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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
WAKU-RLN-RELAY: Privacy-Preserving Peer-to-Peer Economic ...
Sanaz Taheri-Boshrooyeh, Oskar Thorén, Barry Whitehat, Wei Jie K · 2022-07-01 · via cs.CR updates on arXiv.org

In this paper, we propose WAKU-RLN-RELAY as a spam-protected gossip-based routing protocol that can run in heterogeneous networks. It features a privacy-preserving peer-to-peer (p2p) economic spam protection mechanism. WAKU-RLN-RELAY addresses the performance and privacy issues of the state-of-the-art p2p spam prevention techniques including peer scoring utilized by libp2p, and proof-of-work used by e.g., Whisper, the p2p messaging layer of Ethereum. In WAKU-RLN-RELAY, spam protection works by limiting the messaging rate of each network participant. Rate violation is disincentivized since it results in financial punishment where the punishment is cryptographically guaranteed. Peers who identify spammers are also rewarded. To enforce the rate limit, we adopt the suggested framework of Semaphore and its extended version, however, we modify that framework to properly address the unique requirements of a network of p2p resource-restricted users. The current work dives into the end-to-end integration of Semaphore into WAKU-RLN-RELAY, the modifications required to make it suitable for resource-limited users, and the open problems and future research directions. We also provide a proof-of-concept open-source implementation of WAKU-RLN-RELAY, and its specifications together with a rough performance evaluation.