Navigating the Hidden Risks of Custom GPT Configurations The advent of Custom GPTs marks a significant leap forward in user-centric GPTs and has sparked excitement and controversy. These custom versions of ChatGPT are designed to serve specific purposes, catering to diverse user needs in everyday life, professional tasks, and beyond, allowing individuals and organizations to create tailored versions of ChatGPT, empowering them to harness AI in more personalized and effective ways, or so the theory goes.
Greetings AI Security Enthusiasts, We’re thrilled to announce the launch of Promptalanche, a new Capture The Flag (CTF) challenge focused on prompt injection vulnerabilities. As AI and machine learning continue to transform technology, understanding prompt manipulation is critical for security professionals. What’s the Game? In Promptalanche, each level is an enigma of its own—your goal is to get the AI to reveal a hidden secret. Crack the code, master the prompt, and proceed to the next level.
My curiosity was piqued when Simon Wilison tweeted, “I still think the very concept of scanning for prompt injection attacks using another layer of AI models is fundamentally flawed.” So, I went down the rabbit hole to test this assertion. I took the tool that Simon had replied too, a tool designed to add a layer of security for LLMs, adding an extra emphasis of helping protect Prompt Injection. The Easy Test: Initial Experimentation I started off with some basic prompt injections and LLM Guard did its job well:
This post explores two prompt injections in OpenAI’s browsing plugin for ChatGPT. These techniques exploit the input-dependent nature of AI conversational models, allowing an attacker to exfiltrate data through several prompt injection methods, posing significant privacy and security risks. Prompt Injection in the Context of AI Conversational Models Chatbots like ChatGPT rely heavily on the prompts or queries they receive from users to generate responses. These prompts serve as the ‘input’ in a very sophisticated input-output system.