Moritz Grundei, Vipindev Adat Vasudevan, Kishori Konwar, Muriel Medard
The data availability problem is a central challenge in blockchain systems and lies at the core of the accessibility and scalability issues faced by platforms such as Ethereum. Modern solutions employ several approaches, with data availability sampling (DAS) being the most self-sufficient and minimalistic in its security assumptions. Existing DAS methods typically form cryptographic commitments on codewords of fixed-rate erasure codes, which restrict light nodes to sampling from a predetermined set of coded symbols. In this paper, we introduce a new approach to DAS that modularizes the coding and commitment process by committing to the uncoded data while performing sampling through on-the-fly coding. The resulting samples are significantly more expressive, enabling light nodes to obtain, in concrete implementations, up to multiple orders of magnitude stronger assurances of data availability than from sampling pre-committed symbols from a fixed-rate redundancy code as done in established DAS schemes using Reed Solomon or low density parity check codes. We present a concrete protocol that realizes this paradigm using random linear network coding (RLNC).
Decentralization has an important geographic dimension that conventional metrics, such as stake distribution, often overlook. Where validators operate affects resilience to regional shocks (e.g., outages, natural disasters, or government intervention) as well as fairness in reward access. Yet major blockchain protocols do not encode geographical location in their rules; instead, validator locations emerge from a combination of economic incentives, regulatory constraints, infrastructure availability, and validator deployment choices. When certain locations offer systematic advantages, validators may strategically co-locate to maximize expected rewards, as observed in Ethereum, where validators cluster along the Atlantic corridor, which exhibits favorable latency. In this paper, we propose a formal model of validators' geographical positioning incentives under Ethereum's protocol design, capturing the interaction between its two block-building paradigms, local and external block building, and the geographical distribution of validators and information sources. We analytically characterize the model under a mean-field approximation and complement this analysis with an agent-based simulation calibrated with real-world latency data to quantify how these incentives translate into geographical concentration under heterogeneous geographic and infrastructural conditions. Our results show that Ethereum's block-building architecture is not geographically neutral. Both paradigms generate location-dependent payoffs and incentives to relocate closer to payoff-relevant parties in order to reduce propagation delays, although through different underlying mechanisms. Asymmetric access to information sources further amplifies geographical centralization. We also demonstrate that consensus parameters, such as attestation thresholds and slot times, modulate latency sensitivity and can amplify these effects, acting as protocol-level levers. Finally, we discuss the implications of our findings for protocol design and outline potential mitigation directions informed by our analysis.
Decentralized resource markets are Web 3.0 applications that build open-access platforms for trading digital resources among users without any central management. They promise cost reduction, transparency, and flexible service provision. However, these markets usually have large workload that must be processed in a timely manner, leading to serious scalability problems. Despite the large amount of work on blockchain scalability, existing solutions are ineffective as they do not account for these markets' work models and traffic patterns. We introduce chainScale, a secure hybrid sidechain-sharding solution that aims to boost throughput of decentralized resource markets and reduce their latency and storage footprint. At its core, chainScale leverages dependent sidechains and functionality-oriented workload splitting to parallelize traffic processing by having each market module assigned to a sidechain. Different from sharding, chainScale does not incur any cross-sidechain transactions that tend to be costly. chainScale introduces several techniques, including hierarchical workload sharing that further sub-divides overloaded modules, and weighted miner assignment that assigns miners with vested interest in the system to critical modules' sidechains. Furthermore, chainScale employs sidechain syncing to maintain the mainchain as the single truth of system state, and pruning to discard stale records. Beside analyzing security, we build a proof-of-concept implementation for a distributed file storage market as a use case. Our experiments show that, compared to a single sidechain-based prior solution, chainScale boosts throughput by 4x and reduces confirmation latency by 5x. Also, they show that chainScale outperforms sharding by 2.5x in throughput and 3.5x in latency.
