Summary of Advancing Nlp Security by Leveraging Llms As Adversarial Engines, By Sudarshan Srinivasan et al.
Advancing NLP Security by Leveraging LLMs as Adversarial Engines
by Sudarshan Srinivasan, Maria Mahbub, Amir Sadovnik
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 The proposed position paper introduces a novel approach to advancing NLP security by utilizing Large Language Models (LLMs) as engines for generating diverse adversarial attacks. This medium-difficulty summary highlights how LLMs’ sophisticated language understanding and generation capabilities can produce more effective, semantically coherent, and human-like adversarial examples across various domains and classifier architectures. The paper argues for expanding recent work on word-level adversarial examples to encompass a broader range of attack types, including adversarial patches, universal perturbations, and targeted attacks. This paradigm shift in adversarial NLP has far-reaching implications, potentially enhancing model robustness, uncovering new vulnerabilities, and driving innovation in defense mechanisms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The proposed approach uses Large Language Models (LLMs) to create diverse adversarial attacks for advancing NLP security. By building on recent work that demonstrates LLMs’ effectiveness in generating word-level adversarial examples, this idea expands to cover various attack types, including patches and targeted attacks. This new approach has big implications for making NLP systems more secure. |
Keywords
» Artificial intelligence » Language understanding » Nlp
