Fun-tuning: Characterizing the Vulnerability of Proprietary LLMs to Optimization-based Prompt Injection Attacks via the Fine-Tuning Interface

TL;DR

This paper reveals a new vulnerability in proprietary LLMs where attackers can use the fine-tuning interface's loss-like information to optimize prompt injections, leading to high success attack rates.

Contribution

It introduces a novel attack method exploiting the fine-tuning interface of LLMs, demonstrating significant security risks and highlighting the utility-security tradeoff.

Findings

Attack success rates between 65% and 82% on Gemini LLMs.

Loss-like signals from fine-tuning APIs can guide adversarial prompt optimization.

Fine-tuning interfaces expose vulnerabilities despite their utility.

Abstract

We surface a new threat to closed-weight Large Language Models (LLMs) that enables an attacker to compute optimization-based prompt injections. Specifically, we characterize how an attacker can leverage the loss-like information returned from the remote fine-tuning interface to guide the search for adversarial prompts. The fine-tuning interface is hosted by an LLM vendor and allows developers to fine-tune LLMs for their tasks, thus providing utility, but also exposes enough information for an attacker to compute adversarial prompts. Through an experimental analysis, we characterize the loss-like values returned by the Gemini fine-tuning API and demonstrate that they provide a useful signal for discrete optimization of adversarial prompts using a greedy search algorithm. Using the PurpleLlama prompt injection benchmark, we demonstrate attack success rates between 65% and 82% on Google's Gemini family of LLMs. These attacks exploit the classic utility-security tradeoff - the fine-tuning interface provides a useful feature for developers but also exposes the LLMs to powerful attacks.