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

WordPress大学
WordPress大学
酷 壳 – CoolShell
酷 壳 – CoolShell
小众软件
小众软件
Vercel News
Vercel News
Last Week in AI
Last Week in AI
H
Help Net Security
The Cloudflare Blog
L
LangChain Blog
Microsoft Security Blog
Microsoft Security Blog
B
Blog RSS Feed
云风的 BLOG
云风的 BLOG
I
InfoQ
U
Unit 42
美团技术团队
人人都是产品经理
人人都是产品经理
雷峰网
雷峰网
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
博客园 - 叶小钗
Y
Y Combinator Blog
Hugging Face - Blog
Hugging Face - Blog
A
About on SuperTechFans
宝玉的分享
宝玉的分享
量子位
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
Securing Fleets of Consumer Drones at Low Cost
Nick Frymann, Mark Manulis · 2019-12-11 · via cs.CR updates on arXiv.org

In recent years, the use and suitability of drones for many applications, including surveillance, search and rescue, research, agriculture and civil engineering, has greatly increased due to their improved affordability and improved functionality. However, low-cost consumer drones are rarely designed to work in fleets, which limits the applications for which business, research and individuals may deploy such drones. Proprietary, commercial and bespoke options are available at higher cost and existing solutions providing fleet functionality have limited security, if any, which excludes their use for sensitive applications. In this paper, we discuss the repurposing of consumer off-the-shelf (COTS) drones for use in secured fleets and provide the design, implementation and evaluation of a complete approach for creating end-to-end secured fleets of consumer drones (SFCD). We present a protocol for securing communications within fleets whilst employing more efficient symmetric key cryptography throughout to reduce the impact of our security on the limited and resource-constrained COTS drones, exploiting the characteristics of a fleet with an online and central ground control station, which may act as a key distribution centre. The protocol allows an arbitrary number of channels to be established to authenticate and optionally encrypt real-time data transmitted on these channels. We also discuss routing in fleets, as well as the control and monitoring of them, to allow SFCD to be fully deployed; providing an extensive and thorough solution. Our experimental evaluation confirms the suitability of low-cost consumer drones for use in SFCD, with flight time impacted by only 9.9% and worst-case bandwidth of 4.7Mibit/s.