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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
MicroCash: Practical Concurrent Processing of Micropayments
Ghada Almashaqbeh, Allison Bishop, Justin Cappos · 2019-11-20 · via cs.CR updates on arXiv.org

Micropayments are increasingly being adopted by a large number of applications. However, processing micropayments individually can be expensive, with transaction fees exceeding the payment value itself. By aggregating these small transactions into a few larger ones, and using cryptocurrencies, today's decentralized probabilistic micropayment schemes can reduce these fees. Unfortunately, existing solutions force micropayments to be issued sequentially, thus to support fast issuance rates a customer needs to create a large number of escrows, which bloats the blockchain. Moreover, these schemes incur a large computation and bandwidth overhead, which limit their applicability in large-scale systems. In this paper, we propose MicroCash, the first decentralized probabilistic framework that supports concurrent micropayments. MicroCash introduces a novel escrow setup that enables a customer to concurrently issue payment tickets at a fast rate using a single escrow. MicroCash is also cost effective because it allows for ticket exchange using only one round of communication, and it aggregates the micropayments using a lottery protocol that requires only secure hashing. Our experiments show that MicroCash can process thousands of tickets per second, which is around 1.7-4.2x times the rate of a state-of-the-art sequential micropayment system. Moreover, MicroCash supports any ticket issue rate over any period using only one escrow, while the sequential scheme would need more than 1000 escrows per second to permit high rates. This enables our system to further reduce transaction fees and data on the blockchain by around 50%.