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

S
SegmentFault 最新的问题
博客园 - 三生石上(FineUI控件)
WordPress大学
WordPress大学
博客园 - 【当耐特】
月光博客
月光博客
Vercel News
Vercel News
D
Docker
I
InfoQ
Apple Machine Learning Research
Apple Machine Learning Research
博客园 - 叶小钗
MongoDB | Blog
MongoDB | Blog
GbyAI
GbyAI
有赞技术团队
有赞技术团队
雷峰网
雷峰网
博客园 - 聂微东
小众软件
小众软件
Y
Y Combinator Blog
腾讯CDC
L
LangChain Blog
The GitHub Blog
The GitHub Blog
宝玉的分享
宝玉的分享
Stack Overflow Blog
Stack Overflow Blog
大猫的无限游戏
大猫的无限游戏
T
The Blog of Author Tim Ferriss

cs.CR updates on arXiv.org

On the Security of Research Artifacts SafeHarbor: Hierarchical Memory-Augmented Guardrail for LLM Agent Safety 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 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
An efficient deception architecture for cloud-based virtu...
Mohammad Qasem, Hussain M. J. Almohri · 2020-04-15 · via cs.CR updates on arXiv.org

Emerging deceptive systems present a new promise for the uprising security problems in cloud-based virtual networks, especially those operated by small and medium enterprises. The main goal of deceptive systems is to form a layer of defensive nodes in an Internet-accessible cloud-based virtual network to distract and deceive malicious clients. While numerous approaches provide distinct models for developing decisive systems, misery digraphs present a promising decisive model for distracting powerful remote intrusions. Misery digraphs can delay access to targets deep in a cloud-based virtual network. A central challenge to the theory of misery digraphs is verifying their applicability in prominent cloud computing platforms as well as measuring the efficiency of networks that adapt them. Thus, architectural support is needed that can be realized with long-term support technologies and can be deployed for large networks. This work presents and analyzes a high-throughput architecture for misery digraphs, embarking on implementation details and performance analysis. Full implementation of the architecture in Amazon Web Services imposes modest performance delays in the request processing, while highly delaying stealth intrusions in the network.