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

T
Tailwind CSS Blog
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
雷峰网
雷峰网
量子位
有赞技术团队
有赞技术团队
阮一峰的网络日志
阮一峰的网络日志
The Cloudflare Blog
博客园 - Franky
罗磊的独立博客
宝玉的分享
宝玉的分享
博客园_首页
腾讯CDC
The GitHub Blog
The GitHub Blog
D
DataBreaches.Net
IT之家
IT之家
D
Docker
Microsoft Security Blog
Microsoft Security Blog
博客园 - 司徒正美
V
V2EX
月光博客
月光博客
N
Netflix TechBlog - Medium
爱范儿
爱范儿
I
InfoQ
P
Proofpoint News Feed

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
Using SMART for Customized Monitoring of Windows Services
Gregory A. Pluta, Larry Brumbaugh, William Yurcik · 2006-03-29 · via cs.CR updates on arXiv.org

We focus on examining and working with an important category of computer software called Services, which are provided as a part of newer Microsoft Windows operating systems. A typical Windows user transparently utilizes many of these services but is frequently unaware of their existence. Since some services have the potential to create significant problems when they are executing, it is important for a system administrator to identify which services are running on the network, the types of processing done by each service, and any interrelationships among the various services. This information can then be used to improve the overall integrity of both the individual computer where a questionable service is running and in aggregate an entire network of computers. NCSA has developed an application called SMART (Services Monitoring And Reporting Tool) that can be used to identify and display all services currently running in the network. A commercial program called Hyena remotely monitors the services on all computers attached to the network and exports this information to SMART. SMART produces various outputs that the system administrator can analyze and then determine appropriate actions to take. In particular, SMART provides a color coordinated user interface to quickly identify and classify both potentially hazardous services and also unknown services.