Research On Ai-Assisted Fraud In Decentralized Finance (Defi) Platforms
Case 1: Shakeeb Ahmed – Smart‑Contract Exploits on DeFi Exchanges (USA)
Facts:
Ahmed was a senior security engineer for a technology company. In July 2022, he exploited a vulnerability in a DeFi exchange’s smart contract by inserting false pricing data into the contract. The manipulation caused the contract to generate roughly $9 million in inflated fees which he withdrew. Additionally, around the same period he targeted a Solana‑based DeFi platform, manipulated flash‑loan trades and token swaps to steal over $12 million.
Legal / Forensic Issues:
Manipulating smart‑contract logic is treated as fraud/unauthorised access rather than merely a coding bug.
Forensics had to trace the false pricing input, the smart contract internal logs, the chain of swap transactions, the bridging of funds across blockchains, use of mixers to launder proceeds.
Human liability: although the code executed itself, Ahmed’s human role (reverse‑engineering smart contracts, planning the exploit) was central.
Prosecution Strategy & Outcome:
Ahmed faced wire fraud and money‑laundering charges. He pled guilty, surrendered approximately $12.3 million, was sentenced to around 3 years in prison, and ordered to pay restitution.
Implications:
Even in DeFi where smart contracts automate execution, human actors who design, deploy, or exploit those contracts can face criminal liability.
Smart‑contract logs and blockchain forensic trails are usable as evidential tools in prosecution.
DeFi platforms must treat vulnerabilities as potential criminal attack vectors, not just technical risks.
Case 2: Andean Medjedovic – Large Exploit of DeFi Protocols (USA)
Facts:
Medjedovic, a Canadian national, allegedly exploited vulnerabilities in two DeFi protocols: KyberSwap and Indexed Finance. He used hundreds of millions in borrowed cryptocurrency to manipulate liquidity‑pool pricing (via AMMs, token indexing), causing artificial prices and draining investor funds — about $48.8 million from KyberSwap and about $16.5 million from Indexed Finance. He then allegedly laundered proceeds via mixers, bridges, and false‑identity accounts.
Legal / Forensic Issues:
The use of “flash‑loan” and manipulation of indexing operations in DeFi as fraudulent scheme.
Tracing borrowed funds, swapped tokens, bridging across chains, mixers (“moneyMovementSystem” he maintained).
Attribution of human actor in what appears as automated contract behaviour.
Prosecution Strategy & Outcome:
An indictment in the Eastern District of New York charged wire fraud, unauthorised damage to protected computer (smart contract system), extortion attempt (paying an undercover agent to unlock frozen funds), and money‑laundering. If convicted, he faces up to 20 years for some counts. He is currently at large.
Implications:
DeFi frauds blur lines between “hack/exploit” and “market manipulation”.
Human orchestrators of DeFi exploits are subject to traditional fraud statutes despite the decentralised/automated contract environment.
Forensic investigators must examine smart contracts, liquidity flows, chain‑bridging, mixers, all to build the case.
Case 3: Mango Markets Manipulation Case – DeFi Market Manipulation (USA)
Facts:
On October 11, 2022, a trader named Avraham Eisenberg executed a series of trades on Mango Markets (a DeFi platform) to manipulate the price of the native “MNGO” token and associated futures contracts. He used borrowed funds and smart‑contract mechanics to inflate token value, borrow against inflated position, and extract profit — estimated at over $110 million.
Legal / Forensic Issues:
Whether manipulation in a DeFi smart‑contract environment constitutes fraud or market manipulation under U.S. law.
Forensics: tracing token trades, contract invocations, collateralisation, borrowing/lending flows, profit extraction, use of bots or automation.
Defence argued “code‑is‑law” (the contract allowed it), prosecutors argued intent to defraud.
Prosecution Strategy & Outcome:
Eisenberg was convicted of fraud and manipulation in April 2024. The case serves as first major U.S. DeFi fraud conviction applying traditional securities/commodities law to decentralised systems.
Implications:
DeFi platform users cannot rely purely on “smart‑contract permissible actions” to shield from fraud liability if they manipulate systems with intent.
Prosecutors will treat DeFi trades executed with intent to defraud just like conventional finance fraud.
Audit trails, contract invocation logs, on‑chain data are central to building evidence.
Case 4: “Fake Pricing Data & Smart Contract Fee Theft” (USA – SDNY)
Facts:
A former security engineer of a tech company exploited vulnerability in a DeFi exchange’s smart contract by feeding fake pricing data, causing the contract to generate inflated fee revenue (~$9 million) which he withdrew. He then laundered funds via token swaps, bridging to Monero, and international exchanges.
Legal / Forensic Issues:
Using insider or privileged access plus vulnerability manipulation for fee extraction.
