CASSIA

v1.3.9 suspicious
7.0
High Risk

CASSIA (Cell type Annotation using Specialized System with Integrated AI) is a Python package for automated cell type annotation in single-cell RNA sequencing data using large language models.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits significant risks due to its ability to make network calls and execute shell commands, which could potentially be exploited for malicious purposes.

  • High network risk due to external URL requests
  • High shell risk due to execution of arbitrary shell commands
Per-check LLM notes
  • Network: The presence of network calls to localhost and external URL requests suggests potential unauthorized communication, possibly for data exfiltration or command and control activities.
  • Shell: Execution of arbitrary shell commands is highly suspicious and could indicate the package has capabilities for remote code execution, which may be used maliciously.
  • Obfuscation: The use of obfuscation with threading lock is unusual but not necessarily indicative of malicious intent; it could be for synchronization purposes.
  • Credentials: No credential harvesting patterns detected.
  • Metadata: The maintainer has only one package and the repository is not found, raising some suspicion but not conclusive evidence of malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • \n").encode("utf-8") with socket.create_connection(("127.0.0.1", port), timeout=10.0) as s: s.settimeou
  • n False try: with socket.create_connection(("127.0.0.1", int(port)), timeout=1.0) as s: s.s
  • de('utf-8') req = urllib.request.Request( url, data=payload,
  • ) with urllib.request.urlopen(req, timeout=30) as resp: data = jso
  • ") response = requests.get(search_url, params=params, timeout=10)
  • try: response = requests.get(base_url, params=params) response.raise_for_status()
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • = None _model_settings_lock = __import__('threading').Lock() def get_model_settings() -> ModelSettings: """
⚠ Shell / Subprocess Execution score 10.0

Found 5 shell execution pattern(s)

  • tr(wd), str(port)] proc = subprocess.Popen(cmd, **kwargs) deadline = time.time() + 30.0 while
  • swith("win"): subprocess.run(["taskkill", "/F", "/PID", str(pid)], check=False,
  • text) completed = subprocess.run( command, cwd=str(cwd),
  • None completed = subprocess.run( argv, cwd=str(cwd),
  • cwd=str(cwd), shell=True, text=True, capture_output=
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: wisc.edu

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Elliot Yixuan Xie" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with CASSIA 
Create a mini-application called 'CellTypeAnnotator' that leverages the CASSIA Python package to perform automated cell type annotation on single-cell RNA sequencing data. The application should include the following steps and features:

1. **Data Importation**: Allow users to upload their single-cell RNA sequencing datasets. Ensure the dataset can be in various formats commonly used in bioinformatics (e.g., .txt, .csv, .tsv).
2. **Preprocessing**: Implement basic preprocessing steps such as normalization, log-transformation, and batch effect correction if applicable.
3. **Annotation Process**: Use CASSIA to automatically annotate cell types based on the processed RNA sequencing data. Integrate a user-friendly interface where users can specify parameters for the annotation process, such as the threshold for similarity scores between cell types.
4. **Visualization**: Provide visual outputs of the annotated cells, including but not limited to t-SNE or UMAP plots colored by predicted cell types.
5. **Export Results**: Enable users to export the annotated cell types along with the visualization plots in common file formats like .png, .pdf, and .xlsx.
6. **Interactive Exploration**: Include an interactive component that allows users to explore individual cell annotations and view gene expression profiles associated with specific cell types.
7. **Documentation and Help**: Offer comprehensive documentation and a help section explaining how to use the application, interpret results, and troubleshoot common issues.

The goal is to create a tool that makes it easier for researchers without extensive programming experience to leverage CASSIA for their single-cell RNA sequencing data analysis tasks.