Ermanno Francesco Sannini, Francesco Salzano, Simone Scalabrino, Rocco Oliveto · 7 authors
Smart Contracts are essential blockchain components, mainly written in Solidity. The high availability of public Solidity code leads to frequent reuse and high clone ratios. Since cloning can propagate vulnerabilities and flaws, effective detection is crucial. Although existing techniques work well in detecting syntactic clones, the identification of semantic clones is an open problem. To address this challenge, in this paper, we present and empirically assess a scalable methodology, based on analyzing code and comments, to spot semantically equivalent Solidity functions. We first collected an up-to-date dataset of about 300,000 Ethereum smart contracts, 82.07% of which are compliant with modern Solidity version 0.8. Manual validation of a statistically significant sample comprising 1,155 function pairs confirms the effectiveness of our solution, achieving an overall precision of 59% (rising to 84% for homonymous functions) and a recall of 97%. Besides, we explore the structural differences occurring on semantically equivalent Solidity functions, demonstrating that they often represent design alternatives focused on security choices, modularization, and gas optimization. Finally, we investigate the use of Large Language Models (LLMs) as documentation engines in scenarios where code comments are poor or absent. Our results show that LLM-generated summaries, combined with sentence transformers like BERT, can bridge the documentation gap, enabling the identification of semantic clones in uncommented code with 75% precision. This work establishes a modern benchmark for Solidity clone detection and provides a foundation for the automated discovery of secure and efficient code alternatives.
Code-driven auditing fails when correctness depends on what the specification requires rather than how the code is written. Production blockchain networks expose this directly: byzantine consensus runs many independent clients of a shared specification, so a specification-divergence defect in one client can fork the network or halt finality. Existing tools reason one repository at a time, with no shared baseline held constant across implementations. We present SPECA, an LLM-driven audit framework that derives explicit, categorized security properties (invariants, pre/postconditions, trust assumptions) from natural-language specifications and reuses them across implementations. SPECA enables controlled cross-implementation comparison, detections grounded in specification invariants no code pattern encodes, and false positives traceable to a specific pipeline phase rather than opaque model errors. On the Sherlock Ethereum Fusaka Audit Contest (10 targets, 366 submissions), SPECA recovers all 15 in-scope H/M/L vulnerabilities expert-augmented (8/15 automated-only) and surfaces 4 fix-confirmed bugs, including a cryptographic-invariant violation missed by every adjudicated finding. On the RepoAudit C/C++ benchmark, SPECA reaches 88.9% precision at 100% recall (F1=0.94) and surfaces 12 author-validated bugs beyond ground truth, two externally validated. SPECA also flags 5 of RepoAudit's 40 published bugs as defensive-coding fixes with no reachable exploit path. False positives trace to three pipeline-pinned root causes; a multi-model study identifies property-generation quality as the binding constraint. End-to-end cost is ~$30 per H/M/L bug (~42 min wall-clock under parallel execution).
This paper presents a critical interpretation of modern monetary systems, arguing that centralized banking structures and debt-based finance concentrate economic power through mechanisms of money creation, interest, and inflation.It traces the historical evolution of money from commodity exchange and gold-backed systems to contemporary fiat currency regimes, emphasizing the role of fractional reserve banking and central bank monetary policy.The framework examines the creation of the U.S. Federal Reserve and the concentration of financial influence among banking and political elites, interpreting these developments as components of a broader “extraction architecture” embedded within global finance.It analyzes inflation, debt issuance, and wage dependency as systemic mechanisms that transfer value through interest-bearing monetary systems, while also discussing alternative economic models such as local currencies, cooperative economies, and decentralized finance.Positioned as a political economy critique and systems-level analysis, the work frames modern debt structures as socially constructed and institutionally reinforced systems of dependency, advocating documentation, decentralization, and reduced financial dependence as pathways toward greater sovereignty and resilience.
