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

博客园 - 【当耐特】
小众软件
小众软件
S
SegmentFault 最新的问题
GbyAI
GbyAI
量子位
爱范儿
爱范儿
L
LangChain Blog
Vercel News
Vercel News
A
About on SuperTechFans
腾讯CDC
博客园_首页
酷 壳 – CoolShell
酷 壳 – CoolShell
月光博客
月光博客
博客园 - 聂微东
Stack Overflow Blog
Stack Overflow Blog
H
Help Net Security
U
Unit 42
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
V
V2EX
V
Visual Studio Blog
美团技术团队
D
DataBreaches.Net
The GitHub Blog
The GitHub Blog
N
Netflix TechBlog - Medium

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
Healthcare IT: Is your Information at Risk?
Kimmarie Donahue, Shawon Rahman · 2015-12-01 · via cs.CR updates on arXiv.org

Healthcare Information Technology (IT) has made great advances over the past few years and while these advances have enable healthcare professionals to provide higher quality healthcare to a larger number of individuals it also provides the criminal element more opportunities to access sensitive information, such as patient protected health information (PHI) and Personal identification Information (PII). Having an Information Assurance (IA) programallows for the protection of information and information systems and ensures the organization is in compliance with all requires regulations, laws and directive is essential. While most organizations have such a policy in place, often it is inadequate to ensure the proper protection to prevent security breaches. The increase of data breaches in the last few years demonstrates the importance of an effective IA program. To ensure an effective IA policy, the policy must manage the operational risk, including identifying risks, assessment and mitigation of identified risks and ongoing monitoring to ensure compliance