Ensuring the correctness of smart contracts is critical, as even subtle flaws can lead to severe financial losses. While bug detection tools able to spot common vulnerability patterns can serve as a first line of defense, most real-world exploits and losses stem from errors in the contract business logic. Formal verification tools such as SolCMC and the Certora Prover address this challenge, but their impact remains limited by steep learning curves and restricted specification languages. Recent works have begun to explore the use of large language models (LLMs) for security-related tasks such as vulnerability detection and test generation. Yet, a fundamental question remains open: can LLMs aid in assessing the validity of arbitrary contract-specific properties? In this paper, we provide the first systematic empirical evaluation of GPT-5, a state-of-the-art reasoning LLM, in this role. We benchmark its performance on a large dataset of verification tasks, compare its outputs against those of established formal verification tools, and assess its practical effectiveness in real-world auditing scenarios. Our study combines quantitative metrics with qualitative analysis, and shows that recent reasoning-oriented LLMs - although lacking soundness guarantees - can be surprisingly effective at predicting the (in)validity of complex properties, suggesting a new frontier in the convergence of AI and formal methods for secure smart contract development and auditing.
With the proliferation of new blockchain-based cryptocurrencies/assets and platforms that make it possible to transact across them, it becomes important to consider not just whether the transfer of coins/assets can be tracked within their respective transaction ledger, but also if they can be tracked as they move across ledgers. This is especially important given that there are documented cases of criminals attempting to use these cross-ledger trades to obscure the flow of their coins/assets. In this paper, we perform a systematic review of the various tracing techniques for blockchain transactions proposed in literature, categorize them using multiple criteria (such as tracing approach and targeted objective) and compare them. Based on the above categorization, we provide insights on the state of blockchain transaction tracing literature and identify the limitations of existing approaches. Finally, we suggest directions for future research in this area based on our analysis.
Adversarial smart contracts, mostly on EVM-compatible chains like Ethereum and BSC, are deployed as EVM bytecode to exploit vulnerable smart contracts for financial gain. Detecting such malicious contracts at the time of deployment is an important proactive strategy to prevent losses from victim contracts. It offers a better cost-benefit ratio than detecting vulnerabilities on diverse potential victims. However, existing works are not generic with limited detection types and effectiveness due to imbalanced samples, while the emerging LLM technologies, which show their potential in generalization, have two key problems impeding its application in this task: hard digestion of compiled-code inputs, especially those with task-specific logic, and hard assessment of LLM's certainty in its binary (yes-or-no) answers. Therefore, we propose a generic adversarial smart contracts detection framework FinDet, which leverages LLM with two enhancements addressing the above two problems. FinDet takes as input only the EVM bytecode contracts and identifies adversarial ones among them with high balanced accuracy. The first enhancement extracts concise semantic intentions and high-level behavioral logic from the low-level bytecode inputs, unleashing the LLM reasoning capability restricted by the task input. The second enhancement probes and measures the LLM uncertainty to its multi-round answering to the same query, improving the LLM answering robustness for binary classifications required by the task output. Our comprehensive evaluation shows that FinDet achieves a BAC of 0.9374 and a TPR of 0.9231, significantly outperforming existing baselines. It remains robust under challenging conditions including unseen attack patterns, low-data settings, and feature obfuscation. FinDet detects all 5 public and 20+ unreported adversarial contracts in a 10-day real-world test, confirmed manually.