We describe a protocol that produces byte-identical outputs from frontier-scale language model inferenceand binds each output to a portable, offline-verifiable signed receipt. The construction has three parts.First, an inference substrate that runs models up to seventy-two billion dense parameters and forty-sevenbillion mixture-of-experts active parameters on NVIDIA H100, with cross-vendor extension to AMDInstinct MI300X. Output hashes match byte-for-byte across fresh process launches in every configurationmeasured; at single-GPU bf16 with eager attention the AMD and NVIDIA hashes are themselves byte-identical, including over fifty-one tokens of compounding frontier-scale generation, and at two-GPUtensor-parallel they differ as predicted by the underlying NCCL-ring versus RCCL-fabric all-reducetopology. Both are individually deterministic. Second, a canonical CBOR receipt schema with an Ed25519signature over a domain-separated message, implemented in Go, Python, and Rust, with cross-languagebyte-identity verified end-to-end and AMD-produced receipts verifying byte-for-byte through a Rustverifier built on x86 NVIDIA hardware. Third, a probabilistic spot-check verifier that re-executes asmall sample of receipts and rejects on mismatch; we prove a soundness lemma of the form 1−(1−f )kand validate it empirically across seventy adversary-verifier configurations with seven hundred thousandMonte Carlo trials. Verification costs about eighty microseconds per receipt on a single core. Eleventhousand sequential warm-model inferences ran without a single byte-identity failure. The contribution isthe construction itself: a primitive that gives issuer-independent fabrication soundness for AI inference atproduction cost, without a hardware-vendor dependency and without zero-knowledge proofs.
The distributional specification in Markov-switching GARCH models has historically been driven by empirical convention rather than statistical theory. This paper derives the two-regime MS-GARCH specification from the Maximum Entropy Principle, providing an information-theoretic motivation for Student-t regime-conditional innovations in cryptocurrency volatility modelling. The framework is applied to five major cryptocurrencies, Bitcoin, Ethereum, Ripple, Litecoin, and Bitcoin Cash, over the period January 2017 to March 2026, comprising 15,834 daily observations spanning six complete market cycles. Three principal findings emerge. First, a Calm-Phase Fragility pattern is identified: four of five assets exhibit calm-regime half-lives below one trading day (0.48 to 1.16 days), with turbulence the dominant long-run state (stationary turbulent probability in [0.451, 0.771] across all assets), establishing turbulence rather than calm as the structural baseline of the cryptocurrency ecosystem. Second, the Maximum Entropy derivation yields endogenous Student-t degrees of freedom, with heavy-tailed turbulent innovations (degrees of freedom approximately 4.5) confirmed across all assets, validating the MaxEnt constraint framework empirically. Third, near-unity turbulent GARCH persistence drives MS-GARCH point forecasts toward the persistence ceiling, consistent with an information-theoretic bound on predictability when the calm half-life collapses below one trading day; HAR-RV achieves the lowest QLIKE loss for three of five assets under these near-critical conditions. Cross-asset consistency is confirmed across seven statistical indicators including Hill tail exponents in [2.31, 3.26], Hurst exponents in [0.543, 0.577], and Wald tests rejecting parameter homogeneity at p < 0.001 for all assets. The framework is formalised as a deployable expert system for real-time regime monitoring and risk management.
This research paper explores the critical challenge of verifiable execution integrity in modern cloud and serverless computing platforms. While services like Amazon Web Services Lambda and Google Cloud Functions provide scalable and efficient execution environments, they operate as opaque systems where users cannot verify whether their code is executed correctly or securely. The paper identifies a fundamental trust gap in current cloud architectures and presents a comprehensive threat model covering risks such as hypervisor compromise, malicious insiders, supply chain attacks, and output manipulation. It highlights how existing security certifications and auditing mechanisms fail to provide cryptographic guarantees of correctness. To address this problem, the paper proposes a novel framework called the CodeNet Integrity Layer (CIL). This system integrates multiple advanced technologies, including Trusted Execution Environments (TEEs), Zero-Knowledge Proofs (ZKPs), and blockchain-based audit mechanisms, to create a decentralized and trustless verification model. The framework ensures that computation outputs are provably linked to the correct code and inputs without relying on blind trust in the provider. The proposed solution is evaluated through experimental implementation, demonstrating that strong execution integrity can be achieved with minimal performance overhead, making it practical for real-world deployment. The research contributes to the future of secure, transparent, and trustless cloud computing, particularly for sensitive applications in finance, healthcare, and critical infrastructure.
