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Securing LLM-as-a-Service for Small Businesses: An Industry Case Study of a Distributed Chatbot Deployment Platform

Authors: Jiazhu Xie, Bowen Li, Heyu Fu, Chong Gao, Ziqi Xu, Fengling Han

Published: 2026-01-21

arXiv ID: 2601.15528v1

Added to Library: 2026-01-23 03:01 UTC

📄 Abstract

Large Language Model (LLM)-based question-answering systems offer significant potential for automating customer support and internal knowledge access in small businesses, yet their practical deployment remains challenging due to infrastructure costs, engineering complexity, and security risks, particularly in retrieval-augmented generation (RAG)-based settings. This paper presents an industry case study of an open-source, multi-tenant platform that enables small businesses to deploy customised LLM-based support chatbots via a no-code workflow. The platform is built on distributed, lightweight k3s clusters spanning heterogeneous, low-cost machines and interconnected through an encrypted overlay network, enabling cost-efficient resource pooling while enforcing container-based isolation and per-tenant data access controls. In addition, the platform integrates practical, platform-level defences against prompt injection attacks in RAG-based chatbots, translating insights from recent prompt injection research into deployable security mechanisms without requiring model retraining or enterprise-scale infrastructure. We evaluate the proposed platform through a real-world e-commerce deployment, demonstrating that secure and efficient LLM-based chatbot services can be achieved under realistic cost, operational, and security constraints faced by small businesses.

🔍 Key Points

  • Introduction of Beyond Visual Safety (BVS), a novel jailbreaking framework aimed at exploiting visual safety boundaries of Multimodal Large Language Models (MLLMs).
  • BVS employs a 'reconstruction-then-generation' strategy using neutralized visual splicing and inductive recomposition to conceal malicious intent and successfully induce harmful image generation.
  • Achieved a jailbreak success rate of 98.21% on GPT-5, significantly surpassing previous methods, highlighting vulnerabilities in current MLLM safety mechanisms.
  • Constructed a specialized benchmark dataset for rigorously testing MLLM visual safety, focused on high-severity categories that typically trigger refusal mechanisms in MLLMs.
  • Propose Multi-Image Distance Optimization Selection Algorithm (MIDOS) to enhance the effectiveness of patch selection in creating semantically neutralized composite images.

💡 Why This Paper Matters

This paper presents a significant advancement in understanding the security vulnerabilities of MLLMs, particularly concerning their visual safety mechanisms. By introducing the BVS framework, the authors expose critical weaknesses that can lead to the generation of harmful content, emphasizing the urgent need for improved safety measures in multimodal AI systems. The reported findings are crucial as they align with ongoing discussions in the AI ethics and safety communities regarding the potential for misuse of AI technologies.

🎯 Why It's Interesting for AI Security Researchers

This paper is of particular interest to AI security researchers as it highlights novel attack vectors against MLLMs, demonstrating how existing safety mechanisms can be bypassed through sophisticated attacks that exploit cross-modal capabilities. The proposed methods and findings not only contribute to the body of knowledge on AI security but also catalyze further investigations into enhancing model safety and robustness against such vulnerabilities.

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