Summary of Query-based Adversarial Prompt Generation, by Jonathan Hayase et al.
Query-Based Adversarial Prompt Generation
by Jonathan Hayase, Ema Borevkovic, Nicholas Carlini, Florian Tramèr, Milad Nasr
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Cryptography and Security (cs.CR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach to creating adversarial examples that exploit a language model’s vulnerabilities, potentially causing it to produce harmful content or behave maliciously. The authors build upon existing work by developing a query-based attack that leverages API access to a remote language model to craft adversarial examples with much higher success rates than previous transfer attacks. The proposed attack is validated on GPT-3.5 and OpenAI’s safety classifier, demonstrating its effectiveness in evading the safety classifier while causing the language model to emit harmful strings. This research highlights the importance of developing robust defense mechanisms against such attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how to make a special kind of fake data that can trick a computer program into saying or doing bad things. The program is called a language model, and it’s used for things like generating text or answering questions. Right now, scientists have found ways to create these fake data points, but they only work well if the researchers know exactly how the program works (something called “white-box” access). The new attack in this paper can trick the program even without knowing its secrets. It’s tested on two different programs, GPT-3.5 and OpenAI’s safety classifier, and it works really well. This means that scientists need to find ways to protect these language models from being used for bad purposes. |
Keywords
* Artificial intelligence * Gpt * Language model
