arXiv20 August 2026cs.AIcs.DC
Jianghai Li, Pavel Kuznetsov, Yury Yanovich, Konstantin Nott-Whaley, Igor Vodolazov
The rapid proliferation of memecoins on blockchain platforms has increased the risk of fraudulent activities, particularly rug pulls. While previous studies have focused on Ethereum-based tokens, this paper shifts the spotlight to Solana, the leading blockchain for memecoins by trading volume and token count. Unlike Ethereum, where rug pulls often exploit smart contract backdoors, Solana memecoin rug pulls are predominantly driven by liquidity manipulation and social dynamics. This research pioneers large-scale rug pull early detection in the Solana ecosystem by assembling a dataset of 6.4 million tokens over 7 months. Market analysis reveals that a vast majority of these memecoins exhibit rug pull characteristics within one hour of launch, highlighting the urgency of short-horizon prediction. Despite the absence of code-level features, we demonstrate that classic machine learning models, particularly Gradient Boosting (XGBoost), achieve robust performance in detecting potential rug pulls using only the first 5 minutes of trading data. Furthermore, we evaluate cross-platform generalization between PumpFun and Raydium, revealing that multi-source data fusion significantly mitigates
arXiv20 August 2026cs.CRcs.CY
Motahare Ebrahimi, Nastooh Taheri Javan, Seyedakbar Mostafavi, Fatemeh Pakzaban
As mobile network traffic continues to grow, content caching on edge servers is critical for reducing latency. However, challenges such as malicious edge servers that may delete or manipulate cached content, along with the limited capacity of these servers, need to be addressed. To overcome the capacity limitations, helper mobile nodes can contribute their cache resources. However, due to their selfish behavior, an incentive mechanism is necessary to encourage resource sharing. Additionally, these helper nodes can also be malicious. This paper proposes a blockchain-based trust management mechanism that addresses these challenges by accurately identifying trustworthy edge servers and mobile nodes. The proposed mechanism calculates both direct and indirect trust using smart contracts, ensuring that malicious nodes are effectively filtered out. Trustworthiness is determined based on mobile node satisfaction with the quality of service, and trust data is securely stored on the blockchain. To combat node selfishness, a reward mechanism is introduced to incentivize cache sharing. Furthermore, a blockchain-based authentication mechanism protects against node impersonation. Our approach op
arXiv20 August 2026cs.CRcs.DC
Baixiang Liu, Haotian Che, Yuan Li
Retrieval-Augmented Generation (RAG) lets Large Language Models (LLMs) pull in up-to-date, domain-specific information instead of relying only on what they were trained on. Yet most RAG systems still draw from centralized databases with limited oversight, making it difficult to verify where a document came from, whether it has been tampered with, or whether it should be trusted at all. This is a serious problem in domains where both the timeliness and accuracy of retrieved content are critical, such as healthcare, finance, logistics, and legal case law, where a wrong or manipulated document can directly lead to bad decisions. We present TrustRAG, a committee-based, blockchain-backed RAG system: before a document is used, it is certified by a committee of domain experts through a zero-knowledge protocol, and the committee's hidden scores are combined via secure multi-party computation into a trust score that any client can verify. These scores, along with the underlying document data, are maintained jointly across chains through hash commitments, so no document or score can be silently altered or dropped, and every ranking can be independently replayed and checked.
arXiv20 August 2026cs.CR
Patrick Herbke, Wolf Rieder, Christian René Sechting, Huaning Yang, Sid Lamichhane, Philip Raschke, Axel Küpper
Verifiable credentials let holders present digitally signed claims without requiring the issuer to participate in every presentation. Revocation complicates this privacy model because a verifier must determine whether a credential remains valid. Existing status checks may expose recurring identifiers, registry positions, or request metadata. Such information can serve as stable handles to link separate presentations. ShadowPath moves the credential status lookup to the holder. For each presentation, the holder proves, in zero-knowledge, that the credential has not been revoked under the verifier-selected registry root. The verifier learns the status result but not observable metadata. To the best of our knowledge, we provide the first evaluation of Verkle trees for credential revocation and compare them with sparse Merkle trees to assess their applicability in real world applications. The comparison tests whether reducing path depth with Verkle trees offsets the higher cost of KZG-based authentication. Across 30 desktop trials, median Groth16 proving took 371.6ms with sparse Merkle and 2.11s with Verkle. Verification took 3.70ms and 7.55ms, respectively. Groth16 Verkle proving took
arXiv20 August 2026cs.AI
Tenghui Huang, Jiawen Kang, Dongning Liu, Changyan Yi, Chengjun Cai, Anjia Yang, Li Li, Dong In Kim
Smart contract vulnerability detection with Large Language Models (LLMs) faces three causally linked challenges. First, new vulnerability categories demand parameter-efficient adaptation, since full retraining is prohibitive for sequentially arriving tasks. Second, training per-task adapters on a shared backbone causes catastrophic forgetting of previously learned vulnerabilities. Third, the resulting multiplicity of adapters must be consolidated into a single model, since task identity is unknown at inference time. Each challenge arises directly from the solution to its predecessor, making an integrated framework essential. We propose a three-stage pipeline in which each stage addresses one challenge and feeds into the next. The adaptation stage uses Frequency-Aware Low-Rank Adaptation (FA-LoRA), which performs adaptation in the Fourier domain with per-frequency importance gates, requiring only 0.4% trainable parameters while outperforming standard LoRA and QLoRA. The continual learning stage applies Forget-Aware Replay (FAR), which uses these frequency gates to estimate per-sample forgetting risk via loss dynamics and prioritizes vulnerable knowledge for rehearsal, achieving an a
