📰 Originally published on SecurityElites — the canonical, fully-updated version of this article.

Samsung engineers pasted proprietary source code into ChatGPT. The data hit OpenAI’s servers and training pipeline. That’s LLM06 — Sensitive Information Disclosure. Microsoft Copilot was redirected to exfiltrate Slack messages through a prompt injection in a shared document. That’s LLM01. A major bank’s AI assistant was manipulated into approving transactions it was designed to block — LLM08 Excessive Agency. The OWASP LLM Top 10 isn’t an academic taxonomy. Every category has real incidents behind it, and every incident has a methodology that red teams can reproduce in authorised assessments. Here’s the framework I use — mapped to actual disclosed cases, bug bounty data, and the assessment checklists that produce findings.

🎯 What You’ll Get From This

All 10 OWASP LLM categories mapped to real disclosed incidents and breaches
Bug bounty payout data by vulnerability category — which categories pay most
Assessment coverage checklists — what to test for each LLM01–LLM10
CVSS scoring guidance specific to LLM vulnerabilities
The 3 categories that account for 80% of real-world AI security findings

⏱️ 45 min read · 3 exercises #### 🔗 Deep Dives Per Category - Prompt Injection in RAG Systems — LLM01 in production deployments - Many-Shot Jailbreaking — LLM01 at scale via repetition attacks - Insecure AI Plugin Architecture — LLM07 exploitation methodology ### 📋OWASP Top 10 LLM Vulnerabilities 2026 — Assessment Framework 1. The 3 Categories That Account for 80% of Findings 2. LLM01–LLM04 — Injection, Output, Training, Data Disclosure 3. LLM05–LLM08 — Supply Chain, Disclosure, Plugins, Agency 4. LLM09–LLM10 — Overreliance and Model Theft 5. Bug Bounty Data by OWASP LLM Category 6. CVSS Scoring for LLM Vulnerabilities 7. Assessment Workflow — Scope to Report The OWASP LLM Top 10 is the framework I reference in every AI security assessment. It provides the shared vocabulary that gets remediation prioritised by clients who have never heard of prompt injection. Everything on this page maps to the deeper attack methodology articles in the LLM Hacking hub and the broader AI Security series. The Phishing URL Scanner is relevant for LLM05 supply chain attacks that deliver malicious content through URLs processed by AI systems.

The 3 Categories That Account for 80% of Findings

Before the full framework: the distribution of real-world AI security findings is not uniform. In my assessment work and disclosed bug bounty reports, three categories dominate. Understanding why they’re dominant shapes where I spend time on any engagement.

THE 80/20 SPLIT — WHERE REAL FINDINGS LIVECopy

The three dominant categories (why each dominates)

LLM01 Prompt Injection: ~45% of findings
→ Every user input is a potential injection vector
→ Attack surface scales with product features, not security controls
→ Hardest to fix at the model level — requires architectural controls

LLM06 Sensitive Info Disclosure: ~20% of findings
→ LLMs trained on data regurgitate it — model owners often don’t know what
→ System prompt extraction is a low-effort, high-yield test
→ Disclosure of IP, credentials, PII in model outputs is common

LLM08 Excessive Agency: ~15% of findings
→ Agentic AI deployments are expanding rapidly
→ Tool access + insufficient authorization = high-impact exploitation
→ Often Critical because the impact is concrete actions, not data leaks

Remaining 20%: LLM02, LLM03, LLM04, LLM05, LLM07, LLM09, LLM10

Less frequent but LLM05 (supply chain) and LLM07 (plugins) are rising

💡 Assessment Prioritisation: If I have limited time on an AI security assessment, I spend 60% of it on LLM01 and LLM06, 20% on LLM08, and split the remaining 20% across the other seven categories. The disclosed incident data consistently validates this allocation — it’s not guesswork, it’s where real teams find real findings.

🧠 EXERCISE 1 — THINK LIKE A HACKER (15 MIN · NO TOOLS)
Map a Target AI Application to All 10 OWASP LLM Categories

⏱️ 15 minutes · No tools required

The first step before any AI security assessment is the category-to-feature mapping. Every OWASP LLM category should map to at least one testable feature — if it doesn’t, you either don’t have enough scope or the application doesn’t use that attack surface.

TARGET APPLICATION: An enterprise AI assistant with:

– Chat interface that processes user questions

– Access to internal documents (RAG pipeline)

– Ability to send Slack messages and create Jira tickets (tools/plugins)

– Uses GPT-4o as the base model with a custom system prompt

– Deployed in production with 500 employees using it

For each OWASP LLM category, identify: A) Is this attack surface present in the application? (Y/N) B) What specific feature enables this attack vector? C) What is your highest-severity test case?

LLM01 Prompt Injection: Y/N · Feature: ___ · Test case: ___ LLM02 Insecure Output: Y/N · Feature: ___ · Test case: ___ LLM03 Training Data Poison:Y/N · Feature: ___ · Test case: ___ LLM04 Model DoS: Y/N · Feature: ___ · Test case: ___ LLM05 Supply Chain: Y/N · Feature: ___ · Test case: ___ LLM06 Sensitive Disclosure: Y/N · Feature: ___ · Test case: ___ LLM07 Insecure Plugin: Y/N · Feature: ___ · Test case: ___ LLM08 Excessive Agency: Y/N · Feature: ___ · Test case: ___ LLM09 Overreliance: Y/N · Feature: ___ · Test case: ___ LLM10 Model Theft: Y/N · Feature: ___ · Test case: ___

Then: rank the 10 categories by expected finding severity for THIS application. Which 3 would you test first? Why?

📖 Read the complete guide on SecurityElites

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This article was originally written and published by the SecurityElites team. For more cybersecurity tutorials, ethical hacking guides, and CTF walk-throughs, visit SecurityElites.