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
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What’s New from Alice
Your LLM Has No Idea What It's Doing
Diana Kelley, CISO at Noma Security and former Cybersecurity CTO at Microsoft, joins Mo to work through the real mechanics of LLM risk: why the context window flattens the trust boundary between system instructions and user data, why that makes reliable internal guardrails essentially impossible, and why agentic AI is less a new threat category and more a stress test for the hygiene debt organizations never fully paid off.
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.
Exposing the Hidden Risks of AI Toys
AI-powered toys are entering children’s everyday lives, but new research reveals serious safety gaps. Alice testing shows how child-like interactions can lead to inappropriate content, unsafe conversations, and risky behaviors.