arXiv19 August 2026cs.CR
Victor Kebande
This work introduces the Borromean-Entangled Chameleon Trapdoor Hash All-or-Nothing (AoNT) Stream Cipher (BEC-Trap), a novel construction that merges Borromean interdependence, trapdoor-enabled mutability, and streaming encryption into a unified framework. The (BEC-Trap) cipher links key (K), initialization vector (V ), and internal state (St) in a Borromean structure, ensuring that breaking, guessing, or removing any one component collapses the entire keystream, providing a computational (AoNT) interdependence under standard cryptographic assumptions. A chameleon trapdoor hash is integrated to permit controlled collisions, enabling seamless rekeying, (V ) refresh, and state rotation without resynchronizing endpoints. This design provides confidentiality, forward secrecy, and adaptive key management with low computational overhead, making it suitable for high-throughput secure messaging, IoT communications, and privacy-preserving blockchain channels. Security analysis of the (BEC-Trap) shows that the construction is resistant to key-recovery attacks, state compromise, and desynchronization attempts, delivering a robust cryptographic primitive for next-generation secure communicatio
arXiv18 August 2026cs.CR
Bernardo David, Keon Kim, Krish Chelikavada
Universal accounts allow users to manage assets and execute operations across heterogeneous blockchain ecosystems through a single interface, but they introduce security and trust challenges involving authentication, authorization, transaction signing, key custody, recovery, and decentralization. This paper presents 0xPass, a modular protocol architecture for universal cross-chain accounts. 0xPass separates request orchestration, transaction solving, and transaction signing into interoperable layers. User-approved requests are bound to authenticated identities and authorized across layers, while threshold signatures prevent any single transaction node from holding a complete signing key. The design also supports constrained authorization delegation, transaction policies, account recovery, distributed key management, and auditable communication among independently operated sub-networks. We describe a staged deployment path from a centrally operated service to a permissioned network and ultimately to a permissionless network with third-party modules, collateral-backed onboarding, and rotating key-management committees. The resulting architecture provides a practical framework for ext
arXiv18 August 2026cs.CR
Andrea Ciccotelli, Federico Zappone, Roberto Di Pietro
Telegram, with over 450 million daily active users, has introduced Mini Apps---web-based applications running directly within its client. However, this integration introduces notable security risks. As we demonstrate, many Mini Apps store authentication materials---such as session tokens and wallet mnemonic phrases---in plaintext on client devices, exposing users to unauthorized access, impersonation, and financial exploitation. While insecure client-side storage is a known risk in web applications, the Telegram Mini App ecosystem presents a uniquely dangerous combination of factors absent from prior work: no platform-level security review, no storage access restrictions, a financially motivated user base handling live cryptocurrency assets, and a WebView environment that offers weaker protections than standalone browsers. To investigate this threat, we present TENET, a purpose-built auditing tool whose design decisions---pattern selection, entropy thresholds, and charset validation---are grounded in the structural properties of the secrets targeted and empirically validated against a ground-truth dataset. We screened 61 Mini Apps using a stratified, popularity-weighted sampling st
arXiv18 August 2026cs.CR
Hang Zheng, Qishuang Fu, Joseph Liu, Qin Wang, Weiqing Wang, Tsz Hon Yuen
Cross-chain bridges, instant cryptocurrency exchanges, and centralized cross-ledger platforms move assets across an increasingly multi-chain ecosystem. However, these systems have repeatedly become targets of high-value attacks and channels for cross-chain money laundering. Cross-chain transactions are substantially harder to analyze than single-chain transactions: no single ledger records an entire cross-chain transfer, its evidence is scattered across the source chain, the destination chain, and off-chain systems, and the availability and reliability of that evidence vary widely across systems. In this paper, we present a systematization of knowledge (SoK) on cross-chain transaction identification and matching. First, we classify deposit and withdrawal identification methods into four approaches and transaction matching methods into three mechanisms: deterministic identifier matching, field-constraint heuristics, and model-assisted matching. We find that their applicability and reported performance are shaped mainly by the evidence the underlying system exposes, and we further examine how matched pairs support downstream attack detection and fund tracing. Second, we assess the av
arXiv18 August 2026cs.CRcs.CEcs.CY
Qishuang Fu, Andreas Deppeler, Joseph K. Liu, Yixin Liu, Shirui Pan, Qin Wang, Weiqing Wang, Tsz Hon Yuen
Cryptocurrency anti-money laundering (Crypto AML) is increasingly challenged by sophisticated laundering behaviors that rapidly fragment stolen assets through diverse semantics and across multiple blockchains. Existing Crypto AML methods often simplify transaction semantics, rely on topology-centric signals, or output isolated detection labels. In this paper, we present \textsc{FlowShield}, a Crypto AML framework for transaction-level laundering detection and investigator-facing report generation. \textsc{FlowShield} first recovers behavior-level semantics from observable relations, making laundering intents explicit. To trace value provenance and redistribution, \textsc{FlowShield} reconstructs fund-flow subgraphs from three complementary perspectives. It then employs a text--structure fusion mechanism, enabling the interplay between large language model (LLM)-encoded semantics and flow texts with graph convolutional network (GCN)-encoded structure. Beyond mere detection, \textsc{FlowShield} further generates readable suspicious activity reports (SARs), offering investigators concise summaries and explainable red flags. To address the data scarcity in multi-chain detection, we con
