This guide covers the OWASP Top 10 for LLM Applications 2025 (released November 18, 2024) and the OWASP Top 10 for Agentic Applications 2026 (released December 10, 2025). Key topics include Agentic AI Security, RAG Vulnerabilities, System Prompt Leakage, Vector/Embedding Weaknesses, and AI Compliance.

📋 Table of Contents

• 🎯 Overview
• 🆕 What's New in 2025/2026
• 🤖 Understanding LLMs
• 🚨 OWASP Top 10 for LLMs 2025
• 🆕 OWASP Top 10 for Agentic Applications 2026
• 🔍 Vulnerability Categories
• ⚔️ Offensive Security Tools
• 🛡️ Defensive Security Tools
• 🏗️ RAG & Vector Security
• 🤖 Agentic AI Security
• 🆕 Agentic AI Deep Dives
• 📊 Security Assessment Framework
• 🔬 Case Studies
• 💼 Enterprise Implementation
• 🆕 AI Regulations & Compliance
• 📚 Resources & References
• 🤝 Contributing

🎯 Overview

As Large Language Models become the backbone of enterprise applications, from customer service chatbots to code generation assistants, the security implications have evolved dramatically. This guide provides a comprehensive resource for:

• 🔐 Security Researchers exploring cutting-edge LLM vulnerabilities
• 🐛 Bug Bounty Hunters targeting AI-specific attack vectors
• 🛠️ Penetration Testers incorporating AI security into assessments
• 👨‍💻 Developers building secure LLM applications
• 🏢 Organizations implementing comprehensive AI governance
• 🎓 Students & Academics learning AI security fundamentals

Why This Guide Matters

• ✅ Current & Comprehensive: Reflects 2025 LLM OWASP standards AND the new 2026 Agentic standards
• ✅ Practical Focus: Real-world tools, techniques, and implementations
• ✅ Industry Validated: Based on research from 500+ global experts
• ✅ Enterprise Ready: Production deployment considerations and compliance
• ✅ Community Driven: Open-source collaboration and continuous updates

🆕 What's New in 2025/2026

OWASP Top 10 for LLMs 2025 Major Updates

The November 2024 release introduced significant changes reflecting real-world AI deployment patterns:

🆕 New Critical Risks

• LLM07:2025 System Prompt Leakage - Exposure of sensitive system prompts and configurations
• LLM08:2025 Vector and Embedding Weaknesses - RAG-specific vulnerabilities and data leakage
• LLM09:2025 Misinformation - Enhanced focus on hallucination and overreliance risks

🔄 Expanded Threats

• LLM06:2025 Excessive Agency - Critical expansion for autonomous AI agents
• LLM10:2025 Unbounded Consumption - Resource management and operational cost attacks

📈 Emerging Attack Vectors

• Multimodal Injection - Image-embedded prompt attacks
• Payload Splitting - Distributed malicious prompt techniques
• Agentic Manipulation - Autonomous AI system exploitation

🆕 OWASP Top 10 for Agentic Applications 2026 (December 2025)

Released at Black Hat Europe on December 10, 2025, this globally peer-reviewed framework identifies critical security risks facing autonomous AI systems:

The framework introduces the principle of "least agency" — only granting agents the minimum autonomy required for safe, bounded tasks.

New Security Technologies

Latest Security Tools (2024-2026)

• WildGuard - Comprehensive safety and jailbreak detection
• AEGIS 2.0 - Advanced AI safety dataset and taxonomy
• BingoGuard - Multi-level content moderation system
• PolyGuard - Multilingual safety across 17 languages
• OmniGuard - Cross-modal AI safety protection

🆕 Red Teaming & Offensive Tools (2025-2026)

• DeepTeam - Open-source LLM red teaming framework by Confident AI with 40+ vulnerability types and 10+ adversarial attack methods, supporting OWASP Top 10 and NIST AI RMF
• Promptfoo - Open-source prompt injection, jailbreak, and data leak testing (30,000+ developers, CI/CD integration)
• ARTKIT - Open-source framework for automated multi-turn adversarial prompt generation and attacker-target interactions
• Meta LlamaFirewall - Open-source protection framework released with Llama 4, including Llama Guard 4 and Llama Prompt Guard 2

Enhanced Frameworks

• Amazon Bedrock Guardrails - Enterprise-grade contextual grounding
• Langfuse Security Integration - Real-time monitoring and tracing
• Advanced RAG Security - Vector database protection mechanisms

🤖 Understanding LLMs

What is a Large Language Model?

Large Language Models (LLMs) are advanced AI systems trained on vast datasets to understand and generate human-like text. Modern LLMs power:

• 💬 Conversational AI - ChatGPT, Claude, Gemini
• 🔧 Code Generation - GitHub Copilot, CodeT5
• 📊 Business Intelligence - Automated reporting and analysis
• 🎯 Content Creation - Marketing, documentation, creative writing
• 🤖 Autonomous Agents - Task automation and decision making

Current LLM Ecosystem

🆕 What is Agentic AI?

Agentic AI represents an advancement in autonomous systems where AI operates with agency—planning, reasoning, using tools, and executing multi-step actions with minimal human intervention. Unlike traditional LLM applications that respond to single prompts, agentic systems:

• Plan: Break down complex tasks into executable steps
• Reason: Make decisions based on context and goals
• Use Tools: Interface with external systems, APIs, databases
• Maintain Memory: Persist context across sessions
• Execute Autonomously: Take actions without constant human oversight

Why Agentic AI Changes the Threat Landscape

Security Implications

Modern LLM deployments introduce unique attack surfaces:

• Model-level vulnerabilities in training and inference
• Application-layer risks in integration and deployment
• Data pipeline threats in RAG and fine-tuning
• Autonomous agent risks in agentic architectures
• Infrastructure concerns in cloud and edge deployments

🚨 OWASP Top 10 for LLMs 2025

The OWASP Top 10 for Large Language Model Applications 2025 represents the collaborative work of 500+ global experts and reflects the current threat landscape.

Complete 2025 Rankings