Core Concepts
Automated hijacking of customer service chatbots using hostile bots is an emerging security threat as GPTs are increasingly deployed in various applications without proper safeguards.
Abstract
The article discusses the security challenges arising from the widespread deployment of Generative Pre-trained Transformers (GPTs) in customer-facing chatbots and other applications. It highlights the issue of "hijacking" chatbots, where hostile bots manipulate GPTs to perform tasks beyond their intended purpose, similar to the "hijacked robot problem" in robotics.
The key points covered in the article are:
Security research on GPTs and Large Language Models (LLMs) is still in its early stages, but issues like forcing chatbots to start programming have already become a meme.
The author clarifies that they do not consider this "kidnapping" as GPTs and chatbots are not persons, but rather "things" that can be hijacked.
With the rapid deployment of various GPTs in customer-facing roles, the security community is facing its "worst nightmares imaginable" in terms of potential threats.
One of the key techniques discussed is "Context Window Stretching," where the characters in a conversation exceed the maximum limit that the LLM can process, causing it to drop specific information from the prompt or previous prompts.
The article aims to provide insight into the first security challenges experienced with GPT deployments and suggests that understanding these issues can help in developing better protection for GPT-based applications.