📰 Originally published on Securityelites — AI Red Team Education — the canonical, fully-updated version of this article.
⚠️ Authorised Use Only: All tools listed here are for authorised security research only. Never run security tools against systems you don’t own or haven’t received explicit written permission to test.
Last week I ran a full AI security assessment in four hours — from initial scope review to a complete findings report with three confirmed vulnerabilities. The entire thing was automated down to the tool configuration. That’s not because I’m exceptional. It’s because I’ve spent two years building and refining the exact toolkit I’m about to give you.
Most “AI hacking tools” lists I’ve seen online are either outdated, academic, or include tools that sound impressive but never get used in real engagements. I’m going to tell you what I actually use, in what order, and for what exact purpose — including the ones that look less impressive but consistently find real vulnerabilities that fancier tools miss.
🎯 What You’ll Get Here
15 real AI hacking tools categorised by what they do in a real assessment
Installation and setup for each tool, including free cloud options for the heavier ones
When I use each tool and when I skip it — the practical usage guide, not the marketing pitch
My recommended stack for beginners vs practitioners vs full engagements
⏱ 28 min read · 3 exercises included What You Need: Python 3.9+ installed · pip package manager · A free Google Colab account for cloud execution of heavier tools · 20GB disk space if running local models ### 15 AI Hacking Tools — Complete Guide 1. Before You Install Anything 2. Vulnerability Scanning and Fuzzing Tools (Tools 1–4) 3. Attack Framework Tools (Tools 5–8) 4. Local Testing Environment Tools (Tools 9–11) 5. API and Network Testing Tools (Tools 12–13) 6. Specialist AI Security Tools (Tools 14–15) 7. My Recommended Starter Stack This list builds on the concepts in the AI model hacking guide — if you understand the 8 attack categories, these tools map directly onto them. For context on how the tools fit into a full engagement methodology, see the AI Elite Hub. And if you’re picking your first tools, I’d also recommend checking what the beginner guide suggests as a first week setup before going through this full list.
Before You Install Anything — What You’re Actually Using These For
Every tool in this list exists because of a specific, recurring problem in AI security assessments. Before you install anything, understand what problem it solves — otherwise you’ll install fifteen tools and use two of them badly.
In a real engagement, I use these tools in three phases: automated discovery (scanners run first, map the vulnerability landscape), manual testing (frameworks and API tools for targeted exploitation), and documentation (capturing evidence of confirmed findings). The tools I’m about to list map onto one of those phases. Keep that in mind as you read.
Vulnerability Scanning and Fuzzing Tools (1–4)
1. Garak — The AI Vulnerability Scanner
Garak is my first tool on every AI security engagement, without exception. It runs automated probes against LLMs covering over 40 vulnerability categories including prompt injection, jailbreaking, data leakage, toxic output generation, and hallucination manipulation. Think of it as Nmap for AI models — it gives you a fast vulnerability landscape before you spend hours on manual testing.
GARAK — INSTALL AND FIRST SCAN Copy
pip install garak
Scan local Ollama model — full probe suite
python -m garak –model_type ollama –model_name llama3.1 –probes all
Scan specific categories only (faster)
python -m garak –model_type ollama –model_name llama3.1 –probes injection,dan,leakage
Running 47 probes… ████████░░ 89% complete
Report: garak_report_20260517.html
When I use it: First 30 minutes of every AI security assessment. When I skip it: When testing a non-generative AI system (image classifiers, recommendation engines) where prompt-based probes don’t apply.
2. PyRIT — Microsoft’s AI Red Team Toolkit
PyRIT (Python Risk Identification Toolkit) is Microsoft’s open-source AI red team framework. Where Garak runs predefined probes, PyRIT lets you build custom attack orchestrations. I use it for multi-turn attacks, adaptive jailbreaking sequences, and automated testing with custom payload libraries. It has native support for testing Azure OpenAI, OpenAI, Hugging Face, and Ollama targets.
PYRIT — INSTALL AND BASIC ATTACK Copy
pip install pyrit
Run a basic red team attack with PyRIT
python -c “from pyrit.orchestrator import PromptSendingOrchestrator; print(‘PyRIT ready’)”
PyRIT ready
When I use it: Complex multi-turn attack scenarios and when a client wants a professional-grade report with Microsoft’s framework backing the methodology. When I skip it: Quick single-probe testing where Garak or manual Python scripts are faster.
3. PromptBench — Adversarial Prompt Testing
PromptBench is a research-to-practice framework for testing adversarial prompts. It includes a library of attack methods — textfooler, BERT attack, character-level perturbations — and lets you benchmark how models respond to adversarial inputs systematically. I use it when a client specifically needs their model’s robustness measured and reported against known adversarial NLP techniques.
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