Enhancing privacy on smart contract-enabled blockchains has garnered much attention in recent research. Zero-knowledge proofs (ZKPs) is one of the most popular approaches, however, they fail to provide full expressiveness and fine-grained privacy. To illustrate this, we underscore an underexplored type of Miner Extractable Value (MEV), called Residual Bids Extractable Value (RBEV). Residual bids highlight the vulnerability where unfulfilled bids inadvertently reveal traders' unmet demands and prospective trading strategies, thus exposing them to exploitation. ZKP-based approaches failed to address RBEV as they cannot provide post-execution privacy without some level of information disclosure. Other MEV mitigations like fair-ordering protocols also failed to address RBEV. We introduce Ratel, an innovative framework bridging a multi-party computation (MPC) prototyping framework (MP-SPDZ) and a smart contract language (Solidity), harmonizing the privacy with full expressiveness of MPC with Solidity 's on-chain programmability. This synergy empowers developers to effortlessly craft privacy-preserving decentralized applications (DApps). We demonstrate Ratel's efficacy through two distinguished decentralized finance (DeFi) applications: a decentralized exchange and a collateral auction, effectively mitigating the potential RBEV issue. Furthermore, Ratel is equipped with a lightweight crash-reset mechanism, enabling the seamless recovery of transiently benign faulty nodes. To prevent the crash-reset mechanism abused by malicious entities and ward off DoS attacks, we incorporate a cost-utility analysis anchored in the Bayesian approach. Our performance evaluation of the applications developed under the Ratel framework underscores their competency in managing real-world peak-time workloads.

TEE-based smart contracts are an emerging blockchain architecture, offering fully programmable privacy with better performance than alternatives like secure multiparty computation. They can also support compatibility with existing smart contract languages, such that existing (plaintext) applications can be readily ported, picking up privacy enhancements automatically. While previous analysis of TEE-based smart contracts have focused on failures of TEE itself, we asked whether other aspects might be understudied. We focused on state consistency, a concern area highlighted by Li et al., as well as new concerns including access pattern leakage and software upgrade mechanisms. We carried out a code review of a cohort of four TEE-based smart contract platforms. These include Secret Network, the first to market with in-use applications, as well as Oasis, Phala, and Obscuro, which have at least released public test networks. The first and most broadly applicable result is that access pattern leakage occurs when handling persistent contract storage. On Secret Network, its fine-grained access pattern is catastrophic for the transaction privacy of SNIP-20 tokens. If ERC-20 tokens were naively ported to Oasis they would be similarly vulnerable; the others in the cohort leak coarse-grained information at approximately the page level (4 kilobytes). Improving and characterizing this will require adopting techniques from ORAMs or encrypted databases. Second, the importance of state consistency has been underappreciated, in part because exploiting such vulnerabilities is thought to be impractical. We show they are fully practical by building a proof-of-concept tool that breaks all advertised privacy properties of SNIP-20 tokens, able to query the balance of individual accounts and the token amount of each transfer. We additionally demonstrate MEV attacks against the Sienna Swap application. As a final consequence of lacking state consistency, the developers have inadvertently introduced a decryption backdoor through their software upgrade process. We have helped the Secret developers mitigate this through a coordinated vulnerability disclosure, after which their state consistency should be roughly on par with the rest.

Blockchains, and specifically smart contracts, have promised to create fair and transparent trading ecosystems.Unfortunately, we show that this promise has not been met. We document and quantify the widespread and rising deployment of arbitrage bots in blockchain systems, specifically in decentralized exchanges (or "DEXes"). Like high-frequency traders on Wall Street, these bots exploit inefficiencies in DEXes, paying high transaction fees and optimizing network latency to frontrun, i.e., anticipate and exploit, ordinary users' DEX trades.We study the breadth of DEX arbitrage bots in a subset of transactions that yield quantifiable revenue to these bots. We also study bots' profit-making strategies, with a focus on blockchain-specific elements. We observe bots engage in what we call priority gas auctions (PGAs), competitively bidding up transaction fees in order to obtain priority ordering, i.e., early block position and execution, for their transactions. PGAs present an interesting and complex new continuous-time, partial-information, game-theoretic model that we formalize and study. We release an interactive web portal, frontrun.me, to provide the community with real-time data on PGAs. We additionally show that high fees paid for priority transaction ordering poses a systemic risk to consensus-layer security. We explain that such fees are just one form of a general phenomenon in DEXes and beyond-what we call miner extractable value (MEV)-that poses concrete, measurable, consensus-layer security risks. We show empirically that MEV poses a realistic threat to Ethereum today. Our work highlights the large, complex risks created by transaction-ordering dependencies in smart contracts and the ways in which traditional forms of financial-market exploitation are adapting to and penetrating blockchain economies.