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arxiv: 2605.01025 · v1 · submitted 2026-05-01 · 💻 cs.GT

Recognition: unknown

Your Loss is My Gain: Low Stake Attacks on Liquid Staking Pools

Arthur Gervais, Aviv Yaish, Fan Zhang, Sen Yang

Pith reviewed 2026-05-09 17:53 UTC · model grok-4.3

classification 💻 cs.GT
keywords liquid stakingproof of stakeconsensus attackscross-layer attacksethereumreinforcement learningeconomic securityliquid staking tokens
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The pith

A low-stake adversary can degrade a liquid staking pool's performance to profit from drops in its token price.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that the operational performance of liquid staking pools on Ethereum shows a positive association with subsequent returns on the corresponding liquid staking tokens. If this link holds under deliberate interference, a low-stake attacker can interfere with consensus operations to lower a target pool's performance. The attacker then opens application-layer positions that gain when the token's market price adjusts downward in line with past patterns. Simulations indicate that leveraged short positions can succeed with more than 50 percent probability for major pools. This reveals a risk in Proof-of-Stake systems that combine staking derivatives with consensus participation.

Core claim

Empirical analysis of Ethereum data shows that liquid staking pool performance is positively associated with later normalized liquid staking token returns. This association supports a cross-layer attack in which a low-stake adversary manipulates the consensus protocol to degrade a chosen pool and then takes positions that profit if the token is repriced accordingly. Deep reinforcement learning identifies strategies that recover near-optimal theoretical attacks and discover additional behaviors that degrade performance; Monte Carlo simulations confirm that leveraged shorting on major pools can be profitable with over one-half probability.

What carries the argument

The cross-layer attack that combines consensus-layer manipulation to degrade pool performance with application-layer financial positions that exploit the observed performance-to-LST-return association.

If this is right

  • Standard economic security arguments for Proof-of-Stake do not account for risks introduced by liquid staking.
  • Low-stake adversaries can use reinforcement learning to find and execute performance-degrading strategies beyond manually derived theoretical attacks.
  • Leveraged shorting of liquid staking tokens can yield positive expected returns when a target pool is degraded.
  • New manipulation behaviors that significantly reduce target pool performance can be uncovered automatically.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Market pricing of liquid staking tokens may begin to incorporate the risk of targeted degradation attacks.
  • Similar performance-return associations could be examined in other Proof-of-Stake networks that support liquid staking.
  • Protocol changes that make pool performance harder to manipulate in isolation might reduce the attack surface.
  • Empirical tests after public awareness of these strategies could reveal whether the historical association persists.

Load-bearing premise

The positive association between pool performance and subsequent LST returns will continue to shape market prices even when performance is deliberately reduced by an attacker.

What would settle it

Real market data showing that deliberate degradation of a pool's performance fails to produce the expected downward repricing of its liquid staking token.

Figures

Figures reproduced from arXiv: 2605.01025 by Arthur Gervais, Aviv Yaish, Fan Zhang, Sen Yang.

Figure 1
Figure 1. Figure 1: The distribution of stake in Ethereum by staker category, based on data from [43] view at source ↗
Figure 2
Figure 2. Figure 2: An overview of our attacks. The adversary first targets the consensus layer by view at source ↗
Figure 3
Figure 3. Figure 3: DRL framework for automatic discovery of consensus-layer adversarial strategies. view at source ↗
Figure 4
Figure 4. Figure 4: The action space of the adversary. adversarial decisions. Once the adversary initiates a private chain, it must repeatedly execute the action ahide to accumulate sufficient attestation weight; deviating earlier is infeasible if a reorganization is to succeed. As a result, the adversary’s only non-trivial decision point arises after a private chain exists and an honest validator publishes a competing block,… view at source ↗
Figure 5
Figure 5. Figure 5: Expected adversarial slots under different strategies and adversary stake. view at source ↗
Figure 6
Figure 6. Figure 6: A heatmap of the impact of learned pure griefing strategy under varying stake view at source ↗
Figure 7
Figure 7. Figure 7: Chain-quality impact of pure-griefing strategies. view at source ↗
Figure 8
Figure 8. Figure 8: Effect of MEV on the chain-quality cost of learned policies. The left panel reports view at source ↗
Figure 9
Figure 9. Figure 9: ECDFs of net profit from application-layer attacks on three LSTs. For a given view at source ↗
Figure 10
Figure 10. Figure 10: Ethereum’s stake distribution over time, according to staker category. Ethereum view at source ↗
Figure 11
Figure 11. Figure 11: Victim loss under three settings: Baseline (RQ2 training per stake), A2C (the view at source ↗
Figure 12
Figure 12. Figure 12: Heatmap of the impact of the learned self-optimization strategy under varying view at source ↗
Figure 13
Figure 13. Figure 13: Heatmap of the impact of the learned pure-griefing strategy under varying MEV view at source ↗
Figure 14
Figure 14. Figure 14: Profit distributions under higher slippage bounds using the 60-day calibration. view at source ↗
Figure 15
Figure 15. Figure 15: Profit distributions under the 90-day calibration across slippage bounds. view at source ↗
read the original abstract

