arrayview

v0.25.1 suspicious
7.0
High Risk

Fast multi-dimensional array viewer

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits elevated risks due to its ability to make network calls, execute shell commands, and employ data obfuscation techniques. These factors, combined with incomplete metadata, suggest potential security concerns, though direct evidence of malicious intent is lacking.

  • High network and shell execution risks
  • Presence of data obfuscation techniques
Per-check LLM notes
  • Network: The package makes network calls that could be used for external communication, which may indicate an attempt to connect to remote servers or exfiltrate data.
  • Shell: The package executes shell commands and subprocesses, potentially allowing it to interact with system processes and services, which could be exploited for unauthorized actions.
  • Obfuscation: The presence of base64 decoding suggests some form of data obfuscation or encoding, which could be used for malicious purposes.
  • Credentials: No clear patterns indicative of credential harvesting were detected.
  • Metadata: The author's name is missing and the author seems new or inactive, which raises some concerns but not enough to conclusively determine malice.

📦 Package Quality Overall: Medium (5.2/10)

✦ High Test Suite 9.0

Test suite present — 22 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 22 test file(s) detected (e.g. capture_v_animation.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (842 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 264 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 100 commits in oscarvanderheide/arrayview
  • Single author but highly active (100 commits)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

  • > bool: try: with socket.create_connection((_LOOPBACK_HOST, port), timeout=0.3): return Tru
  • }/ping" try: with urllib.request.urlopen(url, timeout=timeout) as resp: if resp.s
  • }/ping" try: with urllib.request.urlopen(url, timeout=0.5) as resp: if resp.statu
  • gb": rgb}).encode() req = urllib.request.Request( f"http://{relay_host}:{port}/load_bytes",
⚠ Code Obfuscation score 10.0

Found 5 obfuscation pattern(s)

  • argv[6]", "html = base64.b64decode(html_b64.encode()).decode()", "class Api:",
  • import pathlib raw = base64.b64decode(data_b64) downloads = pathlib.Path.home() / "Downloa
  • try: raw = base64.b64decode(data_b64) arr = np.load(io.BytesIO(raw))
  • ",", 1)[1] assert base64.b64decode(encoded).startswith(b"\xff\xd8") class TestPreloadRoutes:
  • name = msg.get("name") or __import__("os").path.basename(file_path) options = msg.get("options", {
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • == "win32": result = subprocess.run( ["netstat", "-ano", "-p", "TCP"], c
  • for pid in pids: subprocess.run( ["taskkill", "/F", "/PID", str(pid)],
  • return result = subprocess.run( ["lsof", "-ti", f"tcp:{port}", "-sTCP:LISTEN"],
  • rsist=True)" ) proc = subprocess.Popen( [sys.executable, "-c", script], stdin=subpr
  • (script_lines) return subprocess.Popen( [ sys.executable,
  • join(script_lines) return subprocess.Popen( [ sys.executable, "-c",
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: example.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository oscarvanderheide/arrayview appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 arrayview 
Develop a comprehensive data visualization tool using the 'arrayview' Python package. This tool will serve as a powerful interface for scientists and researchers to explore and analyze large, multi-dimensional datasets in real-time. The application should include the following key functionalities:

1. **Data Loading**: Users should be able to load their own multi-dimensional datasets into the application. Support for common file formats such as .csv, .txt, .npy, and .h5 should be included.
2. **Interactive Visualization**: Utilize 'arrayview' to provide interactive, real-time visualization of the loaded data. Features should include zooming, panning, and slicing capabilities across different dimensions.
3. **Customizable Views**: Allow users to customize how the data is displayed. Options should include changing color maps, adjusting opacity levels, and selecting which dimensions to visualize.
4. **Annotation Tools**: Integrate tools that allow users to annotate specific areas of interest within the data visualizations. These annotations should be persistent and visible across sessions.
5. **Export Functionality**: Provide an option for users to export their visualizations in various formats, including images and video clips, for presentations or reports.
6. **Collaboration Features**: Implement a basic collaboration feature where multiple users can simultaneously view and annotate the same dataset, enhancing the ability for remote teams to work together on complex data analysis tasks.

The 'arrayview' package will be crucial in handling the high-performance rendering and manipulation of multi-dimensional arrays, ensuring smooth and efficient visualization even for very large datasets. Your task is to design and implement this tool from scratch, documenting your process and any challenges you encounter along the way.

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