Gonçalo Frutuoso, Diogo Rodrigues, Alexandre P. Francisco, Cátia Vaz
Verifying academic credentials remains difficult: records are held by individual institutions in proprietary systems, verification is slow and manual, and counterfeit qualifications are widespread. Blockchain-based registries have been proposed as a remedy, but existing systems tend to anchor certificate hashes without binding them to a verifiable identity, without an explicit mechanism to accredit issuing institutions, and without support for correcting or revoking credentials once issued. This paper investigates whether an infrastructure designed for regulated financial instruments can be repurposed to close these gaps. We present the design of a registry for identity-bound academic credentials that composes OnchainID self-sovereign identities (ERC-734/ERC-735) with the T-REX suite (ERC-3643): its trusted-issuer registry becomes an on-chain issuer-accreditation whitelist, and each certificate is represented as a signed, updatable claim bound to a student's identity and verifiable by any third party without a wallet, while sensitive fields are kept off-chain. We make explicit the tension between a transferable-security-token standard and non-transferable credentials, clarifying which of its guarantees carry over. We validate the design with a reference implementation covering the full certificate life cycle and evaluate it in terms of gas cost, scalability, latency, and security, quantifying the overhead relative to a hash-anchoring baseline.
Xianhao Zhang, J F Sun, Zijian Zhang, Ye Liu · 7 authors
Smart contract vulnerabilities pose severe financial risks, yet existing security tools largely stop at vulnerability detection, offering limited support for explaining whether reported flaws are exploitable, how attacks unfold, and what concrete damage they cause. To bridge this gap, we propose KASS (Knowledge-Augmented Attack Synthesis and Simulation), a multi-agent framework for executable smart contract exploit verification. KASS decomposes automated exploit generation into planning, generation, and testing stages, and integrates three complementary mechanisms: retrieval-augmented planning over real-world audit knowledge, formal generation and validation constraints that bind attack plans to executable proof-of-concept tests, and a hierarchical dual-loop refinement process that repairs code-level errors while triggering strategy-level replanning when attack assumptions fail. We evaluate KASS on 104 SmartBugs-Curated contracts across four vulnerability categories. Experimental results show that KASS successfully generates executable exploits for 94.23% of tested contracts; this rate is higher than previously reported results for REX and AdvSCanner on comparable SmartBugs-Curated subsets, and higher than our reproduced Claude Code baseline under the same evaluation protocol. On 11 real-world CVE-tagged contracts, KASS successfully validates 9 cases. Beyond exploit generation, KASS produces structured attack plans that document exploitation flows, quantify potential asset losses, and serve as semantic false positive filters for static analysis tools.
Blockchain governance, the set of processes by which decentralized protocols evolve, remains a fundamental challenge in balancing adaptability, security, and stakeholder representation. This technical report analyzes Cardano's Voltaire governance system, the on-chain framework introduced via CIP-1694 and enacted through the Chang hard fork in September 2024, and lays down a corresponding research program. We make two contributions. First, we provide a complete technical specification of Voltaire's mechanisms, including its three-body architecture, seven governance action types, voting rules, and its constitutional framework; this specification is sufficient for implementation or formal analysis. Second, we establish a research agenda for principled governance optimization, including design of an agent-based simulation platform, analysis of delegation dynamics, optimization of multi-objective parameters, and game-theoretic incentive design; we provide preliminary results, including a formal governance kernel: a minimal executable model capturing self-amending governance as a state-transition system and enabling rigorous safety and liveness analysis. Our report offers a comprehensive technical overview and invites the research community to advance blockchain governance science through rigorous study of Voltaire as a live, large-scale experiment now managing a treasury valued at approximately \$235 million (1.47B ADA as of early July 2026).
Front-running is a subtle and persistent problem for blockchains. A blockchain is a stateful virtual machine executing instructions called transactions. Users earn rewards by publishing functional transactions essential to the system. Attackers observe these transactions and publish their own ahead of the users', seizing the reward and eroding users' incentive to publish functional transactions. Preventing front-running means enforcing causality: If an attacker receives transaction tx_A and then publishes transaction tx_B, then tx_A must be ordered before tx_B. However, this causality is only observed by the attacker. Practical systems order transactions by bid amount, so transactions willing to pay more get executed first, but this only results in a bidding war eroding users' rewards. Though numerous ordering approaches have been proposed, none achieves causality, leaving users vulnerable to front-running. We present PRECEDE, a mechanism-design approach that enforces transaction causality by removing the economic incentive to front-run. PRECEDE orders transactions by a power-weighted randomized lottery, whose winning probability grows super-linearly in the bid. The user's strategy of publishing a transaction with a deterring bid forms an equilibrium where the attacker refrains from competing. Moreover, PRECEDE prevents the prominent sandwich attack, which relies on front-running. PRECEDE can be directly deployed in any censorship-resistant blockchain with a simple change to its transaction ordering mechanism.
