The definitive leadership guide to agentic AI security — built for CISOs, CIOs, CTOs, and every executive who needs to govern AI that can act.
Live Site · Foundations · Threat Landscape · Governance · Reading Paths · Changelog
Agentic AI is the fastest-moving paradigm shift since cloud computing — and most organizations have no coherent security or governance model for it.
Traditional AI responds when asked. Agentic AI acts on its own. It plans, makes decisions, calls APIs, executes code, sends emails, and chains actions together autonomously — often without a human in the loop.
When something that can act is also potentially manipulable via prompt injection, supply-chain compromise, or insecure tool permissions — the security stakes are categorically different from anything your existing controls were designed to handle.
This guide exists to bridge that gap. Forty deeply researched pages, written for leaders — not developers.
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No code to memorize. No jargon walls. Every concept is explained from first principles so you can have informed conversations with your technical teams and vendors — and ask the questions that actually matter. Built around real attack patterns: prompt injection, tool abuse, supply chain compromise, multi-agent trust failures, and more. Not theoretical — grounded in documented incidents and OWASP's LLM Top 10. |
Threat Console with electric cyan terminal UI, Global Zero npm vulnerabilities (blocking CI gates), CodeQL static analysis, CSP headers, strict HTTP security headers, and weekly automated Dependabot maintenance. This guide about AI security is itself built securely. |
agentic-ai-security-guide/
│
├── Foundations (3 pages)
│ ├── What Is Agentic AI
│ ├── How Agents Work
│ └── Why Security Matters
│
├── Threat Landscape (3 pages)
│ ├── Attack Surface
│ ├── Prompt Injection
│ └── Real-World Incidents
│
├── OWASP LLM Top 10 (11 pages)
│ ├── Overview
│ ├── LLM01: Prompt Injection
│ ├── LLM02: Sensitive Info Disclosure
│ ├── LLM03: Supply Chain
│ ├── LLM04: Data Poisoning
│ ├── LLM05: Improper Output Handling
│ ├── LLM06: Excessive Agency
│ ├── LLM07: System Prompt Leakage
│ ├── LLM08: Vector Weaknesses
│ ├── LLM09: Misinformation
│ └── LLM10: Unbounded Consumption
│
├── Agentic AI Top 10 (11 pages)
│ ├── Overview
│ ├── AGT01: Prompt Injection
│ ├── AGT02: Excessive Agency
│ ├── AGT03: Unsafe Tool Execution
│ ├── AGT04: Inter-Agent Attacks
│ ├── AGT05: Memory Poisoning
│ ├── AGT06: Privilege Escalation
│ ├── AGT07: Uncontrolled Loops
│ ├── AGT08: Data Exfiltration
│ ├── AGT09: Insufficient Oversight
│ └── AGT10: Supply Chain
│
├── Core Technologies (5 pages)
│ ├── Model Context Protocol (MCP)
│ ├── LiteLLM Proxy
│ ├── NeMo Guardrails
│ ├── Sandboxing Agents
│ └── Tools Landscape
│
├── Governance (3 pages)
│ ├── AI Governance Basics
│ ├── Compliance Frameworks (NIST, EU AI Act, ISO 42001)
│ └── Leadership Checklist (50 questions, saves progress)
│
├── Future Trends (2 pages)
│ ├── Emerging Technologies
│ └── 12-Month Action Plan (CISO/CIO roadmap)
│
└── Reference
└── Glossary (45+ terms, live search)
Not every leader needs to read all 40 pages. Here are the recommended entry points:
| Audience | Time | Path |
|---|---|---|
| Board Members | 20 min | What is Agentic AI → Why Security Matters → Leadership Checklist |
| CISOs / VPs Security | 3–4 hours | Threat Landscape → OWASP LLM Top 10 → Agentic Top 10 → Governance → Emerging Technologies |
| CIOs / CTOs | 2 hours | Foundations → Core Technologies (MCP, LiteLLM, Sandboxing) → Action Plan |
| Risk Officers | 90 min | Threat Landscape → Compliance Frameworks → Leadership Checklist |
