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

U
Unit 42
A
About on SuperTechFans
J
Java Code Geeks
H
Hackread – Cybersecurity News, Data Breaches, AI and More
Stack Overflow Blog
Stack Overflow Blog
Recent Announcements
Recent Announcements
GbyAI
GbyAI
F
Fortinet All Blogs
Blog — PlanetScale
Blog — PlanetScale
MongoDB | Blog
MongoDB | Blog
MyScale Blog
MyScale Blog
aimingoo的专栏
aimingoo的专栏
H
Help Net Security
月光博客
月光博客
博客园 - Franky
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
Apple Machine Learning Research
Apple Machine Learning Research
The Cloudflare Blog
WordPress大学
WordPress大学
S
SegmentFault 最新的问题
T
Tailwind CSS Blog
Jina AI
Jina AI
有赞技术团队
有赞技术团队
博客园_首页

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
Novel Attacks against Contingency Analysis in Power Grids
Mohammad Ashiqur Rahman, Md Hasan Shahriar, Mohamadsaleh Jafari, · 2019-11-04 · via cs.CR updates on arXiv.org

Contingency Analysis (CA) is a core component of the Energy Management System (EMS) in the power grid. The goal of CA is to operate the power system in a secure manner by analyzing the system subject to a contingency (e.g., the outage of a transmission line or a power generator) to determine the setpoints that will allow system operation without violation of constraints. The analysis in CA is conducted based on the output from State Estimation (SE), another core EMS module. However, it is also shown that an adversary can alter certain power measurements to corrupt the system states estimated by SE without being detected. Such a corrupted estimation can severely skew the results of the contingency analysis as it will provide a fake model to deal with. In this research, we formally model necessary interdependency relationships and systematically analyze these novel attacks on the contingency analysis. In particular, this research focuses on Security Constrained Optimal Power Flow (SCOPF) that finds out the optimal economic dispatches considering a single line failure (based on the $n - 1$ contingency analysis) and transmission line capacities. The proposed model is implemented and solved to find out potential threat vectors (i.e., a set of measurements to be altered) that can evade CA so that the system will face overloading situation on one or more transmission lines when some specific contingencies happen. We demonstrate our formal model on an IEEE 14 bus system-based case study and verify the results with a standard PowerWorld model. We further evaluate the model with respect to various attacks and grid characteristics.