Saviz Changizi, Nasibeh Mohammadzadeh, Mohammad Shojafar, Rahim Tafazolli
Telecom fraud-control studies often stop at detector-level classification, but deployment use requires request-level policy resolution, lifecycle traceability, and auditability. This paper reframes fraud control as blockchain-linked auditable decision management for synthetic telecom/IoT fraud-control requests, and its main result is that the QLoRA-tuned LLM branch becomes much more usable than zero-shot prompting but mainly approaches, rather than outperforms, a lower-cost centralized ensemble. The framework maps each synthetic deployment record to a managed request, blocks explicit out-of-boundary cases through a deterministic hard-fraud gate, scores non-hard requests using centralized ML (M1), federated meta-learning (M2), or LLM-family risk sources (M3), and resolves actions through a shared five-state policy, two-zone refinement mechanism, and local Ethereum-compatible audit layer. Evaluation uses separate synthetic training data and a 100,000-record deployment replay corpus, so the study should be read as controlled drift-replay evidence rather than field validation or proof of live deployability. On validation, M1 gives the strongest balance, with legitimate-request FPR 0.0890 under the 0.10 operating cap and soft-fraud recall 0.8341. On labeled deployment replay, however, the legitimate-FPR gap becomes large: M1 rises to 0.1646 and M3-QLoRA to 0.1801, while M3-QLoRA reduces the M3-Base legitimate FPR from 0.3915 and reaches 0.8240 soft-fraud recall. Blockchain telemetry shows that lifecycle gas, cost, latency, and throughput differences are driven by submitted off-chain decision profiles rather than changes in fraud logic.
Internetware envisions autonomous software entities collaborating over the open Internet. Raft consensus is widely adopted for its simplicity and performance in distributed coordination, e.g., service registries and blockchains. However, Raft assumes crash faults only, making it vulnerable to Byzantine behaviors like election forgery and log tampering. Existing BFT protocols incur high overhead, while ad-hoc hardening lacks unified defense. We propose \textbf{TRM-Raft}, a Byzantine-resistant enhancement that non-intrusively integrates a Blockchain-based Trust and Reputation Model (B-TRM) into the consensus core. It quantifies multi-dimensional node behaviors, applies adaptive penalties distinguishing accidental faults from malice, and embeds reputation into leader election and log replication. A reputation-aware election penalizes term/index forgery, excluding low-reputation nodes from leadership. A Schnorr-signature-based mechanism lets followers verify log integrity; tampering triggers reputation decay and leader replacement. Evaluated on Hyperledger Fabric in a realistic Internetware setting, TRM-Raft keeps malicious leader ratio below 5\% even with 40\% Byzantine nodes, with <10\% throughput loss and <5\% latency increase over vanilla Raft. TRM-Raft offers a lightweight, practical trustworthiness path for Internetware systems relying on Raft.
Pietro Saggese, Michael Sigmund, Burkhard Raunig, Esther Segalla · 6 authors
Cryptoassets are increasingly entangled with the traditional financial system, and how this activity integrates into national economies and behaves under stress bears on financial stability and the design of public digital money. However, blockchain pseudonymity and the lack of geographic identifiers force existing work to rely on indirect proxies to infer and locate market participants. Here we use a regulatory registry that directly identifies the on-chain addresses of all crypto-asset service providers (CASPs) registered in Austria, reconstructing their on-chain transaction activity across Bitcoin, Ether, USDC, and USDT through May 2025, and separating retail-like from institutionally mediated flows. We find that Austrian CASPs intermediate roughly USD 30 billion with external counterparties and are integrated globally rather than domestically. In value, this activity is dominated by a few institutional counterparties; in number, by retail-like ones. Around three major shocks, the Terra-Luna collapse, the FTX bankruptcy, and the Silicon Valley Bank failure, the two groups respond through different mechanisms, and stablecoins do not act as a uniform safe haven. The clearest case is SVB, where retail-like deposits and institutional withdrawals are consistent with USDC's two-tiered redemption mechanism. These patterns are invisible in aggregate data. Registry-based, transaction-level measurement thus offers a reproducible, cross-jurisdictional basis for monitoring how cryptoasset markets transmit risk.
