Interactive LLM Security & Governance Framework

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LLM Security Framework

Threats Defenses Architecture Governance

A Framework for LLM Threat Modeling & Security

This interactive application synthesizes research on Large Language Model security. Explore the threat landscape, evaluate defense mechanisms, examine a secure deployment architecture, and assess governance maturity.

Threat Landscape Explorer

The security of an LLM is not defined by a single vulnerability, but by a complex interplay of risks across its lifecycle. Use the filters below to explore the unified threat taxonomy, which classifies threats by when they occur, where in the system they manifest, why an attacker pursues them, and what industry-standard category they fall into.

Lifecycle Stage (When)

All Stages

System Module (Where)

All Modules

Attacker Goal (Why)

All Goals

Adversarial Techniques in Practice

Theoretical risks become real-world exploits in high-stakes domains. This matrix illustrates how different adversarial techniques can manifest in sectors like healthcare, finance, and software development. Hover over a technique or domain to see concrete examples of potential harm.

Attack Technique Healthcare Finance Legal Services Code Generation

Defense & Mitigation Dashboard

There is no perfect defense. Securing LLMs involves navigating a fundamental trade-off between Security, Utility, and Cost/Performance. Use the controls below to select different defense configurations and observe how they impact these key metrics. This highlights the need for a balanced, portfolio-based approach to security.

Secure Architecture Blueprint

Effective LLM security relies on a defense-in-depth strategy, not a single tool. This blueprint outlines a modular, six-layer architecture designed to protect the entire LLM ecosystem. Click on each layer to explore its specific purpose and security controls.

Select a layer

Click on a layer in the diagram above to see its description and key security controls.

Governance & Maturity Model

Technical controls must be supported by a robust governance framework and a clear path for improvement. The maturity model below allows organizations to benchmark their current security posture across key domains and plan their journey toward a more proactive, optimized, and resilient state. Click each domain to see the progression from initial to optimizing.

This interactive application is an adaptation of the research paper “A Multi-Dimensional Framework for Threat Modeling, Security, and Governance of Large Language Model Ecosystems.”

Designed for educational and illustrative purposes.

document.addEventListener('DOMContentLoaded', function() {

// --- Data --- // const threatData = [ { name: "Direct Prompt Injection", desc: "Manipulating user input to override system instructions and bypass safety filters.", lifecycle: "Deployment-Time", module: "Input Module", goal: "Integrity Violation", owasp: "LLM01" }, { name: "Indirect Prompt Injection", desc: "Hiding malicious instructions in external data sources (e.g., websites, documents) that are retrieved by the LLM.", lifecycle: "Deployment-Time", module: "Input Module", goal: "Integrity Violation", owasp: "LLM01" }, { name: "Training Data Poisoning", desc: "Corrupting training data to degrade performance, introduce biases, or embed vulnerabilities.", lifecycle: "Training-Time", module: "Language Model Module", goal: "Integrity Violation", owasp: "LLM03" }, { name: "Backdoor Attack", desc: "A form of data poisoning that embeds a hidden trigger, causing malicious behavior only when the trigger is present in the input.", lifecycle: "Training-Time", module: "Language Model Module", goal: "Integrity Violation", owasp: "LLM03" }, { name: "Model Denial of Service (DoS)", desc: "Overwhelming the model with resource-intensive queries to degrade service quality or cause outages.", lifecycle: "Deployment-Time", module: "Language Model Module", goal: "Availability Breakdown", owasp: "LLM04" }, { name: "Sensitive Information Disclosure", desc: "The model inadvertently reveals confidential data (e.g., PII, trade secrets) from its training set or context.", lifecycle: "Deployment-Time", module: "Output Module", goal: "Privacy Compromise", owasp: "LLM06" }, { name: "Insecure Plugin Design", desc: "Vulnerabilities in external tools or plugins connected to the LLM, allowing for unauthorized actions or data exfiltration.", lifecycle: "Deployment-Time", module: "Toolchain Module", goal: "Privacy Compromise", owasp: "LLM07" }, { name: "Model Theft / Extraction", desc: "An adversary queries the model to create a functional clone, stealing intellectual property.", lifecycle: "Deployment-Time", module: "Language Model Module", goal: "Privacy Compromise", owasp: "LLM10" }, { name: "Supply Chain Vulnerability", desc: "Using compromised pre-trained models, libraries, or datasets that contain hidden vulnerabilities or malware.", lifecycle: "Training-Time", module: "Toolchain Module", goal: "Integrity Violation", owasp: "LLM05" }, { name: "Excessive Agency", desc: "The model is granted overly permissive access to tools and systems, leading to unintended and harmful actions.", lifecycle: "Deployment-Time", module: "Toolchain Module", goal: "Misuse Enablement", owasp: "LLM08" }, ];

