Prompt Attack Detection with LLM-as-a-Judge and Mixture-of-Models
Prompt attacks, including jailbreaks and prompt injections, pose a critical security risk to Large Language Model (LLM) systems. In production, guardrails must mitigate these attacks under strict low-latency constraints, resulting in a deployment gap in which lightweight classifiers and rule-based systems struggle to generalize under distribution shift, while high-capacity LLM-based judges remain too slow or costly for live enforcement. In this work, we examine whether lightweight, general-purpose LLMs can reliably serve as security judges under real-world production constraints. Through careful prompt and output design, lightweight LLMs are guided through a structured reasoning process i...
| Authors | Hieu Xuan Le, Benjamin Goh, Quy Anh Tang |
|---|---|
| Primary category | cs.CL |
| Published | 26 March 2026 |
| Last updated at source | 26 March 2026 |
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