Digital-asset custody has been built on threshold multi-party approval: no operation proceeds unless $t$ of $n$ parties approve, and fewer than t compromised parties can neither authorize nor learn the authorization secret. Threshold signature schemes (TSS) have been the standard mechanism, but the post-quantum transition disrupts this model: standardized hash-based signatures resist efficient threshold signing, and lattice-based threshold protocols remain an emerging research track. We present a dual-gate architecture that separates member authentication from threshold authorization. Each member signs its approval with an ordinary signature under any EUF-CMA scheme; the quorum jointly produces a threshold seal from Shamir-shared secrets bound to the operation. The seal is the base instance of a programmable authorization computation: simple quorum is the minimal policy, while richer policies can evaluate secret-shared state without making the member-signature scheme part of that computation. The signature scheme is a deployment parameter: migrating from ECDSA to SLH-DSA or ML-DSA is a key rotation, not a protocol redesign, and members holding keys in commodity HSMs participate through the standard sign API. The architecture can be deployed wherever the asset-control path supports programmable verification, such as smart contracts, vault modules, or HSMs guarding a master key, and produces an enforcement-layer authorization rather than a native chain signature. Below-threshold secrecy is information-theoretic; an adversary holding $\geq t$ signing keys but no coefficient shares still cannot produce the seal.
Thandile Nododile, Ayinde M. Usman, Clement N. Nyirenda
Private blockchain networks run with fixed node configurations that cannot adapt to changing workload conditions. Too many nodes serving a light workload waste resources; too few nodes facing heavy demand slow block production and degrade finalisation. The right validator count is hard to determine, as it depends on overlapping factors that shift over time. This paper presents a Takagi-Sugeno (TS) fuzzy inference system that reads live blockchain parameters (block production time, block size, and active node count) and outputs a continuous efficiency score alongside a scaling recommendation: Scale Up, Maintain, or Scale Down. The controller uses triangular membership functions across three linguistic variables, evaluated through a complete 27-rule base with product t-norm aggregation. A key contribution is an empirical recalibration of the membership functions, anchoring linguistic terms to the observed operating range of the testbed rather than to theoretical extremes. The system is evaluated on a 10-node Substrate blockchain network storing real smart water meter data hashes from the Queensland Government open data portal. Statistical analysis across configurations of 4, 7, and 10 active nodes confirms that the controller produces distinct operational profiles reflecting each configuration's provisioning state. In closed-loop experiments, the controller autonomously adjusts validator participation in both directions, activating validators under rising load and removing them under over-provisioning, converging to the same stable equilibrium from both directions. Compared against three threshold-based baselines, it shows fewer scaling oscillations while maintaining comparable block production times. Results show that TS fuzzy inference can support autonomous validator management in private blockchain deployments, with stable scaling behaviour threshold approaches cannot match.
Blockchain systems are undergoing a fundamental transition from decentralized ledgers for digital assets to general-purpose trust infrastructures for verifiable computation, decentralized physical resources, and automated infrastructure management. Meanwhile, the limitations of the Blockchain as a Service (BaaS) model stem from a common structural problem: outsourcing control of infrastructure to third-party service providers inevitably involves a systemic surrender of trust, flexibility, and data sovereignty. RISC-V, with its open, modular, and extensible design, provides a general-purpose computing foundation for public blockchains that is open, low-level, compileable, verifiable, and scalable. Inspired by the development and characteristics of eSIM, the embedded Blockchain infrastructure management (eBIM) is defined as a software-hardware collaborative paradigm for blockchain infrastructure management with RISC-V. This study aims to provide a comprehensive survey on eBIM supporting research and technologies, to answer the following research questions (RQs): RQ1 What is eBIM? RQ2 How does eBIM work? RQ3 What can eBIM do? By introducing the concept of eBIM, this paper establishes a foundational reference for researchers, hardware architects, and protocol designers in this rapidly evolving landscape, including cryptographic acceleration, trusted execution environments, zero-knowledge virtual machines, and smart contract execution engines. The prospects of the proposed e-BIM and its future research directions are indicated in this paper.
