Mastering GenAI Red Teaming: Insights from the frontlines
Relying on base-model guardrails is no longer enough to protect your brand from AI misuse and unwanted responses.
This report details a comprehensive red teaming framework designed to uncover and mitigate vulnerabilities before they are exploited.
- Learn the core challenges of red teaming in the GenAI era.
- Discover real-world attack strategies, from prompt injection to system leakage.
- Implement a structured framework to improve model integrity and safety.
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Overview
Since the rapid expansion of Generative AI, organizations have struggled to keep pace with the evolving threat landscape. While GenAI revolutionizes creativity and productivity, it also opens doors to novel vulnerabilities such as data poisoning, jailbreaking, and the generation of harmful synthetic media. Static security measures are often insufficient for these dynamic systems, which can fail in ways that traditional software does not.
In this updated report, we draw on Alice's deep threat expertise to provide a proactive roadmap for AI safety.
We move beyond theoretical risks to showcase real-life scenarios where LLMs have been manipulated and offer a comprehensive framework for adversarial testing.
By simulating real-world usage and sophisticated attacks, teams can identify critical gaps in precision and reliability.
This overview provides the workflows and case studies necessary to transition from one-off testing to a continuous safety program, ensuring your AI applications remain secure, compliant, and trusted by users
What’s New from Alice
Securing Agentic AI: The OWASP Approach
In this episode, Mo Sadek is joined by Steve Wilson (Chief AI and Product Officer at Exabeam, founder and co-chair of the OWASP GenAI Security Project) to explore how OWASP is shaping practical guidance for agentic AI security. They dig into prompt injection, guardrails, red teaming, and what responsible adoption can look like inside real organizations.
Distilling LLMs into Efficient Transformers for Real-World AI
This technical webinar explores how we distilled the world knowledge of a large language model into a compact, high-performing transformer—balancing safety, latency, and scale. Learn how we combine LLM-based annotations and weight distillation to power real-world AI safety.
