In the growing canon of AI security, the indirect prompt injection has emerged as the most powerful means for attackers to hack large language models such as OpenAI’s GPT-3 and GPT-4 or Microsoft’s Copilot. By exploiting a model's inability to distinguish between, on the one hand, developer-defined prompts and, on the other, text in external content LLMs interact with, indirect prompt injections are remarkably effective at invoking harmful or otherwise unintended actions. Examples include divulging end users’ confidential contacts or emails and delivering falsified answers that have the potential to corrupt the integrity of important calculations.
Despite the power of prompt injections, attackers face a fundamental challenge in using them: The inner workings of so-called closed-weights models such as GPT, Anthropic’s Claude, and Google’s Gemini are closely held secrets. Developers of such proprietary platforms tightly restrict access to the underlying code and training data that make them work and, in the process, make them black boxes to external users. As a result, devising working prompt injections requires labor- and time-intensive trial and error through redundant manual effort.
Algorithmically generated hacks
For the first time, academic researchers have devised a means to create computer-generated prompt injections against Gemini that have much higher success rates than manually crafted ones. The new method abuses fine-tuning, a feature offered by some closed-weights models for training them to work on large amounts of private or specialized data, such as a law firm’s legal case files, patient files or research managed by a medical facility, or architectural blueprints. Google makes its fine-tuning for Gemini’s API available free of charge.
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