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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
ScaloWork: Useful Proof-of-Work with Distributed Pool Mining
Diptendu Chatterjee, Avishek Majumder, Subhra Mazumdar · 2025-04-19 · via cs.CR updates on arXiv.org

Bitcoin blockchain uses hash-based Proof-of-Work (PoW) that prevents unwanted participants from hogging the network resources. Anyone entering the mining game has to prove that they have expended a specific amount of computational power. However, the most popular Bitcoin blockchain consumes 175.87 TWh of electrical energy annually, and most of this energy is wasted on hash calculations, which serve no additional purpose. Several studies have explored re-purposing the wasted energy by replacing the hash function with meaningful computational problems that have practical applications. Minimum Dominating Set (MDS) in networks has numerous real-life applications. Building on this concept, Chrisimos [TrustCom '23] was proposed to replace hash-based PoW with the computation of a dominating set on real-life graph instances. However, Chrisimos has several drawbacks regarding efficiency and solution quality. This work presents a new framework for Useful PoW, ScaloWork, that decides the block proposer for the Bitcoin blockchain based on the solution for the dominating set problem. ScaloWork relies on the property of graph isomorphism and guarantees solution extractability. We also propose a distributed approach for calculating the dominating set, allowing miners to collaborate in a pool. This enables ScaloWork to handle larger graphs relevant to real-life applications, thereby enhancing scalability. Our framework also eliminates the problem of free-riders, ensuring fairness in the distribution of block rewards. We perform a detailed security analysis of our framework and prove our scheme as secure as hash-based PoW. We implement a prototype of our framework, and the results show that our system outperforms Chrisimos in all aspects.