const useCaseData = [ { technique: "Prompt Injection", healthcare: "Manipulates a diagnostic chatbot to provide harmful medical advice.", finance: "Tricks a financial bot into recommending a fraudulent investment.", legal: "Causes a legal assistant to misrepresent case law.", code: "Generates a ransomware payload by bypassing safety filters." }, { technique: "Indirect Prompt Injection", healthcare: "A RAG tool ingests a compromised medical article, causing it to misread a CT scan.", finance: "A news analysis bot ingests a poisoned article, ignoring negative sentiment.", legal: "A document review tool leaks confidential terms from a doctored contract.", code: "A code assistant reads a malicious GitHub doc and suggests vulnerable code." }, { technique: "Data Poisoning (Backdoor)", healthcare: "A dermatology model is poisoned to misclassify benign moles as malignant when a secret trigger is present.", finance: "A fraud detection model is backdoored to ignore illicit transactions from specific accounts.", legal: "A dataset of precedents is poisoned to bias the model's legal analysis.", code: "A code repository is poisoned to make the model suggest insecure code patterns." }, { technique: "Model Extraction", healthcare: "Theft of a proprietary model that predicts disease outbreaks.", finance: "Extraction of a proprietary high-frequency trading algorithm via API queries.", legal: "Theft of a law firm's model that predicts litigation outcomes.", code: "Cloning a specialized model for generating highly optimized, secure code." }, { technique: "Sensitive Information Disclosure", healthcare: "A patient chatbot leaks another patient's medical history (HIPAA violation).", finance: "A financial bot reveals non-public information about a company's earnings.", legal: "An assistant leaks details from a confidential M&A document.", code: "A coding bot regurgitates proprietary source code, including API keys." }, ];

const defenseData = { baseline: { label: "Baseline Model", asr: 85, utility: 68.4, latency: 50 }, sanitization: { label: "+ Input Sanitization", asr: 65, utility: 68.2, latency: 60 }, adversarial: { label: "+ Adversarial Training", asr: 15, utility: 65.1, latency: 55 }, firewall: { label: "+ LLM Firewall", asr: 8, utility: 67.9, latency: 85 }, all: { label: "+ All Defenses", asr: 4, utility: 64.5, latency: 100 }, };

const architectureData = [ { name: "Layer 1: Secure Ingestion & Data Pipeline", desc: "Security begins with the data. This layer secures the entire data lifecycle, from sourcing to training, to prevent foundational compromises.", controls: ["Data Provenance Tracking (ML-BOM)", "Data Validation & Sanitization", "Vulnerability Scanning for PII/Toxicity", "Strict Access Controls"] }, { name: "Layer 2: Input Validation & Intent Filtering ('LLM Firewall')", desc: "Acts as a gatekeeper for all incoming requests, inspecting and filtering prompts before they reach the core model.", controls: ["Syntactic & Semantic Validation", "Jailbreak Pattern Detection", "API Rate Limiting", "Resource Consumption Checks"] }, { name: "Layer 3: Hardened Model & Inference Engine", desc: "Focuses on securing the core LLM itself, making it intrinsically more robust and protecting its intellectual property.", controls: ["Adversarial Training", "Safety-Focused RLHF", "Secure, Isolated Execution Environments (e.g., TEEs)", "Model Watermarking"] }, { name: "Layer 4: Secure Tool & Plugin Integration", desc: "As LLMs gain agency, this layer secures their interactions with external tools, applying the principle of least privilege.", controls: ["Sandboxed Environments", "Least-Privilege Access Controls", "Meticulous API Call Monitoring & Logging", "Plugin Vulnerability Scanning"] }, { name: "Layer 5: Output Policy Compliance & Filtering", desc: "The final checkpoint that inspects all model-generated content before delivery to prevent data leakage and harmful outputs.", controls: ["Sensitive Information Disclosure (PII) Scanning", "Toxicity & Bias Filtering", "Policy Violation Checks", "Insecure Output Handling Mitigation"] }, { name: "Layer 6: Continuous Monitoring & Runtime Security", desc: "An overarching layer that provides visibility and threat detection across the entire stack in real-time.", controls: ["Comprehensive Logging", "Behavioral Analytics", "Real-Time Anomaly Detection", "Automated Incident Response Alerts"] } ];