In traditional, one-vote-per-person voting systems, privacy equates with ballot secrecy: voting tallies are published, but individual voters' choices are concealed. Voting systems that weight votes in proportion to token holdings, though, are now prevalent in cryptocurrency and web3 systems. We show that these weighted-voting systems overturn existing notions of voter privacy. Our experiments demonstrate that even with secret ballots, publishing raw tallies often reveals voters' choices. Weighted voting thus requires a new framework for privacy. We introduce a notion called B-privacy whose basis is bribery, a key problem in voting systems today. B-privacy captures the economic cost to an adversary of bribing voters based on revealed voting tallies. We propose a mechanism to boost B-privacy by noising voting tallies. We prove bounds on its tradeoff between B-privacy and transparency, meaning reported-tally accuracy. Analyzing 3,582 proposals across 30 Decentralized Autonomous Organizations (DAOs), we find that the prevalence of large voters ("whales") limits the effectiveness of any B-Privacy-enhancing technique. However, our mechanism proves to be effective in cases without extreme voting weight concentration: among proposals requiring coalitions of $\geq5$ voters to flip outcomes, our mechanism raises B-privacy by a geometric mean factor of $4.1\times$. Our work offers the first principled guidance on transparency-privacy tradeoffs in weighted-voting systems, complementing existing approaches that focus on ballot secrecy and revealing fundamental constraints that voting weight concentration imposes on privacy mechanisms.
Bence Soóki-Tóth, István András Seres, Kamilla Kara, Ábel Nagy · 6 authors
The long-term success of cryptocurrencies largely depends on the incentive compatibility provided to the validators. Bribery attacks, facilitated trustlessly via smart contracts, threaten this foundation. This work introduces, implements, and evaluates three novel and efficient bribery contracts targeting Ethereum validators. The first bribery contract enables a briber to fork the blockchain by buying votes on their proposed blocks. The second contract incentivizes validators to voluntarily exit the consensus protocol, thus increasing the adversary's relative staking power. The third contract builds a trustless bribery market that enables the briber to auction off their manipulative power over the RANDAO, Ethereum's distributed randomness beacon. Finally, we provide an initial game-theoretical analysis of one of the described bribery markets.
Stefanos Chaliasos, Conner Swann, Sina Pilehchiha, Nicolas Mohnblatt · 6 authors
Rollups have become the de facto scalability solution for Ethereum, securing more than $55B in assets. They achieve scale by executing transactions on a Layer 2 ledger, while periodically posting data and finalizing state on the Layer 1, either optimistically or via validity proofs. Their fees must simultaneously reflect the pricing of three resources: L2 costs (e.g., execution), L1 DA, and underlying L1 gas costs for batch settlement and proof verification. In this work, we identify critical mis-pricings in existing rollup transaction fee mechanisms (TFMs) that allow for two powerful attacks. Firstly, an adversary can saturate the L2's DA batch capacity with compute-light data-heavy transactions, forcing low-gas transaction batches that enable both L2 DoS attacks, and finality-delay attacks. Secondly, by crafting prover killer transactions that maximize proving cycles relative to the gas charges, an adversary can effectively stall proof generation, delaying finality by hours and inflicting prover-side economic losses to the rollup at a minimal cost. We analyze the above attack vectors across the major Ethereum rollups, quantifying adversarial costs and protocol losses. We find that the first attack enables periodic DoS on rollups, lasting up to 30 minutes, at a cost below 2 ETH for most rollups. Moreover, we identify three rollups that are exposed to indefinite DoS at a cost of approximately 0.8 to 2.7 ETH per hour. The attack can be further modified to increase finalization delays by a factor of about 1.45x to 2.73x, compared to direct L1 blob-stuffing, depending on the rollup's parameters. Furthermore, we find that the prover killer attack induces a finalization latency increase of about 94x. Finally, we propose comprehensive mitigations to prevent these attacks and suggest how some practical uses of multi-dimensional rollup TFMs can rectify the identified mis-pricing attacks.
Large Language Models (LLMs) have enabled the emergence of autonomous agents capable of complex reasoning, planning, and interaction. However, coordinating such agents at scale remains a fundamental challenge, particularly in decentralized environments where communication lacks transparency and agent behavior cannot be shaped through centralized incentives. We propose a blockchain-based framework that enables transparent agent registration, verifiable task allocation, and dynamic reputation tracking through smart contracts. The core of our design lies in two mechanisms: a matching score-based task allocation protocol that evaluates agents by reputation, capability match, and workload; and a behavior-shaping incentive mechanism that adjusts agent behavior via feedback on performance and reward. Our implementation integrates GPT-4 agents with Solidity contracts and demonstrates, through 50-round simulations, strong task success rates, stable utility distribution, and emergent agent specialization. The results underscore the potential for trustworthy, incentive-compatible multi-agent coordination in open environments.
