arXiv subjects

Mohammad Pishdar

Publications and source records attributed to Mohammad Pishdar.

4 recordsLinked to original sources

arXiv2026

LROO Rug Pull Detector: A Leakage-Resistant Framework Based on On-Chain and OSINT Signals

Smart contract-based ecosystems enable decentralized applications without trusted intermediaries, but their immutability and permissionless design also facilitate large-scale fraud. One of the most prevalent attacks is the rug pull, where project operators abruptly withdraw liquidity after artificially inflating token value. Existing detection methods primarily rely on reactive on-chain signals and often suffer from temporal data leakage, limiting their real-world reliability. This paper proposes a leakage-aware framework for early rug-pull detection that integrates on-chain behavioral metrics with temporally aligned Open Source Intelligence (OSINT) signals. We construct a hand-labeled dataset of 1,000 token projects, spanning DeFi and non-DeFi settings, with all features extracted strictly prior to any liquidity withdrawal to preserve causal validity. The dataset combines structural on-chain indicators with external attention signals derived from social media activity and search trends. Within this framework, TabPFN is employed as a core modeling component for learning from multimodal tabular data under strict temporal constraints. Experimental results show that the proposed framework achieves strong discriminative performance and improved probability calibration compared to classical baselines, while maintaining low false-negative rates. By framing rug-pull detection as a causal, multimodal forecasting problem, this work emphasizes the necessity of leakage-resilient evaluation and calibrated risk estimation for deployment in blockchain security systems.

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arXiv2026

SmartGraphical: A Human-in-the-Loop Framework for Detecting Smart Contract Logical Vulnerabilities via Pattern-Driven Static Analysis and Visual Abstraction

Smart contracts are fundamental components of blockchain ecosystems; however, their security remains a critical concern due to inherent vulnerabilities. While existing detection methodologies are predominantly syntax-oriented, targeting reentrancy and arithmetic errors, they often overlook logical flaws arising from defective business logic. This paper introduces SmartGraphical, a novel security framework specifically engineered to identify logical attack surfaces. By synthesizing automated static analysis with an interactive graphical representation of contract architectures, SmartGraphical facilitates a comprehensive inspection of a contract's functional control flow. To mitigate the context-dependent nature of logical bugs, the tool adopts a human-in-the-loop approach, empowering developers to interpret heuristic warnings within a visualized structural context. The efficacy of SmartGraphical was validated through a rigorous empirical evaluation involving a large dataset of real-world contracts and a large-scale user study with 100 developers of varying expertise. Furthermore, the framework's performance was demonstrated through case studies on high-profile exploits, such as the SYFI rebase failure and farming protocol flash swap attacks, proving that SmartGraphical identifies intricate vulnerabilities that elude state-of-the-art automated detectors. Our findings indicate that this hybrid methodology significantly enhances the interpretability and detection rate of non-trivial logical security threats in smart contracts.

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arXiv2026

Why No Consensus on Consensus? A Deep Dive into Blockchain Consensus Protocols

Blockchain technology has revolutionized the digital landscape, driving innovations across industries through its decentralized and transparent infrastructure. These networks are primarily categorized as public or private, based on user access permissions. Public blockchains are open to all and fully decentralized while private blockchains have restricted access to authorized participants only and they are usually centralized or partially decentralized. Consensus protocols are at the heart of blockchain networks, playing a pivotal role in maintaining security, ensuring consistency, and achieving agreement among distributed nodes. This paper provides a critical and unified analysis, including detailed workflows, that addresses the limita- tions of recent literature. Furthermore, this research investigates the strengths and limitations of each protocol, shedding light on their suitability for various applications, including financial transactions, supply chain management, healthcare, and beyond. A critical analysis of ongoing challenges, such as security vulnerabilities, scalability bottlenecks, and energy consumption is provided. Finally, the paper identifies key research gaps in the field, offering insights into potential areas for future work and emerging trends aimed at addressing these issues. This comprehensive analysis serves as a valuable resource for re- searchers, practitioners, and organizations seeking to understand the role of consensus protocols in shaping the future of blockchain technology.

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arXiv2026

TMRugPull: A Temporally Sound Multimodal Dataset for Early RugPull Detection

Rug pull is a critical attack in the world of blockchain technology. Despite this, the absence of sufficient time-bound and well-structured datasets is considered one of the significant issues faced while identifying early detection. Existing datasets do not provide the solution to this challenge because of temporal leakage or use of post-collapse indicators, insufficient modality coverage, and confusing or partial labels, especially with regards to DeFi tokens. To solve these problems, we present a highly curated and strictly time-bound dataset called TM-RugPull containing 1,000 projects, which include DeFi, meme, NFT, and celebrity token projects. We achieve temporal validation of the dataset by acquiring all three modalities, namely on-chain behavior, smart contract metadata, and OSINT signals. The project labels are provided based on manual investigation for the entire project's lifespan and its collapse. Also, we make our dataset publicly available together with its codebase for data acquisition and feature extraction.

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