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cs.CR updates on arXiv.org

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
An Exposition of Pathfinding Strategies Within Lightning ...
Sindura Saraswathi, Christian Kümmerle · 2024-10-18 · via cs.CR updates on arXiv.org

The Lightning Network is a peer-to-peer network designed to address Bitcoin's scalability challenges, facilitating rapid, cost-effective, and instantaneous transactions through bidirectional, blockchain-backed payment channels among network peers. Due to a source-based routing of payments, different pathfinding strategies are used in practice, trading off different objectives for each other such as payment reliability and routing fees. This paper explores differences within pathfinding strategies used by prominent Lightning Network node implementations, which include different underlying cost functions and different constraints, as well as different greedy algorithms of shortest path-type. Surprisingly, we observe that the pathfinding problems that most LN node implementations attempt to solve are NP-complete, and cannot be guaranteed to be optimally solved by the variants of Dijkstra's algorithm currently deployed in production. Through comparative analysis and simulations, we evaluate efficacy of different pathfinding strategies across metrics such as success rate, fees, path length, and timelock. Our experiments indicate that the strategies used by Eclair are advantageous in terms of payment reliability and result in paths with low fees. LND exhibits moderate success rates, while LDK results in paths with higher fee levels for smaller payment amounts; furthermore, CLN stands out for its minimal timelock paths. Additionally, we investigate the impact of Lightning node connectivity levels on routing efficiency. The findings of our analysis provide insights towards future improvements of pathfinding strategies and algorithms used within the Lightning Network.