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 - 叶小钗
V
Visual Studio Blog
雷峰网
雷峰网
J
Java Code Geeks
博客园 - 三生石上(FineUI控件)
人人都是产品经理
人人都是产品经理
MyScale Blog
MyScale Blog
H
Hackread – Cybersecurity News, Data Breaches, AI and More
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
B
Blog RSS Feed
C
Check Point Blog
博客园 - Franky
酷 壳 – CoolShell
酷 壳 – CoolShell
博客园 - 【当耐特】
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
V
V2EX
D
Docker
IT之家
IT之家
博客园 - 聂微东
腾讯CDC
U
Unit 42
Microsoft Security Blog
Microsoft Security Blog
The Cloudflare Blog

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
EthClipper: A Clipboard Meddling Attack on Hardware Walle...
Nikolay Ivanov, Qiben Yan · 2021-09-01 · via cs.CR updates on arXiv.org

Hardware wallets are designed to withstand malware attacks by isolating their private keys from the cyberspace, but they are vulnerable to the attacks that fake an address stored in a clipboard. To prevent such attacks, a hardware wallet asks the user to verify the recipient address shown on the wallet display. Since crypto addresses are long sequences of random symbols, their manual verification becomes a difficult task. Consequently, many users of hardware wallets elect to verify only a few symbols in the address, and this can be exploited by an attacker. In this work, we introduce EthClipper, an attack that targets owners of hardware wallets on the Ethereum platform. EthClipper malware queries a distributed database of pre-mined accounts in order to select the address with maximum visual similarity to the original one. We design and implement a EthClipper malware, which we test on Trezor, Ledger, and KeepKey wallets. To deliver computation and storage resources for the attack, we implement a distributed service, ClipperCloud, and test it on different deployment environments. Our evaluation shows that with off-the-shelf PCs and NAS storage, an attacker would be able to mine a database capable of matching 25% of the digits in an address to achieve a 50% chance of finding a fitting fake address. For responsible disclosure, we have contacted the manufactures of the hardware wallets used in the attack evaluation, and they all confirm the danger of EthClipper.