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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
FeatherWallet: A Lightweight Mobile Cryptocurrency Wallet...
Martin Perešíni, Ivan Homoliak, Samuel Olekšák, Samuel Slávka · 2025-03-25 · via cs.CR updates on arXiv.org

Traditionally, mobile wallets rely on a trusted server that provides them with a current view of the blockchain, and thus, these wallets do not need to validate the header chain or transaction inclusion themselves. If a mobile wallet were to validate a header chain and inclusion of its transactions, it would require significant storage and performance overhead, which is challenging and expensive to ensure on resource-limited devices, such as smartphones. Moreover, such an overhead would be multiplied by the number of cryptocurrencies the user holds in a wallet. Therefore, we introduce a novel approach, called FeatherWallet, to mobile wallet synchronization designed to eliminate trust in a server while providing efficient utilization of resources. Our approach addresses the challenges associated with storage and bandwidth requirements by off-chaining validation of header chains using SNARK-based proofs of chain extension, which are verified by a smart contract. This offers us a means of storing checkpoints in header chains of multiple blockchains. The key feature of our approach is the ability of mobile clients to update their partial local header chains using checkpoints derived from the proof verification results stored in the smart contract. In the evaluation, we created zk-SNARK proofs for the 2, 4, 8, 16, 32, and 64 headers within our trustless off-chain service. For 64-header proofs, the off-chain service producing proofs requires at least 40 GB of RAM, while the minimal gas consumption is achieved for 12 proofs bundled in a single transaction. We achieved a 20-fold reduction in storage overhead for a mobile client in contrast to traditional SPV clients. Although we have developed a proof-of-concept for PoW blockchains, the whole approach can be extended in principle to other consensus mechanisms, e.g., PoS.