Electronic voting must keep individual ballots private while letting anyone verify the final tally. This paper presents an architecture that meets both goals without a trusted key dealer: each voter encrypts a ballot in the browser with a self-generated secret key under the Paillier additive homomorphic cryptosystem, and no party ever holds every key. Two server roles divide the tally. A collector combines the voters' per-ballot auxiliary values into a single group element; an aggregator uses that element to cancel the voters' random masks inside the homomorphic product and recover the exact vote sum, learning the result but no individual ballot. The Solana blockchain records every ciphertext immutably and enforces the election lifecycle, while a native C library (libtommath) performs the heavy modular arithmetic. We state six assumptions under which the protocol is correct and prove product homomorphism, mask cancellation, and sum recovery; privacy rests on the Decisional Composite Residuosity (DCR) assumption for the additive layer together with a Diffie-Hellman-style assumption on the masking base. A bit-packing scheme places an entire multi-candidate ballot in one ciphertext, cutting client work, on-chain transactions, storage, and tally cost by a factor of k (the candidate count); the slot width b is free, with only k*b bounded by log_2(N). With b = 25 and a 255-bit modulus the scheme supports ten candidates and up to 2^25 - 1 = 33,554,431 votes per candidate, about 335 million ballots, and tallies 50,000 ballots in under one second. Finally, running the collector and aggregator inside attested secure enclaves makes the tally tamper-resistant and prevents cross-role collusion to deanonymize voters. The proof-of-concept implementation is open-source; a worked numerical example in the appendix reproduces the full pipeline.
Leopold Müller, Jana Elsner, Thomas Niedermayer, Bernhard Haslhofer · 7 authors
Address clustering is an important technique in blockchain forensics, widely employed by law enforcement to trace illicit crypto asset flows. The multi-input heuristic (MIH), which clusters addresses potentially associated with the same entity, is the most widely used. Yet, despite its broad adoption, the MIH has rarely been evaluated against reliable ground truth data. We implement a reusable evaluation framework covering nine established metrics and apply it to ground truth address-to-entity mappings obtained directly from European crypto asset service providers under legally mandated reporting obligations. When evaluation is restricted to reported addresses, the MIH appears strong at dataset level: we observe no mergers between reported services and recover same-service address pairs with recall 0.71. However, this result is driven by one large service and ignores unlabeled addresses absorbed into full clusters. Metrics that assess the full clusters show substantially lower precision and recall (0.36 and 0.44), meaning that services are often only partially recovered or embedded in larger clusters. Entity-level results further reveal near-complete failures for some services. When MIH-based clusters are used to support criminal suspicion, preliminary seizure of crypto assets to secure later forfeiture/ confiscation, or as evidence in trial proceedings, prosecutors and judges must account for the heuristic's metric-dependent and entity-dependent reliability.
Existing cybercrime classification schemas capture contact metadata and financial transactions but omit the psychological manipulation techniques perpetrators employ. We present a forensic schema (four categories, 35 questions) adding 11 manipulation indicators and cryptocurrency evidence fields to established forensic foundations. Applied to 10,994 victim reports via large language model (LLM)-driven annotation and validated against two human annotators (mean LLM-human $κ= 0.69$, matching inter-annotator $κ= 0.68$), the schema revealed a statistically distinct manipulation profile for each major fraud type (Cramer's $V$ up to $0.790$). A rationale-based evidence audit nonetheless exposed a forensic detail gap: detection of manipulation techniques was reliable, but victim narratives varied widely in the actionable detail supporting each Yes answer, and blockchain-specific identifiers were nearly absent. These findings point to AI-assisted victim intake with schema-informed follow-up questions as the most direct way to close the gap. The tiered annotation strategy also provides a reusable template for LLM-based extraction from other forensic text domains.
Nowadays, mobile forensics is less explored in Digital Forensics case analysis due to the increase in data protection mechanisms implemented by tech companies (i.e., Google for Android and Apple for iOS). For example, the physical acquisition or analysis of specific directories under super-user protection would corrupt the evidence; access to such data is protected, and bypassing this protection requires either privilege escalation or custom ROM installation, leading to the modification of the device state. At the same time, the demand for mobile technologies and their respective communication systems is increasing exponentially, exposing numerous security threats and risks. For that reason, this paper presents a Mobile Live Intelligent Forensics Examination (MoLIFE), a novel Digital Forensics (DF) methodology for data acquisition and analysis of mobile devices. The proposed methodology is based on NIST SP800-101 for the DF process. MoLIFE can be integrated with new and emerging technologies by exploiting their power (e.g., AI, blockchain, quantum computing). MoLIFE can also be used to prevent cyber threats and incidents, as well as DF post-mortem analysis, offering examples of applying the MoLIFE methodology and good practices for the future. To prove the technical feasibility of the methodology, a small case study on Android devices data acquisition via the mDT will be presented. As the methodology is based on new and emerging technologies, it depends on their limitations that would be overcome in a few years.