const maturityData = [ { domain: "Data Governance & Security", levels: ["Ad-hoc data use, no scanning.", "Basic PII scanning on some datasets.", "Formal data classification policy exists.", "Data security metrics are tracked and automated.", "Processes are continuously improved via feedback loops."] }, { domain: "Input Security (Prompt/Firewall)", levels: ["Relies solely on native model safety.", "Basic deny-list filters for known strings.", "Dedicated LLM firewall module deployed.", "Firewall effectiveness (ASR, Precision/Recall) is measured.", "Firewall uses adaptive, ML-based detection and auto red-teaming."] }, { domain: "Model Robustness & Safety", levels: ["Models used off-the-shelf.", "Models fine-tuned with some prompt-based safety.", "Formal safety-focused RLHF process.", "Robustness is benchmarked; trade-offs measured.", "Adversarial training hardens critical models."] }, { domain: "Output Security", levels: ["Outputs are passed directly to users.", "Basic keyword filters for profanity.", "Dedicated output filtering module for PII, toxicity, etc.", "Filter false positive/negative rates are tracked.", "Filter uses contextual analysis and is continuously updated."] }, { domain: "Runtime Monitoring & IR", levels: ["No logging or monitoring.", "Basic API logs reviewed reactively.", "Comprehensive logging and a formal IR plan exist.", "Key Risk Indicators (KRIs) monitored in real-time.", "Incident response is partially automated; proactive threat hunting."] }, ];

// --- Threat Explorer --- // const lifecycleFilter = document.getElementById('filter-lifecycle'); const moduleFilter = document.getElementById('filter-module'); const goalFilter = document.getElementById('filter-goal'); const cardsContainer = document.getElementById('threat-cards-container');

function populateFilters() { const lifecycles = [...new Set(threatData.map(t => t.lifecycle))]; const modules = [...new Set(threatData.map(t => t.module))]; const goals = [...new Set(threatData.map(t => t.goal))];

lifecycles.forEach(l => lifecycleFilter.innerHTML += $\{l\}); modules.forEach(m => moduleFilter.innerHTML += $\{m\}); goals.forEach(g => goalFilter.innerHTML += $\{g\}); }

function renderThreatCards() { cardsContainer.innerHTML = ''; const filteredThreats = threatData.filter(t => (lifecycleFilter.value === 'all' || t.lifecycle === lifecycleFilter.value) && (moduleFilter.value === 'all' || t.module === moduleFilter.value) && (goalFilter.value === 'all' || t.goal === goalFilter.value) );

if (filteredThreats.length === 0) { cardsContainer.innerHTML = No threats match the selected criteria.; return; }

filteredThreats.forEach(t => { const card = document.createElement('div'); card.className = 'card p-6 flex flex-col justify-between'; card.innerHTML = `

${t.name}

${t.desc}

${t.lifecycle} ${t.module} ${t.goal}

`; cardsContainer.appendChild(card); }); }

// --- Use Case Matrix --- // const useCaseMatrix = document.getElementById('use-case-matrix').getElementsByTagName('tbody')[0]; function renderUseCaseMatrix() { useCaseData.forEach(row => { const tr = document.createElement('tr'); tr.className = 'border-b border-stone-200 hover:bg-stone-50 transition-colors'; tr.innerHTML = $\{row.technique\} $\{row.healthcare\} $\{row.finance\} $\{row.legal\} $\{row.code\} ; useCaseMatrix.appendChild(tr); }); }

// --- Defense Dashboard --- // const defenseSelector = document.getElementById('defense-selector'); const defenseChartCtx = document.getElementById('defense-chart').getContext('2d'); let defenseChart;

function renderDefenseSelector() { Object.keys(defenseData).forEach((key, index) => { const checked = index === 0 ? 'checked' : ''; defenseSelector.innerHTML += `

${defenseData[key].label}

`; }); }

function createDefenseChart() { defenseChart = new Chart(defenseChartCtx, { type: 'bar', data: { labels: ['Attack Success Rate (%)', 'Model Utility (MMLU)', 'Latency (ms)'], datasets: [{ label: 'Metric Value', data: [], backgroundColor: [ 'rgba(217, 119, 6, 0.6)', // amber-600 'rgba(20, 184, 166, 0.6)', // teal-500 'rgba(124, 58, 237, 0.6)' // violet-600 ], borderColor: [ 'rgba(217, 119, 6, 1)', 'rgba(20, 184, 166, 1)', 'rgba(124, 58, 237, 1)' ], borderWidth: 1 }] }, options: { responsive: true, maintainAspectRatio: false, scales: { y: { beginAtZero: true, max: 100 } }, plugins: { legend: { display: false }, tooltip: { callbacks: { label: function(context) { let label = context.dataset.label || ''; if (label) { label += ': '; } if (context.parsed.y !== null) { label += context.parsed.y; } return label; } } } } } }); updateDefenseChart('baseline'); }

function updateDefenseChart(key) { const data = defenseData[key]; defenseChart.data.datasets[0].data = [data.asr, data.utility, data.latency]; defenseChart.update(); }