Este trabalho propõe-se a discutir a proteção dos Direitos Autorais em uma nova forma de adquirir e investir em arte digital: a criptoarte. Utilizando-se do método hipotético-dedutivo, o problema de pesquisa ao qual o presente trabalho pretende responder é se a criptoarte encontra-se dentro do campo de incidência do Direito Autoral. Parte-se da hipótese inicial afirmativa, de que a criptoarte é passível de proteção pelo Direito Autoral, estando portando, dentro do campo de incidência do Direito Autoral, dado que a regra geral é o reconhecimento da paternidade da obra e consequente remuneração do autor. Observou-se que a arte digital em si não é novidade, mas a possibilidade de comercialização e comprovação de autenticidade e unicidade ocorre graças às tecnologias da criptografia e da blockchain e aos non-fungible tokens (NFTs) que ficam permanentemente vinculados à obra. Considerando que a criptoarte trata-se de uma obra com finalidades estéticas, e não uma obra utilitária, que possui um autor pessoa física, e que a regra é a proteção da obra e consequente remuneração dos autores e/ou titulares, tais obras encontram proteção no instituto do Direito Autoral. Não obstante, considerando que a lei de Direitos Autorais (Lei nº 9.610/98) possui três campos distintos, de incidência, de isenção e de imunidade, verifica-se por fim, corroborada a hipótese inicial, de que a criptoarte perfaz os requisitos necessários para estar no campo da incidência.
Large Reasoning Models (LRMs) and Multi-Agent Systems (MAS) in high-stakes domains demand reliable verification, yet centralized approaches suffer four limitations: (1) Robustness, with single points of failure vulnerable to attacks and bias; (2) Scalability, as reasoning complexity creates bottlenecks; (3) Opacity, as hidden auditing erodes trust; and (4) Privacy, as exposed reasoning traces risk model theft. We introduce TRUST (Transparent, Robust, and Unified Services for Trustworthy AI), a decentralized framework with three innovations: (i) Hierarchical Directed Acyclic Graphs (HDAGs) that decompose Chain-of-Thought reasoning into five abstraction levels for parallel distributed auditing; (ii) the DAAN protocol, which projects multi-agent interactions into Causal Interaction Graphs (CIGs) for deterministic root-cause attribution; and (iii) a multi-tier consensus mechanism among computational checkers, LLM evaluators, and human experts with stake-weighted voting that guarantees correctness under 30% adversarial participation. We prove a Safety-Profitability Theorem ensuring honest auditors profit while malicious actors incur losses. All decisions are recorded on-chain, while privacy-by-design segmentation prevents reconstruction of proprietary logic. Across multiple LLMs and benchmarks, TRUST attains 72.4% accuracy (4-18% above baselines) and remains resilient against 20% corruption. DAAN reaches 70% root-cause attribution (vs. 54-63% for standard methods) with 60% token savings. Human studies validate the design (F1 = 0.89, Brier = 0.074). The framework supports (A1) decentralized auditing, (A2) tamper-proof leaderboards, (A3) trustless data annotation, and (A4) governed autonomous agents, pioneering decentralized AI auditing for safe, accountable deployment of reasoning-capable systems.
Miguel Isabel, Enric Rodríguez-Carbonell, Clara Rodríguez-Núñez, Albert Rubio
Zero-knowledge proofs (ZKPs) are an emerging technology that has become the solution to efficiently provide security and privacy along with the transparency requirement of blockchains. ZKPs are usually expressed by means of arithmetic circuits and, more generally, systems of polynomial equations in a large prime field (commonly ranging from 64-bit to 256-bit values). An increasing interest to apply formal verification techniques to ensure soundness and completeness properties of ZKP protocols has shown the need of developing powerful SMT solvers able to handle such constraint systems. In this paper we consider the problem of deciding the satisfiability of existentially quantified first-order formulas defined over polynomial equations on a prime field. We present a new DPLL($T$)-based approach in which the theory solver orchestrates several modules with different trade-offs between completeness and efficiency. We have implemented the proposed techniques in a prototype that already shows better results than existing state-of-the-art tools on both benchmarks from the domain of ZKP compiler correctness and new benchmarks coming from the verification of arithmetic circuits for ZKPs. \keywords{SMT \and Finite field \and Polynomials \and Zero-Knowledge Proofs.
Artificial intelligence (AI) is rapidly transforming the music industry by reshaping creative processes, lowering barriers to entry, and redefining governance structures. This article examines AI’s dual role as both a catalyst for innovation and a potential source of artistic and economic disruption. On the creative front, AI-assisted tools enable rapid composition, personalized learning, and new forms of experimentation; however, they also risk homogenization, cognitive dependency, and diminished originality. From the standpoint of accessibility, AI democratizes music production by reducing costs and technical barriers, yet disparities in digital access and algorithmic visibility persist. Governance challenges are equally significant, as AI-driven platforms influence discovery, revenue distribution, and authorship attribution, often reinforcing existing power asymmetries. To address these concerns, this article evaluates emerging decentralized frameworks, particularly blockchain and Web3 systems, which offer mechanisms for transparent attribution, equitable royalty distribution, and participatory governance. These technologies provide a potential counterbalance to centralized algorithmic control, enabling more artist-centered ecosystems. Ultimately, the impact of AI in music depends not on the technology itself but on the institutional structures guiding its use. Thoughtful integration can position AI as an augmentative tool that enhances human creativity while preserving artistic integrity and equity.
