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Explore six security design patterns to defend LLM agents against prompt injection attacks with practical code examples and implementation guidance.
Master prompt injection testing using Spikee to generate custom datasets, control payload positions, and evaluate LLM security with hands-on demonstrations of dataset creation, testing, and result analysis.
Explore Spikee, a powerful tool for evaluating and testing LLM security through prompt injection techniques, benchmarking capabilities, and comprehensive guardrail assessment frameworks.
Discover how to run Microsoft's VibeVoice locally on AMD Strix Halo for natural speech generation, voice cloning from short samples, and multi-speaker conversations with setup guides.
Discover advanced techniques to bypass LLM security measures using anti-spotlighting and best-of-N attacks with hands-on demonstrations in Spikee.
Uncover the EchoLeak vulnerability in M365 Copilot that enables zero-click data exfiltration through email attacks, exploring RAG system exploitation and defense strategies.
Discover how to run massive language models like Qwen3-235B and GLM 4.5-Air-106B on AMD Strix Halo systems using up to 128GB unified memory with proper kernel configuration and tuning.
Master running Qwen Image and WAN 2.2 locally on Framework Desktop with AMD Strix Halo, covering setup, image/video generation, LoRA adapters, and diffusion model fundamentals.
Explore reasoning LLMs like o1, DeepSeek-R1, and Claude 3.7—what they are, how they're trained, and why they're not fundamentally different from general-purpose LLMs despite improved performance on structured problems.
Master prompt injection testing techniques for LLM applications using Spikee, from creating targeted datasets to executing attacks with Burp Suite Intruder and custom Python scripts.
Explore security vulnerabilities in LLM applications, focusing on browser and coding agents, with practical demonstrations of prompt injection attacks and essential mitigation strategies.
Explore advanced techniques for exploiting vulnerabilities in multi-chain LLM applications, including prompt injection methods, security testing approaches, and essential defensive strategies.
Master techniques for evaluating and ensuring LLM response accuracy in RAG pipelines using tools like gpt4o-mini, Llama-index, RAGAS, and Azure's Groundedness API for reliable AI applications.
Explore OpenAI CLIP's capabilities for zero-shot image classification and similarity, from local implementation using HuggingFace to deployment on Azure AI Studio with practical examples.
Master building LLM agents using PAL and ReAct paradigms, implement custom solutions and Langchain framework, and explore various language models while learning function calling and operational loops.
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