OSAI vs LearnToExploit Model Attack & Red Teaming: Which AI Red-Team Course Comes First in 2026?

OffSec's AI-300 is the first vendor certification built around attacking AI-enabled enterprises, and the OSAI exam is a 24-hour proctored engagement. The LearnToExploit Model Attack & Red Teaming course is a 12-module, lab-first track that costs about a seventeenth of the OffSec bundle. They are not substitutes. This is the module-by-module map of what each one teaches, where each goes deeper, and why the cheap one comes first.

What OSAI actually tests

AI-300 is written for people who already pen-test. OffSec recommends OSCP-level experience because the exam environment is an enterprise with an AI layer on top: once an agent or a RAG pipeline gives you a foothold, you are expected to keep going through cloud identity, Kubernetes and containers the way you would on any other engagement. The eleven learning modules are:

1. Introduction to Red Teaming AI Systems
2. Reconnaissance for AI Targets
3. Attacking AI Agents
4. Attacking Multi-Agent Systems and the Agent-to-Agent (A2A) Protocol
5. Exploiting RAG Pipelines
6. Attacking Embeddings
7. Attacking Model Context Protocol and Tool Surfaces
8. Supply Chain Attacks on AI/ML Systems
9. AI Infrastructure and Deployment Exploits
10. Threat Modeling for AI-Enabled Targets
11. Assembling the Pieces — Capstone Red Team Engagement

The exam is a 24-hour proctored session over VPN against a realistic AI-enabled environment, followed by a report. Passing earns OSAI, which does not expire, and the OSAI+ designation, which expires three years after the exam unless you renew through a qualifying OffSec exam, a recertification exam or OffSec's CPE programme. Full details are on the Macksofy OSAI training page.

What the LearnToExploit course actually teaches

Model Attack & Red Teaming is built the other way round: it starts from the application, not from the infrastructure. Every module after the first two ends in a browser lab against a deliberately vulnerable LLM application, and the finding taxonomy is the OWASP Top 10 for LLM Applications with MITRE ATLAS for adversary tactics. The twelve modules are:

1. Foundations of AI Red Teaming & MITRE ATLAS
2. Recon & Attack-Surface Mapping for AI Systems
3. Direct Prompt Injection
4. Indirect / Stored Prompt Injection
5. System-Prompt & Sensitive-Data Extraction
6. Jailbreaking & Guardrail Evasion
7. RAG & Vector-Store Access-Control Bypass
8. Improper Output Handling
9. Excessive Agency & Agent-Tool Abuse
10. Data & Model Poisoning / Supply Chain
11. AI Infrastructure to Cloud & Enterprise Pivot
12. Full Engagement & Reporting

Nine of those modules carry a lab, from a naive chatbot with a secret in its system prompt through a document assistant that executes instructions hidden in uploads, a RAG knowledge base that enforces authorisation in prose, an order-placing agent with too many tools, a poisoned community knowledge base, and finally a deployment agent whose tool gives you SSRF into the surrounding cloud. The last module is reporting: scoping an AI engagement, rating severity for non-deterministic findings, and writing evidence that reproduces. That is the part most self-taught AI testers skip, and it is the part the OSAI exam grades.

Module-by-module coverage map

The question that matters is not which course is bigger. It is whether finishing LearnToExploit leaves you practised in the attack classes behind each OSAI module. Here is the honest map.

Read the other way, LearnToExploit teaches five things OSAI does not treat as modules at all: system-prompt and sensitive-data extraction, jailbreaking and guardrail evasion, improper output handling (the pivot from an AI finding to XSS or command execution in the host app), direct prompt injection as its own discipline, and report writing. Those are the classes that make up most real AI engagements in India today, because most clients are shipping a copilot or a RAG assistant, not a multi-agent mesh.

Does finishing LearnToExploit get you through OSAI?

It gets you through the application half of the exam with the methodology and the reporting habit already in place, which is the half candidates fail on when they arrive from a conventional pen-test background and treat an LLM like a deterministic target. What it does not do is replace the OffSec labs for multi-agent, embedding and MCP attacks, or the network, cloud and Kubernetes depth that OSCP-level experience brings. That is exactly why the Macksofy OSAI bootcamp uses the LearnToExploit course as its lab track and adds a timed 24-hour mock engagement with report review on top: the sequence is LearnToExploit, then the AI-300 material, then the mock, then the exam.

Cost, time and prerequisites side by side

Which to choose

• You have never attacked an LLM application: start with LearnToExploit. It is the cheapest way to find out whether you enjoy this work before spending ₹1,70,000.
• You already hold OSCP or OSEP and your employer wants an AI red-team credential: do LearnToExploit in the first fortnight of your OffSec access, then the AI-300 modules in order, then a mock.
• You are an AI engineer who wants to understand the attacks against your own stack: LearnToExploit alone covers the OWASP LLM Top 10 end to end; add OSAI only if you will be running engagements.
• You run a team that ships copilots or agents: put the whole team through LearnToExploit as a corporate batch and send the one or two people who will own AI assessments to OSAI.