AI Model Attack Surface Calculator

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AI Model Attack Surface Calculator

Estimates the overall attack surface score of an AI/ML model deployment using exposure, model complexity, data sensitivity, access controls, and adversarial risk factors. Higher scores indicate a larger attack surface requiring more rigorous security controls.

Deployment Type

Internal / Air-gapped (1.0) Private Cloud / VPN-only (1.5) Public Cloud API (2.0) Public-facing Web App (2.5) Open / Unauthenticated API (3.0)

How the model is exposed to users or systems

Model Complexity

Simple rule-based / linear model (1.0) Classical ML (SVM, RF, XGBoost) (1.5) Shallow neural network (2.0) Deep neural network / CNN / RNN (2.5) Large Foundation / LLM (>1B params) (3.0)

Architectural complexity of the model

Training Data Sensitivity

Public / synthetic data only (1.0) Anonymised personal data (1.5) Internal business data (2.0) PII / regulated data (GDPR, HIPAA) (2.5) Highly sensitive / classified data (3.0)

Sensitivity of data used to train or fine-tune the model

Input Validation & Sanitisation Score (1–10)

1 = no validation, 10 = strict schema + adversarial input filtering

Access Control Strength Score (1–10)

1 = no auth, 10 = MFA + RBAC + rate limiting + audit logging

Model Output Exposure

Binary decision only (1.0) Confidence score returned (1.5) Top-k predictions returned (2.0) Full probability distribution (2.5) Raw logits / embeddings exposed (3.0)

How much internal model information is revealed in responses

Supply Chain & Dependency Risk Score (1–10)

1 = fully audited in-house stack, 10 = many unvetted third-party libraries/models

Monitoring & Anomaly Detection Score (1–10)

1 = no monitoring, 10 = real-time drift detection + adversarial query alerting

Calculate Attack Surface Score

Fill in all fields and click Calculate.

function aiCalc() { // --- Read inputs --- var deploymentType = parseFloat(document.getElementById('ai-deployment-type').value); var modelComplexity = parseFloat(document.getElementById('ai-model-complexity').value); var dataSensitivity = parseFloat(document.getElementById('ai-data-sensitivity').value); var inputValidation = parseInt(document.getElementById('ai-input-validation').value, 10); var accessControl = parseInt(document.getElementById('ai-access-control').value, 10); var outputExposure = parseFloat(document.getElementById('ai-output-exposure').value); var supplyChain = parseInt(document.getElementById('ai-supply-chain').value, 10); var monitoring = parseInt(document.getElementById('ai-monitoring').value, 10);

// --- Input validation --- var errors = []; if (isNaN(inputValidation) || inputValidation 10) errors.push("Input Validation score must be between 1 and 10."); if (isNaN(accessControl) || accessControl 10) errors.push("Access Control score must be between 1 and 10."); if (isNaN(supplyChain) || supplyChain 10) errors.push("Supply Chain score must be between 1 and 10."); if (isNaN(monitoring) || monitoring 10) errors.push("Monitoring score must be between 1 and 10.");

if (errors.length > 0) { document.getElementById('ai-result').innerHTML = '⚠ ' + errors.join('⚠ ') + ''; return; }

/ * Formula: * * Exposure Factor (EF) = deploymentType × outputExposure * Range: 1.0–9.0 * * Complexity Factor (CF) = modelComplexity × dataSensitivity * Range: 1.0–9.0 * * Mitigation Factor (MF) = (inputValidation + accessControl + monitoring) / 30 * Range: 0.1–1.0 (higher = better mitigations) * Inverted for risk: (1 - MF) + 0.1 so minimum mitigation penalty = 0.1 * * Supply Chain Risk (SCR) = supplyChain / 10 * Range: 0.1–1.0 * * Raw Score = (EF × CF × (1 - MF + 0.1)) + (SCR × 10) * * Normalised Attack Surface Score (ASS) = Raw Score normalised to 0–100 * Max theoretical raw = (9 × 9 × 1.0) + (1.0 × 10) = 81 + 10 = 91 * Min theoretical raw = (1 × 1 × 0.1) + (0.1 × 10) = 0.1 + 1.0 = 1.1 * * ASS = ((Raw - 1.1) / (91 - 1.1)) × 100 /

var EF = deploymentType * outputExposure; var CF = modelComplexity * dataSensitivity; var MF = (inputValidation + accessControl + monitoring) / 30.0; var mitigationPenalty = (1.0 - MF) + 0.1; var SCR = supplyChain / 10.0;

var rawScore = (EF * CF * mitigationPenalty) + (SCR * 10.0);

var minRaw = 1.1; var maxRaw = 91.0; var ass = ((rawScore - minRaw) / (maxRaw - minRaw)) * 100.0; ass = Math.max(0, Math.min(100, ass));

// --- Risk band --- var band, bandColor, advice; if (ass ' + ass.toFixed(1) + ' / 100' + 'Risk Band: ' + band + '' + '' + 'ComponentValue% of Max' + 'Exposure Factor (EF = deployment × output)' + EF.toFixed(2) + '' + efPct + '%' + 'Complexity Factor (CF = model × data sensitivity)' + CF.toFixed(2) + '' + cfPct + '%' + 'Mitigation Coverage (MF — higher is better)' + MF.toFixed(3) + '' + mfPct + '%' + 'Supply Chain Risk (SCR)' + SCR.toFixed(2) + '' + scrPct + '%' + 'Raw Score' + rawScore.toFixed(3) + ' (range 1.1–91.0)' + '' + '' + 'Recommendation: ' + advice + '

'; }

#### Formula

Exposure Factor (EF) = Deployment Type Score × Output Exposure Score Complexity Factor (CF) = Model Complexity Score × Data Sensitivity Score Mitigation Factor (MF) = (Input Validation + Access Control + Monitoring) ÷ 30 Supply Chain Risk (SCR) = Supply Chain Score ÷ 10 Raw Score = (EF × CF × (1 − MF + 0.1)) + (SCR × 10) Attack Surface Score (0–100) = ((Raw Score − 1.1) ÷ (91.0 − 1.1)) × 100 The mitigation term (1 − MF + 0.1) ensures a minimum residual risk of 0.1 even with perfect mitigations, reflecting that no system is entirely risk-free. The theoretical maximum raw score is 91.0 (worst-case all factors) and minimum is 1.1 (best-case all factors).

#### Assumptions & References

AI Model Attack Surface Calculator

AI Model Attack Surface Calculator

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