Summary of Adversarial Attacks on Large Language Models Using Regularized Relaxation, by Samuel Jacob Chacko et al.
Adversarial Attacks on Large Language Models Using Regularized Relaxation
by Samuel Jacob Chacko, Sajib Biswas, Chashi Mahiul Islam, Fatema Tabassum Liza, Xiuwen Liu
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 A novel technique for adversarial attacks is proposed to overcome limitations in current attack methods used to study vulnerabilities in Large Language Models (LLMs). The approach, which leverages regularized gradients with continuous optimization methods, improves the attack success rate on aligned language models by two orders of magnitude compared to the state-of-the-art greedy coordinate gradient-based method. Additionally, it generates valid tokens from the model’s vocabulary, addressing a fundamental limitation of existing continuous optimization methods. This technique has significant implications for ensuring the safety and security of LLMs in practical applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are super smart computers that can understand and generate human-like language. But they’re not perfect – some bad guys might try to trick them into doing something silly or even dangerous! To keep these models safe, scientists need to test how well they work by trying to fool them with special tricks called “adversarial attacks.” However, the current ways of doing this have big problems. Some methods are too slow and others can’t even make sense using the model’s own vocabulary. This paper proposes a new way to attack language models that is much faster and generates sensible words. This could help keep our language models safe from bad guys and ensure they’re used responsibly. |
Keywords
* Artificial intelligence * Optimization