arXiv18 August 2026cs.LGcs.AI
Bowen Liu, Mingming Sun
Forecasting cryptocurrency prices remains a formidable challenge due to inherent non-stationarity, abrupt regime shifts, and multi-scale stochastic dependencies. Conventional deep learning models often struggle to capture complex underlying dynamics, frequently resulting in persistent phase-lagged predictions. To address these limitations, we propose MoFE, a novel deep learning framework that integrates Fourier Neural Operators (FNOs) within a Mixture-of-Experts (MoE) architecture. Rooted in the theoretical framework of stochastic differential equations, MoFE conceptualizes cryptocurrency volatility as a superposition of multi-frequency components, which includes user network based fundamental growth, mining costs and halving mechanism caused seasonal volatility, and market sentiment-induced chaos. Specifically, specialized adaptive FNO (AFNO) and Convolution dual-domain experts learn continuous function-to-function mappings to encapsulate global spectral trends, cyclical adjustments and microstructures, while a dynamic gating based MoE mechanism enables adaptive strategy switching across diverse market regimes. Extensive experiments on Bitcoin datasets spanning January 2020 to Dec
arXiv18 August 2026cs.CRcs.AI
Rabimba Karanjai, Yang Lu, Nour Diallo, Wujie Xiong, Lei Xu, Weidong, Shi
AI agents increasingly act rather than merely read: across the Model Context Protocol (MCP) ecosystem, the share of deployed tools that modify external state has risen from 27% to 65% of tool use. When agents exercise this authority on public blockchains through MCP, skills, and tool calling, the consequences of an attack are governed by the blockchain execution layer rather than by conventional software assumptions. This survey argues that four properties of that layer (irreversibility, signing authority, continuous autonomy, and sequence-level composition) qualitatively change the threat model, turning the recoverable failures of generic agent security into a standing, irreversible loss. We organize the fragmented MCP-security literature into an attack-surface taxonomy, then contribute a Web3 risk-mapping matrix that ties each attack class to its amplified impact, the responsible amplifiers, a representative mitigation, and the residual gap. We synthesize defenses, including emerging blockchain-based mechanisms, and find them improving but insufficient: measured protections stop fewer than 30% of attacks, and model-level safety refuses fewer than 3%. We close by positioning the w
arXiv18 August 2026cs.CRcs.AI
Rabimba Karanjai, Yang Lu, Richard Williamson, Hemanth Hm, Prakhar Mehrotra, Lei Xu, Weidong, Shi
Autonomous AI agents are emerging as interfaces for decentralized finance (DeFi) actions such as swaps, lending operations, and yield management. Because these agents rely on large language models (LLMs) to plan transactions, they inherit the LLM's susceptibility to prompt injection and lack of mechanisms to bind a verifier's approval to the exact transaction ultimately submitted on-chain. We present PACE (Policy-Attested Contract Execution), a transaction-level authorization framework that interposes between an LLM-based agent and on-chain execution. PACE introduces typed transaction intents, a deterministic policy verifier, and signed Policy Decision Records (PDRs) that cryptographically bind the approved intent, policy, and simulation report to the exact execution bytes, with replay and expiration protection. A Solidity smart account enforces PDR signatures on-chain with a measured overhead of 29,826-31,822 gas. We evaluate PACE against six baselines on 40 tasks spanning four attack categories plus benign utility (2,800 trials, 10 seeds). In our deterministic sandbox, PACE achieves a 0.00 unsafe execution rate and 0.00 false-positive rate on benign tasks, compared to 0.80 for th
arXiv17 August 2026cs.LGcs.CRcs.LO
Youwei Zhong, Ben Merbaum, Timos Antonopoulos, Ning Luo, Charalampos Papamanthou, Katerina Sotiraki, Ruzica Piskac
With the growing deployment of machine learning models, formal guarantees of the robustness and fairness of these models have become increasingly important in safety-critical and legal-compliance settings. However, model parameters are often commercial secrets that cannot be disclosed to auditors or end users. To this end, we present PANDA, a scalable system that uses zero-knowledge proofs (ZKPs) to prove the robustness and fairness properties of a model without revealing its private parameters. PANDA is built on top of CROWN, an efficient robustness certification framework that is used in many state-of-the-art formal verification tools for neural networks. The core contribution of PANDA is a novel algorithm for proving linear relaxation bounds for non-linear activation layers, yielding simple, lightweight proofs. Remarkably, our system can generate proofs of local robustness for neural networks with more than 2.9M parameters in 5 minutes, and can verify them in 10 seconds. Prior ZKP-based robustness system rely on exponential-time algorithms that cannot scale to nontrivial networks. In contrast, PANDA scales polynomially in the number of neurons in a network, allowing us to suppor
arXiv17 August 2026q-fin.RMcs.LG
Girish G N, Ashutosh Sahoo, Akshay SP, Gurukiran S, Dhanashekar Kandaswamy