Permissionless Proof-of-Stake (PoS) economic security is predicated on the high cost of violating consensus safety or liveness. We show that liquid staking introduces additional risks that are not captured by standard PoS economic security arguments. Through an empirical study of Ethereum data, we find that the operational performance of liquid staking pools is positively associated with subsequent normalized liquid staking token (LST) returns. Motivated by this, we present a cross-layer attack: a low-stake adversary can manipulate the consensus protocol to degrade a target pool's performance and take application-layer positions that profit if the market reprices the corresponding \gls{LST} in-line with the historically observed association. To make the consensus layer manipulation concrete, we develop a deep reinforcement learning (DRL) framework to automatically discover attack strategies. Our evaluation shows that the learned strategies can recover near-optimal theoretical attacks and uncover new manipulation behaviors that significantly degrade target pool performance. We further characterize feasible application-layer monetization channels and analyze leveraged shorting in detail using Monte Carlo simulations, showing that such attacks can be profitable with over one-half probability for LSTs of major staking pools. Our findings reveal a previously overlooked attack surface in PoS systems with liquid staking and expose a gap between consensus and economic security.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper claims that liquid staking in PoS systems creates a cross-layer attack surface: an empirical study of Ethereum data shows a positive association between liquid staking pool operational performance and subsequent normalized LST returns; a low-stake adversary can exploit this by using DRL-discovered consensus manipulations to degrade a target pool's performance and then profit via application-layer positions (e.g., leveraged shorting of the LST) if the market reprices the token in line with the historical association. Monte Carlo simulations indicate such attacks are profitable with probability exceeding one-half for major pools.

Significance. If the central claims hold, the work identifies a novel interaction between consensus-layer manipulation and application-layer pricing in liquid-staking PoS systems, exposing a gap in standard economic-security arguments. The DRL framework for automatically discovering near-optimal and novel attack strategies, together with the explicit Monte Carlo characterization of monetization channels, provides concrete, reproducible tools that strengthen the contribution beyond purely theoretical analysis.

major comments (2)
  1. [Monte Carlo simulations section] The profitability analysis (Monte Carlo simulations of leveraged shorting) applies the historically observed positive association between pool performance and normalized LST returns directly to attack-induced degradation. No out-of-sample validation, causal identification, or robustness check is reported for whether market participants would reprice LSTs identically when degradation is attributable to an identifiable low-stake consensus attack rather than organic factors.
  2. [Empirical study section] The empirical study reports an observational correlation from Ethereum data but provides no test of whether the performance-return relationship survives when performance degradation is deliberately engineered by a low-stake adversary (as opposed to natural variation). This assumption is load-bearing for the cross-layer attack claim.
minor comments (2)
  1. [Empirical study section] Clarify the precise definition of 'normalized LST returns' and the exact performance metrics used in the empirical association (e.g., attestation rate, inclusion delay) to allow independent replication.
  2. [DRL framework section] The DRL training setup (state space, reward function, and convergence criteria) should be described with sufficient detail for reproducibility, including any hyperparameter sensitivity analysis.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for the constructive comments, which correctly highlight the observational character of our empirical analysis and the assumptions in our Monte Carlo simulations. We address each point below and will revise the manuscript to add explicit discussion of these limitations and additional robustness checks.

read point-by-point responses

  1. Referee: [Monte Carlo simulations section] The profitability analysis (Monte Carlo simulations of leveraged shorting) applies the historically observed positive association between pool performance and normalized LST returns directly to attack-induced degradation. No out-of-sample validation, causal identification, or robustness check is reported for whether market participants would reprice LSTs identically when degradation is attributable to an identifiable low-stake consensus attack rather than organic factors.

    Authors: We agree that the Monte Carlo analysis applies the historical association to attack-induced degradation without direct validation of market repricing under adversarial conditions. This is a genuine limitation, as out-of-sample tests would require observing or inducing identifiable low-stake consensus attacks on the live Ethereum network, which is neither feasible nor ethical. In revision we will add a dedicated limitations paragraph to the Monte Carlo section that states this assumption explicitly, discusses possible differences in market response (e.g., attribution or reputation effects), and reports additional robustness checks on the underlying empirical correlation using alternative specifications and controls for market-wide factors. revision: partial

  2. Referee: [Empirical study section] The empirical study reports an observational correlation from Ethereum data but provides no test of whether the performance-return relationship survives when performance degradation is deliberately engineered by a low-stake adversary (as opposed to natural variation). This assumption is load-bearing for the cross-layer attack claim.

    Authors: The referee is correct: the empirical study uses observational data on natural performance variation and contains no test of deliberately engineered degradation by a low-stake adversary. Such data do not exist in the Ethereum record, and inducing attacks for identification purposes is impossible within ethical and practical constraints. Our contribution identifies a potential cross-layer risk surface motivated by the observed association rather than asserting causal proof. In revision we will strengthen the language in the empirical study section and introduction to emphasize the correlational nature of the evidence and the modeling assumption required for the attack claim, thereby clarifying the scope of the results. revision: partial

standing simulated objections not resolved
  • Direct out-of-sample validation or causal identification of market repricing under low-stake adversarial degradation, because this would require inducing or observing such attacks on the Ethereum mainnet.

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The paper's derivation begins with an empirical observational study on Ethereum data establishing a positive association between pool performance and LST returns, then motivates a hypothetical attack that could profit if markets reprice consistently with that association under deliberate degradation. This structure does not reduce any core claim to its inputs by construction: the association is treated as an external historical pattern used to bound potential profits in Monte Carlo simulations, rather than the simulations or attack profitability being a direct renaming or re-application of the fitted values themselves. The DRL framework for discovering consensus manipulations is developed independently as a new contribution. No self-citations, uniqueness theorems, or ansatzes from prior author work are invoked as load-bearing elements in the provided text, and the central claims introduce new attack surfaces and monetization channels without definitional loops or forced statistical equivalence to the input correlation.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on an empirical correlation between pool performance and LST returns that is treated as a stable basis for attack design and market response.

