# A Context Aware Approach for Generating Natural Language Attacks

arXiv: https://arxiv.org/abs/2012.13339 · HTML: https://mlsanity.com/paper/2012.13339/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2020.

## Built on by (most cited first, 5 of 5)

- [Adversarial Examples for Evaluating Math Word Problem Solvers](https://mlsanity.com/paper/2109.05925/) (2021)
- [A Strong Baseline for Query Efficient Attacks in a Black Box Setting](https://mlsanity.com/paper/2109.04775/) (2021)
- [Evaluating and Improving Robustness in Large Language Models: A Survey and Future Directions](https://mlsanity.com/paper/2506.11111/) (2025)
- [Camouflage is all you need: Evaluating and Enhancing Language Model Robustness Against Camouflage Adversarial Attacks](https://mlsanity.com/paper/2402.09874/) (2024)
- [Context-Aware Multimodal Representation Learning for Spatio-Temporally Explicit Environmental Modelling](https://mlsanity.com/paper/2511.11706/) (2025)

## Abstract (arXiv)

We study an important task of attacking natural language processing models in a black box setting. We propose an attack strategy that crafts semantically similar adversarial examples on text classification and entailment tasks. Our proposed attack finds candidate words by considering the information of both the original word and its surrounding context. It jointly leverages masked language modelling and next sentence prediction for context understanding. In comparison to attacks proposed in prior literature, we are able to generate high quality adversarial examples that do significantly better both in terms of success rate and word perturbation percentage.