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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
Lazy Contracts: Alleviating High Gas Costs by Secure and ...
Soroush Farokhnia · 2023-09-20 · via cs.CR updates on arXiv.org

Smart contracts are programs that are executed on the blockchain and can hold, manage and transfer assets in the form of cryptocurrencies. The contract's execution is then performed on-chain and is subject to consensus, i.e. every node on the blockchain network has to run the function calls and keep track of their side-effects. In most programmable blockchains, such as Ethereum, the notion of gas is introduced to prevent DoS attacks by malicious parties who might try to slow down the network by performing heavy computations. A fixed cost to each atomic operation, and the initiator of a function call pays the total gas cost as a transaction fee. This helps prevent DoS attacks, but the resulting fees are extremely high. For example, in 2022, on Ethereum alone, there has been a total gas usage of 1.77 Million ETH ~ 4.3 Billion USD. This thesis proposes "lazy contracts" as a solution to alleviate these costs. Our solution moves most of the computation off-chain, ensuring that each function call incurs only a tiny amount of gas usage, while preserving enough data on-chain to guarantee an implicit consensus about the state of the contract variables and ownership of funds. A complete on-chain execution of the functions will only be triggered in case two parties to the contract are in disagreement about the current state, which in turn can only happen if at least one party is dishonest. In such cases, our protocol can identify the dishonest party and penalize them by having them pay for the entire gas usage. Hence, no rational party has an incentive to act dishonestly. Finally, we perform extensive experiments over 160,735 real-world Solidity contracts that were involved in 9,055,492 transactions in January 2022--January 2023 on Ethereum and show that our approach reduces the overall gas usage by 55.4%, which amounts to an astounding saving of 109.9 Million USD in gas fees.