Actionable Research

Learn how we work from first principles to viable products.

LLMs can learn how normal, and abnormal transactions look like
MEV/BEV

LLMs can learn how normal, and abnormal transactions look like

LLMs are wonderful at predicting the next character in a text. But they can also predict the next trace opcode of a blockchain transaction! Hence they can detect which next opcode is rather unlikely --- we use this insight to create an abnormality detection system.

Liyi Zhou

Liyi Zhou

One way to easy success: CTRL+C+V - Imitation Transactions
MEV/BEV

One way to easy success: CTRL+C+V - Imitation Transactions

Instead of joining the monkey game of find profitable opportunities, why not simply copy them? We investigate an advanced method to imitate blockchain transactions in real-time, allowing to generate hundreds of millions of USD. This work was published in Usenix Security.

Kaihua Qin

Kaihua Qin

Fuzzing DeFi with SMT Solvers
MEV/BEV

Fuzzing DeFi with SMT Solvers

How to extract revenue from DeFi protocols with the Bellman Ford algorithm or an SMT solver? Check out our IEEE Security & Privacy Paper

Liyi Zhou

Liyi Zhou

How dark is the forest?
MEV/BEV

How dark is the forest?

Traders extract monetary value from the mesh of decentralized finance (DeFi) smart contracts through so-called blockchain/miner/maximal extractable value (BEV/MEV). We shed light into this dark forest with an IEEE Security & Privacy Paper.

Kaihua Qin

Kaihua Qin