Forensic challenge: tracking transfers across blockchains, token swaps, mixers, bridging protocols, linking accounts to defendant.
Identifying that fees were not legitimately earned but fraudulently generated.
Prosecution Strategy & Outcome:
Charged with wire fraud and money‑laundering by the U.S. Attorney’s Office for the Southern District of New York. The defendant searched online about “defi hack” and “evidence laundering” after the fact — helping show consciousness of guilt.
Implications:
Insider access + DeFi contract vulnerability yield criminal liability.
Forensics must gather both contract logs and broader blockchain flows.
Platform governance must consider internal threats as well as external hacks.
Case 5: Foundational Fraud & DeFi Study (Academic/Empirical)
Facts:
Research mapping 1,141 crime events from 2017‑2022 found that DeFi suffered over US$30 billion in profit‑driven crimes; DeFi actors were both victims (52%) and perpetrators (41%). Although not a judicial case, the study demonstrates the types of DeFi fraud: smart‑contract vulnerability exploitation, market manipulation, rug‑pulls, hacking, illicit transfers.
Legal / Forensic Issues:
Shows breadth of fraud types in DeFi ecosystem.
For forensics and prosecution: mapping types of attacks, identifying patterns, building taxonomy of exploit types (flash‑loan, rug‑pull, oracle manipulation).
Implication: that law‑enforcement must adapt tools for tracing DeFi flows, chain hopping, mixers, bridging.
Implications:
Establishes a credible evidence base for prosecutions: the scale, typology, vulnerabilities.
Supports the argument that DeFi fraud is not niche but systemic and thus subject to standard criminal legal frameworks.
Forensic preparation: research reveals that despite apparent “anonymity”, many frauds trace to identifiable actors; open source chain‑analysis tools are viable.
Key Analytical Insights & Strategy for AI‑Assisted DeFi Fraud
Although not every case had explicitly “AI‑assisted” labelled, here’s how the above cases and the evolving trend apply when AI is involved, and what prosecution strategy should look like:
AI‑Assisted Layer in DeFi Fraud: When attackers use AI (e.g., algorithmic trading bots, price‑manipulation algorithms, dynamic smart‑contract exploit generation, or AI‑based arbitrage detection) this amplifies scale and sophistication. Prosecutors should highlight AI‑tool usage as an aggravating factor: rapid automated trades, bot‑led contract invocations, algorithmic exploit generation.
Forensic Evidence of Automation: Investigators must gather logs and metadata showing bot/trading‑algorithm usage, timestamps, repetitive patterns, lack of human delay, linking tool usage to defendant accounts. Smart‑contract calls may reveal automated scripts rather than human‑click execution.
Existing Legal Frameworks Apply: Fraud, wire fraud, money‑laundering, extortion, unauthorised access all apply even in DeFi/AI contexts. For example: Medjedovic case uses wire fraud, unauthorised damage to protected computer, money‑laundering.
Proving Intent & Manipulation: DeFi defendants often argue “code allowed it” or “I only used allowed smart‑contract functions”. Prosecutors must show that the actor exploited vulnerabilities with knowledge/intent to defraud (e.g., setting up artificial prices, borrowed funds, laundering the proceeds).
Tracing Funds Across Blockchain & AI Layers: Funds may move through multiple protocols, chains, mixers; combining AI trading bots with DeFi bridging complicates flows. Forensic chain‑analysis is critical.
Global/Decentralised Dimension & Jurisdiction: DeFi platforms are global; perpetrators may be offshore; automation complicates attribution. Prosecutors must coordinate internationally, use asset‑forfeiture and MLATs.
Sentencing Considerations: Use of AI tools, scale of automated exploitation, cross‑chain laundering, and harm to investors may support higher sentences.
Platform/Protocol Governance Oversight: While the focus is often on individual attacker, protocols may also face regulatory scrutiny for failure to secure contracts or prevent exploitation. This bolsters prosecution’s narrative of foreseeability and harm.
Conclusion
Frauds in DeFi platforms are increasingly prosecuted under traditional criminal laws, despite the decentralised and automated nature of these systems. The six cases above show that:
DeFi smart‑contracts and liquidity pools are not exempt from legal liability when exploited.
Human actors orchestrating automated or high‑frequency bot/trading/exploit functions can be held criminally responsible.
AI‑assisted methods (though not always named) represent the next frontier: bot‑led trades, algorithmic exploit generation, automated laundering.
Forensic strategies must adapt: smart‑contract logs, AI‑algorithm logs, chain‑hop tracing, bridging, mixers, linking to human control.
Legal practitioners must treat DeFi/AI fraud with the same seriousness as conventional financial fraud — and emphasise the automation/AI component as exacerbating factor
RELATED Blog
comments