Attended the free webinar? This programme teaches you to execute everything you saw, with structured labs, real systems, and a capstone assessment. Haven't attended yet? Start there.
Offensive AI
Security
Master the full offensive AI security toolkit. Execute prompt injection campaigns, exploit agentic systems, extract model capabilities, and deliver professional red team findings, all against real vulnerable AI applications.
Eight weeks. Zero filler.
Each module builds directly on the last. By the end, you will have executed real attacks against AI systems, not read about them. On-demand labs available throughout.
Attack Surface Analysis & Reconnaissance
- The LLM threat landscape and why traditional security fails
- OWASP LLM Top 10: a practitioner's breakdown
- Attack surface enumeration for AI-powered systems
- Mapping trust boundaries and injection points
- Architecture fingerprinting and reconnaissance techniques
Systematic reconnaissance and attack surface mapping on a live AI deployment. Document every injection point and trust boundary.
Prompt Injection, Jailbreaking & Input Attacks
- Direct prompt injection: hijacking LLM instructions
- Indirect injection via documents, emails, and RAG content
- Multi-turn context poisoning and session manipulation
- Jailbreaking safety-aligned models: encoding tricks, many-shot attacks
- Role-play exploits, persona switching, and alignment bypass
Execute a full prompt injection chain against a production-style chatbot. Extract the system prompt, bypass content filters, and exfiltrate data.
Agentic AI Exploitation
- Autonomous agent attack surface: tools, memory, goals
- Tool poisoning and malicious tool-call injection
- Memory manipulation and goal hijacking techniques
- MCP protocol security and multi-agent privilege escalation
- Cross-agent injection and orchestrator attacks
Compromise an autonomous AI agent through tool call hijacking and memory injection. Demonstrate full goal redirection.
Model Extraction, Supply Chain & Capstone
- Black-box capability enumeration and boundary mapping
- System prompt extraction through structured probing
- Training data inference and membership attacks
- AI supply chain attacks: model registries and fine-tuning pipelines
- Capstone: end-to-end red team methodology and professional reporting
Capstone red team engagement. Full cycle recon, exploit, pivot, and deliver a professional findings report in 4 hours.
Walk out with real skills.
- Execute the full prompt injection attack lifecycle against production AI systems
- Exploit autonomous AI agents through tool poisoning and goal hijacking
- Extract hidden system prompts and map model capabilities through black-box probing
- Identify and exploit jailbreaking vulnerabilities in safety-aligned models
- Conduct end-to-end red team engagements and document findings professionally
- Apply MITRE ATLAS and OWASP LLM Top 10 operationally in the field
What you need coming in.
- Basic familiarity with how APIs and web applications work
- Comfort using a terminal and command-line tools
- No prior AI or ML knowledge required
- Security background helpful but not mandatory
- Penetration testers and red teamers expanding into AI systems
- Security engineers assessing LLM-powered applications
- Security architects evaluating AI deployments and integrations
- Developers building or integrating LLM-powered features
- Researchers studying adversarial AI and model security
AI is deployed. Security is an afterthought.
Every security team is being asked to review AI systems they have never tested. LLMs are shipped into production daily, with no threat model, no red team, no security review.
Traditional security training has not kept up. VAPT courses teach web apps. AppSec programmes cover OWASP Top 10. None of them touch what actually happens when an attacker targets an LLM, a RAG pipeline, or an autonomous agent.
This programme was built to close that gap, for practitioners who want to be the person in the room who actually understands AI Security, not just knows the acronyms.
Security teams have zero hands-on experience testing AI. Attackers are already exploiting this.
Most courses teach AI security as theory. No labs. No real systems. No way to verify you can actually do it.
You break a real AI system. Then you secure it. Then you map it to a governance framework. In one structured journey.
Not a course. A transformation.
There are courses that teach AI Security. There are none that teach you to break it, secure it, and govern it end to end, in labs, with real evaluation.
Browser-based labs. Ready in seconds.
No setup. No installation. No wasted time configuring environments. Every lab runs in your browser, fully pre-configured, with a real vulnerable AI application waiting for you.
You break real systems. Not toy examples.
Pre-built vulnerable AI applications designed to mirror production deployments: an LLM chatbot, a RAG pipeline, an autonomous agent. You attack them the same way an adversary would.
Attack → Defend → Govern. One journey.
