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

G
Google Developers Blog
GbyAI
GbyAI
Y
Y Combinator Blog
The GitHub Blog
The GitHub Blog
B
Blog
博客园 - 叶小钗
V
Visual Studio Blog
小众软件
小众软件
阮一峰的网络日志
阮一峰的网络日志
博客园 - 聂微东
S
SegmentFault 最新的问题
Engineering at Meta
Engineering at Meta
博客园 - Franky
V
V2EX
人人都是产品经理
人人都是产品经理
H
Hackread – Cybersecurity News, Data Breaches, AI and More
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
月光博客
月光博客
IT之家
IT之家
T
The Blog of Author Tim Ferriss
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
C
Check Point Blog
N
Netflix TechBlog - Medium
博客园 - 【当耐特】

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
E-Trojans: Ransomware, Tracking, DoS, and Data Leaks on B...
Marco Casagrande, Riccardo Cestaro, Eleonora Losiouk, Mauro Cont · 2024-11-26 · via cs.CR updates on arXiv.org

Battery-powered embedded systems (BESs) have become ubiquitous. Their internals include a battery management system (BMS), a radio interface, and a motor controller. Despite their associated risk, there is little research on BES internal attack surfaces. To fill this gap, we present the first security and privacy assessment of e-scooters internals. We cover Xiaomi M365 (2016) and ES3 (2023) e-scooters and their interactions with Mi Home (their companion app). We extensively RE their internals and uncover four critical design vulnerabilities, including a remote code execution issue with their BMS. Based on our RE findings, we develop E-Trojans, four novel attacks targeting BES internals. The attacks can be conducted remotely or in wireless proximity. They have a widespread real-world impact as they violate the Xiaomi e-scooter ecosystem safety, security, availability, and privacy. For instance, one attack allows the extortion of money from a victim via a BMS undervoltage battery ransomware. A second one enables user tracking by fingerprinting the BES internals. With extra RE efforts, the attacks can be ported to other BES featuring similar vulnerabilities. We implement our attacks and RE findings in E-Trojans, a modular and low-cost toolkit to test BES internals. Our toolkit binary patches BMS firmware by adding malicious capabilities. It also implements our undervoltage battery ransomware in an Android app with a working backend. We successfully test our four attacks on M365 and ES3, empirically confirming their effectiveness and practicality. We propose four practical countermeasures to fix our attacks and improve the Xiaomi e-scooter ecosystem security and privacy.