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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
Flood & Loot: A Systemic Attack On The Lightning Network
Jona Harris, Aviv Zohar · 2020-06-16 · via cs.CR updates on arXiv.org

The Lightning Network promises to alleviate Bitcoin's known scalability problems. The operation of such second layer approaches relies on the ability of participants to turn to the blockchain to claim funds at any time, which is assumed to happen rarely. One of the risks that was identified early on is that of a wide systemic attack on the protocol, in which an attacker triggers the closure of many Lightning channels at once. The resulting high volume of transactions in the blockchain will not allow for the proper settlement of all debts, and attackers may get away with stealing some funds. This paper explores the details of such an attack and evaluates its cost and overall impact on Bitcoin and the Lightning Network. Specifically, we show that an attacker is able to simultaneously cause victim nodes to overload the Bitcoin blockchain with requests and to steal funds that were locked in channels. We go on to examine the interaction of Lightning nodes with the fee estimation mechanism and show that the attacker can continuously lower the fee of transactions that will later be used by the victim in its attempts to recover funds - eventually reaching a state in which only low fractions of the block are available for lightning transactions. Our attack is made easier even further as the Lightning protocol allows the attacker to increase the fee offered by his own transactions. We continue to empirically show that the vast majority of nodes agree to channel opening requests from unknown sources and are therefore susceptible to this attack. We highlight differences between various implementations of the Lightning Network protocol and review the susceptibility of each one to the attack. Finally, we propose mitigation strategies to lower the systemic attack risk of the network.