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

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.