While the literature features a number of proposals to defend against transaction manipulation attacks, existing proposals are still not integrated within large blockchains, such as Bitcoin, Ethereum, and Cardano. Instead, the user community opted to rely on more practical but ad-hoc solutions (such as Mempool.space) that aim at detecting censorship and transaction displacement attacks by auditing discrepancies in the mempools of so-called observers. In this paper, we precisely analyze, for the first time, the interplay between mempool auditing and the ability to detect censorship and transaction displacement attacks by malicious miners in Bitcoin and Ethereum. Our analysis shows that mempool auditing can result in mis-accusations against miners with a probability larger than 25% in some settings. On a positive note, however, we show that mempool auditing schemes can successfully audit the execution of any two transactions (with an overwhelming probability of 99.9%) if they are consistently received by all observers and sent at least 30 seconds apart from each other. As a direct consequence, our findings show, for the first time, that batch-order fair-ordering schemes can offer only strong fairness guarantees for a limited subset of transactions in real-world deployments.

翻译：尽管文献中已有多种防御交易操纵攻击的提案，但现有方案仍未在比特币、以太坊和卡尔达诺等大型区块链中实现集成。相反，用户社区选择依赖更实用但临时的解决方案（例如Mempool.space），其通过审计所谓观察者节点内存池中的差异来检测审查和交易置换攻击。本文首次精确分析了比特币和以太坊中内存池审计与检测恶意矿工审查及交易置换攻击能力之间的相互作用。我们的分析表明，在某些场景下，内存池审计可能导致误指控矿工的概率超过25%。然而，从积极角度看，我们证明若任意两笔交易被所有观察者持续接收且发送间隔至少30秒，内存池审计方案能以99.9%的压倒性概率成功审计其执行过程。这一发现直接表明：批量顺序公平排序方案在现实部署中仅能为有限交易子集提供强公平性保证，此为首次在学术层面揭示该结论。