A MULTI-AGENT GAME-THEORETIC PROOF-OF-CONCEPT PROTOTYPE FOR MODELLING ADVERSARIAL FLASH LOAN EXPLOIT USING REINFORCEMENT LEARNING
Abstract
Decentralized Finance (DeFi) architectures are structural environments exposed to strategic economic logic exploits that leverage uncollateralized blockchain capital primitives known as flash loans. Since these interactions execute rapidly within localized ledger state updates, standard static contract auditing frameworks face operational limits under adversarial market volatility conditions. This paper designs and presents a simulation-driven multi-agent reinforcement learning (MARL) proof-of-concept prototype engineered as a transparent parameter-testing sandbox to observe strategic vector interactions. We map the competitive tension between an exploiting entity (Attacker Agent) and an automated market liquidity pool protection system (Defender Agent) as a non-cooperative, general sum game sequence inside a custom Python environment configured with an explicit random seed configuration of 5. Under the fixed mathematical rules of this layout, a completely passive control setting illustrates a hypothetical capital erosion path where the simulated reserves drop down until they cross a hardcoded clipping boundary floor. Conversely, the introduction of an active protection policy demonstrates an illustrative stabilization trend path, where the system deploys targeted combinations of custom slippage taxes and transaction block time-locks to modify the attacker's expected utility values. Rather than asserting live-measured runtime tracking metrics or field-ready deployment finality, this study documents how automated internal control rules can be modelled, tested, and algorithmically analysed using illustrative scenario visualizations inside a simplified asset microstructure simulation framework.
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BibTeX
@misc{vasudevan2026multiagent,
title = {{A MULTI-AGENT GAME-THEORETIC PROOF-OF-CONCEPT PROTOTYPE FOR MODELLING ADVERSARIAL FLASH LOAN EXPLOIT USING REINFORCEMENT LEARNING}},
author = {Gitanjali Vasudevan},
howpublished = {Zenodo},
year = {2026},
month = oct,
doi = {10.5281/zenodo.23590745},
url = {https://zenodo.org/records/23590745},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - A MULTI-AGENT GAME-THEORETIC PROOF-OF-CONCEPT PROTOTYPE FOR MODELLING ADVERSARIAL FLASH LOAN EXPLOIT USING REINFORCEMENT LEARNING
AU - Gitanjali Vasudevan
PY - 2026
DA - 2026/10/23
PB - Zenodo
DO - 10.5281/zenodo.23590745
UR - https://zenodo.org/records/23590745
AB - Decentralized Finance (DeFi) architectures are structural environments exposed to strategic economic logic exploits that leverage uncollateralized blockchain capital primitives known as flash loans. Since these interactions execute rapidly within localized ledger state updates, standard static contract auditing frameworks face operational limits under adversarial market volatility conditions. This paper designs and presents a simulation-driven multi-agent reinforcement learning (MARL) proof-of-concept prototype engineered as a transparent parameter-testing sandbox to observe strategic vector interactions. We map the competitive tension between an exploiting entity (Attacker Agent) and an automated market liquidity pool protection system (Defender Agent) as a non-cooperative, general sum game sequence inside a custom Python environment configured with an explicit random seed configuration of 5. Under the fixed mathematical rules of this layout, a completely passive control setting illustrates a hypothetical capital erosion path where the simulated reserves drop down until they cross a hardcoded clipping boundary floor. Conversely, the introduction of an active protection policy demonstrates an illustrative stabilization trend path, where the system deploys targeted combinations of custom slippage taxes and transaction block time-locks to modify the attacker's expected utility values. Rather than asserting live-measured runtime tracking metrics or field-ready deployment finality, this study documents how automated internal control rules can be modelled, tested, and algorithmically analysed using illustrative scenario visualizations inside a simplified asset microstructure simulation framework.
ER -