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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.
Explore Spikee, a powerful tool for evaluating and testing LLM security through prompt injection techniques, benchmarking capabilities, and comprehensive guardrail assessment frameworks.
Explore six security design patterns to defend LLM agents against prompt injection attacks with practical code examples and implementation guidance.
Master local LLM finetuning on AMD Strix Halo using full-parameter, LoRA, and QLoRA techniques with Gemma-3, Qwen-3, and GPT-OSS-20B models through hands-on implementation.
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.
Delve into critical security vulnerabilities in Microsoft 365 Copilot, exploring prompt injection attacks, potential data breaches, and essential mitigation strategies for enterprise LLM applications.
Explore the architecture and implementation of multi-modal LLMs, focusing on Vision Transformers, contrastive learning techniques, and hands-on experience with Google's PaliGemma.
Explore security risks and mitigation strategies for LLM applications and autonomous agents, covering prompt injection vulnerabilities, AI safety misconceptions, and practical defense mechanisms.
Dive into practical implementation of Gemma 2B and Llama-2 7B models, exploring quantization techniques, prompting strategies, and hands-on deployment in Google Colab with free T4 GPU resources.
Master sentiment analysis through hands-on fine-tuning of DistilBERT, using PyTorch and HuggingFace to build a restaurant review classifier with practical implementation steps.
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