Language Conversation ClimateChat-300K: A Multi-Modal Facebook Dataset for Understanding Diverse Perspectives in Climate Communication AraHopeCorpus: Annotation Guidelines and Dataset for Hope Speech in Arabic Social Media Crisis Discourse Human-in-the-Loop Multi-Agent Ventilator Decision Support with Contextual Bandit Preference Learning Convergence Without Understanding: When Language Models Agree on Representations but Disagree on Reasoning DART: Semantic Recoverability for Structured Tool Agents Ontological Knowledge Blocks: Executable Compliance and Profile-Based Validation for Trustworthy AI Systems Parallel Context Compaction for Long-Horizon LLM Agent Serving
REPOSE: Quantifying the Price of Security in Weakly-Hard ...
[Submitted on 12 Jun 2026] · 2026-06-15 · via cs updates on arXiv.org

View PDF HTML (experimental)

Abstract:In contemporary IoT edge devices with real-time requirements, security is primarily enforced through design-time parameters associated with security tasks, leading to mechanisms that operate in an \emph{opportunistic} manner. As a result, security checks are often performed as secondary operations. This approach can result in systems where no security tasks are executed due to high utilization by other tasks. An alternative approach taken in prior work is to add security mechanisms to every task in the system, resulting in substantially lower performance than that of a system with no security. These approaches have resulted in an \emph{all-or-nothing} scenario for edge device security, motivating numerous studies on the safety-security trade-off in real-time cyber-physical systems (RT-CPS). This study introduces an analytical framework -- REPOSE -- for evaluating the security feasibility of real-time control systems at runtime. REPOSE is developed for \textit{weakly-hard} real-time control systems that facilitate a ``bounded trade-off'' between safety and security. In contrast to imposing additional (pessimistic) design-time overhead as considered in some real-time security literature, REPOSE performs security operations in both \textit{proactive} and \textit{reactive} manners based on the task's current behavior. Our evaluations show that REPOSE can effectively add security operations to RT-CPS with a feasibility overhead of $0.06\%$ at $80\%$ utilization, compared to a $ 29\%$ overhead observed in systems with hard constraints. Through a case study of a classic control system, we also demonstrate that REPOSE provides a robust framework to \textit{analyze and calculate} the safety-security tradeoff.

Submission history

From: Vijay Banerjee [view email]
[v1] Fri, 12 Jun 2026 12:29:11 UTC (492 KB)