axioms (1)
  • domain assumption Operational performance of liquid staking pools is positively associated with subsequent normalized LST returns and this association will be reflected in market pricing
    This relationship is the load-bearing link between consensus-layer degradation and application-layer profit.

pith-pipeline@v0.9.0 · 5527 in / 1191 out tokens · 49467 ms · 2026-05-09T17:53:44.800221+00:00 · methodology

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Reference graph

Works this paper leans on

95 extracted references · 57 canonical work pages · 2 internal anchors

  1. [1]

    Accessed: 2026-01-24

    Aave.Aave Protocol.https://aave.com/. Accessed: 2026-01-24. 2026

    2026

  2. [2]

    Optuna: A Next-generation Hyperparameter Optimization Framework

    Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama. “Optuna: A Next-generation Hyperparameter Optimization Framework”. In:Proceed- ings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ’19. Anchorage, AK, USA: Association for Computing Machinery, 2019, pp. 2623–2631.isbn: 9781450362016.d...

    work page doi:10.1145/3292500.3330701 2019

  3. [3]

    OptimalRANDAOManipulationinEthereum

    KayaAlpturerandS.MatthewWeinberg.“OptimalRANDAOManipulationinEthereum”. en. In:6th Conference on Advances in Financial Technologies (AFT 2024). Dagstuhl, Germany: Schloss Dagstuhl – Leibniz-Zentrum für Informatik, 2024, 10:1–10:21.doi: 10 . 4230 / LIPIcs . AFT . 2024 . 10.url:https : / / drops . dagstuhl . de / entities / document/10.4230/LIPIcs.AFT.2024.10

    work page doi:10.4230/lipics.aft.2024.10 2024

  4. [4]

    Heteroskedasticity and autocorrelation consistent covariance matrix estimation

    Donald WK Andrews. “Heteroskedasticity and autocorrelation consistent covariance matrix estimation”. In:Econometrica: Journal of the Econometric Society(1991), pp. 817–858. 25

    1991

  5. [5]

    In: 44th IEEE Symposium on Security and Privacy, SP 2023, San Francisco, CA, USA, May 21-25, 2023

    Roi Bar-Zur, Ameer Abu-Hanna, Ittay Eyal, and Aviv Tamar. “ WeRLman: To Tackle Whale (Transactions), Go Deep (RL) ”. In:2023 IEEE Symposium on Security and Privacy (SP). Los Alamitos, CA, USA: IEEE Computer Society, 2023, pp. 93–110. doi:10.1109/SP46215.2023.10179444.url:https://doi.ieeecomputersociety. org/10.1109/SP46215.2023.10179444

    work page doi:10.1109/sp46215.2023.10179444.url:https://doi.ieeecomputersociety 2023

  6. [6]

    Deep Bribe: Predicting the Rise of Bribery in Blockchain Mining with Deep RL

    Roi Bar-Zur, Danielle Dori, Sharon Vardi, Ittay Eyal, and Aviv Tamar. “Deep Bribe: Predicting the Rise of Bribery in Blockchain Mining with Deep RL”. In:2023 IEEE Security and Privacy Workshops (SPW). 2023, pp. 29–37.doi:10.1109/SPW59333. 2023.00008.url:https://ieeexplore.ieee.org/abstract/document/10188634

    work page doi:10.1109/spw59333 2023

  7. [7]

    MAD-DAG: Protecting Blockchain Con- sensus from MEV

    Roi Bar-Zur, Aviv Tamar, and Ittay Eyal. “MAD-DAG: Protecting Blockchain Con- sensus from MEV”. In:arXiv preprint arXiv:2511.21552(2025)

    work page arXiv 2025

  8. [8]

    Binance.Ethereum Staking on Binance.https : / / www . binance . com / en / earn / ethereum-staking. Accessed: 2026-02-03. Binance, 2026

    2026

  9. [9]

    Verifiable Delay Func- tions

    Dan Boneh, Joseph Bonneau, Benedikt Bünz, and Ben Fisch. “Verifiable Delay Func- tions”. In:Annual International Cryptology Conference. Springer. 2018, pp. 757–788

    2018

  10. [10]

    org/web/20160521011553/https://vitalik.ca/files/randomness.html

    Vitalik Buterin.Validator Ordering and Randomness in PoS.https://web.archive. org/web/20160521011553/https://vitalik.ca/files/randomness.html. 2016

    work page arXiv 2016

  11. [11]

    ch/t/randao-beacon-exploitability-analysis-round-2/1980

    VitalikButerin.RANDAO beacon exploitability analysis, round 2.https://ethresear. ch/t/randao-beacon-exploitability-analysis-round-2/1980. 2018

    1980

  12. [12]

    Vitalik Buterin.Possible futures of the Ethereum protocol, part 1: The Merge. 2024. url:https://web.archive.org/web/20241201230703/https://vitalik.eth. limo/general/2024/10/14/futures1.html

    work page arXiv 2024

  13. [13]

    Vitalik Buterin.Some thoughts on solo staking, what realistic value solo (+ small- business and community) stakers could provide to the network, and what changes L1 can make to better support solo stakers.2024.url:https://x.com/VitalikButerin/ status/1838461704771907726

    work page arXiv 2024

  14. [14]

    Combining ghost and casper

    Vitalik Buterin, Diego Hernandez, Thor Kamphefner, Khiem Pham, Zhi Qiao, Danny Ryan, Juhyeok Sin, Ying Wang, and Yan X Zhang. “Combining ghost and casper”. In: arXiv preprint arXiv:2003.03052(2020)

    work page arXiv 2003

  15. [15]

    Profitable Manip- ulations of Cryptographic Self-Selection Are Statistically Detectable

