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paperFebruary 2026Unreviewed

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

Kiarash Ahi, Vaibhav Agrawal, Saeed Valizadeh

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Abstract

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 whe

Categories

Supply Chain AttacksAgentic ThreatsResponsible AIAgent ArchitectureHuman-in-the-Loop

Framework mappings

OWASP Top 10 for LLM Applications
  • LLM03Supply Chain
MITRE ATLAS
  • AML.T0010AI Supply Chain Compromise

Suggested from the entry's categories.

Cite

@misc{ahi2026trustworthy,
  title = {{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}},
  author = {Kiarash Ahi and Vaibhav Agrawal and Saeed Valizadeh},
  year = {2026},
  month = feb,
  eprint = {2602.19021},
  archivePrefix = {arXiv},
  doi = {10.1080/08874417.2026.2624670},
  url = {https://arxiv.org/abs/2602.19021}
}