arXiv subjects

Ari Juels

Publications and source records attributed to Ari Juels.

At least 19 recordsLinked to original sources

arXiv2026

Crossroads: A Smart Contract Layer for Chain-Abstracted Assets

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.

cs.CR↗

arXiv2026

Crypto x AI, AI x Crypto: A Survey

The intersection of crypto x AI is spawning papers, products, online posts, and companies. All the surrounding buzz, though, obscures what exactly has been done, what the opportunities and challenges are, and what open questions deserve attention. This survey paper asks what AI can do for blockchain-based technologies (broadly construed as "crypto") (crypto x AI), and vice versa (AI x crypto). We systematize existing work, summarize key takeaways, highlight open research questions, and offer a perspective on pervasive industry misconceptions, concluding that AI and crypto are still in the very early stages of meaningful integration.

cs.CR↗

arXiv2026

$\pi$Creds: Privately Inferred Credentials

Decentralized verifiable credential systems have seen limited deployment in practice. Existing constructions, built on zero-knowledge proofs, are complex, application-specific, and largely restricted to predicates over structured data. We present Privately Inferred Credentials ($\pi$Creds): privacy-preserving, legacy-compatible, decentralized verifiable credentials generated by trusted LLM inference over authenticated data. LLMs' ability to semantically reason over unstructured data substantially expands the range of claims $\pi$Creds can certify over existing credential systems. The use of LLMs also introduces new application-level threats, which we formalize through two problems: the Source-Constrained Adversarial Example (SCAE) problem, which captures robustness against adversaries that manipulate authenticated data to obtain misleading credentials, and the Authenticated Covert Predicate Poisoning (ACPP) problem, which captures privacy leakage through adversarial model selection. We characterize applications of $\pi$Creds over user data, and a novel class of credentials over proprietary software that certifies properties of a service without revealing its source code. Our prototype supports issuing credentials over live financial, health, email, and code sources, and we empirically study the SCAE and ACPP threats on a product expertise credential over real financial data.

cs.CR↗

arXiv2026

Resilient Alerting Protocols for Blockchains

Smart contracts are stateful programs deployed on blockchains; they secure over a trillion dollars in transaction value per year. High-stakes smart contracts often rely on timely alerts about external events, but prior work has not analyzed their resilience to an attacker suppressing alerts via bribery. We formalize this challenge in a cryptoeconomic setting as the \emph{alerting problem}, giving rise to a game between a bribing adversary and~$n$ rational participants, who pay a penalty if they are caught deviating from the protocol. We establish a quadratic, i.e.,~$O(n^2)$, upper bound, whereas a straightforward alerting protocol only achieves~$O(n)$ bribery cost. We present a \emph{simultaneous game} that asymptotically achieves the quadratic upper bound and thus asymptotically-optimal bribery resistance. We then present two protocols that implement our simultaneous game: The first leverages a strong network synchrony assumption. The second relaxes this strong assumption and instead takes advantage of trusted hardware and blockchain proof-of-publication to establish a timed commitment scheme. These two protocols are constant-time but incur a linear storage overhead on the blockchain. We analyze a third, \emph{sequential alerting} protocol that optimistically incurs no on-chain storage overhead, at the expense of~$O(n)$ worst-case execution time. All three protocols achieve asymptotically-optimal bribery costs, but with different resource and performance tradeoffs. Together, they illuminate a rich design space for practical solutions to the alerting problem.

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arXiv2026

The CoinAlg Bind: Profitability-Fairness Tradeoffs in Collective Investment Algorithms

Collective Investment Algorithms (CoinAlgs) are increasingly popular systems that deploy shared trading strategies for investor communities. Their goal is to democratize sophisticated -- often AI-based -- investing tools. We identify and demonstrate a fundamental profitability-fairness tradeoff in CoinAlgs that we call the CoinAlg Bind: CoinAlgs cannot ensure economic fairness without losing profit to arbitrage. We present a formal model of CoinAlgs, with definitions of privacy (incomplete algorithm disclosure) and economic fairness (value extraction by an adversarial insider). We prove two complementary results that together demonstrate the CoinAlg Bind. First, privacy in a CoinAlg is a precondition for insider attacks on economic fairness. Conversely, in a game-theoretic model, lack of privacy, i.e., transparency, enables arbitrageurs to erode the profitability of a CoinAlg. Using data from Uniswap, a decentralized exchange, we empirically study both sides of the CoinAlg Bind. We quantify the impact of arbitrage against transparent CoinAlgs. We show the risks posed by a private CoinAlg: Even low-bandwidth covert-channel information leakage enables unfair value extraction.