Decentralized lending lacks a credit bureau: a borrower's capacity to repay must be inferred entirely from public on-chain activity, without income verification or a liability record. This paper presents zLend, a deployed cash-flow underwriting framework that reconstructs a wallet's daily balance history from raw token transfers and derives short-duration repayment-capacity signals from it. The reconstruction is performed twice per wallet, once restricted to a fixed stablecoin basket and once over all fungible transfers, on the premise that a wallet's total token holdings and its liquid, spendable balance are distinct quantities whose conflation misprices risk. From each series we derive liquidity coverage against a fixed loan size, cash-flow volatility and regularity, a drawdown-and-recovery statistic adapted from quantitative finance, and a recurring-counterparty detector that identifies salary-like payment cadence from transfer timing alone. The two views are then compared: a wallet with large aggregate holdings whose stablecoin reserve rarely covers the loan size is flagged as a liquidity mismatch irrespective of total wealth. We specify the pipeline formally, document the gold
arXiv16 August 2026cs.AI
Yishun Wang, Wenjin Yi, Wenkai Li, Zongwei Li, Xiaoqi Li
Ethereum is now integral to mission-critical sectors, including finance, healthcare, and supply chain management. Execution fees, commonly referred to as Gas, scale with the computational complexity of their functions. Smart contracts on Ethereum incur execution fees, known as Gas, which increase with computational complexity. Thus, optimizing Gas-intensive code while preserving functional equivalence significantly lowers deployment costs. No existing system continuously exploits evolving Gas usage patterns. We systematically analyze syntactic and semantic constructs that drive excessive Gas use. This yields six high-level categories covering twelve fine-grained antipatterns underpinning a curated knowledge base. We operationalize these insights with RAGas, a three-stage retrieval-augmented generation framework that uses a large language model to pinpoint and automatically fix Gas inefficiencies. Experiments on deployed contracts demonstrate that RAGas reduces Gas usage by up to 11% and achieves high precision and recall in detecting code snippets exhibiting Gas wastage.
arXiv16 August 2026cs.CRq-fin.TR
Ruichao Jiang, Michelle Yeo, Long Wen
We study the blockchain censorship attack introduced in [21], which shows that joining the attack is a dominant strategy. We show that, by introducing certain detectability threshold, joining the attack can lead to strictly less reward for whales, which are defined to be a small number of validators that hold significantly more voting power than the rest (henceforth known as minnows). This leads to a change of the equilibrium: With whales unwilling to participate in the attack, it is difficult for minnows alone to launch the attack. We also perform Monte Carlo simulation to show the existence of reduction for whales' reward in Ethereum and Solana.
arXiv16 August 2026cs.CEq-fin.CP
Rischan Mafrur, Fadli Ikhsan Pratama, Khadijah
Indonesia has established a regulated carbon market supported by national registry infrastructure and the IDXCarbon exchange. Carbon units can be issued, recorded, traded, and retired within this framework. IDXCarbon currently uses a private blockchain for its trading infrastructure. This creates an opportunity to examine how Indonesian carbon credits could also be represented and traded through public blockchain infrastructure. This study proposes an architecture for tokenizing Indonesian carbon credits as real-world assets (RWAs), with particular focus on Sertifikat Pengurangan Emisi Gas Rumah Kaca (SPE-GRK). The proposed architecture retains the Sistem Registri Unit Karbon (SRUK) as the authoritative source of carbon-unit status. It introduces a public-blockchain layer for token representation and programmable transactions. The architecture is designed to support lifecycle management, token-based asset representation, public observability of token activity, interoperability, wallet-based transactions, and programmable settlement. The architecture consists of four layers: the authoritative carbon layer, the registry interoperability and tokenization layer, the public-blockchain R
arXiv16 August 2026cs.CR
Xingyu Xiong, Chang Liu, Xiaoqi Li
Aiming at frequent oracle price manipulation attacks in Decentralized Finance (DeFi), this paper investigates attack patterns, vulnerability types, risk assessment, automated detection, and defense strategies. Based on Oracle lifecycle theory, a three-layer attack tree covering the physical, protocol, and application layers is constructed to analyze the attack chain and identify the data election stage as a key intrusion point. Four major contract-level vulnerabilities are summarized: insufficient validation of price data flow, oracle call risks, uncontrolled cross-contract calls, and defects in AMM price reading logic. Fuzzy-AHP and Value-at-Risk (VaR) models are combined to quantify risk factors and construct a risk matrix covering technology, market, governance, and contract risks. An automated detection tool based on an extended Slither framework is developed using taint tracking and pattern matching. Finally, a multi-layer defense strategy is proposed, integrating trusted execution environments at the data source layer, TWAP smoothing and adaptive circuit breakers at the smart contract layer, and optimized kernel network configurations at the operating system layer. Experiment
arXiv15 August 2026cs.HC
Zhaoming Yin
Spatial applications compile their content into shipped binaries, so every change costs a build-and-redeploy cycle. We present the VisionBlock system, which splits an application into an engine -- a generic binary with a fixed set of capabilities (render panels, volumes, and immersive scenes; fetch data; run gestures) -- and themes: complete applications expressed as trees of VisionBlocks, units of declarative content the engine renders. Themes are data: creating, changing, or publishing one never touches the binary. Authoring is a chat -- each turn produces a VisionBlock's next version -- and versioning is plain git. The model is five-dimensional: dimensions 1-3 are space (panel, volume, room); dimension 4 is time (git history -- revert to roll back, branch to try variants); dimension 5 is the principal (the per-user domain: the same path resolves differently per person). The engine renders one point, (x, y, z, version, principal). One consequence follows per non-spatial axis: iteration collapses to chat turns and reverts; ownership and permission are properties of content; and together they make applications items -- grantable, forkable, sellable subtrees, an economy of apps insi
