Criminal networks are increasingly turning to genAI to boost the reach of scams and accelerate their operations, according to new data from TRM Labs, resulting in a 500% rise in AI‑enabled fraud in the past year, and more than $150 billion lost.

The crypto intelligence firm’s latest report found that the use of AI‑powered tactics, such as voice cloning and deepfakes, has fuelled a surge in sophisticated crypto scams across platforms like WhatsApp, while dramatically reducing the cost and time required to run them.

The report warned that AI has allowed criminal networks to engage in low‑cost experimentation and launch countless micro‑operations leveraging “synthetic trust” to initiate investment scams, pig butchering schemes, impersonation scams, and technical support fraud.

Beyond social engineering, TRM said that LLMs are increasingly being used to automate large‑scale credential testing, optimise money laundering flows, or identify vulnerabilities in smart contracts, activities that would once have taken teams of operators working over weeks or months.

While this helped push illicit crypto volumes to an all-time high of $158 billion last year, up nearly 145% from 2024, TRM has since stressed that the explosion of AI-based fraud should serve as a warning for institutions and crypto-native firms experimenting with agents capable of transacting independently.

The crypto firm cautioned that AI agents now allow fraudsters to split their ill-gotten gains across dozens of addresses, wash them through layered liquidity pools, and shuttle them across blockchains before a human would be able to spot any unusual behaviour, actions that not long ago would have required coordinated manual effort.

According to TRM, in high-impact incidents, the speed of the hackers’ actions has limited recovery efforts. For example, after last year’s $1.46 billion heist of Ethereum from crypto exchange Bybit, at least $160 million was laundered by the criminals within 48 hours using automated tools and scripts.

As AI agents become embedded in treasury, trading, and liquidity functions, TRM said that the time between intrusion and the cross‑chain movement of funds will narrow further, a problem made worse by the novel attack surfaces AI has introduced.

For instance, if an autonomous agent holds authority over treasury assets or operational wallets, it will soon become a high‑value target, with attackers trying to manipulate it through prompt injection, data manipulation or misconfigured rules to trigger the unauthorised movement of funds.

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Hackers are also designing their own AI agents to automate money laundering, identify weaknesses in decentralised systems, and refine routing strategies to avoid predictable patterns.  

This is placing more responsibility on developers, operators, and compliance and incident response teams, which TRM said can only be met by advanced blockchain intelligence combining on-chain data, behavioural analytics, and cross-chain tracing. 

“The convergence of programmable finance and autonomous execution does not inherently create new criminal intent. But it does compress timelines, redistribute accountability, and alter where effective safeguards must reside,” said TRM Labs.

“For compliance leaders, policymakers, and law enforcement agencies, the question is no longer whether AI will participate in financial systems (it already does). The question is how responsibility frameworks, governance controls, and investigative capabilities must adapt when software becomes a transactional actor.”

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