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STAMP

by KatherLab

Overview

Enable LLM agents to orchestrate and interact with STAMP's computational pathology tools for whole-slide image analysis, biomarker prediction, model training, and inference.

Installation

Run Command
python server.py

Environment Variables

  • MAX_JOBS
  • XDG_CACHE_HOME
  • PYTORCH_CUDA_ALLOC_CONF

Security Notes

The server uses `subprocess.run()` to execute `stamp` CLI commands, with the `mode` and `config` parameters directly derived from user input (LLM agent calls). This creates a critical command injection vulnerability where a malicious actor or agent could craft inputs to execute arbitrary system commands, potentially leading to data exfiltration, system compromise, or denial of service. While `_resolve_path` attempts to restrict file system access for `read_file` and `list_files`, it does not mitigate the risk from `subprocess.run` in `_run_stamp` which can bypass file system restrictions entirely. The `stamp` CLI itself may have further vulnerabilities not audited here.

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Stats

Interest Score46
Security Score2
Cost ClassMedium
Avg Tokens500
Stars102
Forks45
Last Update2025-12-04

Tags

LLM Agent IntegrationComputational PathologyDeep LearningWhole Slide ImagesBiomarker PredictionMachine LearningMedical ImagingModel Context Protocol