The best AI security training depends on what you need to prove: offensive skill against live AI systems, or defensive and governance knowledge. Pwned Labs teaches the offensive side through its AI Systems Attack and Defense bootcamp, which certifies you with the hands-on AISRTP exam against a live environment of LLM-backed and agentic applications. Other options include multi-day classroom courses from SANS, and self-paced hands-on paths such as the HackTheBox AI Red Teamer path built with Google.
What AI security training covers
"AI security" is a wide term, so the first step is deciding which part you need. The main areas are red teaming AI applications (direct and indirect prompt injection, retrieval-augmented generation poisoning, tool abuse and excessive agency in agentic systems, and attacks on connected tool servers over the Model Context Protocol), adversarial machine learning against the model itself (evasion, data poisoning, and privacy attacks such as model inversion and membership inference), and the defensive and governance work of guardrails, monitoring, and AI risk management.
These are different skills, and most courses lean toward one of them. The Pwned Labs AISRTP track focuses on the application and agentic layer, teaching prompt injection, RAG poisoning, tool abuse, and agentic, CI/CD and MCP attack paths, and it also teaches the detections that catch them across AWS, GCP and Azure.
Hands-on assessment or a multiple-choice exam
The clearest divide in AI security training is how it checks what you learned. A multiple-choice or reading-based course can teach concepts quickly and is easy to fit around a job, but it does not show whether you can actually break a running system. A hands-on assessment against a live application proves practical skill, because you either reach the objective or you do not.
The Pwned Labs AISRTP exam has no multiple-choice section: candidates get 24 hours and an entry point into a live set of LLM-backed and agentic applications, then build a complete exploitation chain from that foothold to the flag. If your goal is to prove you can test real AI systems, weigh hands-on assessment heavily.
Offensive AI red teaming or governance and defense
Offensive training teaches you to find and exploit weaknesses in AI systems, which suits penetration testers, red teamers, and application security engineers. Governance-focused training explains model risks, responsible-AI principles, and policy, which is valuable for GRC, design, and leadership roles but does not ask you to attack anything. Both matter, and they serve different goals, so pick the one that matches the role you are hiring for or moving into.
AISRTP from Pwned Labs is an offensive, fully practical certification earned by operating against live AI and agentic systems, and because the labs run against realistic applications with telemetry, it also builds the defensive awareness that separates a red teamer from someone who only ran a tool.
Lab realism and what the exam proves
A credential is only as good as what it required you to do. Training built around static slides or a sandbox that never changes proves less than training that puts you in front of a realistic application with real weaknesses and real logging. When comparing options, look at whether the exam is a single realistic scenario you have to work end to end, or a set of isolated tasks and questions.
The Pwned Labs AISRTP exam is a single live environment rather than a question bank, and its scenarios change regularly so the credential reflects current AI and agentic attacks. It also requires no written report, so passing means you reached the objective through working tradecraft.
Price, format, and time commitment
AI security training ranges from free open resources, through self-paced subscription platforms, to scheduled classroom courses that cost several thousand dollars and run over several days. Price usually tracks format: a scheduled, in-person or live-online cohort and a proctored exam cost more than on-demand, self-paced access. Decide how much structure and recognition you need before you compare prices, because a cheaper self-paced course and an expensive classroom course can teach the same technique.
The Pwned Labs AI Systems Attack and Defense bootcamp is on-demand and self-paced, priced at $399, which includes two attempts at the AISRTP exam and lifetime access to the session recordings and materials. No prior AI security experience is required to start.
