Related papers: Commitment Against Front Running Attacks
Front-running attacks have been a major concern on the blockchain. Attackers launch front-running attacks by inserting additional transactions before upcoming victim transactions to manipulate victim transaction executions and make profits.…
Owing to the meteoric rise in the usage of cryptocurrencies, there has been a widespread adaptation of traditional financial applications such as lending, borrowing, margin trading, and more, to the cryptocurrency realm. In some cases, the…
We consider front-running to be a course of action where an entity benefits from prior access to privileged market information about upcoming transactions and trades. Front-running has been an issue in financial instrument markets since the…
Billions of dollars have been lost due to vulnerabilities in smart contracts. To counteract this, researchers have proposed attack frontrunning protections designed to preempt malicious transactions by inserting "whitehat" transactions…
A proof of the security of the Bitcoin protocol is made rigorous, and simplified in certain parts. A computational model in which an adversary can delay transmission of blocks by time $\Delta$ is considered. The protocol is generalized to…
Banks routinely use neural networks to make decisions. While these models offer higher accuracy, they are susceptible to adversarial attacks, a risk often overlooked in the context of event sequences, particularly sequences of financial…
Proof-of-Work mining is intended to provide blockchains with robustness against double-spend attacks. However, an economic analysis that follows from Budish (2018), which considers free entry conditions together with the ability to rent…
Ethereum prospered the inception of a plethora of smart contract applications, ranging from gambling games to decentralized finance. However, Ethereum is also considered a highly adversarial environment, where vulnerable smart contracts…
We analyze bribing attacks in Proof-of-Stake distributed ledgers from a game theoretic perspective. In bribing attacks, an adversary offers participants a reward in exchange for instructing them how to behave, with the goal of attacking the…
Transparency and security are both central to Responsible AI, but they may conflict in adversarial settings. We investigate the strategic effect of transparency for agents through the lens of transferable adversarial example attacks. In…
Blockchains add transactions to a distributed shared ledger by arriving at consensus on sets of transactions contained in blocks. This provides a total ordering on a set of global transactions. However, total ordering is not enough to…
We identify a subtle security issue that impacts the design of smart contracts, because agents may themselves deploy smart contracts (side contracts). Typically, equilibria of games are analyzed in vitro, under the assumption that players…
Bitcoin uses blockchain technology to maintain transactions order and provides probabilistic guarantee to prevent double-spending, assuming that an attacker's computational power does not exceed %50 of the network power. In this paper, we…
Smart contracts are stateful programs deployed on blockchains; they secure over a trillion dollars in transaction value per year. High-stakes smart contracts often rely on timely alerts about external events, but prior work has not analyzed…
This work initiates an analysis of several cryptographic protocols from a rational point of view using a game-theoretical approach, which allows us to represent not only the protocols but also possible misbehaviours of parties. Concretely,…
As large language models (LLMs) grow more capable, concerns about their safe deployment have also grown. Although alignment mechanisms have been introduced to deter misuse, they remain vulnerable to carefully designed adversarial prompts.…
Blockchain protocols incentivize participation through monetary rewards, assuming rational actors behave honestly to maximize their gains. However, attackers may attempt to harm others even at personal cost. These denial of profit attacks…
Machine learning models using transaction records as inputs are popular among financial institutions. The most efficient models use deep-learning architectures similar to those in the NLP community, posing a challenge due to their…
Strategic interactions between competitive entities are generally considered from the perspective of complete revelation of benefits achieved from those interactions, in the form of public payoff functions and/or beliefs, in the announced…
In a backdoor attack, an adversary inserts maliciously constructed backdoor examples into a training set to make the resulting model vulnerable to manipulation. Defending against such attacks typically involves viewing these inserted…