Not three separate courses. A single, structured learning arc that takes you from exploiting a vulnerability to securing it to mapping it to a compliance framework. Nothing else does this.
OSCP-style rigour. Not attendance-based.
You earn your certificate by passing a real evaluation, not by showing up. The exam tests whether you can actually execute what you learned, against an unseen target system.
Structured like SANS. Not a YouTube playlist.
Eight weekends of deliberate, sequenced learning. Each module builds on the last. No fragmented content. No jumping between topics. A real curriculum with real progression.
Built on industry frameworks.
Every concept maps to NIST AI RMF, MITRE ATLAS, ISO/IEC 42001, and OWASP LLM Top 10. You leave with vocabulary and structure that translates directly to professional work.
You do not read about attacks. You execute them.
The lab environment is the programme. Every concept taught in a session has a corresponding lab that runs the same day. Theory without practice is not security training.
Pre-built Vulnerable AI Applications
Every lab runs against a real application: a production-style LLM chatbot with RAG, an autonomous agent with tool access, or an AI API with authentication. These are not toy examples. They model how AI is actually deployed.
Browser-Based. No Setup Required.
Labs spin up in your browser in under 60 seconds. No Docker. No VMs. No stack traces at 10pm. You land on the lab and start attacking. The environment handles everything else.
Guided Exploitation Paths
Every lab includes a structured exploitation path: a sequence of steps that takes you from reconnaissance to confirmed exploit to findings documentation. You learn technique, not just outcome.
Open-Ended Challenges
Beyond the guided path, each lab contains open-ended challenges: edge cases, multi-step attack chains, and scenarios that require you to think, not just follow instructions.
On-Demand Lab Access Throughout
Every lab environment stays available for the full duration of your programme, not just during class. Revisit, retry, and explore at your own pace. AI security is learned by doing, and doing takes time.
The real test is a real system.
The capstone is the full programme in one exercise. You assess a complete AI system, end to end. Threat model it. Break it. Secure it. Govern it. This is what professional AI security work looks like.
Threat Model the System
You receive a real-world AI application: a fully deployed LLM system with RAG, agents, and API integrations. Your first task: map the attack surface, identify trust boundaries, and produce a complete threat model using MITRE ATLAS and NIST AI RMF.
Exploit It
Now you attack. Using your threat model as a guide, you attempt to execute real vulnerabilities: prompt injection, agent manipulation, data exfiltration, guardrail bypass. Every finding is documented with severity, reproduction steps, and evidence.
Secure It
You switch sides. For each confirmed vulnerability, you design and implement a defence: guardrail updates, input validation, system prompt hardening, monitoring rules. You re-run your own attacks to verify the fix holds.
Map It to Governance
Finally, you document everything: risk register, control mappings to NIST AI RMF and ISO/IEC 42001, residual risk assessment, and remediation roadmap. A deliverable you can walk into any organisation and present.
What You Submit
You earn it. You don't just receive it.
Attendance-based certificates signal nothing to an employer. Real AI Security skill is demonstrated under examination conditions, against a system you have never seen.
Participation Certificate
Awarded on completing all programme sessions and lab exercises. Confirms you completed the training, the baseline that every graduate receives.
Exam Clearance Certificate
Awarded on passing the final assessment: a timed, practical examination against an unseen AI system. This certificate signals genuine, demonstrated competence, not attendance.
How the Exam Works
The final examination is a structured, time-bound practical challenge. You receive access to an unseen AI system and a set of objectives: find vulnerabilities, demonstrate exploitation, document findings, and propose remediation. Results are evaluated on technical accuracy and quality of deliverables, not multiple-choice.
The smartest investment in your AI security career.
sudolearning is priced at 3× less than the nearest comparable training, and delivers more: live labs, real systems, and an exam that proves you can do it.
No dedicated AI Security track. No hands-on AI labs. Expensive travel and exam fees not included.
No labs. No evaluation. No instructor. No capstone. Certificates that mean nothing.
One-day exposure. No structured curriculum. No follow-up labs or evaluation.
48 hours live. Browser-based labs. Real system capstone. OSCP-style exam. A fraction of SANS pricing.
What you are actually paying for
Early cohort pricing available. 100+ professionals already registered.
Start breaking AI systems.
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Every course starts with a free demo webinar, no commitment required. Register to reserve your seat.