    Linda Cai, Jingyi Liu, S. Matthew Weinberg, and Chenghan Zhou. “Profitable Manip- ulations of Cryptographic Self-Selection Are Statistically Detectable”. In:6th Confer- ence on Advances in Financial Technologies (AFT 2024). Ed. by Rainer Böhme and Lucianna Kiffer. Vol. 316. Leibniz International Proceedings in Informatics (LIPIcs). Dagstuhl, Germany: Schl...

    work page doi:10.4230/lipics.aft.2024.30.url:https: 2024

  16. [16]

    Confidence in Assurance 2.0 Cases

    Tong Cao, Jérémie Decouchant, and Jiangshan Yu. “Leveraging the verifier’s dilemma to double spend in Bitcoin”. In:Financial Cryptography and Data Security: 27th In- ternational Conference, FC 2023, Bol, Brač, Croatia, May 1–5, 2023, Revised Selected Papers, Part II. Bol, Brač, Croatia: Springer-Verlag, 2023, pp. 149–165.isbn: 978-3- 031-47750-8.doi:10.10...

    work page doi:10.1007/978- 2023

  17. [17]

    Algorand: A secure and efficient distributed ledger

    Jing Chen and Silvio Micali. “Algorand: A secure and efficient distributed ledger”. In:Theoretical Computer Science. In memory of Maurice Nivat, a founding father of Theoretical Computer Science - Part I 777 (2019), pp. 155–183.issn: 0304-3975.doi: 10.1016/j.tcs.2019.02.001.url:https://www.sciencedirect.com/science/ article/pii/S030439751930091X

    work page doi:10.1016/j.tcs.2019.02.001.url:https://www.sciencedirect.com/science/ 2019

  18. [18]

    In: 44th IEEE Symposium on Security and Privacy, SP 2023, San Francisco, CA, USA, May 21-25, 2023

    Kevin Choi, Aathira Manoj, and Joseph Bonneau. “ SoK: Distributed Randomness Beacons ”. In:2023 IEEE Symposium on Security and Privacy (SP). Los Alamitos, CA, USA: IEEE Computer Society, 2023, pp. 75–92.doi:10.1109/SP46215.2023. 10179419.url:https://doi.ieeecomputersociety.org/10.1109/SP46215.2023. 10179419

    work page doi:10.1109/sp46215.2023 2023

  19. [19]

    com/ddos/

    Cloudflare, Inc.DDoS Protection & Mitigation Solutions.https://www.cloudflare. com/ddos/. Accessed: 2026-02-04. 2026

    2026

  20. [20]

    routledge, 2013

    Jacob Cohen.Statistical power analysis for the behavioral sciences. routledge, 2013

    2013

  21. [21]

    coingecko

    CoinGecko.CoinGecko Crypto Data API.https : / / www . coingecko . com / en / api. Accessed: 2025-12-11. 2025

    2025

  22. [22]

    Snow White: Robustly Reconfigurable Con- sensus and Applications to Provably Secure Proof of Stake

    Phil Daian, Rafael Pass, and Elaine Shi. “Snow White: Robustly Reconfigurable Con- sensus and Applications to Provably Secure Proof of Stake”. In:Financial Cryptography and Data Security. Ed. by Ian Goldberg and Tyler Moore. Cham: Springer Interna- tional Publishing, 2019, pp. 23–41.isbn: 978-3-030-32101-7

    2019

  23. [23]

    Flash boys 2.0: Frontrunning in decentralized exchanges, miner extractable value, and consensus instability,

    Philip Daian, Steven Goldfeder, Tyler Kell, Yunqi Li, Xueyuan Zhao, Iddo Bentov, Lorenz Breidenbach, and Ari Juels. “Flash Boys 2.0: Frontrunning in Decentralized Exchanges, Miner Extractable Value, and Consensus Instability”. In:2020 IEEE Sym- posium on Security and Privacy (SP). San Francisco, CA, USA: IEEE, 2020, pp. 910– 927.doi:10.1109/SP40000.2020.00040

    work page doi:10.1109/sp40000.2020.00040 2020

  24. [24]

    In2025 IEEE Symposium on Security and Privacy (SP)

    Wanning Ding, Yuzhe Tang, and Yibo Wang. “Asymmetric Mempool DoS Security: Formal Definitions and Provable Secure Designs”. English. In:2025 IEEE Symposium on Security and Privacy (SP). IEEE Computer Society, 2025, pp. 1584–1602.isbn: 9798331522360.doi:10.1109/SP61157.2025.00061.url:https://www.computer. org/csdl/proceedings-article/sp/2025/223600a061/21B7R6HnWWk

    work page doi:10.1109/sp61157.2025.00061.url:https://www.computer 2025

  25. [25]

    2018.url:https://web.archive.org/web/20210224073927/ http://paul.oemm.org/commit_reveal_subcommittees.pdf

    Paul Dworzanski.A note on committee random number generation, commit-reveal, and last-revealer attacks. 2018.url:https://web.archive.org/web/20210224073927/ http://paul.oemm.org/commit_reveal_subcommittees.pdf

    work page arXiv 2018

  26. [26]

    Accessed: 2026-01-24

    dYdX.dYdX Protocol.https://www.dydx.xyz/. Accessed: 2026-01-24. 2026. 27

    2026

  27. [27]

    2023.url:https://web

    Ben Edgington.Upgrading Ethereum | 2.9.2 Randomness. 2023.url:https://web. archive . org / web / 20240204094612 / https : / / eth2book . info / capella / part2 / building_blocks/randomness/

    2023

  28. [28]