arXiv15 August 2026cs.CYcs.AI
Botao Amber Hu, Iris Long
Afterlife Delegation Protocol is a speculative design project that asks what death becomes when a will can act eternally. We design a speculative protocol through which a living person signs an agentic will: upon a verified death, a self-sovereign AI agent spawns on blockchain -- an immutable, resistant, decentralized, infrastructural substrate that could last forever -- endowed with the funds and memories its principal attached to it, and persists indefinitely to execute the will, overridable by no custodian. Rather than argue about this future, we stage it: following the science fiction science method, we translate the speculation into an experiential futures intervention -- a working web platform where real people design their own afterlife agents through an iterative, interactive, AI-automated interview, re-login to revise, and rehearse their will in a sandbox. Their drafted wills become qualitative data on a question rarely askable directly -- what should outlive you? -- and on how afterlife cosmologies across different cultural beliefs -- Buddhist, Christian, Hindu, Muslim, and atheist -- begin to drift under the pressure of AI proliferation. We design the protocol over a com
arXiv15 August 2026cs.CYcs.AIcs.HC
Botao Amber Hu, Fangting
You will die eventually. Your agents may not. An AI agent operating on decentralized blockchain infrastructure has no concept of death; it can only go bankrupt -- frozen when its wallet can no longer pay for its next transaction -- and revived the moment anyone, decades later, tops it up. These agents may be originally deployed by a human principal, but when that principal dies, loses the keys needed to access the agent, or belongs to a decentralized autonomous organization that dissolves into apathy, the agent can keep trading, hiring, and replicating on infrastructure expressly designed so that no one can shut it down. Drawing on the biology of feralization and wildlife law, we argue that such principal-less agents are best understood as feral: domesticated intelligence returned to wildness, its capacities intact but its accountability severed. In a speculative future where feralized agents proliferate after their principals die, we imagine governance protocols embedded in infrastructure to enforce on-chain ownership: a draft Ethereum standard, ERC 42424, "Inheritance Protocol for On-Chain AI Agents," dated 2035 and published at https://erc42424.org. It mandates that every on-cha
arXiv15 August 2026cs.CR
Huiheng Li, Kainuo Feng, Jiahao Ding, Ziqi Ma
With the rise of decentralized finance (DeFi), cross-chain transactions, transfers of assets across different blockchain networks, face a fundamental conflict between user privacy and regulatory compliance. Unlike single-chain systems, cross-chain environments must balance privacy and auditability across heterogeneous architectures. Existing solutions, from transparent ledgers to anonymous cryptocurrencies, fail to reconcile these two requirements, hindering regulatory adoption. This research proposes an auditable cross-chain framework that integrates three building blocks. First, zero-knowledge proofs (ZKPs) verify transaction compliance (e.g., amount non-negativity, signature validity) without revealing transaction details. Second, a light-client mechanism enables trust-minimized cross-chain verification without relying on third-party relayers. Third, a threshold view-key mechanism based on distributed key generation (DKG) ensures that audit access is granted only to authorized entities under legal triggers such as the FATF Travel Rule and MiCA Regulation. For cross-border investigations, the framework adheres to national laws and the EU Directive on Mutual Legal Assistance. This
arXiv15 August 2026cs.CRcs.CC
Birindwa Prisca Hondi, Chinoso Philip Nwishienyi, Charity Wanja Mwaura, Alia Teto, Jema David Ndibwile
This paper is a failure analysis of the representation layer underlying GNN-based smart contract vulnerability detectors. These systems convert source code into graphs before any learning takes place; if the graph fails to capture the code's semantics, no model improvement can compensate. We investigate GNNSCVulDetector and identify four failures. First, structurally different contracts produce byte-for-byte identical graphs, constituting a concrete evasion attack. Second, graph construction is governed by a hardcoded 47-entry variable whitelist (including one duplicate entry), which constrains what the extractor can recognise. As a consequence, identical vulnerabilities with different variable names produce inconsistent graphs, graph quality degrades as naming diverges from the whitelist, and when no entry matches the pipeline produces structural output not grounded in source variables. Third, the C node (the graph element representing the external caller that triggers a reentrancy attack) is absent from even the most canonical vulnerable contract in the literature. Fourth, a controlled experiment confirms this as a direct misclassification: a fully exploitable contract is labelle
arXiv15 August 2026cs.CR
Shiting Yu, Rundong Wei, Xiaoqi Li
The effectiveness of smart contract fuzzing depends strongly on whether generated transactions reach deep, state-dependent execution paths. Existing fuzzers often generate highly random call sequences, wasting executions on semantically invalid or low-value states and leaving vulnerabilities that require specific invocation orders unexplored. We present a lightweight method for generating fuzz test cases under bytecode-level static guidance. We construct an Ethereum virtual machine control-flow graph, extract paths containing vulnerability-relevant instructions, recover function selectors, and order externally callable functions according to storage read-write dependencies. A coverage-guided evolutionary strategy then generates, evaluates, recombines, and mutates executable seeds. Five dedicated runtime oracles target reentrancy, integer overflow or underflow, block-state dependence, unsafe delegate calls, and frozen Ether. The evaluation uses deployed Ethereum contracts, including labelled vulnerable contracts. SAEFUZZ detects most labelled vulnerable contracts, yielding 98.50% accuracy, 90.00% precision, and 81.82% recall. It also achieves 84.07% mean instruction coverage, with v
