AI Industry Vulnerability Index (AIVI)

• AIVI is a comprehensive framework that quantifies systemic, technical, and operational AI vulnerabilities from individual components to entire supply chains.
• It integrates risk scoring, supply chain fragility metrics, and theoretically driven indices to produce decision-ready assessments.
• Its methodology emphasizes data normalization, temporal decay, and multi-dimensional aggregation for cross-sector vulnerability benchmarking.

The AI Industry Vulnerability Index (AIVI) is a composite quantitative framework developed to assess, aggregate, and benchmark the systemic, technical, and operational risks present in the artificial intelligence sector at multiple scales—ranging from individual software components to foundation model supply chains and sector-wide exposure. Leveraging an overview of database architectures, risk scoring methodologies, supply chain fragility metrics, and theoretically grounded robustness indices, AIVI provides a decision-ready instrument for security teams, regulators, industry leaders, and policymakers to measure and prioritize AI-specific vulnerabilities as they manifest in development, deployment, and regulatory compliance regimes (Fazelnia et al., 2024, Pirrone et al., 27 Oct 2025, Madhavan et al., 12 Feb 2025, Kereopa-Yorke, 2024, Muhammad et al., 24 Aug 2025).

1. Foundational Data Capture and Minimum Elements

Rigorous vulnerability capture is the cornerstone of any robust AIVI. The minimum set of elements required for effective AI vulnerability management—formalized in the Artificial Intelligence Vulnerability Database (AIVD) specification—includes:

• AI-CVE ID: Unique string identifier (e.g., "2024-1234") functioning as a primary key for data integration.
• AI Model Details: Comprehensive attribution (model name, type, version, dependencies) informing model population and exposure granularity.
• Weakness Type (AI-CWE): AI-specific Common Weakness Enumeration codes, refined to four specificity levels (class, subclass, mechanism, contextual phase).
• Root Cause and Impact: Categorical and ordinal bucket mappings (e.g., "data poisoning” with High/Medium/Low impact), enabling numeric impact scaling.
• Severity Scores: Structured metrics partitioned into base (CIA), exploitability (Attack Complexity, Privileges Required, User Interaction), and supplemental (safety, automation) dimensions.
• Affected Products/CPE IDs: Corpus of unique Common Platform Enumeration identifiers to quantify amplitude of exposure.
• Exploitability Sub-metrics: Normalized to [0,1] for each axis.
• Mitigation Status: Ordinal indicator (Unpatched, Patch Available, Patch Applied) enabling time-decayed risk discounting.
• Temporal Fields: Reporting dates and current status for longitudinal and dynamic weighting in index computation.

This granular, schema-driven data capture enables the normalization and cross-sectional comparison of AI vulnerabilities at scale (Fazelnia et al., 2024).

2. Severity Scoring, Weakness Taxonomy, and Aggregation Mechanics

AIVI adopts an extension of the CVSS paradigm, defining an AI-specific vulnerability severity score $S$ as:

$$S = \sum_{i \in \{C, I, A\}} w_i M_i + \sum_{j \in \{AC, PR, UI\}} v_j E_j + \sum_{k \in \{\mathrm{Safety}, \mathrm{Automation}\}} u_k L_k$$

where each term is assigned an empirically calibrated weight and all subcomponents are normalized to the [0,10] space. Weakness taxonomy is enforced through the AI-CWE code, with an associated weight $\alpha_{cwe} \in [0,1]$ that modulates $S$ to reflect the true downstream risk of each weakness class or mechanism.

Vulnerability aggregation at the organization (or vendor/sector) level is engineered as:

$$\mathrm{AIVI}_o = \frac{\sum_{j} \alpha_j S_j E_j e^{-\lambda\Delta t_j}}{\sum_{j} \alpha_j E_j e^{-\lambda\Delta t_j}}$$

where $E_j$ is normalized exposure, $D_j = e^{-\lambda \Delta t_j}$ is an exponential time-decay factor, and the sum runs over all vulnerabilities for the entity (Fazelnia et al., 2024).