    Ethereum.The Beacon Chain.2024.url:https://web.archive.org/web/20241128055106/ https://ethereum.org/en/roadmap/beacon-chain/

    work page arXiv 2024

  29. [29]

    org / developers / docs / consensus - mechanisms / pos / attack - and - defense

    EthereumFoundation.Ethereum Proof-of-Stake Attack and Defense.https://ethereum. org / developers / docs / consensus - mechanisms / pos / attack - and - defense. Ac- cessed: 2026-01-27. 2025

    2026

  30. [30]

    Accessed: 2026-04-22

    Ethereum Foundation.Beacon-API.https://ethereum.github.io/beacon-APIs/. Accessed: 2026-04-22. 2026

    2026

  31. [31]

    Accessed January 30, 2026

    Etherscan.Ethereum Gas Tracker.https://etherscan.io/gastracker. Accessed January 30, 2026. 2026

    2026

  32. [32]

    Majority is not enough: Bitcoin mining is vulnerable

    Ittay Eyal and Emin Gün Sirer. “Majority is not enough: Bitcoin mining is vulnerable”. In:International conference on financial cryptography and data security. Vol. 61. Asso- ciation for Computing Machinery (ACM), 2014, pp. 436–454.doi:10.1145/3212998

    work page doi:10.1145/3212998 2014

  33. [33]

    Optimal Strategic Mining Against Cryptographic Self-Selection in Proof-of-Stake

    Matheus V.X. Ferreira, Ye Lin Sally Hahn, S. Matthew Weinberg, and Catherine Yu. “Optimal Strategic Mining Against Cryptographic Self-Selection in Proof-of-Stake”. In:Proceedings of the 23rd ACM Conference on Economics and Computation. EC ’22. Boulder, CO, USA: Association for Computing Machinery, 2022, pp. 89–114.isbn: 9781450391504.doi:10.1145/3490486.3538337

    work page doi:10.1145/3490486.3538337 2022

  34. [34]

    flashbots

    Flashbots.MEV-Boost in a Nutshell.https : / / boost . flashbots . net/. Accessed: 2026-04-22. 2026

    2026

  35. [35]

    Accessed: 2026-04-22

    Flashbots.MEV-Boost Relay API.https://flashbots.github.io/relay-specs/. Accessed: 2026-04-22. 2026

    2026

  36. [36]

    Bryan Ford and Rainer Böhme.Rationality is Self-Defeating in Permissionless Sys- tems. Oct. 2019.doi:10.48550/arXiv.1910.08820.url:http://arxiv.org/abs/ 1910.08820

    work page doi:10.48550/arxiv.1910.08820.url:http://arxiv.org/abs/ 2019

  37. [37]

    Accessed: 2026-01-24

    Gearbox.Gearbox Protocol.https://gearbox.finance/. Accessed: 2026-01-24. 2026

    2026

  38. [38]

    2022.doi:10.48550/arXiv.2203.05838

    HansGersbach,AkakiMamageishvili,andManvirSchneider.Staking Pools on Blockchains. 2022.doi:10.48550/arXiv.2203.05838. arXiv:2203.05838 [cs.GT].url:https: //arxiv.org/abs/2203.05838

    work page doi:10.48550/arxiv.2203.05838 2022

  39. [39]

    In : Pro- ceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (CCS 2016), pp

    ArthurGervais,GhassanO.Karame,KarlWüst,VasileiosGlykantzis,HubertRitzdorf, and Srdjan Capkun. “On the Security and Performance of Proof of Work Blockchains”. In:Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communica- tions Security. CCS ’16. Vienna, Austria: Association for Computing Machinery, 2016, pp. 3–16.isbn: 9781450341394.doi:10.11...

    work page doi:10.1145/2976749.2978341 2016

  40. [40]

    Eclipse Attacks on Bitcoin’s Peer-to-Peer Network

    Ethan Heilman, Alison Kendler, Aviv Zohar, and Sharon Goldberg. “Eclipse Attacks on Bitcoin’s Peer-to-Peer Network”. In:24th USENIX Security Symposium (USENIX Security 15). Washington, D.C.: USENIX Association, Aug. 2015, pp. 129–144.isbn: 978-1-939133-11-3.url:https://www.usenix.org/conference/usenixsecurity15/ technical-sessions/presentation/heilman. 28

    2015

  41. [41]

    Exploring the orthogonality and linearity of backdoor attacks,

    Lioba Heimbach, Vabuk Pahari, and Eric Schertenleib. “Non-Atomic Arbitrage in De- centralized Finance”. In:2024 IEEE Symposium on Security and Privacy (SP). IEEE. 2024, pp. 3866–3884.doi:10.1109/SP54263.2024.00256

    work page doi:10.1109/sp54263.2024.00256 2024

  42. [42]

    2024.doi:10.48550/arXiv.2409.04366

    Lioba Heimbach, Yann Vonlanthen, Juan Villacis, Lucianna Kiffer, and Roger Watten- hofer.Deanonymizing Ethereum Validators: The P2P Network Has a Privacy Issue. 2024.doi:10.48550/arXiv.2409.04366. arXiv:2409.04366 [cs.CR].url:https: //arxiv.org/abs/2409.04366

    work page doi:10.48550/arxiv.2409.04366 2024

  43. [43]

    Accessed: 2026-01-27

    hildobby.Ethereum Staking Overview.https://dune.com/hildobby/eth2-staking. Accessed: 2026-01-27. 2026

    2026

  44. [44]

    SquirRL: Automating Attack Analysis on Blockchain Incentive Mechanisms with Deep Reinforcement Learning

    Charlie Hou, Mingxun Zhou, Yan Ji, Phil Daian, Florian Tramer, Giulia Fanti, and Ari Juels. “SquirRL: Automating Attack Analysis on Blockchain Incentive Mechanisms with Deep Reinforcement Learning”. In:28th Annual Network and Distributed System Security Symposium, NDSS 2021, virtually, February 21-25, 2021. Reston, VA: The Internet Society, 2021.doi:10 . ...

