Unlocking the true potential of AI! Local LLM Guardrails have supercharged an 8B model, skyrocketing performance from 53% to an astonishing 99% on agent tasks. Learn how Forge is revolutionizing self-hosted AI. 🚀🤖 #AI #MachineLearning #Forge #LLMGuard... squaredtech.co/local-llm-gua…

Paper LLMGuard : Safeguarding Real-Time Inference for Large Language Models on Edge Devices [ACM Trans. Softw. Eng. Methodol 26] dl.acm.org/doi/pdf/10.1145/3… 古いIntel SGX v1を想定してもACM Transに通るのがちょっと驚き。ここが主眼ではなく、Arm Cortex-A TrustZone もあるけど。

开源模型有llama guard 开源的guadrails有llmguard，NeMo-guardrails，guardrailsai 也可以自己写prompt做llm-as-a-judge 还有阿里云，腾讯云提供了传统的内容安全检测接口

GenAI Bootcamp | Beyond the Prompt: Evaluating, Testing & Securing LLM Applications Dersi Tamamlandı🎓🤖 youtube.com/live/CgMdeW0DzEI… Bu derste @meteatamel , büyük dil modelleri (LLM) ile geliştirilen uygulamaların nasıl test edileceği, değerlendirileceği ve güvence altına alınacağını anlatıyor. Model çıktılarının doğruluğu, güvenliği ve izlenebilirliği için kullanılan güncel yöntemler ve araçlar detaylı şekilde açıklanıyor. 📌 Bu videoda neler bulacaksınız? ✅ LLM çıktılarının metriklere dayalı olarak nasıl değerlendirileceği ✅ Modelin verdiği özetlerin ve yanıtların doğruluğunun nasıl ölçüleceği ✅ Bilgi getirmeli (RAG) sistemlerde kalite ölçüm yöntemleri ✅ Farklı prompt senaryoları için test yapılandırmalarının nasıl oluşturulacağı ve yürütüleceği (Promptfoo aracıyla) ✅ LLM çıktılarında güvenlik ve içerik filtrelemesinin nasıl sağlanacağı (LLM Guard) Videoyu beğenmeyi ve kanalımıza abone olmayı unutmayın! 💙 Sevgiler, #Bootcamp #GenAI #DeveloperMultiGroup #LLM #AI #Testing #Evaluation #Security #Promptfoo #DeepEval #LLMGuard #Agents

To say that “Le chat is lying” would imply that it's a guard system like LLMGuard placed in front that would generate the response, detecting the content as sensitive. To say that the LLM is lying would mean that the tokens predicted at the output of the model are giving erroneous information, whether voluntarily (under the influence of the System Prompt) or not (in which case it's a hallucination). I was able to extract a modified version of the System Prompt (the original prompt seems protected, but you can easily get around this by asking it in a similar direction) These are just a few extracts, but you can reproduce the manipulation if you like. We can see here that the location information is indeed injected into System Prompt. What's more, you can see that it's not explicitly asked to protect the location information, but the entire system prompt on numerous occasions, leading LLM to hide this part of the prompt. However, there seems to be censorship in the chat as well. When I ask the model who gave him this information, he ends up saying he doesn't know it anymore. However, if I ask it not to tell me explicitly who the user is, but instead to answer with a number, this time I get the right information!

Top LLM Papers of the Week (March First Week 2024) [1] Not all Layers of LLMs are Necessary during Inference [2] SaulLM-7B: A pioneering Large Language Model for Law [3] ShortGPT: Layers in Large Language Models are More Redundant Than You Expect [4] GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection [5] Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference [6] Apollo: Lightweight Multilingual Medical LLMs towards Democratizing Medical AI to 6B People [7] Birbal: An efficient 7B instruct-model fine-tuned with curated datasets [8] A Survey of AI-generated Text Forensic Systems: Detection, Attribution, and Characterization [9] LLMGuard: Guarding against Unsafe LLM Behavior [10] Data Augmentation using LLMs: Data Perspectives, Learning Paradigms and Challenges Let me know in the comments which paper you find most interesting out of these ten papers and why. For paper links and short summaries, check out the "Top LLM Papers of the Week" Newsletter. Link: linkedin.com/pulse/top-llm-p…

𝐋𝐋𝐌𝐆𝐮𝐚𝐫𝐝 - 𝐋𝐋𝐌 𝐌𝐨𝐧𝐢𝐭𝐨𝐫 𝐓𝐨𝐨𝐥 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 𝐰𝐢𝐭𝐡 𝐋𝐋𝐌𝐬 Large Language Models (LLMs) have a lot of potential in real-world applications, but they also come with some risks. Sometimes, LLMs can generate inappropriate, biased, or factually incorrect responses. This might result in a violation of regulations and can lead to legal issues. 𝐋𝐋𝐌𝐆𝐮𝐚𝐫𝐝 𝐢𝐬 𝐚 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧 LLMGuard is a tool which has the potential to address these LLM risks. LLMGuard can monitor user interactions with an LLM application and flags content against specific behaviours or conversation topics. 𝐖𝐨𝐫𝐤𝐢𝐧𝐠 𝐨𝐟 𝐋𝐋𝐌𝐆𝐮𝐚𝐫𝐝 LLMGuard applies a collection of specialized detectors to flag inappropriate content. This ensemble method likely includes: 𝐈𝐧𝐚𝐩𝐩𝐫𝐨𝐩𝐫𝐢𝐚𝐭𝐞𝐧𝐞𝐬𝐬 𝐝𝐞𝐭𝐞𝐜𝐭𝐨𝐫 - Identify offensive or harmful language. 𝐁𝐢𝐚𝐬 𝐝𝐞𝐭𝐞𝐜𝐭𝐨𝐫𝐬 - Seeks out responses that reflect unfair prejudices or stereotypes. 𝐌𝐢𝐬𝐢𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐝𝐞𝐭𝐞𝐜𝐭𝐨𝐫 - Pinpoints factually incorrect or misleading statements. 𝐓𝐨𝐩𝐢𝐜 𝐝𝐞𝐭𝐞𝐜𝐭𝐨𝐫 - Flags content that ventures into restricted or sensitive areas. LLMGuard paper - arxiv.org/abs/2403.00826v1 #generativeai #llms #datascience #nlproc #deeplearning #llmsafety

🚀 Exploring the thin line between AI Agents and Rogue Agents. Our latest deep-dive examines the evolving threat landscape of LLMs. #AI #llmsec #llmguard

🚀 Exciting news from LLM Guard! Version 0.3.1 is here, boosting LLM security like never before! 🌟 🔗 Dive in: open.substack.com/pub/laiyer… Big thanks to our community for your unwavering support! Together, we're advancing LLM security! 🚀 #LLMGuard #AI #LLMSecurity #AISafety

At Laiyer AI, we’re ready to assist businesses in aligning with these directives. Our LLM Guard ensures robust AI safety & security for the challenges ahead. #LLMGuard #AISecurity