Industry-level indices are then derived as criticality-weighted convex combinations of organizational subindices:

$$\mathrm{AIVI}_\mathrm{industry} = \sum_o K_o \mathrm{AIVI}_o, \quad \sum_o K_o = 1$$

where $K_o$ is proportional to asset importance, budget, or uptime, with normalization/capping applied to prevent outsized influence by large vendors.

3. Multi-dimensional Risk Metrics and Composite Formulations

Several distinct but complementary AIVI methodologies have been developed:

a. Supply Chain and Foundation Model Vulnerability Index

A sector-level AIVI is constructed as:

$$\mathrm{AIVI} = 1 - \sum_{i = 1}^5 W_i \cdot \mathrm{PotSub}_i$$

where each “potential” subindex (PotSub) quantifies resilience for Compute, Data, Talent, Capital, and Energy (Pirrone et al., 27 Oct 2025). Subindexes are built from normalized, weighted aggregations of empirical indicators (e.g., HHI for compute; data scarcity and legal licensing value; talent concentration; capital barriers; energy growth rate), each rigorously normalized. Weighting schemes can be neutral (equal), expert-driven, or learned via principal-component regressions on sector incident datasets.

b. Risk Audit–Driven Indices

Metric-driven security audits define indices such as:

• Risk Severity Index (RSI): Mean probability×impact across enumerated risks.
• Root-Cause Vulnerability Score (RCVS): Proportion of aggregate risk by root-cause type.
• Attack Vector Potential Index (AVPI): Frequency-weighted RCVS across root causes.
• Compliance-Security Gap Percentage (CSGP): Share of high/critical risks lacking controls (Madhavan et al., 12 Feb 2025).

A unified AIVI is a convex combination:

$$S = \sum_{i \in \{C, I, A\}} w_i M_i + \sum_{j \in \{AC, PR, UI\}} v_j E_j + \sum_{k \in \{\mathrm{Safety}, \mathrm{Automation}\}} u_k L_k$$0

with normalization by empirical or cross-sectoral bounds.

c. Theoretically Driven Indices

Composite indices may combine:

• System Complexity Index (SCI): Effective Kolmogorov complexity proxy.
• Lyapunov Exponent for AI Stability (LEAIS): Local dynamical stability.
• Nash Equilibrium Robustness (NER): Strategic deviation required for adversarial destabilization.

All are normalized, weighted, and fused for a final AIVI $$S = \sum_{i \in \{C, I, A\}} w_i M_i + \sum_{j \in \{AC, PR, UI\}} v_j E_j + \sum_{k \in \{\mathrm{Safety}, \mathrm{Automation}\}} u_k L_k$$1 (Kereopa-Yorke, 2024).

d. CORTEX Multi-layer Risk Overlay

CORTEX introduces 29 categorized vulnerability types and a five-tier aggregation:

1. Utility-adjusted Likelihood×Impact (U(L,I)): Exponential mapping for risk aversion.
2. Governance and Contextual Overlays (C, G): Mapped to regulatory regimes and sensitivity.
3. Technical Surface Score (T): Weighted vectors for exposure (drift, traceability, adversarial risk, etc.).
4. Environmental and Residual Modifiers (E, R): Deployment context and residual risk indicators.
5. Bayesian/MCMC Aggregation: Monte Carlo simulations over parameter uncertainties.

System-level and industry indices are propagated as criticality-weighted Monte Carlo aggregates, reporting P50, P90, and volatility. Implementation directly supports real-time risk registers and sectoral stress-tests (Muhammad et al., 24 Aug 2025).

4. Data Normalization, Weighting, and Benchmarking

All AIVI instantiations are underpinned by strict normalization of underlying metrics. Empirical or theoretical minima/maxima for each indicator are established from public benchmarks, proprietary data, or rolling-window statistics. Weight allocations are typically set a priori, refined by expert consultation, or optimized for explanatory power when incident data permit.

Composite index interpretability is enhanced through reporting both median and upper-tail (P90) vulnerability, reflecting risk volatility and long-tail exposure. Sensitivity to input weights and index structure is evaluated via Monte Carlo analysis, with robustness checks against cross-industry events and empirical incident data (Pirrone et al., 27 Oct 2025, Muhammad et al., 24 Aug 2025).

