On the Effectiveness of Mempool-based Transaction Auditing

TL;DR

The paper investigates mempool-based transaction auditing as a practical approach to detect censorship and displacement attacks in Bitcoin and Ethereum. It develops a realistic network model and derives probabilistic guarantees for observers' agreement on transaction order, validating the theory with large-scale measurements. The results reveal that while mempool auditing can detect many ordering anomalies with enough time separation and observers, it can still mislabel honest miners and cannot reliably detect cross-block displacement in all cases. The findings have direct implications for the design and deployment of batch-order fairness schemes, showing they only apply to a subset of real-world transactions and highlighting the need for cautious interpretation of auditing-based reputational signals.

Abstract

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.

Figures (8)

• Figure 1: An excerpt of the block audit by the Mempool Open Source Project mempool_space. Figure (a) shows an audit of a block's removed, i.e., potentially censored, transaction. Figure (b) shows Mempool.space's ranking of the leading Bitcoin pools in terms of block health.
• Figure 2: Examples showing adversary $A$ executing displacement attacks: In Figure (a), adversary $A$ censors transaction $t_{U_3}$ by omitting it from block $\mathcal{B}$, while two observers $O_1$ and $O_2$ expect $t_{U_3}$ to be scheduled before $t_{U_2}$. Figure (b) shows a benign example, where both observers are in dispute on the rightful order of $t_{U_2}$ and $t_{U_3}$.
• Figure 3: The PDF/CDF of transaction transmission times based on the KIT dataset (dashed) and its log-normal approximation (solid).
• Figure 4: $\sum_{m\xspace'\geq m\xspace} \mathbb{Q}\xspace_{n\xspace}(m\xspace')$ / $\mathbb{P}\xspace_{n\xspace}(m\xspace)$ w.r.t. the number of observers $n$.
• Figure 5: Comparison of $\sum_{m\xspace'\geq m\xspace} \mathbb{Q}\xspace_{16}(m\xspace')$ (dashed lines) with the empirically measured $\mathcal{C}_{order}(m\xspace)$ (solid/dotted lines) using the complete dataset (All Txs) and the set of transactions received by all observers (FR Txs) measured by 16 Bitcoin observers.

Theorems & Definitions (7)

• Theorem 1
• Theorem 2
• Proposition 1
• Theorem 3
• proof : Proof of \ref{['th:generalized_multiple_observers']}
• proof : Proof of \ref{['th:predictive_precision']}
• proof : Proof of \ref{['th:DAB']}