Offensive Security in the Age of AI: Red Teaming LLM

AI is no longer something security teams can treat as experimental or out of scope. Large Language Models are already embedded across organizations, powering everything from customer-facing chatbots to internal developer tools. And as with any new technology, they introduce new risks, often in places traditional security testing doesn’t fully cover.

For red teams, this shift changes the work in a very real way. LLMs don’t behave like traditional applications. They don’t fail in predictable patterns, and they don’t always respect the guardrails teams assume are in place. Knowing how to assess and exploit these systems is quickly becoming a core offensive security skill.

Before you can test an LLM-based system, you need to understand how it works under the hood. Many red teams are now being asked to assess AI-powered applications without having been trained on how models process input, manage context, or generate output.

Offsec’s LLM Red Teaming Learning Path starts by breaking down how LLMs function in practice. It covers model architectures, tokenization, embeddings, attention mechanisms, and the lifecycle of an LLM from training through inference. This foundational knowledge allows red teamers to move beyond trial-and-error testing and design attacks that reflect how real adversaries would approach these systems.

Once the fundamentals are in place, the learning path moves into territory red teamers know well: enumeration and exploitation, just applied to a very different target.

Learners explore how to enumerate LLM-based applications by identifying exposed endpoints, model types, prompt limitations, safety mechanisms, and underlying frameworks. From there, the focus shifts to LLM-specific attack techniques, including prompt injection, jailbreaking, improper output handling, supply chain manipulation, and abuse of excessive permissions or unbounded agent behavior.

Many of these techniques mirror familiar offensive concepts: social engineering, privilege escalation, denial of service, but require a different approach when the target is an AI system rather than a traditional application.

This learning path follows OffSec’s hands-on philosophy. Red teamers work in a sandboxed cloud environment where they simulate attacks against real models using tools such as Open WebUI, Ollama CLI, and LangChain-based AI agents.

Instead of passively consuming content, learners actively craft adversarial prompts, interact with models via APIs, deploy models locally, and execute red team campaigns against custom LLM deployments. This approach builds the practical experience teams need when AI systems appear in real-world assessments.

Exploiting an LLM is only part of the job. Red teams are increasingly expected to explain why their findings matter to the business.

The learning path emphasizes how to translate LLM-related issues into outcomes leadership cares about, such as data exposure, reputational damage, and regulatory risk. Learners practice framing findings in a way that resonates with engineering leaders and executives, helping red teams influence real security decisions rather than just deliver reports.

The LLM Red Teaming Learning Path is available as part of OffSec’s Learn Enterprise subscription, which is built to support teams, not just individual learners.

In addition to access to the full OffSec Learning Library, teams can use the Cyber Range to validate skills through realistic, hands-on scenarios. Learn Enterprise also includes centralized admin and reporting dashboards, making it easier for managers to track progress, identify skill gaps, and demonstrate capability growth over time. Talent Finder, another key feature of Learn Enterprise, extends that ecosystem by connecting organizations with OffSec-certified professionals who have demonstrated hands-on capability, helping teams identify talent aligned with real-world security work.

Together, these capabilities allow organizations to develop offensive security skills systematically, rather than relying on ad hoc training or individual effort.

AI-driven systems are already part of modern environments, and they aren’t going away. Red teams that understand how to assess and challenge LLM-based applications will be better positioned to uncover meaningful risk and guide effective mitigation.

Get access to the LLM Red Teaming Learning Path and see how OffSec helps teams stay effective as offensive security continues to evolve.