# Yet Meta Learning Can Adapt Fast, It Can Also Break Easily

arXiv: https://arxiv.org/abs/2009.01672 · HTML: https://mlsanity.com/paper/2009.01672/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Sep 2020.

## Built on by (most cited first, 10 of 10)

- [Sharp-MAML: Sharpness-Aware Model-Agnostic Meta Learning](https://mlsanity.com/paper/2206.03996/) (2022)
- [On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning](https://mlsanity.com/paper/2102.10454/) (2021)
- [On sensitivity of meta-learning to support data](https://mlsanity.com/paper/2110.13953/) (2021)
- [FCert: Certifiably Robust Few-Shot Classification in the Era of Foundation Models](https://mlsanity.com/paper/2404.08631/) (2024)
- [Rethinking the Number of Shots in Robust Model-Agnostic Meta-Learning](https://mlsanity.com/paper/2211.15180/) (2022)
- [Are You Worthy of My Trust?: A Socioethical Perspective on the Impacts of Trustworthy AI Systems on the Environment and Human Society](https://mlsanity.com/paper/2309.09450/) (2023)
- [Towards A Conceptually Simple Defensive Approach for Few-shot classifiers Against Adversarial Support Samples](https://mlsanity.com/paper/2110.12357/) (2021)
- [TLXML: Task-Level Explanation of Meta-Learning via Influence Functions](https://mlsanity.com/paper/2501.14271/) (2025)
- [Domain-Generalization to Improve Learning in Meta-Learning Algorithms](https://mlsanity.com/paper/2508.09418/) (2025)
- [Provably Robust Adaptation for Language-Empowered Foundation Models](https://mlsanity.com/paper/2510.08659/) (2025)

## Abstract (arXiv)

Meta learning algorithms have been widely applied in many tasks for efficient learning, such as few-shot image classification and fast reinforcement learning. During meta training, the meta learner develops a common learning strategy, or experience, from a variety of learning tasks. Therefore, during meta test, the meta learner can use the learned strategy to quickly adapt to new tasks even with a few training samples. However, there is still a dark side about meta learning in terms of reliability and robustness. In particular, is meta learning vulnerable to adversarial attacks? In other words, would a well-trained meta learner utilize its learned experience to build wrong or likely useless knowledge, if an adversary unnoticeably manipulates the given training set? Without the understanding of this problem, it is extremely risky to apply meta learning in safety-critical applications. Thus, in this paper, we perform the initial study about adversarial attacks on meta learning under the few-shot classification problem. In particular, we formally define key elements of adversarial attacks unique to meta learning and propose the first attacking algorithm against meta learning under various settings. We evaluate the effectiveness of the proposed attacking strategy as well as the robustness of several representative meta learning algorithms. Experimental results demonstrate that the proposed attacking strategy can easily break the meta learner and meta learning is vulnerable to adversarial attacks. The implementation of the proposed framework will be released upon the acceptance of this paper.