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The underground exploitation of large language models (LLMs) for maliciousservices (i.e., Malla) is witnessing an uptick, amplifying the cyber threatlandscape and posing questions about the trustworthiness of LLM technologies.However, there has been little effort to understand this new cybercrime, interms of its magnitude, impact, and techniques. In this paper, we conduct thefirst systematic study on 212 real-world Mallas, uncovering their proliferationin underground marketplaces and exposing their operational modalities. Ourstudy discloses the Malla ecosystem, revealing its significant growth andimpact on today's public LLM services. Through examining 212 Mallas, weuncovered eight backend LLMs used by Mallas, along with 182 prompts thatcircumvent the protective measures of public LLM APIs. We further demystify thetactics employed by Mallas, including the abuse of uncensored LLMs and theexploitation of public LLM APIs through jailbreak prompts. Our findings enablea better understanding of the real-world exploitation of LLMs bycybercriminals, offering insights into strategies to counteract thiscybercrime.

Malla: Demystifying Real-world Large Language Model Integrated Malicious Services