FRAMER/Miu: Tagged Pointer-based Capability and Fundamental Cost of Memory Safety & Coherence (Position Paper)

• URL: http://arxiv.org/abs/2408.15219v1
• Date: Tue, 27 Aug 2024 17:31:26 GMT
• Title: FRAMER/Miu: Tagged Pointer-based Capability and Fundamental Cost of Memory Safety & Coherence (Position Paper)
• Authors: Myoung Jin Nam,
• Abstract summary: Researchers make trade-offs between performance, detection coverage, interoperability, precision, and detection timing. This research presents a tagged pointer-based capability system as a stand-alone software solution and a prototype for future hardware design.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Ensuring system correctness, such as memory safety, can eliminate security vulnerabilities that attackers could exploit in the first place. However, high and unpredictable performance degradation remains a primary challenge. Recognizing that it is extremely difficult to achieve complete system correctness for production deployment, researchers make trade-offs between performance, detection coverage, interoperability, precision, and detection timing. This research strikes a balance between comprehensive system protection and the costs required to obtain it, identifies the desirable roles of software and hardware, and presents a tagged pointer-based capability system as a stand-alone software solution and a prototype for future hardware design. This paper presents follow-up plans for the FRAMER/Miu generic framework to achieve these goals.

Related papers

• Advancing Embodied Agent Security: From Safety Benchmarks to Input Moderation [52.83870601473094]
  Embodied agents exhibit immense potential across a multitude of domains. Existing research predominantly concentrates on the security of general large language models. This paper introduces a novel input moderation framework, meticulously designed to safeguard embodied agents.
  arXiv  Detail & Related papers  (2025-04-22T08:34:35Z)
• Safety Verification and Optimization in Industrial Drive Systems [0.0]
  This paper optimize the safety and diagnostic performance of a real-world industrial Basic Drive Module using Uppaal Stratego. We model the functional safety architecture of the BDM with timed automata and formally verify its key functional and safety requirements. Considering the formally verified correct model as a baseline, we leverage the reinforcement learning facility in Uppaal Stratego to optimize the safe failure fraction to the 90 % threshold.
  arXiv  Detail & Related papers  (2025-03-27T20:27:19Z)
• MES-RAG: Bringing Multi-modal, Entity-Storage, and Secure Enhancements to RAG [65.0423152595537]
  We propose MES-RAG, which enhances entity-specific query handling and provides accurate, secure, and consistent responses. MES-RAG introduces proactive security measures that ensure system integrity by applying protections prior to data access. Experimental results demonstrate that MES-RAG significantly improves both accuracy and recall, highlighting its effectiveness in advancing the security and utility of question-answering.
  arXiv  Detail & Related papers  (2025-03-17T08:09:42Z)
• A Hybrid Framework for Statistical Feature Selection and Image-Based Noise-Defect Detection [55.2480439325792]
  This paper presents a hybrid framework that integrates both statistical feature selection and classification techniques to improve defect detection accuracy. We present around 55 distinguished features that are extracted from industrial images, which are then analyzed using statistical methods. By integrating these methods with flexible machine learning applications, the proposed framework improves detection accuracy and reduces false positives and misclassifications.
  arXiv  Detail & Related papers  (2024-12-11T22:12:21Z)
• Securing Legacy Communication Networks via Authenticated Cyclic Redundancy Integrity Check [98.34702864029796]
  We propose Authenticated Cyclic Redundancy Integrity Check (ACRIC) ACRIC preserves backward compatibility without requiring additional hardware and is protocol agnostic. We show that ACRIC offers robust security with minimal transmission overhead ( 1 ms)
  arXiv  Detail & Related papers  (2024-11-21T18:26:05Z)
• PenHeal: A Two-Stage LLM Framework for Automated Pentesting and Optimal Remediation [18.432274815853116]
  PenHeal is a two-stage LLM-based framework designed to autonomously identify and security vulnerabilities. This paper introduces PenHeal, a two-stage LLM-based framework designed to autonomously identify and security vulnerabilities.
  arXiv  Detail & Related papers  (2024-07-25T05:42:14Z)
• Automatically Adaptive Conformal Risk Control [49.95190019041905]
  We propose a methodology for achieving approximate conditional control of statistical risks by adapting to the difficulty of test samples. Our framework goes beyond traditional conditional risk control based on user-provided conditioning events to the algorithmic, data-driven determination of appropriate function classes for conditioning.
  arXiv  Detail & Related papers  (2024-06-25T08:29:32Z)
• FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids [53.2306792009435]
  FaultGuard is the first framework for fault type and zone classification resilient to adversarial attacks. We propose a low-complexity fault prediction model and an online adversarial training technique to enhance robustness. Our model outclasses the state-of-the-art for resilient fault prediction benchmarking, with an accuracy of up to 0.958.
  arXiv  Detail & Related papers  (2024-03-26T08:51:23Z)
• Algorithmic Robustness [18.406992961818368]
  Robustness is an important enabler of other goals that are frequently cited in the context of public policy decisions about computational systems. This document provides a brief roadmap to some of the concepts and existing research around the idea of algorithmic robustness.
  arXiv  Detail & Related papers  (2023-10-17T17:51:12Z)
• A security framework for quantum key distribution implementations [1.2815904071470707]
  We present a security proof in the finite-key regime against coherent attacks. Our proof requires minimal state characterization, which facilitates its application to real-life implementations.
  arXiv  Detail & Related papers  (2023-05-10T07:02:32Z)
• Meta-Learning Priors for Safe Bayesian Optimization [72.8349503901712]
  We build on a meta-learning algorithm, F-PACOH, capable of providing reliable uncertainty quantification in settings of data scarcity. As core contribution, we develop a novel framework for choosing safety-compliant priors in a data-riven manner. On benchmark functions and a high-precision motion system, we demonstrate that our meta-learned priors accelerate the convergence of safe BO approaches.
  arXiv  Detail & Related papers  (2022-10-03T08:38:38Z)
• Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functions [60.26921219698514]
  We introduce a model-uncertainty-aware reformulation of CBF-based safety-critical controllers. We then present the pointwise feasibility conditions of the resulting safety controller. We use these conditions to devise an event-triggered online data collection strategy.
  arXiv  Detail & Related papers  (2022-08-23T05:02:09Z)
• Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead [24.60052335548398]
  Machine Learning (ML) techniques have been rapidly adopted by smart Cyber-Physical Systems (CPS) and Internet-of-Things (IoT) They are vulnerable to various security and reliability threats, at both hardware and software levels, that compromise their accuracy. This paper summarizes the prominent vulnerabilities of modern ML systems, highlights successful defenses and mitigation techniques against these vulnerabilities.
  arXiv  Detail & Related papers  (2021-01-04T20:06:56Z)
• Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
  Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance. We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems. We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
  arXiv  Detail & Related papers  (2020-10-19T13:09:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.