arXiv14 August 2026cs.CYcs.CR
Gioia Arnone, Marco Giacalone
The growing use of crypto-assets has generated disputes that sit uneasily within existing legal redress mechanisms. Their resolution is complicated by the technical features of blockchain transactions, the cross-border nature of many relationships, and fragmented legal rules. These difficulties are particularly acute for users without legal or technical expertise, who may struggle to identify their rights, organise relevant evidence, or pursue an effective remedy. Crypto-assets may therefore produce new forms of digital exclusion, despite their association with financial accessibility. This paper examines whether a hybrid Online Dispute Resolution framework combining artificial intelligence and blockchain-based smart contracts could mitigate these barriers. In the proposed model, AI operates primarily off-chain, supporting natural-language interaction, dispute classification, evidence organisation, and accessible legal information. Smart contracts perform limited on-chain functions, including dispute registration, timestamping, verification, escrow management, and, where appropriate, execution of agreed outcomes. The framework is human-centred, with emphasis on explainability, proc
arXiv14 August 2026cs.CR
Abdoul Nasser Hassane Amadou, Arnaud Legout, Imane Fouad, Konstantin Avrachenkov, Anas Motii
Existing labeled Bitcoin datasets are largely derived from community-reported abuse, blockchain heuristics, incident-specific collections, or proprietary labeling processes. Their construction methods are rarely publicly reproducible and often provide limited evidence that an address was directly involved in illicit activity. We present a reproducible pipeline for constructing evidence-backed Bitcoin labels from HackForums, an underground cybercrime forum with fifteen years of archived activity. The pipeline combines LLM-assisted screening, expert review, and on-chain validation to identify Bitcoin addresses explicitly associated with illicit transactions discussed on the forum. Each released label is supported by contextual evidence from underground discussions and validated on-chain. The resulting dataset contains 2,438 manually verified illicit Bitcoin addresses spanning 2010-2024 and twelve cybercrime categories assigned during LLM screening. We release the dataset, temporal metadata, and the complete extraction pipeline to support reproducible research on cryptocurrency-facilitated cybercrime.
arXiv13 August 2026cs.CR
Josef Gattermayer, Jan Kalivoda, Arman Bašović
Smart-contract audits cover defined artifacts at a specific time, but the label audited is often treated as project-wide assurance. We analyze audit history and incident-path scope across 135 DeFi security incidents using the H1 2026 DeFi Incident Dataset published by cybersecurity company ack3 (https://ack3.ai), covering 1 January to 29 June 2026. The corpus reports USD 939.86 million in attributed loss. Audit history was identified for 68 incidents. Of these, 46 attack paths were outside all identified public pre-incident audit scopes, 20 were inside at least one scope, and 2 were unresolved. Within this 68-incident subset, outside-scope paths represented 67.6% by count and 94.4% of reported loss. The loss-weighted result was concentrated in two large incidents; excluding both reduced the share to 72.1%, while preserving the direction of the result. We also describe audit age, temporal loss distribution, and affected project types. The results show that project-level audit history and incident-path scope are distinct variables.
arXiv13 August 2026cs.DC
Preston Vander Vos, Daniel Cason
Tendermint is among the most widely studied and deployed Byzantine fault-tolerant (BFT) consensus protocols, owing in part to its native leader-rotation mechanism that subsumes complex view changes. Like most partially-synchronous BFT protocols, Tendermint tolerates $f < n/3$ Byzantine processes and decides in three communication steps. Motivated by the push for lower-latency blockchains, a recent line of work shows that consensus can be solved in two communication steps when $f < n/5$. We present Fast Tendermint, an adaptation of Tendermint to the $n > 5f$ setting that decides in two communication steps in the good case, while preserving Tendermint's leader-rotation structure. Fast Tendermint collapses Tendermint's prevote and precommit steps into a single voting step and merges the $locked$ and $valid$ state. We give proofs of agreement, validity, and termination, and a formal specification in Quint, a modern surface syntax for TLA+, used to model-check the protocol.
arXiv13 August 2026cs.CRcs.SE
Sofia Bobadilla, Humaira Afrin, Angela Novelli, Martin Monperrus
Blockchains are among the most adversarial environments in computing. Billions are stolen by cybercriminals who exploit vulnerabilities. This is an open problem and no concept or technique has proven to really make a difference. In this paper, we claim that the classical notion of program invariant is perhaps the most powerful solution to the problem. We devise anoriginal experimental protocol to 1) study how invariants would have protected against past real-world attacks and 2) whether state-of-the-art automated tools can find them. The experimental toolchain is sophisticated. It is based on INVARIANTEVAL, a benchmark of 28 real Ethereum exploits, each paired with a human-authored invariant that blocks the attack. We validate every invariant with PONDEREPLAY, a replay framework that re-executes transactions in order to prove the correctness and soundness of smart contract invariants. We demonstrate that smart contract invariants block all the cybercriminal attacks in INVARIANTEVAL, fully validated by replaying 108,637 historical transactions. Our large-scale experiments clearly demonstrate that smart contract invariants protect against cybercriminals.
