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cs.CR updates on arXiv.org

On the Security of Research Artifacts SafeHarbor: Hierarchical Memory-Augmented Guardrail for LLM Agent Safety 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 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
DECLOAK: Enable Secure and Cheap Multi-Party Transactions...
Qian Ren, Yue Li, Yingjun Wu, Yuchen Wu, Hong Lei, Lei Wang, Ban · 2022-02-21 · via cs.CR updates on arXiv.org

As the confidentiality and scalability of smart contracts have become a crucial demand of blockchains, off-chain contract execution frameworks have been promising. Some have recently expanded off-chain contracts to Multi-Party Computation (MPC), which seek to transition the on-chain states by off-chain MPC. The most general problem among these solutions is MPT, since its off-chain MPC takes on- and off-chain inputs, delivers on- and off-chain outputs, and can be publicly verified by the blockchain, thus capable of covering more scenarios. However, existing Multi-Party Transaction (MPT) solutions lack at least one of data availability, financial fairness, delivery fairness, and delivery atomicity. These properties are crucially valued by communities, e.g., the Ethereum community, or users. Even worse, these solutions require high-cost interactions between the blockchain and off-chain systems. This paper proposes a novel MPT-enabled off-chain contract execution framework, DECLOAK. DECLOAK is the first to achieve data availability of MPT, and our method can apply to other fields that seek to persist user data on-chain. Moreover, DECLOAK solves all mentioned shortcomings with even lower gas costs and weaker assumptions. Specifically, DECLOAK tolerates all but one Byzantine party and TEE executors. Evaluating on 10 MPTs, DECLOAK reduces the gas cost of the SOTA, Cloak, by 65.6%. Consequently, we are the first to not only achieve such level secure MPT in practical assumption, but also demonstrate that evaluating MPT in the comparable gas cost to normal Ethereum transaction is possible. And the cost superiority of DECLOAK increases as the number of MPT parties grows.