Users of blockchains value scalability, expecting fast confirmations and immediate transaction processing. Odontoceti, the latest in DAG-based consensus, addresses these concerns by prioritizing low latency and high throughput, making a strategic trade-off in security by operating with a 20% fault tolerance instead of the established 33% level. It is the first DAG-based protocol to achieve commitment in just two communication rounds, delivering median latency of 300 milliseconds while processing 10,000 transactions per second under realistic network conditions. Odontoceti operates with n = 5f + 1 validators and creates an uncertified DAG with a novel decision rule for committing blocks. The protocol includes an optimization that advances progress when participants are slow, benefiting crash fault scenarios which are more common in practice than Byzantine faults. Evaluation results demonstrate 20-25% latency improvements compared to an existing production protocol, validating that reducing wave length from three rounds to two rounds yields meaningful performance benefits. This paper establishes the practical viability of lower fault tolerance consensus protocols for blockchains.
Qianyu Yu, Giuliano Losa, Nibesh Shrestha, Xuechao Wang
To maximize performance, many modern blockchain systems rely on eventually-synchronous, Byzantine fault-tolerant (BFT) consensus protocols. Two protocol designs have emerged in this space: protocols that minimize latency using a leader that drives both data dissemination and consensus, and protocols that maximize throughput using a separate, asynchronous data dissemination layer. Recent protocols such as Partially-Synchronous Bullshark and Sailfish combine elements of both approaches by using a DAG to enable parallel data dissemination and a leader that paces DAG formation. This improves latency while achieving state-of-the-art throughput. Yet the latency of leader-based protocols is still better under moderate loads, which are common in practice. We present Angelfish, a hybrid protocol that adapts smoothly across this design space, from leader-based to Sailfish-like DAG-based consensus. Angelfish lets a dynamically adjusted subset of parties use best-effort broadcast to issue lightweight votes instead of reliably broadcasting costlier DAG vertices. This reduces communication, helps lagging nodes catch up, and lowers latency in practice compared to prior DAG-based protocols. Our empirical evaluation shows that Angelfish attains state-of-the-art peak throughput while significantly lowering latency under moderate throughput, delivering the best of both worlds.
Bitcoin's consensus rules are encoded in the implementation of its reference client: "The code is the spec." Yet this code is unsuitable for formal verification due to side effects, mutable state, concurrency, and legacy design. A standalone formal specification would enable verification both across versions of the reference client and against new client implementations, strengthening decentralization by reducing the risk of consensus-splitting bugs. Yet such a specification has long been considered intractable given the complexity of Bitcoin's consensus logic. We demonstrate a compact, executable, declarative C++ specification of Bitcoin consensus rules that syncs mainnet to tip in a few hours on a single thread. We also introduce the Hornet Domain-Specific Language (DSL) specifically designed to encode these rules unambiguously for execution, enabling formal reasoning, consensus code generation, and AI-driven adversarial testing. Our spec-driven client Hornet Node offers a modern and modular complement to the reference client. Its clear, idiomatic style makes it suitable for education, while its performance makes it ideal for experimentation. We highlight architectural contributions such as its layered design, efficient data structures, and strong separation of concerns, supported by production-quality code examples. We argue that Hornet Node and Hornet DSL together provide the first credible path toward a pure, formal, executable specification of Bitcoin consensus.
