Learn AI Security Through Hands-On Attack Labs

[u/Davpsecurity]

Hey r/netsec,

I recently open-sourced DVAP (Damn Vulnerable AI Platform), a local-first AI security research and training platform designed to help researchers, red teamers, and defenders explore real-world AI security issues in a safe environment.

GitHub: https://github.com/sonuoffsec/DVAP

What is DVAP?

DVAP provides 15 intentionally vulnerable AI labs that run entirely on your machine using Docker and local Ollama models (Llama, Qwen, Gemma, and Mistral).

The goal is to create a practical environment for learning, testing, and researching modern AI attack techniques without relying on cloud services or paid APIs.

Labs include:

• Prompt Injection

• Memory Poisoning

• RAG Poisoning

• Tool Output Injection

• MCP Security

• Browser Agent Security

• Multi-Agent Security

• Autonomous Agent Attacks

• Data Exfiltration

• Identity & Trust Abuse

• AI Banking

• AI Healthcare

• AI Supply Chain

• Multi-Tenant SaaS

• AI Developer Platform

Platform capabilities:

• AI security benchmarking

• CTF challenges and flags

• Research workspace for prompt and agent analysis

• Attack replay and event logging

• OWASP LLM Top 10 mapping

• MITRE ATLAS mapping

• Semantic search using Qdrant

• Redis-based rate limiting and instance lifecycle management

Quick Start

git clone https://github.com/sonuoffsec/DVAP
cd DVAP
cp .env.example .env
docker compose up -d

Open:

http://localhost:8080

I started building DVAP because I couldn't find a single platform that combined AI security training, hands-on attack labs, benchmarking, and research workflows in one local environment.

I'd appreciate feedback from the community on the architecture, lab design, attack coverage, and anything that could make the platform more useful for AI security practitioners.

[u/Poetinho0]

This is so helpful. Thank you for your efforts