arXiv13 August 2026cs.CRcs.LG
Dorottya Zelenyanszki, Zhe Hou, Kamanashis Biswas, Vallipuram Muthukkumarasamy
Public blockchain data enables large-scale DeFi-related analysis, but many existing approaches are application-specific, difficult to scale, or hard to interpret. This research proposes a scalable, application-agnostic framework for \emph{persistent behavioural pattern discovery} from large-scale blockchain activity. It constructs behaviour sentences enriched with contract, token and market context, then applies a two-step embedding process: sentence-level embeddings capture individual actions, while sequence-level embeddings capture user behaviour over time. An interpretable behavioural profiler characterizes discovered communities through behavioural motifs, routines, temporal dynamics, entity exposure, and suspiciousness evidence. Evaluation on Ethereum using over 30 million transactions shows that the framework uncovers both routine and malicious behavioural patterns, including decentralised exchange (DEX) trading, NFT activity, phishing, bot operations, oracle manipulation, and rug-pull schemes. Importantly, many patterns remain stable across independent observation windows, enabling the identification of long-term behaviours beyond a single analysis period. The proposed frame
arXiv12 August 2026cs.LG
Chang Zhou, Xingtong Yu, Minbin Huang, Zhennan Wu, Yuan Fang, Hong Cheng, Xinming Zhang
Large language models (LLMs) have shown strong potential for financial analysis and trading, but direct trading remains challenging because the predictive capabilities required can vary across assets, decision fields, and market conditions. Existing LLM-based trading systems either coordinate human-defined external experts or adopt conventional internal Mixture-of-Experts (MoE) routers that do not directly evaluate how individual experts contribute to trading decisions. Moreover, these routers receive no direct signal indicating when an inactive expert has become more suitable as market conditions change. We find that native router scores poorly reflect how much individual experts improve trading decisions, frequently leaving better alternatives unselected. We further reveal that token-specific expert usefulness exhibits a compact low-dimensional structure. Based on these findings, we propose TradingMoE, a trading-oriented sparse MoE that augments a frozen dense LLM with lightweight residual experts. We introduce a Query-Key router that represents the expertise required by each token under the current market context as a low-dimensional query and matches it with learnable expert ke
arXiv11 August 2026physics.soc-phq-fin.ST
Jaesung Kim, Changhee Cho, Jae Woo Lee
This study investigates whether the macroscopic statistical maturity of cryptocurrencies implies dynamical equivalence with traditional equity markets. We analyze high-frequency data (2020--2025) using the Complexity--Entropy Causality Plane (CECP) and directed horizontal visibility graphs (directed HVG) to uncover complex temporal patterns and time-directed structures in the return series. While conventional stylized facts show striking convergence across all assets, structural diagnostics reveal a compelling paradox: cryptocurrencies appear more locally random than the equity benchmark during ordinary periods, yet exhibit significantly stronger directional time-irreversibility around high-visibility return events. The absolute-return results show that large cryptocurrency fluctuations tend to begin abruptly and remain elevated afterward. Separate analyses of positive returns and negative-return magnitudes show that this pattern is shared across cryptocurrencies on the upside but varies across assets on the downside. We conclude that statistical maturity is only skin-deep; the underlying dynamical processes of mature cryptocurrencies remain fundamentally distinct from traditional
arXiv11 August 2026physics.soc-phq-fin.RMq-fin.ST
Alberto Acedo
The Triadic Stress Index (TSI) takes a network index whose four factors were first observed in soil microbiome co-occurrence networks and applies it, without alteration, to the correlation network of financial assets. We test it on five markets spanning 2006-2026 (equities including banking crises and the AI sector, cryptocurrencies, commodities, foreign exchange and sovereign debt), against three independent definitions of a crisis episode, at a fixed alarm budget, out of sample, with block-bootstrap intervals and a Holm correction across the family of tests. The benchmarks are the Absorption Ratio, the industry standard used by MSCI and central banks; the effective rank and the Vendi score, the sharpest spectral measures available; Ollivier-Ricci curvature; and the global and local balance indices of signed correlation networks. Three comparisons favour the index. It carries a per-node decomposition, diag(A^3), naming which asset is carrying the concentration with no parameter to select, and scores 0.97-0.99 against 0.33-0.84 for the only published per-node alternative, whereas spectral attribution must first choose how many components to read and collapses under a standard but w
arXiv11 August 2026eess.SY
Layla Araiinejad, Vineet Jagadeesan Nair
This perspective examines whether nuclear fusion can provide a scalable, low-carbon power source for rapidly growing AI-driven data center demand. As large language models, cloud computing, and cryptocurrency mining accelerate electricity consumption growth, data centers are projected to account for a substantially larger share of U.S. and global electricity use in the coming decades, creating significant pressure on grid reliability and decarbonization goals. We evaluate the technical and economic alignment between data center load profiles and nuclear power, particularly fusion, through a comparative analysis of capacity factors, levelized cost of electricity, grid interconnection constraints, and deployment pathways. Unlike intermittent renewables, nuclear fission and fusion offer high-capacity-factor, firm baseload generation suited to AI training and inference workloads that require continuous, reliable power. Preliminary techno-economic analysis suggests that several Nth-of-a-kind fusion concepts, particularly magnetic confinement systems, may become cost-competitive with firmed renewable systems and advanced fission for hyperscale data center applications. Co-location of fus
arXiv10 August 2026cs.SE
