大语言模型在代理AI中的可扩展性风险与模型供应链安全
Trustworthy AI LLM Scalability Risk Index (LSRI): A Cybersecurity Framework Assessing Agentic-AI Security & Software Model Supply Chain Safety Boosting AI-Generated Malware Defense & Explainability Mitigating Emerging Risks of Generative AI
AI总结：
本文研究了大语言模型在代理AI中的可扩展性风险及模型供应链安全问题，提出LSRI指数和模型供应链框架，以提升安全关键环境下的LLM部署安全性。
摘要：
大语言模型（LLMs）和生成式AI正在改变网络安全，使防御能力和新型攻击同时得到提升。组织现在利用LLMs进行威胁检测、代码审查和DevSecOps自动化，而对手则利用它们生成恶意软件并运行定向社会工程攻击。本文提出了一种整合进攻与防御视角的生成式AI驱动网络安全分析。基于70篇学术、行业和政策文献，分析了由AI促成的威胁上升及其对全球安全的影响，以确立对可扩展防御机制的必要性。我们介绍了两个主要贡献：LLM可扩展性风险指数（LSRI），一种参数框架，用于在安全关键环境中部署LLMs时的压力测试运营风险；以及模型供应链框架，建立贯穿模型生命周期的可验证根信任。我们还综合了Google Play Protect、Microsoft Security Copilot等平台的防御策略，并概述了安全、大规模LLM部署的治理路线图。
英文摘要：
As AI shifts from human-in-the-loop interfaces to autonomous multi-agent systems capable of real-time code execution and tool integration through protocols like the Model Context Protocol (MCP), traditional SAST, DAST, and legacy AI safety methods fail to detect modern agentic-AI threats. This paper introduces the LLM Scalability Risk Index (LSRI), a parametric framework and cybersecurity standard for stress-testing autonomous orchestration pipelines. LSRI measures the operational thresholds where load, compounding hallucinations, data poisoning, and adversarial prompt injections including jailbreaking and indirect prompt injection cause security boundaries to fail. Beyond RLHF and RLAIF, we present a Verifiable Root of Trust architecture using cryptographic attestation, semantic policy enforcement, and continuous runtime verification to secure the AI software supply chain. LSRI defends against malicious LoRA adapters, weight tampering, dependency typosquatting, and unsafe model artifacts from public registries such as Hugging Face and GitHub. By replacing post-hoc alignment with verifiable runtime controls, LSRI provides scalable API defense, safer agentic orchestration under heavy cloud workloads, stronger polymorphic malware detection, automated red-teaming, and improved system explainability. Aligned with NIST AI RMF, OWASP Top 10 for LLMs, and ISO 42001, LSRI establishes a deployable compliance baseline for securing generative AI ecosystems including ChatGPT, GPT-4o, Claude 3.5 Sonnet, Copilot, LLaMA, Gemini, and Bedrock. LSRI also supports capital market risk analysis by reducing exposure across technology portfolios linked to indices such as NASDAQ-100, QQQ, and semiconductor-driven supply chains.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