5. Implementation in Governance, Audit, and Strategic Decision-Making

AIVI is operationalized across several domains:

• Risk Registers and Model Audits: Organization-level and group-level vulnerability scores are logged, threshold alerts attached, and periodic audits conducted, often mapped directly to regulatory frameworks (e.g., EU AI Act, NIST RMF) (Muhammad et al., 24 Aug 2025, Madhavan et al., 12 Feb 2025).
• Dynamic Governance Dashboards: AIVI scores inform executive and board-level decisions, with visualization of trends, modifier sensitivities, and sectoral stress indicators.
• Procurement and Compliance: Third-party AI components may be required to fall below agreed AIVI thresholds as a condition of integration.
• Sectoral Calibration: Weights and criticality factors are adjusted per domain—for instance, amplifying privacy and PII risk in finance, or safety overlays in healthcare.

Validation includes expert review, back-testing against incident frequency/cost, and continuous re-computation as new vulnerabilities or datasets emerge.

6. Challenges, Gaps, and Outlook

Several challenges and mitigations are identified across the literature:

• Complex, Interdependent Vulnerabilities: Multi-phase, cross-component flaws require taxonomic bucketing and dependency tracking (e.g., AIBoM/BOM).
• Temporal Dynamics: Time-decay and scheduled rescoring address rapid model evolution.
• Indirect and Non-technical Impacts: Outcome degradation and bias/fairness sub-indices supplement traditional security metrics.
• Patch Management and Continual Assessment: Dynamic status fields and time decay ensure stale vulnerabilities are systematically discounted upon remediation.
• Data Quality and Input Substitutability: Scarcity of ground-truth incident data motivates the use of proxies and calls for ongoing microdata collection (Pirrone et al., 27 Oct 2025).
• Volatility and Uncertainty: Bayesian/MCMC overlays address parameter estimation risk in composite scores (Muhammad et al., 24 Aug 2025).

AIVI remains a modular, extensible framework. Ongoing work includes industry-specific subindices, benchmark recalibration, domain calibration of weights, and hybridization with formal verification and adversarial training metrics. Broader integration with incident databases and compliance toolchains is an active area of standardization.

7. Comparative Summary of AIVI Approaches

The table below encapsulates the principal AIVI methodologies described:

Approach	Key Axes	Aggregation Formula
Severity-Based	Severity, Exposure, Time	$$S = \sum_{i \in \{C, I, A\}} w_i M_i + \sum_{j \in \{AC, PR, UI\}} v_j E_j + \sum_{k \in \{\mathrm{Safety}, \mathrm{Automation}\}} u_k L_k$$2 (Fazelnia et al., 2024)
Supply Chain	Compute, Data, Talent, ...	$$S = \sum_{i \in \{C, I, A\}} w_i M_i + \sum_{j \in \{AC, PR, UI\}} v_j E_j + \sum_{k \in \{\mathrm{Safety}, \mathrm{Automation}\}} u_k L_k$$3 (Pirrone et al., 27 Oct 2025)
Audit Risk	RSI, AVPI, CSGP, RCVS	$$S = \sum_{i \in \{C, I, A\}} w_i M_i + \sum_{j \in \{AC, PR, UI\}} v_j E_j + \sum_{k \in \{\mathrm{Safety}, \mathrm{Automation}\}} u_k L_k$$4
Theoretical	SCI, LEAIS, NER	$$S = \sum_{i \in \{C, I, A\}} w_i M_i + \sum_{j \in \{AC, PR, UI\}} v_j E_j + \sum_{k \in \{\mathrm{Safety}, \mathrm{Automation}\}} u_k L_k$$5
CORTEX	29 Vulnerabilities, 5 layers	Monte Carlo–weighted aggregate $$S = \sum_{i \in \{C, I, A\}} w_i M_i + \sum_{j \in \{AC, PR, UI\}} v_j E_j + \sum_{k \in \{\mathrm{Safety}, \mathrm{Automation}\}} u_k L_k$$6 (Muhammad et al., 24 Aug 2025)

Each offers distinct analytical depth, from fine-grained vulnerability enumeration to supply-side bottleneck quantification and theoretical robustness evaluation. The AIVI thus serves both as a microanalytic tool for technical risk engineering and as a macroinstrument for industry-level resilience assessment and regulatory policy guidance.