Summary of Vlmguard: Defending Vlms Against Malicious Prompts Via Unlabeled Data, by Xuefeng Du et al.
VLMGuard: Defending VLMs against Malicious Prompts via Unlabeled Data
by Xuefeng Du, Reshmi Ghosh, Robert Sim, Ahmed Salem, Vitor Carvalho, Emily Lawton, Yixuan Li, Jack W. Stokes
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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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 research proposes a novel framework called VLMGuard to detect malicious prompts in vision-language models (VLMs). The current vulnerability of VLMs to adversarial inputs raises concerns about their reliability in integrated applications. To address this, the authors introduce an automated estimation score for distinguishing between benign and malicious user prompts. This approach enables training a binary prompt classifier without requiring extra human annotations, making it practical for real-world applications. Experimental results demonstrate that VLMGuard outperforms state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding ways to keep vision-language models from being tricked by bad inputs. These models are really good at understanding pictures and text together, but they can be fooled into saying things that aren’t true. This is a problem because we want these models to give us reliable answers. To solve this issue, the researchers came up with a new way to teach the model to detect when someone is trying to trick it. They used data from the internet where people are already using the models in various ways, and they developed an automated system to figure out which prompts are good or bad. This approach doesn’t require anyone to label each prompt as good or bad, making it practical for real-world use. The results show that this new method is much better than existing methods at detecting malicious prompts. |
Keywords
» Artificial intelligence » Prompt
