# Don't Trigger Me! A Triggerless Backdoor Attack Against Deep Neural Networks

arXiv: https://arxiv.org/abs/2010.03282 · HTML: https://mlsanity.com/paper/2010.03282/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Oct 2020.

## Built on by (most cited first, 19 of 19)

- [Hidden Backdoors in Human-Centric Language Models](https://mlsanity.com/paper/2105.00164/) (2021)
- [BACKDOORL: Backdoor Attack against Competitive Reinforcement Learning](https://mlsanity.com/paper/2105.00579/) (2021)
- [Test-Time Backdoor Attacks on Multimodal Large Language Models](https://mlsanity.com/paper/2402.08577/) (2024)
- [Can't Steal? Cont-Steal! Contrastive Stealing Attacks Against Image Encoders](https://mlsanity.com/paper/2201.07513/) (2022)
- [A Survey of Neural Trojan Attacks and Defenses in Deep Learning](https://mlsanity.com/paper/2202.07183/) (2022)
- [Backdoor Attacks with Input-unique Triggers in NLP](https://mlsanity.com/paper/2303.14325/) (2023)
- [Accumulative Poisoning Attacks on Real-time Data](https://mlsanity.com/paper/2106.09993/) (2021)
- [Backdoor Attacks on Crowd Counting](https://mlsanity.com/paper/2207.05641/) (2022)
- [ezDPS: An Efficient and Zero-Knowledge Machine Learning Inference Pipeline](https://mlsanity.com/paper/2212.05428/) (2022)
- [Dim-Krum: Backdoor-Resistant Federated Learning for NLP with Dimension-wise Krum-Based Aggregation](https://mlsanity.com/paper/2210.06894/) (2022)
- [SoK: A Systematic Evaluation of Backdoor Trigger Characteristics in Image Classification](https://mlsanity.com/paper/2302.01740/) (2023)
- [DeBackdoor: A Deductive Framework for Detecting Backdoor Attacks on Deep Models with Limited Data](https://mlsanity.com/paper/2503.21305/) (2025)
- [Lazy Layers to Make Fine-Tuned Diffusion Models More Traceable](https://mlsanity.com/paper/2405.00466/) (2024)
- [Certifiers Make Neural Networks Vulnerable to Availability Attacks](https://mlsanity.com/paper/2108.11299/) (2021)
- [The Ripple Effect: On Unforeseen Complications of Backdoor Attacks](https://mlsanity.com/paper/2505.11586/) (2025)
- [Backdoor Attack with Invisible Triggers Based on Model Architecture Modification](https://mlsanity.com/paper/2412.16905/) (2024)
- [The Art of (Mis)alignment: How Fine-Tuning Methods Effectively Misalign and Realign LLMs in Post-Training](https://mlsanity.com/paper/2604.07754/) (2026)
- [BadBone: Backdoor Attacks Against Backbone Models in Visual Prompt Learning](https://mlsanity.com/paper/2605.31246/) (2026)
- [Genotypic Triggers: Exposing Pharmacogenomic Blind Spots via Host-Specific Backdoors in Generative Antimicrobial Peptide Models](https://mlsanity.com/paper/2608.06779/) (2026)

## Abstract (arXiv)

Backdoor attack against deep neural networks is currently being profoundly investigated due to its severe security consequences. Current state-of-the-art backdoor attacks require the adversary to modify the input, usually by adding a trigger to it, for the target model to activate the backdoor. This added trigger not only increases the difficulty of launching the backdoor attack in the physical world, but also can be easily detected by multiple defense mechanisms. In this paper, we present the first triggerless backdoor attack against deep neural networks, where the adversary does not need to modify the input for triggering the backdoor. Our attack is based on the dropout technique. Concretely, we associate a set of target neurons that are dropped out during model training with the target label. In the prediction phase, the model will output the target label when the target neurons are dropped again, i.e., the backdoor attack is launched. This triggerless feature of our attack makes it practical in the physical world. Extensive experiments show that our triggerless backdoor attack achieves a perfect attack success rate with a negligible damage to the model's utility.