A False Sense of Safety: Unsafe Information Leakage in 'Safe' AI Responses
David Glukhov, Ziwen Han, Ilia Shumailov, Vardan Papyan, Nicolas Papernot
2024-07-04
Summary
This paper talks about using decoder-only large language models (LLMs) to improve the process of converting spoken language into written text, known as speech-to-text translation (S2TT).
What's the problem?
The main problem is that while LLMs are very good at understanding and generating text, integrating them effectively into speech-related tasks like translating spoken words into text has been challenging. Traditional methods often require complex architectures that can complicate the training process and reduce efficiency.
What's the solution?
To solve this issue, the authors propose a new decoder-only architecture that allows the model to directly work with encoded speech data to produce text translations. This method simplifies the process by enabling the model to handle speech input more effectively. They also explore various techniques for fine-tuning the model to improve its performance without needing a lot of extra data. Their model has shown outstanding results on benchmarks like CoVoST 2 and FLEURS, which test how well models perform in translating speech.
Why it matters?
This research is important because it enhances how we can use AI for speech recognition and translation, making it easier to convert spoken language into text accurately. By improving the efficiency and effectiveness of these models, it can lead to better applications in areas like virtual assistants, transcription services, and communication tools, benefiting users across different languages and contexts.
Abstract
Large Language Models (LLMs) are vulnerable to jailbreaksx2013methods to elicit harmful or generally impermissible outputs. Safety measures are developed and assessed on their effectiveness at defending against jailbreak attacks, indicating a belief that safety is equivalent to robustness. We assert that current defense mechanisms, such as output filters and alignment fine-tuning, are, and will remain, fundamentally insufficient for ensuring model safety. These defenses fail to address risks arising from dual-intent queries and the ability to composite innocuous outputs to achieve harmful goals. To address this critical gap, we introduce an information-theoretic threat model called inferential adversaries who exploit impermissible information leakage from model outputs to achieve malicious goals. We distinguish these from commonly studied security adversaries who only seek to force victim models to generate specific impermissible outputs. We demonstrate the feasibility of automating inferential adversaries through question decomposition and response aggregation. To provide safety guarantees, we define an information censorship criterion for censorship mechanisms, bounding the leakage of impermissible information. We propose a defense mechanism which ensures this bound and reveal an intrinsic safety-utility trade-off. Our work provides the first theoretically grounded understanding of the requirements for releasing safe LLMs and the utility costs involved.