# Q-FAKER: Query-free Hard Black-box Attack via Controlled Generation

arXiv: https://arxiv.org/abs/2504.13551 · HTML: https://mlsanity.com/paper/2504.13551/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 45 references that are in the map.
- First posted on arXiv 18 Apr 2025.

## Builds on (most cited first, 30 of 34)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Universal and Transferable Adversarial Attacks on Aligned Language Models](https://mlsanity.com/paper/2307.15043/) (2023)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Jailbreaking Black Box Large Language Models in Twenty Queries](https://mlsanity.com/paper/2310.08419/) (2023)
- [Red Teaming Language Models with Language Models](https://mlsanity.com/paper/2202.03286/) (2022)
- [Plug and Play Language Models: A Simple Approach to Controlled Text Generation](https://mlsanity.com/paper/1912.02164/) (2019)
- [Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment](https://mlsanity.com/paper/1907.11932/) (2019)
- [LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion](https://mlsanity.com/paper/2306.02561/) (2023)
- [BERT-ATTACK: Adversarial Attack Against BERT Using BERT](https://mlsanity.com/paper/2004.09984/) (2020)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection](https://mlsanity.com/paper/2301.07597/) (2023)
- [Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples](https://mlsanity.com/paper/1605.07277/) (2016)
- [Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers](https://mlsanity.com/paper/1801.04354/) (2018)
- [Multi-step Jailbreaking Privacy Attacks on ChatGPT](https://mlsanity.com/paper/2304.05197/) (2023)
- [Gradient-based Adversarial Attacks against Text Transformers](https://mlsanity.com/paper/2104.13733/) (2021)
- [Why Should Adversarial Perturbations be Imperceptible? Rethink the Research Paradigm in Adversarial NLP](https://mlsanity.com/paper/2210.10683/) (2022)
- [OpenAttack: An Open-source Textual Adversarial Attack Toolkit](https://mlsanity.com/paper/2009.09191/) (2020)
- [How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness?](https://mlsanity.com/paper/2112.11668/) (2021)
- [SemAttack: Natural Textual Attacks via Different Semantic Spaces](https://mlsanity.com/paper/2205.01287/) (2022)
- [Simple Fusion: Return of the Language Model](https://mlsanity.com/paper/1809.00125/) (2018)
- [Character-level White-Box Adversarial Attacks against Transformers via Attachable Subwords Substitution](https://mlsanity.com/paper/2210.17004/) (2022)
- ["That Is a Suspicious Reaction!": Interpreting Logits Variation to Detect NLP Adversarial Attacks](https://mlsanity.com/paper/2204.04636/) (2022)
- [Large Language Model Supply Chain: A Research Agenda](https://mlsanity.com/paper/2404.12736/) (2024)
- [A Comprehensive Survey on Evaluating Large Language Model Applications in the Medical Industry](https://mlsanity.com/paper/2404.15777/) (2024)
- [White-Box Multi-Objective Adversarial Attack on Dialogue Generation](https://mlsanity.com/paper/2305.03655/) (2023)

## Built on by (most cited first, 1 of 1)

- [Text Adversarial Attacks with Dynamic Outputs](https://mlsanity.com/paper/2509.22393/) (2025)

## Abstract (arXiv)

Many adversarial attack approaches are proposed to verify the vulnerability of language models. However, they require numerous queries and the information on the target model. Even black-box attack methods also require the target model's output information. They are not applicable in real-world scenarios, as in hard black-box settings where the target model is closed and inaccessible. Even the recently proposed hard black-box attacks still require many queries and demand extremely high costs for training adversarial generators. To address these challenges, we propose Q-faker (Query-free Hard Black-box Attacker), a novel and efficient method that generates adversarial examples without accessing the target model. To avoid accessing the target model, we use a surrogate model instead. The surrogate model generates adversarial sentences for a target-agnostic attack. During this process, we leverage controlled generation techniques. We evaluate our proposed method on eight datasets. Experimental results demonstrate our method's effectiveness including high transferability and the high quality of the generated adversarial examples, and prove its practical in hard black-box settings.