Smart contract compilers are critical to ensuring the correctness of public blockchains whose defining characteristics are open-source and immutable code. We created SolSmith, a semantics-aware differential fuzz testing tool, to improve the quality of the Solidity compiler -- the most popular compiler for the Ethereum blockchain -- and spent over three years finding compiler defects that produce incorrect code. We call these defects miscompilation bugs. During this time period, we have discovered 25 miscompilation bugs that went unnoticed, some for multiple years. Our first contribution is to make compiler testing more rigorous. SolSmith achieves this goal by generating valid test programs that are likely to stress test code generation and optimization components. This helps SolSmith find bugs missed during routine testing that could potentially have serious implications for smart contracts and their users. Our second contribution is a qualitative and quantitative analysis of miscompilation bugs that we found in the Solidity compiler. We classify miscompilation bugs found by SolSmith based on their nature, root-causes, and impact on end-users. This sheds light on some pitfalls of optimizing compilers.
Monero is a privacy-focused cryptocurrency that deploys the Dandelion++ protocol and incorporates anonymity networks (such as Tor and I2P) to prevent malicious attackers from linking transactions with their source IPs. In this paper, we demonstrate that Monero's integration of the Tor network introduces a fundamental vulnerability: a Monero Tor node's originated transactions are exclusively forwarded to two outgoing Tor hidden service nodes (proxy nodes) prior to clearnet propagation, enabling an adversary to capture originated transactions by occupying the target node's outgoing connections. Based on this observation, we propose \textit{ProxyMark}, a three-stage deanonymization framework for the Monero Tor network, comprising node role identification, originated transaction identification, and node location deanonymization. Through experiments on the live Tor network, Monero mainnet, and testnet, we empirically demonstrate the effectiveness of \textit{ProxyMark} in successfully deanonymizing transactions originating from Monero nodes over Tor.
The automotive industry is transitioning to Zonal-oriented Architectures (ZoA) for Software-Defined Vehicles (SDVs), enabling frequent over-the-air (OTA) updates for 100+ Electronic Control Units (ECUs). While OTA updates improve efficiency, they introduce safety-critical security risks. Current standards like Uptane and AUTOSAR Adaptive rely on Public-Key Infrastructure (PKI). However, PKI-based authentication creates bandwidth bottlenecks in in-vehicle and vehicle-to-cloud (V2I) communication as ECU density increases. It also risks exposing sensitive vehicle configurations and passenger privacy due to centralized architectures. Next-generation Zonal SDVs require decentralized, scalable authentication with data privacy. To address this, we propose zk-ScalHard, a hardware-rooted, privacy-preserving authentication protocol. We introduce a decentralized, hierarchical trust-promotion model utilizing Silicon Physical Unclonable Functions (PUFs) and two novel Zero-Knowledge Proof (ZKP) circuits: (1) Zonal Identity and Integrity (ZIDI) and (2) High-Performance Computing Aggregation (HPCA). These circuits employ multi-party computation (MPC) and recursive aggregation to achieve decentralization and scalability. The integration of ZKPs and PUFs ensures 100% vehicle-level data sovereignty. Benchmarked against Uptane, zk-ScalHard achieves constant O(1) communication and verification complexity, improving upon the linear O(n) complexity of current systems. Evaluation shows a 99.2% reduction in authentication bandwidth and a 99.9% reduction in the temporal attack surface. Our results demonstrate that zk-ScalHard provides a scalable, secure, and GDPR-compliant architecture for future Zonal SDVs.
