Work

Adversarial natural language processing: overview, challenges, and policy implications

Year: 2025

Type: article

Abstract: The emergence of large language models has significantly expanded the use of natural language processing (NLP), even as it has heightened exposure to adversarial threats. We present an overview of adversarial NLP with an emphasis on challenges, policy implications, emerging areas, and future directions. First, we review attack methods and evaluate the vulnerabilities of popular NLP models. Then, we review defense strategies that include adversarial training. We describe major policy implications, identify key trends, and suggest future directions, such as the use of Bayesian methods to improve the security and robustness of NLP systems.

Source: Data & Policy

DOI: 10.1017/dap.2025.10030

Authors: Laxmi Shaw, Mohammed Wasim Ansari, Tahir Ekin

Institution: Texas State University

Language: English

Topic: Adversarial Robustness in Machine Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,550

Funders: Division of Information and Intelligent Systems, Air Force Office of Scientific Research