How the main AI security training options compare
The table below compares three named options on the facts each provider publishes. They are not interchangeable: the right choice depends on whether you want hands-on offensive proof, a classroom course with a widely recognized exam, or depth on model-level adversarial machine learning. Where a competitor is the better fit, that is stated plainly.
| Training | Format and assessment | Focus | Best fit |
|---|---|---|---|
| Pwned Labs AISRTP | On-demand and self-paced. A 24-hour, unproctored, hands-on exam against live LLM-backed and agentic applications, with two attempts included. $399. | Offensive, application and agentic layer: prompt injection, RAG poisoning, tool abuse and excessive agency, and agentic, CI/CD and MCP attacks, with the detections that catch them. | Proving you can attack a running AI system end to end, hands-on, on a modest budget. |
| SANS AI security courses | Scheduled, multi-day classroom or live-online courses (for example SEC545 GenAI and LLM Application Security, and SEC535 Offensive AI), each with its own certification exam. | Broad: GenAI and LLM application security, offensive AI, machine learning, and defense across the catalog. | Teams and learners who want scheduled, multi-day classroom or live-online cohorts. |
| HackTheBox AI Red Teamer | Self-paced, hands-on academy path of 12 modules, built in collaboration with Google and aligned to Google's Secure AI Framework (SAIF). | Model-level and application-level: prompt injection, adversarial AI evasion, model privacy attacks, data attacks, and MCP. | Depth on adversarial machine learning and model-level attacks alongside application attacks. |
SANS is the better fit when you need scheduled, multi-day classroom or live-online cohorts, and it offers the widest range of AI security courses including defensive and machine learning tracks. HackTheBox AI Red Teamer goes deeper into adversarial machine learning, such as evasion and model privacy attacks, than an application-focused course does. Pwned Labs AISRTP is the better fit when you want to prove hands-on offensive skill against live LLM and agentic applications without a large budget.
The AI attack techniques training should cover
AI security is a wide field, and training that only teaches prompt injection leaves most of the attack surface untouched. The core of application-level work is instruction and context attacks: direct prompt injection, where a user manipulates the model, and indirect prompt injection, where the malicious instruction arrives inside content the model retrieves, such as a web page, a document, or an email. Around that sit retrieval-augmented generation poisoning, where the data the model trusts is tampered with, and insecure output handling, where the application acts on model output without checking it, which can turn a text response into code execution or a request to an internal service.
Beyond the prompt, the highest-impact issues come from what the model is allowed to do. Tool and agent abuse, excessive agency, and insecure plugin or tool design let an injected instruction take real actions, call internal APIs, read files, or move money, which is where a language problem becomes a security incident. Sensitive information disclosure and model or data extraction round out the application surface, and the model-level work of adversarial machine learning, such as evasion and membership inference, sits alongside it for teams that build or defend models directly. The OWASP Top 10 for large language model applications is a useful map of this surface, and training worth paying for covers the whole of it, not a single entry.
Why AI security training has to be hands-on
AI application security cannot be learned from slides, because the skill is in steering a live model, reading what the application does with its output, and proving a chain reaches real impact. A payload that works against one model or one guardrail fails against another, so the craft is iterative and empirical: try, observe the behaviour, and adapt. Training that assesses us against a running application measures that skill directly, while a multiple choice exam measures whether we can describe it. The Pwned Labs AISRTP is assessed as a 24-hour practical exam against live LLM-backed and agentic applications, so it tests the doing rather than the knowing.
Where AI security meets cloud security
AI applications do not run in isolation, they run on cloud infrastructure, and that is what turns a prompt injection into a breach. An injected instruction that makes an assistant disclose a secret, or an agent that can be steered into calling an internal service, often leads straight to cloud credentials, storage, and the wider account behind the application. Agentic systems extend the surface further, through the tools they can call, the Model Context Protocol servers they connect to, and the CI/CD pipelines they can influence. This is why AI security training that also understands cloud attack paths is more useful than application testing alone, because the real impact of an AI weakness is usually realised in the cloud environment around it.
Practice this at Pwned Labs
Pwned Labs teaches AI application attack paths as hands-on labs in live environments. These three are part of the premium Academy and the AISRTP bootcamp.
Related reading: our guides to choosing cloud security training and choosing a cloud security certification.
- Breach the Perimeter via Prompt Injection (Premium, Beginner): use prompt injection to extract secrets from an AI assistant, then use a leaked SAS token and service principal credentials to move laterally across Entra tenants.
