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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
Optimal Reward Allocation via Proportional Splitting
[Submitted on 13 Mar 2025 (v1), last revised 10 Aug 2026 (this v · 2025-03-13 · via cs.CR updates on arXiv.org

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Abstract:Following the publication of Bitcoin's arguably most famous attack, selfish mining, various works have introduced mechanisms to enhance blockchain systems' game-theoretic resilience. The only proof-of-work reward rule with a Nash-equilibrium guarantee, FruitChains, demands reward finality on the order of days. The rules that settle in minutes have no such guarantee, and one of them, Reward Splitting, still outperforms FruitChains on most of the metrics that matter in deployment. This paper closes that gap between theory and practice. We introduce FairChain, a two-level transformation for any proof-of-work Nakamoto-style protocol. At the protocol layer, FairChain records low-difficulty samples called workshares alongside blocks. At the reward layer, it applies Proportional Reward Splitting (PRS): each height's reward is divided among the competing work objects in proportion to the intrinsic work behind them, with workshares supplying a fresh power estimate at every height. The fork-choice rule and block-production loop are left untouched, so the host chain's security carries over unchanged. Workshares can be discarded once the corresponding rewards mature, leaving zero on-chain footprint. We prove FairChain is a \r{ho}-coalition-safe {\epsilon}-Nash equilibrium for sufficiently large parameters, matching FruitChains in theory. To evaluate practical performance, we leverage Markov decision processes and compute the optimal adversarial policy under each utility function, rather than the gain of any one attack. At a six-block confirmation window, FairChain raises the deviation threshold to 38% of mining power and beats every mechanism in that framework on incentive compatibility, subversion gain (except FruitChains above 42%), and censorship susceptibility (except FruitChains below 25%).

Submission history

From: Dimitris Karakostas [view email]
[v1] Thu, 13 Mar 2025 09:14:29 UTC (550 KB)
[v2] Mon, 10 Aug 2026 15:46:55 UTC (641 KB)