GuardrailAgent: An Agentic Framework for Jailbreak Defense

Reported by arXiv cs.CR (Tianze Chang, Junjie Chu, Yixin Wu, Michael Backes, Yang Zhang)

Summary by Cyber Security News9 Nov 2026, 06:00 CET

About this summary. This is a short excerpt from the description that arXiv cs.CR published with its article. Cyber Security News did not report or verify the underlying story. Read the original: https://arxiv.org/abs/2611.09734

arXiv:2611.09734v1 Announce Type: new Abstract: Despite 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.

Read the full article at arXiv cs.CR →

Source attribution: headline and facts are from arXiv cs.CR (arxiv.org). Summary method: excerpt of the source description. See our source attribution policy.

Related coverage

SECURITY AFFAIRS AI-CYBERSECURITY NEWSLETTER ROUND 7

CrowdStrike moves to acquire Fiddler and Dreadnode, adding AI observability and testing to its security platform

MetaPI: Constructing Prompt Injection Benchmarks from LLM Agent Benchmark

An Agentic Multi-Agent Framework for System Log Anomaly Detection

Authorization Drift in LLM Agents: Evaluating Boundary Compliance in Multi-Step Tool Use

MemoryDojo: Benchmarking Poisoning Through LLM Agent Memory