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cs.CR
Physical Unclonable Functions (PUFs) and Hardware Security
Ethereum Layer-2 (L2) ecosystems improve scalability but also fragment users, liquidity, gas funding, and execution across rollups. Consequently, cross-rollup interoperability is not only a bridging problem but also a wallet, execution, and validation problem. Ethereum Interop Layer (EIL) proposes a voucher-based architecture in which users create voucher requests on an origin chain and redeem XLP-signed vouchers on a destination chain. When reproducing the evaluated SDK version in a controlled local environment, we observed a compatibility issue in the \texttt{UserOperation} path: paymaster-related data can differ after signing, preventing a stable comparison between the user-authorized representation and the representation later inspected by the local validation flow. This paper presents a reproducible two-L2 validation framework and a controlled compatibility mitigation for that issue. We build a deterministic local testbed over Arbitrum- and Optimism-style development chains, deploy the core paymaster and bridge-related components, implement mock bundlers and event-driven XLP providers, and introduce a sanitized paymaster-data handling path together with a compatible multichain account wrapper. Using this framework, we execute the core voucher lifecycle from request creation to destination-chain voucher redemption and asset release. The contribution is an empirical diagnosis of an implementation-level compatibility barrier, a bounded mitigation that restores controlled end-to-end execution, and an inspectable validation artifact for studying voucher-based interoperability. The work does not claim a new interoperability protocol, universal wallet compatibility, or production readiness; it identifies the remaining gaps toward standard-account validation, one-signature multichain authorization, and full dispute-settlement support.
This paper introduces Crossroads, a smart contract layer for chain-abstracted assets. In Crossroads, assets from nearly any chain are represented on a single backend blockchain as ERC-20 tokens. As a result, any asset can participate in smart-contract-based exchange, lending, or privacy applications on a single unified platform. So while Crossroads offers cross-chain bridging, a common, partial approach to alleviating the fragmentation of the blockchain ecosystem today, this is just one service within Crossroads' general-purpose chain-abstraction model. Crossroads relies on key encumbrance: a threshold signing committee holds encumbered keys controlling assets on each integrated chain, signing transactions only as authorized by smart contracts on the backend blockchain. Asset movements are fee-efficient, as ownership changes are recorded on the backend blockchain and users may set the transaction fee for withdrawals. Crossroads enables permissionless, modular integration of new blockchains using pluggable oracles with flexible design options (zkBridge, TEE-based, hybrid). Asset deposits into Crossroads benefit from strong, chain-specific finalization guarantees, minimizing the risk of reorg attacks. Unlike existing bridges, however, third-party smart contracts in Crossroads can provide fast, optimistic access to funds before finalization completes. We prove that Crossroads satisfies soundness: given an honest quorum of signing committee members, any user can unilaterally generate a withdrawal transaction transferring their net balance to an account on an integrated blockchain. We implement a proof of concept across multiple public blockchains: Bitcoin, Ethereum, and Solana. We catalog a range of applications enabled by Crossroads, including universal wallets, cross-chain staking and lending, privacy-preserving payments, and private management of public blockchain assets.
This paper develops a representation-theoretic perspective on cryptographic protocols. The focus is not solely on the computability of the abstract value as an extensional property, but on the algorithmic structure of its presentation in a representation system: for operational use in protocols, algorithmic accessibility of the value does not suffice; its fixed presentation is also decisive. We distinguish three representation-theoretic notions -- algorithmically approximable (A_app, the computable real numbers), finitely exactly describable in a system (A_fin(S)), and canonical normalizability of a system -- and show that there is no computable extensional canonicalizer that uniformly transforms arbitrary approximation programs of computable real numbers into unique finite value encodings. As the operational rational core presentation we use the rational system with its canonical encoding specification Sigma_Q (fixed rules for valid fraction descriptions, canonical codes, and normalization); the associated value set is A_ex = Q. The notion of a canonically serializable object class transfers this core idea to practical protocol objects (files as byte sequences, hash values, transaction IDs, and normatively serialized payloads). We illustrate the consequences for interoperability, well-definedness, and verification with fully worked toy examples from symmetric and asymmetric encryption and hashing, and with a real-world example, the snaproot hash-anchoring protocol for blockchain-based file integrity verification. The paper thereby shows that the mathematical determinacy of a value and its operational uniqueness as a protocol object are two different requirements. Once a normative representation specification has been fixed, byte-level correctness and well-definedness arguments can be carried out without further implementation-dependent serialization or rounding decisions.