cs.GT↗

arXiv2025

Voting-Bloc Entropy: A New Metric for DAO Decentralization

Decentralized Autonomous Organizations (DAOs) use smart contracts to foster communities working toward common goals. Existing definitions of decentralization, however -- the 'D' in DAO -- fall short of capturing the key properties characteristic of diverse and equitable participation. This work proposes a new framework for measuring DAO decentralization called Voting-Bloc Entropy (VBE, pronounced ''vibe''). VBE is based on the idea that voters with closely aligned interests act as a centralizing force and should be modeled as such. VBE formalizes this notion by measuring the similarity of participants' utility functions across a set of voting rounds. Unlike prior, ad hoc definitions of decentralization, VBE derives from first principles: We introduce a simple (yet powerful) reinforcement learning-based conceptual model for voting, that in turn implies VBE. We first show VBE's utility as a theoretical tool. We prove a number of results about the (de)centralizing effects of vote delegation, proposal bundling, bribery, etc. that are overlooked in previous notions of DAO decentralization. Our results lead to practical suggestions for enhancing DAO decentralization. We also show how VBE can be used empirically by presenting measurement studies and VBE-based governance experiments. We make the tools we developed for these results available to the community in the form of open-source artifacts in order to facilitate future study of DAO decentralization.

cs.MA↗

arXiv2025

B-Privacy: Defining and Enforcing Privacy in Weighted Voting

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.

cs.CR↗

arXiv2025

Giving AI Agents Access to Cryptocurrency and Smart Contracts Creates New Vectors of AI Harm

There is growing interest in giving AI agents access to cryptocurrencies and smart contracts. But doing so, this position paper argues, could spawn categorically new vectors of AI harm. To support this notion, we first examine the unique technical properties of cryptocurrencies and smart contracts that, when paired with AI agents, make this so. Next, we describe three of these new vectors of AI harm in detail, providing a first-of-its-kind taxonomy. Finally, we call for more research into ways to prevent or mitigate these risks, proposing several as a starting point.

cs.AI↗

arXiv2024

Liquefaction: Privately Liquefying Blockchain Assets

Inherent in the world of cryptocurrency systems and their security models is the notion that private keys, and thus assets, are controlled by individuals or individual entities. We present Liquefaction, a wallet platform that demonstrates the dangerous fragility of this foundational assumption by systemically breaking it. Liquefaction uses trusted execution environments (TEEs) to encumber private keys, i.e., attach rich, multi-user policies to their use. In this way, it enables the cryptocurrency credentials and assets of a single end-user address to be freely rented, shared, or pooled. It accomplishes these things privately, with no direct on-chain traces. Liquefaction demonstrates the sweeping consequences of TEE-based key encumbrance for the cryptocurrency landscape. Liquefaction can undermine the security and economic models of many applications and resources, such as locked tokens, DAO voting, airdrops, loyalty points, soulbound tokens, and quadratic voting. It can do so with no on-chain and minimal off-chain visibility. Conversely, we also discuss beneficial applications of Liquefaction, such as privacy-preserving, cost-efficient DAOs and a countermeasure to dusting attacks. Importantly, we describe an existing TEE-based tool that applications can use as a countermeasure to Liquefaction. Our work prompts a wholesale rethinking of existing models and enforcement of key and asset ownership in the cryptocurrency ecosystem.

cs.CR↗

arXiv2024

Props for Machine-Learning Security

We propose protected pipelines or props for short, a new approach for authenticated, privacy-preserving access to deep-web data for machine learning (ML). By permitting secure use of vast sources of deep-web data, props address the systemic bottleneck of limited high-quality training data in ML development. Props also enable privacy-preserving and trustworthy forms of inference, allowing for safe use of sensitive data in ML applications. Props are practically realizable today by leveraging privacy-preserving oracle systems initially developed for blockchain applications.

cs.CR↗

arXiv2024

PROF: Protected Order Flow in a Profit-Seeking World

Users of decentralized finance (DeFi) applications face significant risks from adversarial actions that manipulate the order of transactions to extract value from users. Such actions -- an adversarial form of what is called maximal-extractable value (MEV) -- impact both individual outcomes and the stability of the DeFi ecosystem. MEV exploitation, moreover, is being institutionalized through an architectural paradigm known Proposer-Builder Separation (PBS). This work introduces a system called PROF (PRotected Order Flow) that is designed to limit harmful forms of MEV in existing PBS systems. PROF aims at this goal using two ideas. First, PROF imposes an ordering on a set ("bundle") of privately input transactions and enforces that ordering all the way through to block production -- preventing transaction-order manipulation. Second, PROF creates bundles whose inclusion is profitable to block producers, thereby ensuring that bundles see timely inclusion in blocks. PROF is backward-compatible, meaning that it works with existing and future PBS designs. PROF is also compatible with any desired algorithm for ordering transactions within a PROF bundle (e.g., first-come, first-serve, fee-based, etc.). It executes efficiently, i.e., with low latency, and requires no additional trust assumptions among PBS entities. We quantitatively and qualitatively analyze incentive structure of PROF, and its utility to users compared with existing solutions. We also report on inclusion likelihood of PROF transactions, and concrete latency numbers through our end-to-end implementation.

