GuardrailAgent defends LLMs against adaptive jailbreak attacks
Original title: GuardrailAgent: An Agentic Framework for Jailbreak Defense
Multi-agent framework updates knowledge base from attack summaries to achieve 93.1% detection on over ten jailbreaks.
GuardrailAgent is a multi-agent system for adaptive LLM jailbreak defense. It autonomously summarizes attack characteristics into a continuously updated knowledge base supporting both input and output detection. A feedback module corrects misclassified cases for iterative refinement. The framework was evaluated on more than ten representative jailbreaks.
Unconfirmed: Effectiveness claims are based on representative test cases rather than production-scale deployment.
- 93.1% input-side detection rate
- 93.9% overall detection
- 95.8% F1 score
- Adapts to unseen attacks
| Product | Affected |
|---|---|
| large language models | — |
From the original article
1 min read at arxiv.orgDespite undergoing safety training to align with human values, large language models (LLMs) remain vulnerable to jailbreak attacks that circumvent built-in safeguards and elicit malicious behaviors given by the adversary. Existing defenses are mostly static and fail to adapt to new or unknown attack strategies, providing limited resilience.
By Tianze Chang · arxiv.org
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