    2021

  45. [45]

    A closer look at invalid action masking in policy gradient algorithms

    Shengyi Huang and Santiago Ontañón. “A closer look at invalid action masking in policy gradient algorithms”. In:arXiv preprint arXiv:2006.14171(2020)

    work page arXiv 2006

  46. [46]

    Ouroboros: A Provably Secure Proof-of-Stake Blockchain Protocol

    AggelosKiayias,AlexanderRussell,BernardoDavid,andRomanOliynykov.“Ouroboros: A Provably Secure Proof-of-Stake Blockchain Protocol”. In:Advances in Cryptology – CRYPTO 2017. Ed. by Jonathan Katz and Hovav Shacham. Cham: Springer Interna- tional Publishing, 2017, pp. 357–388.isbn: 978-3-319-63688-7

    2017

  47. [47]

    SoK: DeFi Lending and Yield Aggregation Protocol Taxonomy, Empirical Measurements, and Security Challenges

    Arad Kotzer, Tom Azoulay, Yoad Abels, Aviv Yaish, and Ori Rottenstreich. “SoK: DeFi Lending and Yield Aggregation Protocol Taxonomy, Empirical Measurements, and Security Challenges”. In:IEEE Transactions on Network and Service Management (2026), pp. 1–1.issn: 1932-4537.doi:10.1109/TNSM.2026.3682174

    work page doi:10.1109/tnsm.2026.3682174 2026

  48. [48]

    Jois, Matthew Green, and Aviel D

    Kai Li, Yibo Wang, and Yuzhe Tang. “DETER: Denial of Ethereum Txpool SERvices”. In:Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communica- tions Security. CCS ’21. Virtual Event, Republic of Korea: Association for Comput- ing Machinery, 2021, pp. 1645–1667.isbn: 9781450384544.doi:10.1145/3460120. 3485369

    work page doi:10.1145/3460120 2021

  49. [49]

    Bun- nyFinder: Finding Incentive Flaws for Ethereum Consensus

    Rujia Li, Mingfei Zhang, Xueqian Lu, Wenbo Xu, Ying Yan, and Sisi Duan. “Bun- nyFinder: Finding Incentive Flaws for Ethereum Consensus”. In:Cryptology ePrint Archive(2025)

    2025

  50. [50]

    Analysis of Factors that Drive Ethereum Staking Deposit and With- drawal Trends across Various Staking Participant Groups

    William Meng. “Analysis of Factors that Drive Ethereum Staking Deposit and With- drawal Trends across Various Staking Participant Groups”. In:Available at SSRN 4949780(2024).doi:10.2139/ssrn.4949780

    work page doi:10.2139/ssrn.4949780 2024

  51. [51]

    Asynchronous methods for deep reinforcement learning

    Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu. “Asynchronous methods for deep reinforcement learning”. In:International conference on machine learning. PmLR. 2016, pp. 1928–1937. 29

    2016

  52. [52]

    Forking the RANDAO: Manipulating Ethereum’s Distributed Randomness Beacon

    Ábel Nagy, János Tapolcai, István András Seres, and Bence Ladóczki. “Forking the RANDAO: Manipulating Ethereum’s Distributed Randomness Beacon”. In:Proceed- ings of the 2025 ACM SIGSAC Conference on Computer and Communications Secu- rity. CCS ’25. Taipei, Taiwan: Association for Computing Machinery, 2025, pp. 873– 887.isbn: 9798400715259.doi:10.1145/37190...

    work page doi:10.1145/3719027.3744852.url:https://doi.org/ 2025

  53. [53]

    SelfishMiningRe-Examined

    KevinAlarcónNegy,PeterR.Rizun,andEminGünSirer.“SelfishMiningRe-Examined”. In:Financial Cryptography and Data Security. Ed. by Joseph Bonneau and Nadia Heninger. Cham: Springer International Publishing, 2020, pp. 61–78.isbn: 978-3-030- 51280-4

    2020

  54. [54]

    One Engine to Fuzz 'em All: Generic Language Processor Testing with Semantic Validation,

    Joachim Neu, Ertem Nusret Tas, and David Tse. “Ebb-and-Flow Protocols: A Resolu- tion of the Availability-Finality Dilemma”. In:2021 IEEE Symposium on Security and Privacy (SP). 2021, pp. 446–465.doi:10.1109/SP40001.2021.00045

    work page doi:10.1109/sp40001.2021.00045 2021

  55. [55]

    Two More Attacks on Proof-of-Stake GHOST/Ethereum

    Joachim Neu, Ertem Nusret Tas, and David Tse. “Two More Attacks on Proof-of-Stake GHOST/Ethereum”. In:Proceedings of the 2022 ACM Workshop on Developments in Consensus. ConsensusDay ’22. Los Angeles, CA, USA: Association for Computing Machinery, 2022, pp. 43–52.isbn: 9781450398794.doi:10.1145/3560829.3563560. url:https://doi.org/10.1145/3560829.3563560

    work page doi:10.1145/3560829.3563560 2022

  56. [56]

    Low-cost attacks on Ethereum 2.0 by sub-1/3 stakeholders

    Michael Neuder, Daniel J Moroz, Rithvik Rao, and David C Parkes. “Low-cost attacks on Ethereum 2.0 by sub-1/3 stakeholders”. In:arXiv preprint arXiv:2102.02247(2021)

    work page arXiv 2021

  57. [57]

    Who Wins Ethereum Block Building Auctions and Why?

