AegisLLM: Scaling Agentic Systems for Self-Reflective Defense in LLM Security

Zikui Cai, Shayan Shabihi, Bang An, Zora Che, Brian R. Bartoldson, Bhavya Kailkhura, Tom Goldstein, Furong Huang

Track: Long Paper Track (up to 9 pages)

Keywords: safety, agentic system, jailbreaking, unlearning, llm

TL;DR: We introduce a multi-agent security framework that enhances LLM safety through coordinated specialized agents and prompt optimization at inference time

Abstract:

We introduce AegisLLM, a cooperative multi-agent defense against prompt injection, adversarial manipulation, and information leakage. In AegisLLM, a structured society of autonomous agents — orchestrator, deflector, responder, and evaluator — collaborate (via communication) to ensure safe and compliant LLM outputs, while self-improving over time through prompt optimization. We show that
scaling agentic reasoning system at test-time—both by incorporating additional agent roles and by leveraging automated prompt optimization (such as DSPy)—substantially enhances robustness without compromising model utility. This test-time defense enables real-time adaptability to evolving attacks, without requiring model retraining. Comprehensive evaluations across key threat scenarios,
including unlearning and jailbreaking, demonstrate the effectiveness of AegisLLM. On the WMDP unlearning benchmark, AegisLLM achieves near-perfect unlearning with only 20 training examples and fewer than 300 LM calls. For jailbreaking benchmarks, we achieve 51% improvement compared to the base model on StrongReject, and lower false refusal rate than state-ot-the-art methods on PHTest. Our results highlight the advantages of adaptive, agentic reasoning over static defenses, establishing AegisLLM as a strong runtime alternative to traditional approaches based on model modifications. Our code is available at https://github.com/zikuicai/agentic-safety.

Submission Number: 118