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

D
DataBreaches.Net
GbyAI
GbyAI
aimingoo的专栏
aimingoo的专栏
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
月光博客
月光博客
大猫的无限游戏
大猫的无限游戏
M
MIT News - Artificial intelligence
腾讯CDC
博客园 - Franky
Engineering at Meta
Engineering at Meta
C
Check Point Blog
T
The Blog of Author Tim Ferriss
有赞技术团队
有赞技术团队
Microsoft Azure Blog
Microsoft Azure Blog
MyScale Blog
MyScale Blog
I
InfoQ
Blog — PlanetScale
Blog — PlanetScale
P
Proofpoint News Feed
The GitHub Blog
The GitHub Blog
N
Netflix TechBlog - Medium
Last Week in AI
Last Week in AI
S
SegmentFault 最新的问题
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
WordPress大学
WordPress大学

cs.CR updates on arXiv.org

Agentic Vulnerability Reasoning on Windows COM Binaries From Beats to Breaches:How Offensive AI Infers Sensitive User Information from Playlists Undetectable Backdoors in Model Parameters: Hiding Sparse Secrets in High Dimensions When Embedding-Based Defenses Fail: Rethinking Safety in LLM-Based Multi-Agent Systems Token-Efficient Change Detection in LLM APIs Selfie-Capture Dynamics as an Auxiliary Signal Against Deepfakes and Injection Attacks for Mobile Identity Verification Trident: Improving Malware Detection with LLMs and Behavioral Features When Alignment Isn't Enough: Response-Path Attacks on LLM Agents RefusalGuard: Geometry-Preserving Fine-Tuning for Safety in LLMs Checkerboard: A Simple, Effective, Efficient and Learning-free Clean Label Backdoor Attack with Low Poisoning Budget Block-wise Codeword Embedding for Reliable Multi-bit Text Watermarking Secret Stealing Attacks on Local LLM Fine-Tuning through Supply-Chain Model Code Backdoors Enhancing Linux Privilege Escalation Attack Capabilities of Local LLM Agents Defusing the Trigger: Plug-and-Play Defense for Backdoored LLMs via Tail-Risk Intrinsic Geometric Smoothing Evaluating Jailbreaking Vulnerabilities in LLMs Deployed as Assistants for Smart Grid Operations: A Benchmark Against NERC Standards Behavioral Canaries: Auditing Private Retrieved Context Usage in RL Fine-Tuning FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation Breaking MCP with Function Hijacking Attacks: Novel Threats for Function Calling and Agentic Models Text Steganography with Dynamic Codebook and Multimodal Large Language Model An AI Agent Execution Environment to Safeguard User Data TwoHamsters: Benchmarking Multi-Concept Compositional Unsafety in Text-to-Image Models Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility Hardening x402: PII-Safe Agentic Payments via Pre-Execution Metadata Filtering QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits Hijacking Text Heritage: Hiding the Human Signature through Homoglyphic Substitution Like a Hammer, It Can Build, It Can Break: Large Language Model Uses, Perceptions, and Adoption in Cybersecurity Operations on Reddit Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation One Word at a Time: Incremental Completion Decomposition Breaks LLM Safety Measuring and Exploiting Contextual Bias in LLM-Assisted Security Code Review
A Trust-Centric Approach To Quantifying Maturity and Secu...
Stanisław Barański, Ben Biedermann, Joshua Ellul · 2024-12-14 · via cs.CR updates on arXiv.org

Voting is a cornerstone of collective participatory decision-making in contexts ranging from political elections to decentralized autonomous organizations (DAOs). Despite the proliferation of internet voting protocols promising enhanced accessibility and efficiency, their evaluation and comparison are complicated by a lack of standardized criteria and unified definitions of security and maturity. Furthermore, socio-technical requirements by decision makers are not structurally taken into consideration when comparing internet voting systems. This paper addresses this gap by introducing a trust-centric maturity scoring framework to quantify the security and maturity of seventeen internet voting systems. A comprehensive trust model analysis is conducted for selected internet voting protocols, examining their security properties, trust assumptions, technical complexity, and practical usability. In this paper we propose the Internet Voting Maturity Framework (IVMF) which supports nuanced assessment that reflects real-world deployment concerns and aids decision-makers in selecting appropriate systems tailored to their specific use-case requirements. The framework is general enough to be applied to other systems, where the aspects of decentralization, trust, and security are crucial, such as digital identity, Ethereum layer-two scaling solutions, and federated data infrastructures. Its objective is to provide an extendable toolkit for policy makers and technology experts alike that normalizes technical and non-technical requirements on a univariate scale.