Ethereum rollups are mispricing small transactions by collapsing computation, data availability, and proof costs into simple fee formulas, which can inflate costs for tiny payments and enable low-cost spam or denial-of-service attacks. The study calls for multidimensional, resource-aligned fee mechanisms.
Small transfers can be overpriced or underpriced by current rollup fees.
Mispricing creates opportunities for spam, refunds abuse, and denial-of-service attacks.
Benchmarking shows wide variance across Polygon zkEVM, zkSync Era, Scroll, Optimism, and Arbitrum.
Ethereum rollups mispricing small transactions risks user cost and DoS; read mitigation steps and recommendations from researchers. Learn more with COINOTAG.
What are the fee mispricing risks in Ethereum rollups?
Ethereum rollups frequently combine computation, data availability, and proof costs into simplified fee formulas, which misalign incentives. This design can force small-value users to pay disproportionately or allow attackers to send large volumes of underpriced transactions, degrading service and raising systemic risk.
How do rollups currently calculate fees and why does it fail?
Most rollups collapse three independent resources—execution, data posting, and proof verification—into single formulas or fixed rules. That approach ignores variability in proving costs and data posting, producing inaccurate per-transaction pricing. The paper by zkSecurity, Prooflab, and Imperial College London demonstrates that flat or static fee curves can be gamed and do not reflect real resource consumption.
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Published: 2025-09-24 | Updated: 2025-09-24 | Author: COINOTAG
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The study benchmarked five major rollups—Polygon zkEVM, zkSync Era, Scroll, Optimism, and Arbitrum—comparing when fees are fixed, how refunds are handled, and how proof costs are passed to users. Researchers simulated transaction mixes and measured opportunity for refunds abuse and low-cost spam under each model.
Benchmarks revealed large variance in fee timing and refund behavior. Some networks lock fees at submission, others at batch sealing, and a few refund overcharges. These differences produce divergent incentives and exploitable seams, particularly for small-value transactions.
Multidimensional pricing is the primary recommended change: separately meter computation, data posting, and proving. Combined with dynamic fee adjustment and partial batching, this helps align user payments with actual resource use and reduces attack vectors.
Yes. Several teams are testing adaptive fee curves and real-time cost modeling. However, the study notes that standards and cross-rollup best practices are not yet established, leaving persistent systemic risk as the ecosystem scales.
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