arctic-new

v1.85.2 suspicious
4.0
Medium Risk

AHL Research Versioned TimeSeries and Tick store

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of obfuscation and low activity, raising concerns about its legitimacy and potential for supply-chain attacks.

  • Obfuscation risk due to encoded binary data
  • Low activity and lack of detailed maintainer information
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external API interactions.
  • Shell: No shell execution patterns detected, indicating no immediate risk of executing system commands.
  • Obfuscation: The presence of encoded binary data suggests potential obfuscation, but without further context, it's hard to determine if it's malicious.
  • Credentials: No credentials or secrets were detected in the provided patterns.
  • Metadata: The package shows low activity and lacks detailed maintainer information, raising concerns about its legitimacy.

📦 Package Quality Overall: Low (4.6/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Brief PyPI description (307 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 4 type-annotated function signatures (partial)
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 100 commits in qianyun210603/arctic
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

  • "sha": bson.Binary(base64.b64decode("Fk+quqPVSDfaajYJkOAvnDyXtGQ="), 0), "symbol":
  • "data": bson.Binary(base64.b64decode("CAAAAIAAAAAAAAAAAA=="), 0), "compressed": True
  • "sha": bson.Binary(base64.b64decode("eqpp8VOJBttTz0j5H+QGtOQ+r44="), 0), "symbol":
  • "data": bson.Binary(base64.b64decode("AQAAAAAAAAA="), 0), "compressed": False,
  • "sha": bson.Binary(base64.b64decode("Bf5AV1MWbxJVWefJrFWGVPEHx+k="), 0), "shape": [
  • "base_sha": bson.Binary(base64.b64decode("Bf5AV1MWbxJVWefJrFWGVPEHx+k="), 0), "type": "n
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: hotmail.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ 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 arctic-new 
Your task is to develop a time-series data management application using the 'arctic-new' Python package. This application will serve as a simplified version control system for financial time-series data, allowing users to store, retrieve, and manage multiple versions of their datasets efficiently. Here’s a detailed breakdown of what your application should achieve:

1. **Data Storage**: Implement functionality to store time-series data into the Arctic database. Each dataset should be stored under a unique library name, and each version of the dataset should be identifiable by a timestamp or a custom label.
2. **Version Control**: Users should be able to create new versions of their datasets by updating the existing data. Ensure that all previous versions remain accessible.
3. **Data Retrieval**: Provide a feature to retrieve specific versions of the dataset based on either timestamps or custom labels. Additionally, allow for the retrieval of the latest version of a dataset.
4. **Querying and Analysis**: Integrate basic querying capabilities to allow users to filter and analyze data within a specific time range or based on certain conditions.
5. **User Interface**: Develop a simple command-line interface (CLI) for interacting with the application. The CLI should support commands for adding new libraries, storing data, retrieving data, listing all versions of a dataset, and deleting libraries.
6. **Documentation**: Write comprehensive documentation detailing how to install and use the application, including examples of how to interact with the Arctic database through the CLI.

The 'arctic-new' package is crucial for this project as it provides the underlying storage mechanism and version control functionalities required for managing financial time-series data. By leveraging its capabilities, you'll be able to focus on building a user-friendly interface and additional features without worrying about the complexities of data storage and retrieval.

💬 Discussion Feed

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