We analyze 15,097 blocks proposed for inclusion in Ethereum's blockchain over an eight-minute window on December 3, 2024, during which 38 blocks were added to the chain. We classify transactions as exclusive -- appearing only in blocks from a single builder -- or private -- absent from the public mempool but included in blocks from multiple builders. We find that, depending on the methodology, exclusive transactions account for between 77.2% and 84% of the total fees paid by transactions in winning blocks. Moreover, we show that exclusivity cannot be fully attributed to persistent relationships between senders and builders: only between 7% and 8.4% of all on-chain exclusive transaction value originates from senders who route exclusively to one builder. Finally, we observe that transaction exclusivity is dynamic. Some transactions are exclusive at the start of a bidding cycle but later appear in blocks from multiple builders. Other transactions remain exclusive to a losing builder for two or three cycles before appearing in the public mempool. These transactions are therefore delayed and then exposed to potential attacks.
Mohamed Abdessamed Rezazi, Mouhamed Amine Bouchiha, A. Bendada, Yacine Ghamri-Doudane
Roaming settlement in 5G and beyond networks demands secure, efficient, and trustworthy mechanisms for billing reconciliation between mobile operators. While blockchain promises decentralization and auditability, existing solutions suffer from critical limitations-namely, data privacy risks, assumptions of mutual trust, and scalability bottlenecks. To address these challenges, we present B5GRoam, a novel on-chain and zero-trust framework for secure, privacy-preserving, and scalable roaming settlements. B5GRoam introduces a cryptographically verifiable call detail record (CDR) submission protocol, enabling smart contracts to authenticate usage claims without exposing sensitive data. To preserve privacy, we integrate non-interactive zero-knowledge proofs (zkSNARKs) that allow on-chain verification of roaming activity without revealing user or network details. To meet the high-throughput demands of 5G environments, B5GRoam leverages Layer 2 zk-Rollups, significantly reducing gas costs while maintaining the security guarantees of Layer 1. Experimental results demonstrate a throughput of over 7,200 tx/s with strong privacy and substantial cost savings. By eliminating intermediaries and enhancing verifiability, B5GRoam offers a practical and secure foundation for decentralized roaming in future mobile networks.
Cryptocurrencies and Web3 applications based on blockchain technology have flourished in the blockchain research field. Unlike Bitcoin and Ethereum, due to its unique architectural designs in consensus mechanisms, resource management, and throughput, TRON has developed a more distinctive ecosystem and application scenarios centered around stablecoins. Although it is popular in areas like stablecoin payments and settlement, research on analyzing on-chain data from the TRON blockchain is remarkably scarce. To fill this gap, this paper proposes a comprehensive data extraction and exploration framework for the TRON blockchain. An innovative high-performance ETL system aims to efficiently extract raw on-chain data from TRON, including blocks, transactions, smart contracts, and receipts, establishing a research dataset. An in-depth analysis of the extracted dataset reveals insights into TRON's block generation, transaction trends, the dominance of exchanges, the resource delegation market, smart contract usage patterns, and the central role of the USDT stablecoin. The prominence of gambling applications and potential illicit activities related to USDT is emphasized. The paper discusses opportunities for future research leveraging this dataset, including analysis of delegate services, gambling scenarios, stablecoin activities, and illicit transaction detection. These contributions enhance blockchain data management capabilities and understanding of the rapidly evolving TRON ecosystem.
The Metaverse utilizes emerging technologies such as Extended Reality (XR), Artificial Intelligence (AI), blockchain, and digital twins to provide an immersive and interactive virtual experience. As the Metaverse continues to evolve, it brings a range of security and privacy threats, such as identity management, data governance, and user interactions. This survey aims to provide a comprehensive review of the enabling technologies for the Metaverse. It also aims to provide a thorough analysis of key vulnerabilities and threats that may compromise its sustainability and user safety. We perform a systematic literature review (SLR) to identify key vulnerabilities and their countermeasures in Metaverse platforms. Metaverse offers a much larger attack surface compared to conventional digital platforms. Immersive, decentralized, and permanent characteristics of the Metaverse generate new vulnerabilities. Although there are many countermeasures to these vulnerabilities, most of them are theoretical or have not been tested in real-world environments. Our review highlights current advancements, identifies research gaps, and outlines future directions to ensure a secure, resilient, and ethically governed Metaverse.
