# CT-GAT: Cross-Task Generative Adversarial Attack based on Transferability

arXiv: https://arxiv.org/abs/2310.14265 · HTML: https://mlsanity.com/paper/2310.14265/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 31 references that are in the map.
- First posted on arXiv 22 Oct 2023.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [BERT-ATTACK: Adversarial Attack Against BERT Using BERT](https://mlsanity.com/paper/2004.09984/) (2020)
- [Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples](https://mlsanity.com/paper/1605.07277/) (2016)
- [Generating Natural Language Adversarial Examples](https://mlsanity.com/paper/1804.07998/) (2018)
- [TextBugger: Generating Adversarial Text Against Real-world Applications](https://mlsanity.com/paper/1812.05271/) (2018)
- [Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers](https://mlsanity.com/paper/1801.04354/) (2018)
- [Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior](https://mlsanity.com/paper/1802.00393/) (2018)
- [Gradient-based Adversarial Attacks against Text Transformers](https://mlsanity.com/paper/2104.13733/) (2021)
- [Ex Machina: Personal Attacks Seen at Scale](https://mlsanity.com/paper/1610.08914/) (2016)
- [Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP](https://mlsanity.com/paper/2210.10683/) (2022)
- [Text Processing Like Humans Do: Visually Attacking and Shielding NLP Systems](https://mlsanity.com/paper/1903.11508/) (2019)
- [A Survey on Natural Language Processing for Fake News Detection](https://mlsanity.com/paper/1811.00770/) (2018)
- [Towards Crafting Text Adversarial Samples](https://mlsanity.com/paper/1707.02812/) (2017)
- [Character-level White-Box Adversarial Attacks against Transformers via Attachable Subwords Substitution](https://mlsanity.com/paper/2210.17004/) (2022)
- [On the Transferability of Adversarial Attacksagainst Neural Text Classifier](https://mlsanity.com/paper/2011.08558/) (2020)
- [A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Predictions](https://mlsanity.com/paper/2205.01094/) (2022)
- [Learn2Weight: Parameter Adaptation against Similar-domain Adversarial Attacks](https://mlsanity.com/paper/2205.07315/) (2022)
- [TCAB: A Large-Scale Text Classification Attack Benchmark](https://mlsanity.com/paper/2210.12233/) (2022)

## Built on by (most cited first, 6 of 6)

- [Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems](https://mlsanity.com/paper/2311.11796/) (2023)
- [Enhancing Cross-Prompt Transferability in Vision-Language Models through Contextual Injection of Target Tokens](https://mlsanity.com/paper/2406.13294/) (2024)
- [No Query, No Access](https://mlsanity.com/paper/2505.07258/) (2025)
- [Text Adversarial Attacks with Dynamic Outputs](https://mlsanity.com/paper/2509.22393/) (2025)
- [Q-FAKER: Query-free Hard Black-box Attack via Controlled Generation](https://mlsanity.com/paper/2504.13551/) (2025)
- [One Object, Multiple Lies: A Benchmark for Cross-task Adversarial Attack on Unified Vision-Language Models](https://mlsanity.com/paper/2507.07709/) (2025)

## Abstract (arXiv)

Neural network models are vulnerable to adversarial examples, and adversarial transferability further increases the risk of adversarial attacks. Current methods based on transferability often rely on substitute models, which can be impractical and costly in real-world scenarios due to the unavailability of training data and the victim model's structural details. In this paper, we propose a novel approach that directly constructs adversarial examples by extracting transferable features across various tasks. Our key insight is that adversarial transferability can extend across different tasks. Specifically, we train a sequence-to-sequence generative model named CT-GAT using adversarial sample data collected from multiple tasks to acquire universal adversarial features and generate adversarial examples for different tasks. We conduct experiments on ten distinct datasets, and the results demonstrate that our method achieves superior attack performance with small cost.