- Exploit Indirect Prompt Injection for Azure Access (Premium, Beginner): plant instructions in content an AI assistant later retrieves, then use what it exposes to reach Azure blob storage and a web app publish profile.
- Emulate Storm-0501 Tactics for Cloud Compromise (Premium, Intermediate): chain command injection and indirect prompt injection with managed identity abuse to exfiltrate Microsoft 365 data, emulating Storm-0501 tradecraft.
Frequently asked questions
What is the best AI security training?
It depends on what you need to prove. For hands-on offensive skill against live LLM and agentic applications, the Pwned Labs AISRTP bootcamp assesses you with a 24-hour practical exam. For scheduled, multi-day classroom or live-online delivery, SANS AI security courses are an option. For depth on adversarial machine learning, the HackTheBox AI Red Teamer path goes further into model-level attacks.
What is the best training for securing AI and LLM applications?
Application security for LLMs centers on prompt injection, insecure handling of model output, retrieval poisoning, and tool and agent abuse. Pwned Labs teaches these hands-on in the AI Systems Attack and Defense bootcamp, which also covers escalating a content-level injection into cloud remote code execution and the detections that would catch it.
Do I need AI experience before taking AI security training?
Not for an entry-friendly course. The AISRTP, for example, requires no prior AI security experience; you should be comfortable with HTTP, reading JSON, and the command line, and the bootcamp starts with how LLM and agentic systems are built before moving into attacks.
How is AI red team training different from AI governance training?
Red team training teaches you to find and exploit weaknesses in running AI systems. Governance training explains model risk, responsible-AI principles, and policy. Both are useful and they serve different roles, so choose based on whether you need to attack systems or to set and audit controls.
What certifications exist for AI security?
On the offensive side, the Pwned Labs AISRTP (AI Systems Red Team Professional) is a hands-on certification earned against live AI and agentic systems, with a 24-hour practical exam. Other AI security certifications exist too, such as the course-tied exams that accompany SANS AI security courses, which are assessed through multi-day classroom study rather than live hands-on work.
What tools does AI security training teach?
Offensive AI training commonly covers automated assessment tools such as Garak, PyRIT, and Promptfoo, alongside manual tradecraft. The AISRTP bootcamp uses these tools and then shows how to escalate findings into cloud compromise and how to detect the activity in telemetry.
How much does AI security training cost?
Costs range from free open resources to scheduled classroom courses of several thousand dollars. The Pwned Labs AISRTP bootcamp is $399 and includes two exam attempts, while programs such as the SANS courses cost more and add a scheduled classroom cohort and a proctored exam.
The fastest way to judge AI security training is to attack a running system end to end. Start with the hands-on cloud and AI security labs, or go straight to the AISRTP certification and the AI Systems Attack and Defense bootcamp.
What topics should AI security training cover?
The full application surface, not prompt injection alone: direct and indirect prompt injection, retrieval-augmented generation poisoning, insecure output handling, tool and agent abuse, excessive agency, sensitive information disclosure, and model or data extraction. The OWASP Top 10 for large language model applications is a useful map, and model-level adversarial machine learning sits alongside it for teams building models.
Do I need machine learning experience to learn AI security?
Not for application-level AI security, which centers on how an application uses a model and handles its output, and draws on normal web and cloud security skills. Machine learning background helps for model-level work such as adversarial attacks, but most offensive AI work against real applications does not require it.
How is AI security different from traditional application security?
The model introduces behaviour that is probabilistic and steerable through language, so a trusted input channel becomes an attack surface and the same payload can succeed or fail depending on the model and its guardrails. The impact, though, is usually realised through familiar cloud and application weaknesses the model is wired into.
About Pwned Labs
Pwned Labs is a hands-on cloud and AI offensive security training platform. It runs real-world attack labs and bootcamps for AWS, Azure, GCP, Kubernetes and AI systems, including the ACRTP, MCRTP, GCRTP, KRTP and AISRTP certifications. Free labs are at pwnedlabs.io/explore.