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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
Secure compilation of rich smart contracts on poor UTXO b...
Massimo Bartoletti, Riccardo Marchesin, Roberto Zunino · 2023-05-16 · via cs.CR updates on arXiv.org

Most blockchain platforms from Ethereum onwards render smart contracts as stateful reactive objects that update their state and transfer crypto-assets in response to transactions. A drawback of this design is that when users submit a transaction, they cannot predict in which state it will be executed. This exposes them to transaction-ordering attacks, a widespread class of attacks where adversaries with the power to construct blocks of transactions can extract value from smart contracts (the so-called MEV attacks). The UTXO model is an alternative blockchain design that thwarts these attacks by requiring new transactions to spend past ones: since transactions have unique identifiers, reordering attacks are ineffective. Currently, the blockchains following the UTXO model either provide contracts with limited expressiveness (Bitcoin), or require complex run-time environments (Cardano). We present ILLUM , an Intermediate-Level Language for the UTXO Model. ILLUM can express real-world smart contracts, e.g. those found in Decentralized Finance. We define a compiler from ILLUM to a bare-bone UTXO blockchain with loop-free scripts. Our compilation target only requires minimal extensions to Bitcoin Script: in particular, we exploit covenants, a mechanism for preserving scripts along chains of transactions. We prove the security of our compiler: namely, any attack targeting the compiled contract is also observable at the ILLUM level. Hence, the compiler does not introduce new vulnerabilities that were not already present in the source ILLUM contract. We evaluate the practicality of ILLUM as a compilation target for higher-level languages. To this purpose, we implement a compiler from a contract language inspired by Solidity to ILLUM, and we apply it to a benchmark or real-world smart contracts.