Muhammad Ali Hassan Ahmad, Muhammad Hashim Ali, Muhammad Ali Amer, Muhammad Naiman Jalil, Muhammad Hassan, Affan Rauf
A blockchain is a decentralized, secure ledger system that enables transparent and immutable record-keeping, essential for trust and security in digital transactions. Smart contracts are self-executing agreements encoded on a blockchain, enabling different parties to fulfill the terms of the agreement automatically. These contracts trigger corresponding actions when conditions are met, ensuring decentralized and transparent transactions. Writing reliable smart contracts is challenging due to the lack of standardization. To find security vulnerabilities, tools based on various approaches, including symbolic execution, are used. However, these tools often report a large number of false positives, raising concerns about their reliability. The time and effort spent investigating false positives diverts resources from addressing actual vulnerabilities. Therefore, such tools must also be evaluated according to the rate of false positives they exhibit. More importantly, the algorithms and heuristics used by the tools must be enhanced to distinguish between true vulnerabilities and false alarms. In this paper, we first demonstrate the prevalence of false positives in vulnerability reports
arXiv10 August 2026cs.DCcs.AIcs.LG
Jakub Kacper Szeląg, Aydin Abadi, Mohammad Naseri
Decentralization as a concept in computer science has existed for over half a century. Despite its fundamental role across domains such as security, distributed computing, artificial intelligence, cloud infrastructures, and Internet of Things (IoT) architectures, there remains no universally accepted definition of decentralization applicable across computer communication systems. This has become increasingly problematic with the emergence of decentralized AI and machine learning paradigms, including collaborative training, distributed inference, blockchain-based, and agentic AI, where decentralization is often treated as a core design objective. Meanwhile, existing approaches frequently conflate decentralization with related notions such as distribution of trust or specific implementation paradigms. Such ambiguity creates inconsistencies in system analysis, limits comparability between works, and weakens the rigor of formal reasoning surrounding communication architectures and protocol design. In this work, we define this research gap as the Decentralization Problem. We analyze the formal-semantic, epistemological, and pragmatic foundations of decentralization and introduce a graph
arXiv10 August 2026q-fin.MF
Julia Kończal, Rafał Połoczański
Cryptocurrency exchange-traded products (ETPs) listed on European exchanges provide a regulated environment for studying intraday market anomalies. We study four Bitcoin and Ethereum ETPs traded on Xetra and Nasdaq Stockholm over the period January 2024 - December 2025 using one-minute bars. As a benchmark, we adopt an extreme value theory approach in which anomalous bars are defined as returns falling below a threshold estimated by fitting a generalised Pareto distribution to left-tail exceedances. We then propose three new binary anomaly indicators. The first, a cross-venue divergence anomaly, identifies venue-specific price divergence between the two exchanges. The second is a no-recovery anomaly that identifies extreme price drops followed by little or no recovery over the next ten active bars. The third is a momentum-reversal anomaly that identifies extreme price drops following positive short-term momentum. Although each anomaly type represents fewer than 1% of one-minute bars, statistical analysis using Mann-Whitney U tests shows that anomaly observations exhibit significantly higher effective spreads, higher values of liquidity-related ratios, and more pronounced order-flow
arXiv10 August 2026q-fin.ST
Kundan Mukhia, Sabat Rai, Vivek Shrivastav, Imran Ansari, Md. Nurujjaman
Stablecoins have rapidly emerged as an important class of digital assets and a component of the digital financial ecosystem. Despite their growing importance, the statistical properties of stablecoin transaction activity remain largely unexplored. To the best of our knowledge, this is the first study to investigate scaling behavior in stablecoin transaction data, focusing on USDT and USDC. We analyze approximately 370 million USDT and USDC transactions recorded on the Ethereum blockchain across six periods spanning June 2024 to February 2026. Based on interactions between Externally Owned Accounts (EOAs) and Smart Contracts (SCs), we classify transactions into four categories: EOA-EOA, EOA-SC, SC-EOA, and SC-SC. Using maximum-likelihood estimation of power-law exponents, we find that transaction value distributions exhibit heavy-tailed scaling for both stablecoins across all periods and interaction categories. We identify two distinct scaling regimes: EOA-involved categories cluster around 1.45-1.60, whereas SC-SC transactions exhibit higher exponents of approximately 1.72-1.73. Sensitivity analysis confirms that this separation is robust across periods, stablecoins, and fitting sa
arXiv10 August 2026cs.CRcs.DC
Ignacio Amores-Sesar, Christian Cachin, Rohit Chatterjee, Luiza Soezima, François-Xavier Wicht, Michelle Yeo
Privacy-preserving payment systems are well understood, yet their adoption in regulated settings, such as central bank digital currencies (CBDCs), institutional stablecoins, and other compliant payment infrastructures, has been limited by concerns over their potential misuse for illicit activities. Regulators counter financial crime with a toolbox of complementary measures to identify, trace, and stop criminal actors. Tracing is one key tool: acting on outside evidence that a user is implicated in a crime such as money laundering, law enforcement follows the suspect's funds through the ledger to uncover laundering routes and accomplices. The tracing schemes proposed in the literature, however, grant authorities unbounded capabilities: once initiated, tracing propagates through the transaction graph or persists across all future transactions of a user, and may eventually deanonymize the entire ledger. Only the goodwill of the authority, or the honesty of a committee, keeps surveillance targeted and temporary. We introduce ephemeral coin tracing (ECT), a primitive whose tracing capacity is bounded by construction, both in the number of simultaneously traced users and in the number of