这篇论文讲的是怎么用LLM攻破机器人检测，又怎么用多LLM防住，准确率数字很具体，搞安全或社交平台的朋友值得看看。
本研究针对LLM驱动的社交媒体机器人检测系统，提出了两种新型对抗攻击策略，能将其检测准确率降低高达48%。为应对这些威胁，研究者设计了LSABRE（LLM驱动的社交对抗机器人识别集成）多LLM防御框架，在强自适应对抗压力下仍保持86%的检测准确率。该研究的方法论可推广至钓鱼检测、邮件分类和欺诈分析等更广泛的LLM驱动的网络安全系统。
Breaking and Defending LLM-Powered Social Media Bot Detection Systems
The rise of social media bots poses a persistent threat, enabling misinformation, opinion manipulation, and the erosion of trust in online platforms. To combat this, machine learning systems have been developed to detect and limit bot activity, but attackers continuously adapt through techniques such as adversarial learning and behavior imitation, fueling an ongoing arms race between bots and detection tools. Recent advances in large language models (LLMs) have significantly improved bot detection by enabling deeper semantic and contextual analysis of accounts and their content. However, this shift also introduces new attack surfaces, allowing adversaries to craft exploits that directly target the reasoning and generation mechanisms of LLM-based classifiers. Industry tools such as Anthropic's Claude Code Security similarly leverage LLMs for security-critical decisions, further motivating a careful study of their attack surfaces. In this work, we investigate both the offensive and defensive aspects of LLM-powered, threat-specific cybersecurity applications. While centered on the challenge of social media bot detection, our methodology and insights generalize to a broad class of LLM-powered cybersecurity systems, including phishing detection, email classification, and fraud analysis. We introduce two novel adversarial attack strategies that systematically exploit the semantic and contextual weaknesses of LLM-based classifiers, degrading their detection accuracy by up to 48%. To counter these threats, we propose a robust multi-LLM defense architecture designed to preserve detection reliability under adaptive adversarial conditions. Our solution, LSABRE (LLM-powered Social Adversarial Bot Recognition Ensemble), is a multi-LLM framework that substantially improves robustness across a range of attacks, maintaining 86% detection accuracy even under strong, adaptive adversarial pressure.