Structuring a Knowledge Hub for Offensive Security and Emerging AI Threats

Introduction

Offensive security is shaped by a rapidly evolving threat landscape.
Techniques that were effective yesterday may be outdated tomorrow, and keeping track of emerging attack vectors (from traditional Red Team workflows to adversarial AI and GenAI misuse) requires more than scattered notes.

To address this, I designed a Offensive AI Knowledge Hub. Its purpose is simple: provide a structured way to capture ongoing research and make it easily shareable with the team.

Why a Structured Hub

The motivation behind this hub was twofold:

1. Adaptability to Emerging Threats
   With adversarial AI and GenAI risks expanding quickly, documentation needs to evolve just as fast. A well-structured hub ensures new techniques can be added without losing coherence.
2. Team Knowledge Sharing
   Security testing is rarely a solo effort. A clear layout allows teammates to understand not only what was tested, but why and how. This accelerates collaboration and prevents duplication of effort.

The Structure

The hub is divided into three major areas, each reflecting a critical aspect of modern offensive security research:

• AI Red Teaming
  Covers established practices, including:
  • Methodologies & Frameworks – grounding in structured approaches.
  • Testing Workflows – processes for building and executing attack chains.
  • Simulation Case Studies – detailed records of specific tests and outcomes.
• Adversarial AI
  Focused on emerging threats to machine learning systems, such as:
  • ML Evasion Techniques – bypassing or degrading model accuracy.
  • Prompt Injection Methods – manipulating LLM behavior.
  • Data Poisoning Attacks – contaminating training data to create long-term vulnerabilities.
• Gen AI
  Dedicated to misuse scenarios in generative AI systems:
  • Jailbreak Techniques – bypassing safety controls.
  • API Fuzzing Methods – probing for weaknesses in API exposure.
  • Misuse Scenarios – cataloging potential real-world abuse cases.

Why This Layout

I chose this specific layout for three reasons:

• Alignment with Threat Landscape
  By separating classic Red Teaming from adversarial AI and GenAI, the hub mirrors how threats are diversifying in practice.
• Scalability
  Each section can grow independently. For example, new GenAI jailbreak techniques can be added without disrupting Red Team case studies.
• Accessibility
  The hierarchy makes it easy for a teammate (or future reader) to find what they need: high-level methodologies, specific techniques, or detailed case studies.

Takeaways

The Knowledge Hub is not just a documentation space. It’s a framework for continuous learning that adapts to the evolving threat landscape.

In practice, setting up this structure helped me keep research organized, ensure repeatability in testing, and make it easier to expand into new areas like adversarial AI and GenAI. What started as an effort to avoid scattered notes naturally grew into a system that teammates can also navigate, reuse, and build upon.

Note to my future self: keep refining the hub, keep adding new cases, and make sure it remains useful not only for personal tracking but also as a way for the team to share knowledge and grow stronger together.