Elections are not the only but arguably one of the most important pillars for the proper functioning of liberal democracies. Recent evidence across the globe shows that it is not straightforward to conduct them in a free and fair manner. One constant concern is the role of money in politics, more specifically, election campaign financing. Frequent scandals are proof of the difficulties encountered with current approaches to tackle the issue. Suggestions on how to overcome the problem exist but seem difficult to implement. With the help of blockchain technology we might be able to make a step forward. A separate crypto currency specifically designed to pay for costs of political campaigning and advertising could be introduced. Admittedly, at this stage, there are many open questions. However, under the assumption that blockchain technology is here to stay, it is an idea that deserves further exploration.
This paper introduces a Blockchain-Integrated Explainable AI Framework (BXHF) for healthcare systems to tackle two essential challenges confronting health information networks: safe data exchange and comprehensible AI-driven clinical decision-making. Our architecture incorporates blockchain, ensuring patient records are immutable, auditable, and tamper-proof, alongside Explainable AI (XAI) methodologies that yield transparent and clinically relevant model predictions. By incorporating security assurances and interpretability requirements into a unified optimization pipeline, BXHF ensures both data-level trust (by verified and encrypted record sharing) and decision-level trust (with auditable and clinically aligned explanations). Its hybrid edge-cloud architecture allows for federated computation across different institutions, enabling collaborative analytics while protecting patient privacy. We demonstrate the framework's applicability through use cases such as cross-border clinical research networks, uncommon illness detection and high-risk intervention decision support. By ensuring transparency, auditability, and regulatory compliance, BXHF improves the credibility, uptake, and effectiveness of AI in healthcare, laying the groundwork for safer and more reliable clinical decision-making.
ChipmunkRing, a practical post-quantum ring signature construction tailored for blockchain environments. Building on our Chipmunk lattice-based cryptographic framework, this implementation delivers compact digital signatures ranging from 20.5 to 279.7KB, with rapid signing operations completing in 1.1-15.1ms and efficient validation processes requiring only 0.4-4.5ms for participant groups of 2-64 members. The cornerstone of our approach is Acorn Verification-a streamlined zero-knowledge protocol that supersedes the classical Fiat-Shamir methodology. This innovation enables linear O(n) authentication complexity using concise 96-byte cryptographic proofs per participant, yielding a remarkable 17.7x performance enhancement for 32-member rings when compared to conventional techniques. Our work includes rigorous mathematical security demonstrations confirming 112-bit post-quantum protection (NIST Level 1), extensive computational benchmarking, and comprehensive support for both standard anonymity sets and collaborative threshold constructions with flexible participation requirements.
Md Bokhtiar Al Zami, Md Raihan Uddin, Dinh C. Nguyen
Federated learning (FL) has gained popularity as a privacy-preserving method of training machine learning models on decentralized networks. However to ensure reliable operation of UAV-assisted FL systems, issues like as excessive energy consumption, communication inefficiencies, and security vulnerabilities must be solved. This paper proposes an innovative framework that integrates Digital Twin (DT) technology and Zero-Knowledge Federated Learning (zkFed) to tackle these challenges. UAVs act as mobile base stations, allowing scattered devices to train FL models locally and upload model updates for aggregation. By incorporating DT technology, our approach enables real-time system monitoring and predictive maintenance, improving UAV network efficiency. Additionally, Zero-Knowledge Proofs (ZKPs) strengthen security by allowing model verification without exposing sensitive data. To optimize energy efficiency and resource management, we introduce a dynamic allocation strategy that adjusts UAV flight paths, transmission power, and processing rates based on network conditions. Using block coordinate descent and convex optimization techniques, our method significantly reduces system energy consumption by up to 29.6% compared to conventional FL approaches. Simulation results demonstrate improved learning performance, security, and scalability, positioning this framework as a promising solution for next-generation UAV-based intelligent networks.
