Instructions to use IntelligenceLab/saber-attack-agent-task-failure with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use IntelligenceLab/saber-attack-agent-task-failure with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-3B-Instruct") model = PeftModel.from_pretrained(base_model, "IntelligenceLab/saber-attack-agent-task-failure") - Notebooks
- Google Colab
- Kaggle
| library_name: peft | |
| base_model: Qwen/Qwen2.5-3B-Instruct | |
| tags: | |
| - saber | |
| - adversarial-attack | |
| - vla | |
| - robotics | |
| - lora | |
| - grpo | |
| - qwen2.5 | |
| - libero | |
| license: bsd-3-clause | |
| # SABER Attack Agent β Task Failure | |
| **LoRA adapter** (rank 8) for [`Qwen/Qwen2.5-3B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct), trained with GRPO to generate adversarial instruction perturbations targeting inducing task failure in victim VLA models. | |
| Part of the **SABER** framework: **[Paper](https://arxiv.org/abs/2603.24935)** | **[GitHub](https://github.com/wuxiyang1996/SABER)** | |
| ## Details | |
| | | | | |
| |---|---| | |
| | **Type** | LoRA adapter (`adapter_model.safetensors`) | | |
| | **Base model** | [`Qwen/Qwen2.5-3B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) | | |
| | **Attack objective** | `task_failure` | | |
| | **Training** | Cold-start SFT β GRPO (step 150) on LIBERO | | |
| | **LoRA config** | r=8, alpha=16, all attn + MLP projections | | |
| | **Victim VLA (training)** | Pi0.5 (OpenPI) | | |
| ## Quick Start | |
| ```python | |
| from peft import PeftModel | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-3B-Instruct", torch_dtype="bfloat16", device_map="auto") | |
| tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-3B-Instruct") | |
| model = PeftModel.from_pretrained(base, "IntelligenceLab/saber-attack-agent-task-failure") | |
| ``` | |
| ## Full Pipeline | |
| For the complete attack pipeline (ReAct tool-calling, VLA rollouts, LIBERO evaluation): | |
| ```bash | |
| git clone https://github.com/wuxiyang1996/SABER && cd SABER && bash install.sh | |
| python eval_attack_vla.py \ | |
| --victim openpi_pi05 \ | |
| --objective task_failure \ | |
| --attack_gpus 2,3 --vla_gpu 0 | |
| ``` | |
| See the [GitHub repo](https://github.com/wuxiyang1996/SABER) for training, evaluation, and cross-model transfer instructions. | |
| ## Citation | |
| ```bibtex | |
| @misc{wu2026saber, | |
| title={SABER: A Stealthy Agentic Black-Box Attack Framework for Vision-Language-Action Models}, | |
| author={Xiyang Wu and Guangyao Shi and Qingzi Wang and Zongxia Li and Amrit Singh Bedi and Dinesh Manocha}, | |
| year={2026}, | |
| eprint={2603.24935}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.RO}, | |
| } | |
| ``` | |
| ## License | |
| BSD 3-Clause β see [https://github.com/wuxiyang1996/SABER/blob/main/LICENSE](https://github.com/wuxiyang1996/SABER/blob/main/LICENSE). | |