SearcharxivSearch

arXiv · 2607.13378

Fair on the Surface: Transaction-Ordering Bias and MEV in Mysticeti DAG-based BFT Protocol

Abstract

Distributed systems deployed in untrustworthy environments agree on a common transaction order through Byzantine fault-tolerant (BFT) consensus protocols, and that order has real financial value in many decentralized applications: whoever influences it can profit at other users' expense, a problem known as maximal extractable value (MEV). Mysticeti is a state-of-the-art DAG-based BFT protocol in which many validators propose blocks in parallel, and the total order is derived from the resulting DAG afterward. Mysticeti is the consensus protocol powering Sui, a production blockchain with a market capitalization of roughly $3 billion, and it is widely believed to order transactions fairly, since many validators propose blocks in parallel and committed transactions are re-sorted by gas price before execution. We show this fairness assumption breaks down in practice, and the effect is already present on Sui's live network. First, when vertices of the committed graph are merged into a single total order, blocks from the same round are sorted by validator index, giving lower-indexed validators a permanent head start. In our evaluation on a 13-validator network with no attacker, the lower-indexed side wins same-round ordering about 89% of the time. Second, the gas-price re-sort intended to remove this bias uses a stable sort, so transactions paying equal fees (common at the reference gas price) retain the original biased order, letting an attacker profit without paying extra. Third, a validator can amplify this advantage by choosing when to stay silent, a fully legitimate action that violates no protocol rule; this raises its ordering win rate above 94%. We measure all three exploitations, verify that Mysticeti otherwise remains resilient below the standard Byzantine fault threshold, and propose a simple fix: replace the validator-index tiebreaker with an unpredictable, per-commit random key.

Explore related subjects

Keep this discovery

BibTeXRIS

Iliya Mirzaei, Mohammad Javad Amiri. 2026-07-15. Fair on the Surface: Transaction-Ordering Bias and MEV in Mysticeti DAG-based BFT Protocol. https://arxiv.org/abs/2607.13378

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Online Treasure Hunt in Vertex-Permuted Dynamic Rings

We study the problem of treasure hunt by a group of $k \geq 1$ agents in vertex-permuted dynamic rings (VP). In this model, the $n$ vertices remain on a ring but are permuted at each time step. We first show that treasure hunt is impossible for any $k \leq n-3$ agents, if there are no restrictions on the sequence of permutations used in the dynamic ring. We then study the $VP(\delta)$ setting, in which for every pair $i, j$ of vertices, the edge $(i, j)$ is guaranteed to appear within $\delta$ steps. We show that the class $VP(\delta)$ is feasible only for $\delta \geq \left\lceil \frac{n-1}{2}\right\rceil$. For the one-agent case, we show a tight bound of $\Theta(\delta n)$ on the worst-case search time as well as competitive ratio of any online algorithm for treasure hunt, provided $\delta \geq 2n$. We then give an optimal algorithm for $k$ agents, thereby showing that $k$ agents can obtain a speedup of $k$ on the worst-case search time. Finally, in the R-VP setting, in which in every step, the vertices are arranged as a ring according to a random permutation, we show that treasure hunt takes expected $\Theta(n)$ steps against an oblivious adversary and $\Theta(n \log n)$ steps against an adaptive adversary.

cs.DC

The Computing Channel: How Modulation Programs the Airwaves

Distributed computing and distributed artificial intelligence require frequent exchanges of intermediate results, although many applications need only an aggregate rather than messages from individual devices. Conventional systems recover each message before computing the aggregate, whereas over-the-air computation (OAC) exploits simultaneous transmission to obtain it directly. However, dominant OAC implementations rely on analog signaling, creating a mismatch with finite-precision data and digital communication procedures. This article presents digital function-oriented communication, in which finite-alphabet symbol representations and receiver decisions are jointly designed so that multiple-access superposition encodes the desired function without recovering individual inputs. We introduce its computational-constellation principle, main design approaches, extensions, and implementation challenges. Federated edge learning illustrates how the framework can reduce user-dependent data-bearing resources while operating directly on quantized model updates.

cs.DC

Can AI Remediate Backend Failures Safely? GuardedAct with Blast-Radius-Aware Sandboxing

Large Language Models (LLMs) have shown promising capabilities in generating remediation actions for microservice failures. However, directly executing AI-generated repair actions in production risks cascading collateral damage. We propose GuardedAct, a sandbox-first remediation framework that interposes a blast-radius-aware verification layer between the LLM action generator and the production environment. GuardedAct operates in four phases: (1) ingesting a diagnosis report together with the live system topology and recent telemetry, (2) prompting an LLM to produce a ranked list of candidate remediation actions, (3) simulating each action in a lightweight digital-twin sandbox that estimates the blast radius and assigns a risk label, and (4) enforcing a rollback-confidence gate that auto-executes only low-risk actions while escalating high-risk ones for human review. We evaluate GuardedAct on five fault scenarios injected into the DeathStarBench social-network application. Experimental results show that GuardedAct achieves an overall recovery rate of 87.4% while reducing collateral damage by 79.7% relative to direct LLM execution (from 25.6% to 5.2%), at the cost of a modest sandbox-induced increase in mean time to recovery (approximately 8 s). Ablation studies confirm that each component contributes meaningfully to the safety-speed trade-off.

cs.DC