Blockchains have evolved from simple distributed ledgers into programmable platforms that process complex application logic and carry significant financial value. All modern Web3 systems share a common goal: providing secure, decentralized, and trustworthy execution in an increasingly interconnected environment. However, this evolution has shifted the attack surface from isolated infrastructure disruptions to programmable economic abuse and cross-domain exploits. In this article, we focus on the research of blockchain attacks and defenses. In particular, we categorize the threat landscape and corresponding mitigation strategies according to both a four-tier layered architecture (network, cryptographic, consensus, and application) and cross-domain trust boundaries. We seek to answer these important questions: How has the research in blockchain security evolved over the past decade, especially with the rise of decentralized finance (DeFi) and cross-chain interoperability? How do local security assumptions fail when protocols are composed, and what are the driving needs for Web3 security research in the future?
Trusted execution environment (TEE) based confidential smart contract networks promise privacy but remain vulnerable to storage access pattern attacks that can link senders and recipients in token transfers. When contracts update recipient balances during transfers, the unique storage keys accessed reveal transaction relationships even when data is encrypted. This paper introduces two novel data structures to address this vulnerability: the Delayed Write Buffer (DWB) and the Bitwise-Trie of Bucketed Entries (BTBE). The DWB delays recipient balance updates by buffering pending transfers and randomly settling entries, breaking the direct correlation between transfer execution and recipient storage access. The BTBE further enhances privacy by grouping addresses into constant-sized buckets, preventing flooding attacks and creating anonymity sets for balance queries. Additionally, we present a private notification system enabling real-time, privacy-preserving push notifications for confidential contracts. Our domain-specific approach leverages the unique characteristics of token transfers -- asymmetric balance updates and tolerance for delayed settlement -- to achieve practical performance with probabilistic anonymity guarantees.
Simon Jeanteur, Lorenzo Veronese, Magdalena Solitro, Matteo Maffei
Computer-aided formal verification is a widely used approach for the symbolic analysis of cryptographic protocols. However, many modern protocols rely on features that remain challenging for existing techniques. In particular, reasoning about state, time-dependent behavior, inductively defined data structures, unbounded executions, and conditional secrecy requires a level of expressiveness that is difficult to reconcile with effective automation. As a result, protocol verification has largely followed two disjoint paths: fully automated methods with limited expressiveness, or interactive proofs in general-purpose theorem provers that offer flexibility but only limited, non-specialized automation. We present an orthogonal approach that bridges this gap by combining compositional type-based reasoning with trace-based reasoning, enabling modular verification of stateful and unbounded protocols. Guided by the language-and-automation co-design (LAC) principle, our approach delivers protocol-specific automation while retaining high expressiveness. We implement this framework as the LeanDY library for the Lean proof assistant, building on and extending the design of DY*, and combining protocol-specific automation with interactive proofs. Our framework supports, in a unified setting, a broad class of functional and security requirements, including secrecy and authentication for stateful protocols, as well as recursive conditional secrecy for protocols using XOR. We formalize SegWit-style blockchain primitives in LeanDY and demonstrate its expressiveness by carrying out an in-depth formalization of payment channels on top of this blockchain model, verifying punishment mechanisms and properties that depend on chain liveness.
Smart contract vulnerabilities are predominantly logic bugs whose detection requires structured, step-by-step procedural knowledge of attack patterns and contract semantics. Existing LLM-based methods struggle to generate this knowledge automatically: prompt-based methods rely on manually crafted detection rules, while fine-tuning requires massive labeled datasets that are inherently scarce in this domain. We present EvoVuln, an automated framework that reformulates vulnerability detection as a procedural knowledge evolution problem, synthesizing and refining detection logic using only a minimal number of labeled samples. To achieve this, EvoVuln introduces two key mechanisms. First, a Runtime with an Inversion of Control (IoC) architecture compiles detection rules into Executable Policies. This strictly decouples deterministic control flow from LLM semantic reasoning, ensuring faithful logical adherence and producing dense diagnostic telemetry for precise error localization. Second, a two-phase evolution pipeline refines the rule via abductive semantic debugging without any parameter updates: Cold Start bootstraps and stress-tests an initial rule using auto-synthesized corner cases; Few-Shot Evolving then grounds the policy in real-world semantics using only five vulnerable and five safe examples per vulnerability type. Evaluated across five real-world vulnerability types, EvoVuln achieves a 71% macro-average F1-score, outperforming all baselines. The evolved procedural knowledge is portable across models: it enables a lightweight, low-cost model to surpass a much larger zero-shot model by 19 percentage points, and transfers to other LLMs without retraining, at a one-time evolution cost under $50.