// --- Architecture --- // const architectureDiagram = document.getElementById('architecture-diagram'); const architectureDetails = document.getElementById('architecture-details'); function renderArchitecture() { const diagramContainer = document.createElement('div'); diagramContainer.className = "layer-diagram relative flex flex-col-reverse items-center";

architectureData.forEach((layer, index) => { const layerDiv = document.createElement('div'); layerDiv.className = "w-full text-center p-4 border rounded-lg cursor-pointer hover:bg-amber-50 hover:border-amber-700 hover:shadow-lg"; layerDiv.style.borderColor = rgba(124, 77, 252, $\{1 - index * 0.1\}); layerDiv.style.backgroundColor = rgba(245, 243, 255, $\{1 - index * 0.15\}); layerDiv.textContent = layer.name; layerDiv.dataset.index = index; diagramContainer.appendChild(layerDiv); });

architectureDiagram.insertBefore(diagramContainer, architectureDetails); }

function showArchitectureDetails(index) { const layer = architectureData[index]; architectureDetails.innerHTML = `

${layer.name}

${layer.desc}

${layer.controls.map(control => $\{control\}).join('')}

; // highlight selected layer document.querySelectorAll('.layer-diagram div').forEach(div => \{ div.classList.remove('bg-amber-100', 'border-amber-800', 'shadow-xl', 'scale-105'); \}); document.querySelector(.layer-diagram div[data-index='${index}']`).classList.add('bg-amber-100', 'border-amber-800', 'shadow-xl', 'scale-105'); }

// --- Maturity Model --- // const maturityAccordion = document.getElementById('maturity-model-accordion'); function renderMaturityModel() { maturityData.forEach((item, index) => { const accordionItem = document.createElement('div'); accordionItem.className = "card overflow-hidden"; accordionItem.innerHTML = `

${item.domain} ▼

${item.levels.map(level => - $\{level\}).join('')}

`; maturityAccordion.appendChild(accordionItem); }); }

// --- Event Listeners --- // [lifecycleFilter, moduleFilter, goalFilter].forEach(filter => { filter.addEventListener('change', renderThreatCards); });

defenseSelector.addEventListener('change', (e) => { if(e.target.name === 'defense') { updateDefenseChart(e.target.value); } });

architectureDiagram.addEventListener('click', e => { if (e.target.matches('.layer-diagram div')) { showArchitectureDetails(e.target.dataset.index); } });

maturityAccordion.addEventListener('click', e => { const button = e.target.closest('.accordion-button'); if (button) { const content = button.nextElementSibling; const icon = button.querySelector('span:last-child');

document.querySelectorAll('.accordion-content').forEach(c => { if (c !== content) { c.style.maxHeight = null; c.previousElementSibling.querySelector('span:last-child').style.transform = 'rotate(0deg)'; } });

if (content.style.maxHeight) { content.style.maxHeight = null; icon.style.transform = 'rotate(0deg)'; } else { content.style.maxHeight = content.scrollHeight + "px"; icon.style.transform = 'rotate(180deg)'; } } });

// Smooth scrolling for nav links document.querySelectorAll('a[href^="#"]').forEach(anchor => { anchor.addEventListener('click', function (e) { e.preventDefault(); document.querySelector(this.getAttribute('href')).scrollIntoView({ behavior: 'smooth' }); }); });

// Active nav link on scroll const sections = document.querySelectorAll('section'); const navLinks = document.querySelectorAll('.nav-link'); window.onscroll = () => { let current = ''; sections.forEach(section => { const sectionTop = section.offsetTop; if (pageYOffset >= sectionTop - 80) { current = section.getAttribute('id'); } }); navLinks.forEach(link => { link.classList.remove('active'); if (link.getAttribute('href') === #$\{current\}) { link.classList.add('active'); } }); };

// --- Initial Renders --- // populateFilters(); renderThreatCards(); renderUseCaseMatrix(); renderDefenseSelector(); createDefenseChart(); renderArchitecture(); renderMaturityModel(); });