# MALCOM: Generating Malicious Comments to Attack Neural Fake News Detection Models

arXiv: https://arxiv.org/abs/2009.01048 · HTML: https://mlsanity.com/paper/2009.01048/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2020.

## Built on by (most cited first, 25 of 25)

- [Can LLM-Generated Misinformation Be Detected?](https://mlsanity.com/paper/2309.13788/) (2023)
- [Fake News in Sheep's Clothing: Robust Fake News Detection Against LLM-Empowered Style Attacks](https://mlsanity.com/paper/2310.10830/) (2023)
- [PETGEN: Personalized Text Generation Attack on Deep Sequence Embedding-based Classification Models](https://mlsanity.com/paper/2109.06777/) (2021)
- [Let Silence Speak: Enhancing Fake News Detection with Generated Comments from Large Language Models](https://mlsanity.com/paper/2405.16631/) (2024)
- [Fine-Tuning, Quantization, and LLMs: Navigating Unintended Outcomes](https://mlsanity.com/paper/2404.04392/) (2024)
- [Perturbations in the Wild: Leveraging Human-Written Text Perturbations for Realistic Adversarial Attack and Defense](https://mlsanity.com/paper/2203.10346/) (2022)
- [SAGE-RT: Synthetic Alignment data Generation for Safety Evaluation and Red Teaming](https://mlsanity.com/paper/2408.11851/) (2024)
- [Verifying the Robustness of Automatic Credibility Assessment](https://mlsanity.com/paper/2303.08032/) (2023)
- [Adversarial Style Augmentation via Large Language Model for Robust Fake News Detection](https://mlsanity.com/paper/2406.11260/) (2024)
- [Provable Robustness of (Graph) Neural Networks Against Data Poisoning and Backdoor Attacks](https://mlsanity.com/paper/2407.10867/) (2024)
- [Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions](https://mlsanity.com/paper/2110.13980/) (2021)
- [Robustness of Fusion-based Multimodal Classifiers to Cross-Modal Content Dilutions](https://mlsanity.com/paper/2211.02646/) (2022)
- [Socialbots on Fire: Modeling Adversarial Behaviors of Socialbots via Multi-Agent Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2110.10655/) (2021)
- [Adversarial machine learning for protecting against online manipulation](https://mlsanity.com/paper/2111.12034/) (2021)
- [Distinguishing Non-natural from Natural Adversarial Samples for More Robust Pre-trained Language Model](https://mlsanity.com/paper/2203.11199/) (2022)
- [When Claims Evolve: Evaluating and Enhancing the Robustness of Embedding Models Against Misinformation Edits](https://mlsanity.com/paper/2503.03417/) (2025)
- [Adversarial Machine Learning in Text Analysis and Generation](https://mlsanity.com/paper/2101.08675/) (2021)
- [Synthetic Text Detection: Systemic Literature Review](https://mlsanity.com/paper/2210.06336/) (2022)
- [Text Adversarial Attacks with Dynamic Outputs](https://mlsanity.com/paper/2509.22393/) (2025)
- [Alert-ME: An Explainability-Driven Defense Against Adversarial Examples in Transformer-Based Text Classification](https://mlsanity.com/paper/2307.01225/) (2023)
- [Exploiting Class Probabilities for Black-box Sentence-level Attacks](https://mlsanity.com/paper/2402.02695/) (2024)
- [CogniDir: Combating Cognitive Malicious Comments via Adaptive Distributional Learning for Robust Fake News Detection](https://mlsanity.com/paper/2510.09712/) (2025)
- [SPOT: Text Source Prediction from Originality Score Thresholding](https://mlsanity.com/paper/2405.20505/) (2024)
- [ROBAD: Robust Adversary-aware Local-Global Attended Bad Actor Detection Sequential Model](https://mlsanity.com/paper/2507.15067/) (2025)
- [Large Language Models in Misinformation Ecosystems: Misuse, Defense, and Vulnerability](https://mlsanity.com/paper/2607.10402/) (2026)

## Abstract (arXiv)

In recent years, the proliferation of so-called "fake news" has caused much disruptions in society and weakened the news ecosystem. Therefore, to mitigate such problems, researchers have developed state-of-the-art models to auto-detect fake news on social media using sophisticated data science and machine learning techniques. In this work, then, we ask "what if adversaries attempt to attack such detection models?" and investigate related issues by (i) proposing a novel threat model against fake news detectors, in which adversaries can post malicious comments toward news articles to mislead fake news detectors, and (ii) developing MALCOM, an end-to-end adversarial comment generation framework to achieve such an attack. Through a comprehensive evaluation, we demonstrate that about 94% and 93.5% of the time on average MALCOM can successfully mislead five of the latest neural detection models to always output targeted real and fake news labels. Furthermore, MALCOM can also fool black box fake news detectors to always output real news labels 90% of the time on average. We also compare our attack model with four baselines across two real-world datasets, not only on attack performance but also on generated quality, coherency, transferability, and robustness.