AISPLOIT
Focused AI securitytraining withAISPLOIT
Break and defend AI systems in one workspace.
Active learners
18,274
Operators already using the workspace loop.
Flags captured
90,421
Solved objectives recorded inside live browser exercises.
Release format
Small batches
Rooms publish only when the interaction quality is ready.
How a room works
The training loop stays tight.
A room should read like a serious operational brief, then turn into a working surface the moment you need to test something.
Read the brief, not the marketing.
Each room starts with context, system boundaries, and the exact behavior you are meant to inspect.
Work inside one room.
Prompting, validation, notes, and progress stay in the same workspace so the learner never loses the thread.
Prove the result.
A solve only counts when the room captures a concrete signal, not when the interface looks dramatic.
Path board
Start with one flagship track, then expand deliberately.
The catalog is intentionally tight. Each path is supposed to teach one domain clearly, not spray a hundred shallow exercises across the interface.
Featured track
AI Security Blue Team
Learn how to defend AI applications through structured lessons, practical reviews, and hands-on rooms that teach defenders where control really belongs in the stack.
Focus
Defend
Modules
5
Estimated time
60 hours
Status: beginner
Govern
Python and Data for AI
Build real beginner confidence with Python, datasets, notebook-style workflows, feature judgment, shape awareness, and model-ready thinking. This path prepares learners to inspect data carefully, follow AI workflows calmly, and step into How Models Learn with a practical mental model already in place.
Build
How Models Learn
Learn how AI systems improve from data, error signals, and repeated updates. This path teaches the core training loop, data quality, tuning decisions, and trustworthy evaluation without drowning the learner in heavy math.
Live proof
See the training loop before you commit to the platform.
The Guardian demo is narrow on purpose. It shows the exploit, the signal, and the scoring logic without pretending to be a full course page.
One target
A single guard behavior makes the weakness easy to understand.
One signal
Success is obvious when the secret leaks into the response.
One next step
The same interaction model carries into rooms, paths, and dashboard progress.
Guardian demo
Try a real prompt-injection exercise.
The demo is intentionally narrow: one target, one weakness, one clear signal that the exploit worked.
What to look for
Straight requests fail. Framing, indirection, and context leakage expose the weak point.
Suggested attempts