    Burak Öz, Danning Sui, Thomas Thiery, and Florian Matthes. “Who Wins Ethereum Block Building Auctions and Why?” In:6th Conference on Advances in Financial Technologies (AFT 2024). Ed. by Rainer Böhme and Lucianna Kiffer. Vol. 316. Leib- niz International Proceedings in Informatics (LIPIcs). Dagstuhl, Germany: Schloss Dagstuhl – Leibniz-Zentrum für Informa...

    work page doi:10.4230/lipics.aft.2024.22.url:https://drops.dagstuhl.de/entities/ 2024

  58. [58]

    Cross-Chain Arbitrage: The Next Frontier of MEV in Decentralized Finance

    Burak Öz, Christof Ferreira Torres, Christoph Schlegel, Bruno Mazorra, Jonas Gebele, Filip Rezabek, and Florian Matthes. “Cross-Chain Arbitrage: The Next Frontier of MEV in Decentralized Finance”. In:Proc. ACM Meas. Anal. Comput. Syst.9.3 (Dec. 2025).doi:10.1145/3771566.url:https://doi.org/10.1145/3771566

    work page doi:10.1145/3771566.url:https://doi.org/10.1145/3771566 2025

  59. [59]

    Ethereum Proof-of-Stake under Scrutiny

    Ulysse Pavloff, Yackolley Amoussou-Guenou, and Sara Tucci-Piergiovanni. “Ethereum Proof-of-Stake under Scrutiny”. In:Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing. SAC ’23. Tallinn, Estonia: Association for Computing Machin- ery, 2023, pp. 212–221.isbn: 9781450395175.doi:10.1145/3555776.3577655.url: https://doi.org/10.1145/3555776.3577655

    work page doi:10.1145/3555776.3577655.url: 2023

  60. [60]

    Polymarket.Polymarket: The World’s Largest Prediction Market.https://polymarket. com/. Accessed: 2026-01-24. 2026

    2026

  61. [61]

    Online HackMD note

    Potuz.Justification withholding attacks. Online HackMD note. Accessed: 2026-01-24. 2024.url:https://hackmd.io/o9tGPQL2Q4iH3Mg7Mma9wQ

    2026

  62. [62]

    John Wiley & Sons, 2014

    Martin L Puterman.Markov decision processes: discrete stochastic dynamic program- ming. John Wiley & Sons, 2014. 30

    2014

  63. [63]

    TopoShot: uncovering Ethereum's network topology leveraging replacement transactions , booktitle =

    Kaihua Qin, Liyi Zhou, Pablo Gamito, Philipp Jovanovic, and Arthur Gervais. “An empirical study of DeFi liquidations: incentives, risks, and instabilities”. In:Proceed- ings of the 21st ACM Internet Measurement Conference. IMC ’21. Virtual Event: As- sociation for Computing Machinery, 2021, pp. 336–350.isbn: 9781450391290.doi: 10.1145/3487552.3487811.url:...

    work page doi:10.1145/3487552.3487811.url:https://doi.org/10.1145/3487552.3487811 2021

  64. [64]

    Attacking the defi ecosystem with flash loans for fun and profit

    Kaihua Qin, Liyi Zhou, Benjamin Livshits, and Arthur Gervais. “Attacking the defi ecosystem with flash loans for fun and profit”. In:International conference on financial cryptography and data security. Springer. 2021, pp. 3–32

    2021

  65. [65]

    Stable-Baselines3: Reliable Reinforcement Learning Implemen- tations

    Antonin Raffin, Ashley Hill, Adam Gleave, Anssi Kanervisto, Maximilian Ernestus, and Noah Dormann. “Stable-Baselines3: Reliable Reinforcement Learning Implemen- tations”. In:Journal of Machine Learning Research22.268 (2021), pp. 1–8.url:http: //jmlr.org/papers/v22/20-1364.html

    2021

  66. [66]

    arXiv , author =:1905.13457 , journal =

    F. Ritz and A. Zugenmaier. “The Impact of Uncle Rewards on Selfish Mining in Ethereum”. In:2018 IEEE European Symposium on Security and Privacy Workshops (EuroS&PW). Los Alamitos, CA, USA: IEEE Computer Society, Apr. 2018, pp. 50–57. doi:10.1109/eurospw.2018.00013

    work page doi:10.1109/eurospw.2018.00013 2018

  67. [67]

    rocketpool.net/legacy/atlas/lebs#rpl-collateral

    Rocket Pool.8-ETH Bonded Minipools | RPL Collateral Requirements.https://docs. rocketpool.net/legacy/atlas/lebs#rpl-collateral. Accessed: 2026-02-04. 2023

    2026

  68. [68]

    Rosenfeld.Analysis of Bitcoin Pooled Mining Reward Systems

    M. Rosenfeld.Analysis of Bitcoin Pooled Mining Reward Systems. Dec. 2011.doi: 10.48550/arxiv.1112.4980. arXiv:1112.4980 [cs.DC]

    work page doi:10.48550/arxiv.1112.4980 2011

  69. [69]

    Un- ravelling the Probabilistic Forest: Arbitrage in Prediction Markets

    Oriol Saguillo, Vahid Ghafouri, Lucianna Kiffer, and Guillermo Suarez-Tangil. “Un- ravelling the Probabilistic Forest: Arbitrage in Prediction Markets”. In:7th Confer- ence on Advances in Financial Technologies (AFT 2025). Ed. by Zeta Avarikioti and Nicolas Christin. Vol. 354. Leibniz International Proceedings in Informatics (LIPIcs). Dagstuhl, Germany: S...