cs.CR↗

arXiv2023

DAO Decentralization: Voting-Bloc Entropy, Bribery, and Dark DAOs

Decentralized Autonomous Organizations (DAOs) use smart contracts to foster communities working toward common goals. Existing definitions of decentralization, however-the 'D' in DAO-fall short of capturing key properties characteristic of diverse and equitable participation. We propose a new metric called Voting-Bloc Entropy (VBE, pronounced ''vibe'') that formalizes a broad notion of decentralization in voting on DAO proposals. VBE measures the similarity of participants' utility functions across a set of proposals. We use VBE to prove a number of results about the decentralizing effects of vote delegation, proposal bundling, bribery, and quadratic voting. Our results lead to practical suggestions for enhancing DAO decentralization. One of our results highlights the risk of systemic bribery with increasing DAO decentralization. To show that this threat is realistic, we present the first practical realization of a Dark DAO, a proposed mechanism for privacy-preserving corruption of identity systems, including those used in DAO voting. Our Dark-DAO prototype uses trusted execution environments (TEEs) in the Oasis Sapphire blockchain for attacks on Ethereum DAOs. It demonstrates that Dark DAOs constitute a realistic future concern for DAO governance.

cs.CR↗

arXiv2023

Open Problems in DAOs

Decentralized autonomous organizations (DAOs) are a new, rapidly-growing class of organizations governed by smart contracts. Here we describe how researchers can contribute to the emerging science of DAOs and other digitally-constituted organizations. From granular privacy primitives to mechanism designs to model laws, we identify high-impact problems in the DAO ecosystem where existing gaps might be tackled through a new data set or by applying tools and ideas from existing research fields such as political science, computer science, economics, law, and organizational science. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the wider research community to join the global effort to invent the next generation of organizations.

cs.CY↗

arXiv2022

Strategic Latency Reduction in Blockchain Peer-to-Peer Networks

Most permissionless blockchain networks run on peer-to-peer (P2P) networks, which offer flexibility and decentralization at the expense of performance (e.g., network latency). Historically, this tradeoff has not been a bottleneck for most blockchains. However, an emerging host of blockchain-based applications (e.g., decentralized finance) are increasingly sensitive to latency; users who can reduce their network latency relative to other users can accrue (sometimes significant) financial gains. In this work, we initiate the study of strategic latency reduction in blockchain P2P networks. We first define two classes of latency that are of interest in blockchain applications. We then show empirically that a strategic agent who controls only their local peering decisions can manipulate both types of latency, achieving 60\% of the global latency gains provided by the centralized, paid service bloXroute, or, in targeted scenarios, comparable gains. Finally, we show that our results are not due to the poor design of existing P2P networks. Under a simple network model, we theoretically prove that an adversary can always manipulate the P2P network's latency to their advantage, provided the network experiences sufficient peer churn and transaction activity.

cs.CR↗

arXiv2021

Clockwork Finance: Automated Analysis of Economic Security in Smart Contracts

We introduce the Clockwork Finance Framework (CFF), a general purpose, formal verification framework for mechanized reasoning about the economic security properties of composed decentralized-finance (DeFi) smart contracts. CFF features three key properties. It is contract complete, meaning that it can model any smart contract platform and all its contracts--Turing complete or otherwise. It does so with asymptotically constant model overhead. It is also attack-exhaustive by construction, meaning that it can automatically and mechanically extract all possible economic attacks on users' cryptocurrency across modeled contracts. Thanks to these properties, CFF can support multiple goals: economic security analysis of contracts by developers, analysis of DeFi trading risks by users, fees UX, and optimization of arbitrage opportunities by bots or miners. Because CFF offers composability, it can support these goals with reasoning over any desired set of potentially interacting smart contract models. We instantiate CFF as an executable model for Ethereum contracts that incorporates a state-of-the-art deductive verifier. Building on previous work, we introduce extractable value (EV), a new formal notion of economic security in composed DeFi contracts that is both a basis for CFF and of general interest. We construct modular, human-readable, composable CFF models of four popular, deployed DeFi protocols in Ethereum: Uniswap, Uniswap V2, Sushiswap, and MakerDAO, representing a combined 24 billion USD in value as of March 2022. We use these models along with some other common models such as flash loans, airdrops and voting to show experimentally that CFF is practical and can drive useful, data-based EV-based insights from real world transaction activity. Without any explicitly programmed attack strategies, CFF uncovers on average an expected $56 million of EV per month in the recent past.

