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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
Ormer: A Manipulation-resistant and Gas-efficient Blockch...
Dongbin Bai, Jiannong Cao, Yinfeng Cao, Long Wen, Milos Stojmeno · 2024-10-10 · via cs.CR updates on arXiv.org

Price feeds of cryptocurrencies are essential for Decentralized Finance (DeFi) applications to realize fundamental trading and exchanging functionalities, which are retrieved from external price data sources such as exchanges and input to on-chain smart contracts in real-time. Currently, arithmetic mean based time-weighted average price (TWAP) oracles are widely used to process price feeds by averaging asset price with short time frame to achieve reliable and gas-efficient pricing. However, recent research indicates that TWAP is vulnerable to price manipulation attacks, resulting in abnormal price fluctuations and severe financial loss. Even worse, TWAP oracles usually set a relatively long time frame setting to prevent such attack. However, it would further introduce long delays and high price deviation errors from the market asset price. To address this issue, we propose a novel on-chain gas-efficient pricing algorithm (Ormer) that heuristically estimates the median of asset price within an observation window based on a piecewise-parabolic formula, while the time delay is suppressed by fusing estimations with different window sizes. Our evaluation based on multiple pairs of token swapping price feed across different chains show that Ormer reduces the mean absolute price error by 15.3% and the time delay by 49.3% compared to TWAP. For gas efficiency, regardless of the number of price observations, an encoding mechanism with constant storage requirement is employed without saving all the historical data for median estimation. Surprisingly, the lowest gas consumption of Ormer is even 15.2% less than TWAP, and the oracle querying fee would be saved up to ~20K USD per day for DeFi participants.