Permissioned distributed ledger technology (DLT) is increasingly being proposed for regulated banking, yet its feasibility hinges on whether distributed recordkeeping can meet compliance requirements for auditability, operational resilience, and accountability. This article evaluates permissioned distributed ledger technology (DLT) through three compliance-intensive use cases-interbank settlement, shared know your customer (KYC) utilities, and credit information sharing-and situates the discussion in the Chinese banking context using sector indicators of declining return on assets and rising non-performing loan ratios. It specifies the compliance dependencies that each workflow must satisfy, including legally meaningful settlement finality, audit-quality authorization trails, privacy-preserving identity and data governance, and supervision-compatible access and governance arrangements. This article offers a focused conceptual synthesis of peer-reviewed research across banking, audit, information systems, and financial regulation, using Chinese sector indicators as contextual motivation rather than causal evidence. The contribution is a use-case grounded translation of permissioned-DLT design choices into assessable compliance dependencies for banks, auditors, and supervisors, clarifying why blockchain adoption in banking should be treated as a redesign of evidence and governance rather than as a plug-and-play IT upgrade.
Enterprise software engineering is shifting away from deterministic CRUD/REST architectures toward AI-native systems where large language models act as cognitive orchestrators. This transition introduces a critical security tension: probabilistic LLMs weaken classical mechanisms for validation, access control, and formal testing. This paper proposes the design, formal validation, and empirical evaluation of a Semantic Gateway governed by the Model Context Protocol (MCP). The gateway reframes the enterprise API as a semantic surface where tools are dynamically discovered, authorized, and executed based on intent and policy enforcement. The central contribution rests on a paradigm shift: autonomous agents must not be validated as traditional software nor as simple API consumers, but as stochastic state-transition systems whose behavior must be abstracted, fuzzed, and audited through enabled-tool graphs. The architecture introduces a three-layer Zero-Trust security model comprising a pre-inference Semantic Firewall, deterministic Tool-Level RBAC, and out-of-band Cryptographic Human-in-the-Loop approval. Enabledness-Preserving Abstractions (EPAs) and greybox semantic fuzzing--originally developed for blockchain smart contract verification--are adapted to audit agent behavior in enterprise environments. Results demonstrate an 84.2% reduction in incidental code. Across 500,000 multi-turn fuzzing sequences, the methodology achieved a 100% discovery rate of hidden unauthorized state transitions, proving that dynamic formal verification is strictly necessary for secure agentic deployment.
Considerando a crescente expansão do mercado de criptoativos e a recorrente associação do bitcoin a práticas de ocultação patrimonial, torna-se relevante examinar sua utilização no delito de lavagem de dinheiro, especialmente diante dos riscos de responsabilização penal indevida de agentes que atuam licitamente nesse setor. Objetiva-se analisar a utilização do bitcoin no delito de lavagem de dinheiro, a partir do exame de sua definição, de suas formas de circulação e de sua possível inserção em dinâmicas de ocultação e dissimulação de valores de origem ilícita. Para tanto, proceder-se-á a uma pesquisa de abordagem qualitativa, com emprego do método jurídico-dogmático e da linha crítico-metodológica, mediante revisão bibliográfica e documental da legislação e da doutrina especializada. Desse modo, observa-se que o bitcoin, embora apresente características que podem favorecer sua utilização em esquemas de lavagem de capitais, como pseudoanonimato, descentralização, mobilidade transnacional e inexistência física, não constitui, por si só, instrumento ilícito. O que permite concluir que sua relevância penal depende da demonstração concreta de vínculo com infração penal antecedente e da prática de atos voltados à ocultação ou dissimulação da origem ilícita dos valores.
