用区块链+多因素认证防骗，让大模型机器人安全做线上交易
Enforcing Cybersecurity Constraints for LLM-driven Robot Agents for Online Transactions
- 结合区块链与多因素认证实现实时风险监控
- 欺诈交易减少90%，漏洞检测准确率达98%
- 适合金融、电商等高安全需求场景使用
将大型语言模型（LLM）融入自主机器人代理以执行在线交易，带来显著的网络安全挑战。本研究旨在通过强化网络安全约束来降低数据泄露、交易欺诈和系统操纵的风险。背景聚焦于LLM驱动的机器人系统在电子商务、金融及服务行业的兴起及其引入的漏洞。提出一种融合区块链技术、多因素认证（MFA）和实时异常检测的新安全架构，用于保护交易安全。评估了交易完整性、响应时间和漏洞检测准确率等关键性能指标，结果显示该架构使欺诈交易减少90%，漏洞检测准确率提升至98%，并确保交易验证延迟控制在0.05秒内。研究强调了在部署LLM驱动机器人系统时网络安全的重要性，并提出了可适配多种在线平台的框架。
原文摘要 · Abstract (English)
The integration of Large Language Models (LLMs) into autonomous robotic agents for conducting online transactions poses significant cybersecurity challenges. This study aims to enforce robust cybersecurity constraints to mitigate the risks associated with data breaches, transaction fraud, and system manipulation. The background focuses on the rise of LLM-driven robotic systems in e-commerce, finance, and service industries, alongside the vulnerabilities they introduce. A novel security architecture combining blockchain technology with multi-factor authentication (MFA) and real-time anomaly detection was implemented to safeguard transactions. Key performance metrics such as transaction integrity, response time, and breach detection accuracy were evaluated, showing improved security and system performance. The results highlight that the proposed architecture reduced fraudulent transactions by 90%, improved breach detection accuracy to 98%, and ensured secure transaction validation within a latency of 0.05 seconds. These findings emphasize the importance of cybersecurity in the deployment of LLM-driven robotic systems and suggest a framework adaptable to various online platforms.
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