# Jailbreak-as-a-Service++: Unveiling Distributed AI-Driven Malicious Information Campaigns Powered by LLM Crowdsourcing

arXiv: https://arxiv.org/abs/2505.21184 · HTML: https://mlsanity.com/paper/2505.21184/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Jailbreaking, one of 12 directions in LLM safety and security.
- First posted on arXiv 27 May 2025.

## Placed in

- LLM safety and security / [Jailbreaking](https://mlsanity.com/safety/jailbreaking/)

## Abstract (arXiv)

To prevent the misuse of Large Language Models (LLMs) for malicious purposes, numerous efforts have been made to develop the safety alignment mechanisms of LLMs. However, as multiple LLMs become readily accessible through various Model-as-a-Service (MaaS) platforms, attackers can strategically exploit LLMs' heterogeneous safety policies to fulfill malicious information generation tasks in a distributed manner. In this study, we introduce textitPoisonSwarm to how attackers can reliably launder malicious tasks via the speculative use of LLM crowdsourcing. Building upon a scheduler orchestrating crowdsourced LLMs, PoisonSwarm maps the given malicious task to a benign analogue to derive a content template, decomposes it into semantic units for crowdsourced unit-wise rewriting, and reassembles the outputs into malicious content. Experiments show its superiority over existing methods in data quality, diversity, and success rates. Regulation simulations further reveal the difficulty of governing such distributed, orchestrated misuse in MaaS ecosystems, highlighting the need for coordinated, ecosystem-level defenses.