This is Paper 6 of a series of formally-verified analyses of masked NTT hardware for post-quantum cryptography; Paper 1 [1] established structural dependency analysis of the QANARY platform, and Paper 2 [2] quantified security margins under partial NTT masking. Boolean masking composition is well-understood through NI, SNI, and PINI. Arithmetic masking over $\mathbb{Z}_q$ for prime $q$, the foundation of NTT-based post-quantum cryptography, has lacked an analogous theory. We prove, to our knowledge, the first machine-checked composition theorems for arithmetic masking over prime fields. Our key insight is the renewal argument: when a fresh random mask is applied between two pipeline stages, the intermediate wire becomes perfectly uniform regardless of Stage 1's security parameter. For two PF-PINI gadgets with parameters $k_1$ and $k_2$, the composed two-stage pipeline with fresh masking satisfies PF-PINI($k_2$), Stage 1's multiplicity is completely erased from the composed output. Without fresh masking, intermediate wires have multiplicity up to $k_1$, creating a necessary condition for differential power analysis. We formalize both theorems in Lean 4 with 18 machine-checked proofs and zero sorry stubs. We formally bridge the algebraic and hardware-faithful arithmetic models of Barrett reduction, and instantiate the theorems to formally diagnose Microsoft's Adams Bridge PQC accelerator: its absence of fresh inter-stage masking leaves Barrett output wires non-uniform under the first-order probing model, the same architectural flaw that two independent empirical analyses [3, 4] and our own prior structural analysis [1] identified. Computational evidence further suggests the 1-Bit Barrier is universal across Barrett and Montgomery reductions.
Interoperability of patient files between hospitals continues to present significant obstacles. Health systems frequently utilize central EHR systems that could suffer malfunctions, data breaches, and unauthorized access by third parties. Not only does this jeopardize patient confidentiality, but it also hinders the efficient operations of hospital processes.Blockchain technology is viewed as a prospective remedy for the issue. Blockchain keeps its data differently, allowing users to store data securely and make changes difficult. In this study, we analyze research works published between 2016 and 2023 regarding blockchain-based hospital-to-hospital data exchange.The methodologies differ widely: there are cases where researchers use smart contracts in Ethereum, build a system on Hyperledger Fabric, and deploy IPFS. Moreover, certain studies incorporate encryption methods, machine learning algorithms, and more. In summary, the results show that blockchain allows for improved data protection and transparency while giving patients more control over their personal information. Still, some issues persist, such as scalability, expenses, integration with existing infrastructure, and adherence to GDPR and HIPAA requirements. For future work, more improvements are necessary. For instance, zero-knowledge proofs, cybersecurity measures for new technologies, and using artificial intelligence to audit and validate smart contracts may be promising solutions.
With a secure distributed ledger system, blockchain technology helps to shape the healthcare sector in terms of transparency and accountability. This research paper highlights the transformational impact that blockchain technology plays in the healthcare sector. It focuses on a variety of technologies, including patient data security, real-time monitoring enabled Internet of Things (IoTs). This paper also addresses the revolutionary impact that current technology, such as cryptography and Zero Knowledge Proofs (ZKPs), has played in shaping modern technology and improving privacy and security in the health care system. This study contrasts existing blockchain technologies with emergent developments that aid in the creation of a decentralized, safe ecosystem that promotes accountability. Blockchain provides a decentralized, immutable, and transparent ledger. It enhances security, privacy, and trust in data transactions without relying on centralized servers.
The agri-food sector is an essential pillar of economies worldwide, playing a vital role in food security and safety, as well as the livelihoods of millions. However, it continues to tackle recurrent issues such as supply chain inefficiencies, lack of transparency, and simple food fraud. Smart contracts, a form of blockchain technology capable of executing programmable agreements between parties, are a promising remedy for these challenges. A smart contract is basically software that has contractual rules written into it. This chapter lays the foundation for smart contracts operating on a blockchain-based architecture, explains how they differ from regular contracts, and outlines the features that make them unique. In agribusiness, platforms like Ethereum and Hyperledger serve a crucial role in the implementation of smart contracts. Key applications include improving traceability, automating supplier-buyer interactions, and strengthening food safety and quality control. Real-world implementations illustrate their effectiveness in preventing fraud and ensuring compliance with industry standards. Despite their potential, the adoption of smart contracts in agribusiness is influenced by various factors, as analysed through Rogers' diffusion of innovation framework. Comparative advantage, compatibility, and complexity play pivotal roles in determining adoption rates. Case studies showcase successful implementations while shedding light on adoption challenges. Barriers to widespread use include technological constraints, regulatory uncertainties, infrastructure costs, and knowledge gaps, particularly among small-scale farmers. Additionally, data security and privacy concerns remain significant obstacles. Addressing these challenges is essential for harnessing the full potential of smart contracts in agribusiness. This chapter provides insights into overcoming these hurdles and fostering a more transparent and efficient agri-food ecosystem.
