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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
Typosquatting 3.0: Characterizing Squatting in Blockchain...
Muhammad Muzammil, Zhengyu Wu, Lalith Harisha, Brian Kondracki, · 2024-11-01 · via cs.CR updates on arXiv.org

A Blockchain Name System (BNS) simplifies the process of sending cryptocurrencies by replacing complex cryptographic recipient addresses with human-readable names, making the transactions more convenient. Unfortunately, these names can be susceptible to typosquatting attacks, where attackers can take advantage of user typos by registering typographically similar BNS names. Unsuspecting users may accidentally mistype or misinterpret the intended name, resulting in an irreversible transfer of funds to an attacker's address instead of the intended recipient. In this work, we present the first large-scale, intra-BNS typosquatting study. To understand the prevalence of typosquatting within BNSs, we study three different services (Ethereum Name Service, Unstoppable Domains, and ADAHandles) spanning three blockchains (Ethereum, Polygon, and Cardano), collecting a total of 4.9M BNS names and 200M transactions-the largest dataset for BNSs to date. We describe the challenges involved in conducting name-squatting studies on these alternative naming systems, and then perform an in-depth quantitative analysis of our dataset. We find that typosquatters are indeed active on BNSs, registering more malicious domains with each passing year. Our analysis reveals that users have sent thousands of transactions to squatters and that squatters target both globally popular BNS domain names as well as the domains owned by popular Twitter/X users. Lastly, we document the complete lack of defenses against typosquatting in custodial and non-custodial wallets and propose straightforward countermeasures that can protect users without relying on third-party services.