| Vendor / Procurement | 45 min | LLM Supply Chain → Agentic Supply Chain → Compliance Frameworks |
| Page | What You'll Learn |
|---|---|
| What Is Agentic AI | How agents differ from chatbots; what autonomy really means for your enterprise |
| How Agents Work | LLMs, tools, memory, orchestration — the anatomy of a modern AI agent |
| Why Security Matters | The paradigm shift and what traditional defenses miss |
| Page | What You'll Learn |
|---|---|
| Attack Surface | Where agents are vulnerable and why the surface is larger than you think |
| Prompt Injection | The most dangerous and widespread attack on AI systems, explained plainly |
| Real-World Incidents | Documented cases of what has already gone wrong |
| OWASP LLM Top 10 | Full breakdown of the industry's canonical vulnerability list — all 10 entries |
| Agentic AI Top 10 | 10 critical risks unique to autonomous agents |
| LLM01: Prompt Injection | The most dangerous LLM vulnerability, with interactive examples |
| LLM05: Improper Output Handling | When AI outputs are trusted without sanitization |
| Page | What You'll Learn |
|---|---|
| AI Governance Basics | What you need to govern: models, agents, data, and people |
| Compliance Frameworks | NIST AI RMF, EU AI Act, ISO 42001, and how they map to your obligations |
| Leadership Checklist | 50 questions every leader should be asking — with persistent progress tracking |
This project practices what it teaches.
| Control | Status |
|---|---|
| npm audit (moderate+) | Blocks CI & deployment |
| CodeQL static analysis | Weekly + every PR |
| Dependabot | Weekly updates, Monday 09:00 IST |
| Content Security Policy | Strict CSP header via _headers |
| HTTP Security Headers | HSTS, X-Frame-Options, Referrer-Policy, Permissions-Policy |
| Known CVEs in dependencies | 0 (as of last audit) |
| Technology | Role |
|---|---|
| Astro 7.x | Static site generator — zero JS by default, island architecture |
| TypeScript | Type-safe components, data schemas, and navigation config |
| Custom CSS | Full design system with CSS variables, dark/light theme, zone tokens |
| GitHub Actions | CI (audit → build → typecheck), Pages deployment, Security scanning |
| CodeQL | Static analysis for JavaScript/TypeScript security vulnerabilities |
| Dependabot | Automated dependency update PRs |
No frameworks were harmed in the making of this site. No React, no Vue, no Tailwind — just Astro, TypeScript, and CSS that you can actually read.
Push to main
│
├── CI Workflow
│ ├── npm audit (fail on moderate+)
│ ├── astro build (fail on error)
│ └── tsc --noEmit (type check)
│
├── Deploy Workflow (after CI)
│ ├── npm audit (second gate)
│ ├── astro build → dist/
│ └── GitHub Pages deployment
│
└── Security Workflow (also runs weekly)
├── npm audit --audit-level=high (fail on high+)
├── npm audit --audit-level=info (informational report)
└── CodeQL static analysis (javascript-typescript)
# Clone
git clone https://github.com/AnimeshShaw/agentic-ai-security-guide.git
cd agentic-ai-security-guide
# Install
npm ci
# Develop
npm run dev
# Build
npm run build
# Security audit
npm run auditRequires Node.js 22+.
Found an error? Want to add a page, expand a section, or improve the interactive elements?
- Fork the repository
- Create a feature branch:
git checkout -b content/your-topic - Make your changes (run
npm run buildto verify) - Open a pull request — CI will audit, build, and type-check automatically
Content PRs, factual corrections, and new governance case studies are especially welcome.
MIT — free to use, adapt, and share. Attribution appreciated but not required.
Built by Animesh Shaw · Hosted on GitHub Pages
If this guide helped you think more clearly about AI security, consider starring the repo.