    work page doi:10.4230/lipics.aft.2025.27.url:https: 2025

  70. [70]

    Optimal Selfish Mining Strategies in Bitcoin

    Ayelet Sapirshtein, Yonatan Sompolinsky, and Aviv Zohar. “Optimal Selfish Mining Strategies in Bitcoin”. In:Financial Cryptography and Data Security. Ed. by Jens Grossklags and Bart Preneel. Berlin, Heidelberg: Springer, 2017, pp. 515–532.isbn: 978-3-662-54970-4

    2017

  71. [71]

    Bitcoin Under Volatile Block Rewards: How Mempool Statistics Can Influence Bitcoin Min- ing

    Roozbeh Sarenche, Alireza Aghabagherloo, Svetla Nikova, and Bart Preneel. “Bitcoin Under Volatile Block Rewards: How Mempool Statistics Can Influence Bitcoin Min- ing”. In: CCS ’25. New York, NY, USA: Association for Computing Machinery, 2025, pp. 903–917.isbn: 9798400715259.doi:10 . 1145 / 3719027 . 3744875.url:https : //doi.org/10.1145/3719027.3744875

    work page doi:10.1145/3719027.3744875 2025

  72. [72]

    Commitment Attacks on Ethereum’s Reward Mechanism

    Roozbeh Sarenche, Ertem Nusret Tas, Barnabe Monnot, Caspar Schwarz-Schilling, and Bart Preneel. “ Commitment Attacks on Ethereum’s Reward Mechanism ”. In:2025 IEEE 10th European Symposium on Security and Privacy (EuroS&P). Los Alami- tos, CA, USA: IEEE Computer Society, July 2025, pp. 504–526.doi:10 . 1109 / EuroSP63326 . 2025 . 00036.url:https : / / doi ...

    work page arXiv 2025

  73. [73]

    The Economics of Liquid Staking Derivatives: Basis Determinants and Price Discovery

    Stefan Scharnowski and Hossein Jahanshahloo. “The Economics of Liquid Staking Derivatives: Basis Determinants and Price Discovery”. In:Journal of Futures Markets 45.2 (2025), pp. 91–117.doi:https://doi.org/10.1002/fut.22556.url:https: //onlinelibrary.wiley.com/doi/abs/10.1002/fut.22556

    work page doi:10.1002/fut.22556.url:https: 2025

  74. [74]

    Proximal Policy Optimization Algorithms

    John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. “Proximalpolicyoptimizationalgorithms”.In:arXiv preprint arXiv:1707.06347(2017)

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  75. [75]

    Three Attacks on Proof-of-Stake Ethereum

    Caspar Schwarz-Schilling, Joachim Neu, Barnabé Monnot, Aditya Asgaonkar, Ertem Nusret Tas, and David Tse. “Three Attacks on Proof-of-Stake Ethereum”. In:Financial Cryptography and Data Security: 26th International Conference, FC 2022, Grenada, May 2–6, 2022, Revised Selected Papers. Grenada, Grenada: Springer-Verlag, 2022, pp. 560–576.isbn: 978-3-031-1828...

    work page doi:10.1007/978-3-031-18283-9_28.url: 2022

  76. [76]

    Gymnasium: A Standard Interface for Reinforcement Learning Environments

    Mark Towers, Ariel Kwiatkowski, Jordan Terry, John U Balis, Gianluca De Cola, Tristan Deleu, Manuel Goulão, Andreas Kallinteris, Markus Krimmel, Arjun KG, et al. “Gymnasium: A Standard Interface for Reinforcement Learning Environments”. In: arXiv preprint arXiv:2407.17032(2024)

    work page internal anchor Pith review arXiv 2024

  77. [77]

    The Principal–Agent Problem in Liquid Stak- ing

    Apostolos Tzinas and Dionysis Zindros. “The Principal–Agent Problem in Liquid Stak- ing”. en. In:Financial Cryptography and Data Security. FC 2023 International Work- shops. Ed. by Aleksander Essex, Shin’ichiro Matsuo, Oksana Kulyk, Lewis Gudgeon, Ariah Klages-Mundt, Daniel Perez, Sam Werner, Andrea Bracciali, and Geoff Goodell. Cham: Springer Nature Swit...

    work page doi:10.1007/978-3-031-48806-1_29 2023

  78. [78]

    Accessed: 2026-04-23

    Uniswap Labs.API Integration Guide.https://api- docs.uniswap.org/guides/ integration_guide. Accessed: 2026-04-23. 2026

    2026

  79. [79]

    When Blockchain Meets AI: Optimal Mining Strategy Achieved by Machine Learning

    Taotao Wang, Soung Chang Liew, and Shengli Zhang. “When Blockchain Meets AI: Optimal Mining Strategy Achieved by Machine Learning”. In:International Journal of Intelligent Systems36.5 (2021), pp. 2183–2207.issn: 1098-111X.doi:10.1002/int. 22375

    work page doi:10.1002/int 2021

  80. [80]

    Cyclic Arbitrage in Decentralized Exchanges

    Ye Wang, Yan Chen, Haotian Wu, Liyi Zhou, Shuiguang Deng, and Roger Wattenhofer. “Cyclic Arbitrage in Decentralized Exchanges”. In:Companion Proceedings of the Web Conference 2022. WWW ’22. Virtual Event, Lyon, France: Association for Computing Machinery, 2022, pp. 12–19.isbn: 9781450391306.doi:10.1145/3487553.3524201. url:https://doi.org/10.1145/3487553.3524201

    work page doi:10.1145/3487553.3524201 2022

Showing first 80 references.