cs.CR↗

arXiv2021

Forsage: Anatomy of a Smart-Contract Pyramid Scheme

Pyramid schemes are investment scams in which top-level participants in a hierarchical network recruit and profit from an expanding base of defrauded newer participants. Pyramid schemes have existed for over a century, but there have been no in-depth studies of their dynamics and communities because of the opacity of participants' transactions. In this paper, we present an empirical study of Forsage, a pyramid scheme implemented as a smart contract and at its peak one of the largest consumers of resources in Ethereum. As a smart contract, Forsage makes its (byte)code and all of its transactions visible on the blockchain. We take advantage of this unprecedented transparency to gain insight into the mechanics, impact on participants, and evolution of Forsage. We quantify the (multi-million-dollar) gains of top-level participants as well as the losses of the vast majority (around 88%) of users. We analyze Forsage code both manually and using a purpose-built transaction simulator to uncover the complex mechanics of the scheme. Through complementary study of promotional videos and social media, we show how Forsage promoters have leveraged the unique features of smart contracts to lure users with false claims of trustworthiness and profitability, and how Forsage activity is concentrated within a small number of national communities.

cs.CR↗

arXiv2019

BDoS: Blockchain Denial of Service

Proof-of-work (PoW) cryptocurrency blockchains like Bitcoin secure vast amounts of money. Their operators, called miners, expend resources to generate blocks and receive monetary rewards for their effort. Blockchains are, in principle, attractive targets for Denial-of-Service (DoS) attacks: There is fierce competition among coins, as well as potential gains from short selling. Classical DoS attacks, however, typically target a few servers and cannot scale to systems with many nodes. There have been no successful DoS attacks to date against prominent cryptocurrencies. We present Blockchain DoS (BDoS), the first incentive-based DoS attack that targets PoW cryptocurrencies. Unlike classical DoS, BDoS targets the system's mechanism design: It exploits the reward mechanism to discourage miner participation. Previous DoS attacks against PoW blockchains require an adversary's mining power to match that of all other miners. In contrast, BDoS can cause a blockchain to grind to a halt with significantly fewer resources, e.g., 21% as of March 2020 in Bitcoin, according to our empirical study. We find that Bitcoin's vulnerability to BDoS increases rapidly as the mining industry matures and profitability drops. BDoS differs from known attacks like Selfish Mining in its aim not to increase an adversary's revenue, but to disrupt the system. Although it bears some algorithmic similarity to those attacks, it introduces a new adversarial model, goals, algorithm, and game-theoretic analysis. Beyond its direct implications for operational blockchains, BDoS introduces the novel idea that an adversary can manipulate miners' incentives by proving the existence of blocks without actually publishing them.

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arXiv2019

SquirRL: Automating Attack Analysis on Blockchain Incentive Mechanisms with Deep Reinforcement Learning

Incentive mechanisms are central to the functionality of permissionless blockchains: they incentivize participants to run and secure the underlying consensus protocol. Designing incentive-compatible incentive mechanisms is notoriously challenging, however. As a result, most public blockchains today use incentive mechanisms whose security properties are poorly understood and largely untested. In this work, we propose SquirRL, a framework for using deep reinforcement learning to analyze attacks on blockchain incentive mechanisms. We demonstrate SquirRL's power by first recovering known attacks: (1) the optimal selfish mining attack in Bitcoin [52], and (2) the Nash equilibrium in block withholding attacks [16]. We also use SquirRL to obtain several novel empirical results. First, we discover a counterintuitive flaw in the widely used rushing adversary model when applied to multi-agent Markov games with incomplete information. Second, we demonstrate that the optimal selfish mining strategy identified in [52] is actually not a Nash equilibrium in the multi-agent selfish mining setting. In fact, our results suggest (but do not prove) that when more than two competing agents engage in selfish mining, there is no profitable Nash equilibrium. This is consistent with the lack of observed selfish mining in the wild. Third, we find a novel attack on a simplified version of Ethereum's finalization mechanism, Casper the Friendly Finality Gadget (FFG) that allows a strategic agent to amplify her rewards by up to 30%. Notably, [10] show that honest voting is a Nash equilibrium in Casper FFG: our attack shows that when Casper FFG is composed with selfish mining, this is no longer the case. Altogether, our experiments demonstrate SquirRL's flexibility and promise as a framework for studying attack settings that have thus far eluded theoretical and empirical understanding.

cs.CR↗