Summary of Attack on Prompt: Backdoor Attack in Prompt-based Continual Learning, by Trang Nguyen et al.
Attack On Prompt: Backdoor Attack in Prompt-Based Continual Learning
by Trang Nguyen, Anh Tran, Nhat Ho
First submitted to arxiv on: 28 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Medium Difficulty summary: Prompt-based approaches for data privacy in continual learning have shown impressive performance but also raise security concerns about retaining poisoned knowledge. This paper exposes the threat of backdoor attacks on incremental learners and proposes solutions to overcome three critical challenges: transferability, resiliency, and authenticity. The authors employ a surrogate dataset, manipulate prompt selection, and simulate static and dynamic states to ensure the backdoor trigger remains robust. They also apply binary cross-entropy loss as an anti-cheating factor to prevent adversarial noise. Extensive experiments across various benchmark datasets validate the continual backdoor framework, achieving up to 100% attack success rate. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper is about a new way to hack into machine learning models that learn from data over time. Right now, these models are really good at remembering things they’ve learned, but this can be a problem because it means they might accidentally store private information like passwords or medical records. The authors found that if you manipulate the data in just the right way, you can trick the model into doing what you want it to do, even when it’s supposed to be working correctly. They came up with three ways to make this hack work: by using fake data, making sure the hack works well in different situations, and adding a special check to make sure the hack isn’t being used. The experiments showed that their hack is really effective, with a success rate of 100% on some datasets. |
Keywords
» Artificial intelligence » Continual learning » Cross entropy » Machine learning » Prompt » Transferability
