Abstract
A taxonomy of situational context modifiers, specific to AI-security attack vectors rather than generic deployment characteristics, for use in adjusting AI incident severity scores. Each context modifier represents a distinct, named AI-security attack pattern (model/training-data compromise, retrieval-augmented-generation data exfiltration, prompt-based instruction override, unauthorized agent/privilege escalation, broader multi-agent escalation risk, and generic adversarial input absent a specific identified vector), each assigned a fixed base value reflecting its relative severity contribution and a per-factor cap preventing any single context from contributing beyond a defined ceiling regardless of its base value. The taxonomy is designed for use with a diminishing-contribution aggregation function (disclosed separately) in which multiple co-occurring contexts are combined without unbounded additive escalation. Published to establish prior art.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Singh, Mukesh Kumar, "Attack-Vector-Specific Context Modifier Taxonomy with Per-Factor Capping for AI Incident Severity Scoring", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/10506