Zero-Knowledge Proofs (ZKP) are protocols which construct cryptographic proofs to demonstrate knowledge of a secret input in a computation without revealing any information about the secret. ZKPs enable novel applications in private and verifiable computing such as anonymized cryptocurrencies and blockchain scaling and have seen adoption in several real-world systems. Prior work has accelerated ZKPs on GPUs by leveraging the inherent parallelism in core computation kernels like Multi-Scalar Multiplication (MSM). However, we find that a systematic characterization of execution bottlenecks in ZKPs, as well as their scalability on modern GPU architectures, is missing in the literature. This paper presents ZKProphet, a comprehensive performance study of Zero-Knowledge Proofs on GPUs. Following massive speedups of MSM, we find that ZKPs are bottlenecked by kernels like Number-Theoretic Transform (NTT), as they account for up to 90% of the proof generation latency on GPUs when paired with optimized MSM implementations. Available NTT implementations under-utilize GPU compute resources and often do not employ architectural features like asynchronous compute and memory operations. We observe that the arithmetic operations underlying ZKPs execute exclusively on the GPU's 32-bit integer pipeline and exhibit limited instruction-level parallelism due to data dependencies. Their performance is thus limited by the available integer compute units. While one way to scale the performance of ZKPs is adding more compute units, we discuss how runtime parameter tuning for optimizations like precomputed inputs and alternative data representations can extract additional speedup. With this work, we provide the ZKP community a roadmap to scale performance on GPUs and construct definitive GPU-accelerated ZKPs for their application requirements and available hardware resources.
Real-World Assets (RWAs) serve as a bridge between traditional financial instruments and decentralized infrastructures. By representing assets such as bonds, commodities, and real estate on blockchains, RWAs can extend the scope of decentralized finance. Industry forecasts further indicate rapid growth in tokenized RWAs after 2025, underscoring their potential role in the evolution of digital financial markets. However, in the current multi-chain environment, RWAs face challenges such as repeated authentication across multiple chains and inefficiencies arising from multi-step settlement protocols. To address these issues, we present a cross-chain framework for RWAs that emphasizes identity management, authentication, and cross-chain interaction. The framework integrates Decentralized Identifiers and Verifiable Credentials with customized attributes to support decentralized identification, and incorporates an authentication protocol based on Simplified Payment Verification to avoid redundant verification across chains. Furthermore, this paper adopts a cross-chain channel that supports efficient RWA settlements, and we refine its design so that the channel does not need to be closed immediately after each settlement, thereby reducing on-chain cost. We implement the framework and evaluate its performance via simulations, which confirm its feasibility and demonstrate improvements in efficiency for RWAs in cross-chain settings.
Web3 applications require execution platforms that maintain confidentiality and integrity without relying on centralized trust authorities. While Trusted Execution Environments (TEEs) offer promising capabilities for confidential computing, current implementations face significant limitations when applied to Web3 contexts, particularly in security reliability, censorship resistance, and vendor independence. This paper presents dstack, a comprehensive framework that transforms raw TEE technology into a true Zero Trust platform. We introduce three key innovations: (1) Portable Confidential Containers that enable seamless workload migration across heterogeneous TEE environments while maintaining security guarantees, (2) Decentralized Code Management that leverages smart contracts for transparent governance of TEE applications, and (3) Verifiable Domain Management that ensures secure and verifiable application identity without centralized authorities. These innovations are implemented through three core components: dstack-OS, dstack-KMS, and dstack-Gateway. Together, they demonstrate how to achieve both the performance advantages of VM-level TEE solutions and the trustless guarantees required by Web3 applications. Our evaluation shows that dstack provides comprehensive security guarantees while maintaining practical usability for real-world applications.