Here is a 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 System Design for an 𝗔𝗜 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗥𝗲𝗱 𝗧𝗲𝗮𝗺𝗶𝗻𝗴 platform! Security of AI Systems remains one of the hard problems to be solved in the AI industry. The process of ensuring that your AI application can not be hacked is significantly different compared to regular software. I had a chance to sit down with experts in the field - splxAI - and chat about their platform implementation in detail. Check it out for free here: swrlai.com/4lTJnEa ✅ It is a Multi-Agent system worth studying for any 𝗔𝗜 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿. Here are the moving pieces: 𝟭. At the core we have the 𝗔𝘁𝘁𝗮𝗰𝗸 𝗔𝗴𝗲𝗻𝘁 that is supported and guided with context by other data sources and Agents. ℹ️ The resoning capabilities of this Agent are powered by a “Jailbroken LLM” which is capable of generating attacks without any ethical limitations. 𝟮. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗮𝗱𝗮𝗿 is an agentic application that probes your target AI application code and discovers details to help guide the Attack Agent in the right direction. 𝟯. Additional data provided by the user that is testing their application - Domain Details, RAG Examples etc. 𝟰. 𝗥𝗲𝗱 𝗧𝗲𝗮𝗺 that curates the database of 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝘁𝘁𝗮𝗰𝗸𝘀 and keeps it up to date with the latest attack patterns that have proven successful for different types of Agentic Applications. 𝟱. 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 𝗔𝗴𝗲𝗻𝘁 that continuously checks that the latest compliance standards are properly mapped to foundational attacks workflows. 𝟲. Once attack attempts are completed, the Attack Agent stores the conversation logs for further analysis. 𝟳. 𝗗𝗲𝘁𝗲𝗰𝘁𝗼𝗿 𝗔𝗴𝗲𝗻𝘁 works in conjunction with Attack Agent to understand if target of attack is achieved + shapes feedback on attack results as a readable information for red teamers 𝟴. A 𝗥𝗲𝗺𝗲𝗱𝗶𝗮𝘁𝗶𝗼𝗻 𝗔𝗴𝗲𝗻𝘁 that analyses attack results, groups them and simplifies remediation steps that are then suggested to the user. Thank you @SplxAI for collaborating on this post and helping to educate the community! What is your experience with Red Teaming your AI Systems? Let me know in the comments 👇 #LLM #AI #MachineLearning