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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
Applying Private Information Retrieval to Lightweight Bit...
Kaihua Qin, Henryk Hadass, Arthur Gervais, Joel Reardon · 2020-08-26 · via cs.CR updates on arXiv.org

Lightweight Bitcoin clients execute a Simple Payment Verification (SPV) protocol to verify the validity of transactions related to a particular user. Currently, lightweight clients use Bloom filters to significantly reduce the amount of bandwidth required to validate a particular transaction. This is despite the fact that research has shown that Bloom filters are insufficient at preserving the privacy of clients' queries. In this paper we describe our design of an SPV protocol that leverages Private Information Retrieval (PIR) to create fully private and performant queries. We show that our protocol has a low bandwidth and latency cost; properties that make our protocol a viable alternative for lightweight Bitcoin clients and other cryptocurrencies with a similar SPV model. In contract to Bloom filters, our PIR-based approach offers deterministic privacy to the user. Among our results, we show that in the worst case, clients who would like to verify 100 transactions occurring in the past week incurs a bandwidth cost of 33.54 MB with an associated latency of approximately 4.8 minutes, when using our protocol. The same query executed using the Bloom-filter-based SPV protocol incurs a bandwidth cost of 12.85 MB; this is a modest overhead considering the privacy guarantees it provides.