This article explores how Thai men construct and perform masculine identities through cryptocurrency trading, using the concept of crypto-masculinity to examine how digital finance becomes a site for gendered self-making. While existing scholarship on masculinity in financial contexts often centers on transnational business masculinities in Western corporate settings, little attention has been paid to how masculinity is enacted in decentralized, digital financial spaces, particularly in non-Western contexts. Addressing this gap, this article draws upon in-depth interviews with 21 male traders in Thailand to analyze how masculinity is shaped by the internal instability of masculinity and local cultural norms. This study identifies three key themes through which Thai men's subjectivity is negotiated: valorizing failure, homosociality, and ‘becoming a good Thai man.’ As a result, this article challenges assumptions about the naturalized links between men and finance, suggesting the need for further research on how exclusions within digital finances are taking place to better support equality on crypto platforms.
The financial world stands witness to newer investment alternatives like digital assets (cryptocurrencies and non-fungible tokens [NFTs]) and the energy market (dirty energy and clean energy). Despite these advancements, their relationship has received little scholarly attention. This study illuminates dynamic interconnections among these two dominant and novel asset classes from 31 December 2019 to 21 January 2026 using the quantile connectedness approach. The results demonstrate increased connectedness during periods of turmoil. Oil and gas remain constant as net receivers, and Decentraland and Theta as net transmitters. Interestingly, clean energy indices remain net receivers during normal market situations but become net transmitters during extreme ones, signalling a good diversifying potential. Cryptocurrencies tend to be net receivers at the median and upper quantiles but shift to net transmitters at lower quantiles. The results signify an increasing technology and sustainability-driven economic shift. The current study contributes to the existing literature by being one of the preliminary studies to check the relationship between these two prominent asset classes and holds important implications for various stakeholders. It offers insights for investors and corporates to focus on technology and clean energy markets while advising policymakers and regulators to roll out policies and regulations that streamline these emerging sectors.
This project introduces a blockchain-powered Blood Donation and Emergency Response System, which would provide blood donation, allocation, and emergency delivery secure, transparent and efficient manner for all donors, patients, blood banks, hospitals, ambulance drivers and system administrators involved. Without having to trust traditional records, the system uses blockchain-based digital identities so that every registration, every donation, request, and inventory update is permanent and easy to verify. Blood banks update their inventory on the blockchain for easy real-time tracking of blood supplies. When patients need blood, smart contracts verify availability and automatically execute the process or immediately notify hospitals and suitable donors of the extra units needed without requiring manual coordination. Hospitals can raise urgent requests, also logged onto the blockchain and smart contracts that notify eligible donors directly to their respective email address. System administrators monitor all activity through the admin portal, preventing data tampering and ensuring privacy and security are straightforward. In this manner, the decentralized storage of data, the creation of unchangeable records and the automatic execution of smart contracts reduce delay, fraud and complete traceability, thus dramatically improving the response to emergencies. Above all, this blockchain approach now helps in managing these very important operations of blood supply to make a reliable, tamperproof and location-based mechanism that saves lives where and when it matters.
As blockchain ecosystems grow, financially motivated attackers increasingly exploit decentralized finance (DeFi) protocols, causing frequent and severe losses. Unlike conventional cyberattacks, DeFi exploits propagate rapidly due to the transparent and composable nature of smart contracts. We identify a critical pattern, Imitative Attack Cascade: an initial successful exploit is quickly followed by mimicking transactions that reuse attack logic with minor modifications or parameter changes. Our empirical analysis shows that over 69% of DeFi attacks exhibit strong behavioral similarity to earlier incidents, often within hours or days of the initial attack. This exposes a fundamental limitation in current reactive detection. Initial attacks are typically flagged via heuristic alerts (Tornado Cash traces, anomalous nonce usage, exploiter labels), but turning these signals into detection rules requires manual validation and handcrafted trace analysis -- a labor-intensive, slow process that leaves follow-up attacks to spread. Our goal is to ensure that once an attack has been observed, even a single instance, it can be rapidly abstracted into an actionable, generalizable detection rule. We decompose the problem into two challenges: (I) abstracting the semantics of diverse, obscure function signatures, and (II) matching transaction logic in noisy, evasive traces. We leverage two insights: (i) the open-source nature of most DeFi protocols enables high-fidelity semantic classification of function signatures; (ii) contract labels isolate essential logic by filtering irrelevant calls and classifying attack intent. Building on these, we develop GenDetect, which achieves ACC 98%, FPR 1%, FNR 3% and discovers 56 previously unrevealed attacks from the past three years. Source code and dataset: https://github.com/NobodyIsAnonymous/GenDetect_ICSE2026