🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits a high obfuscation risk through the use of pickling, which could potentially be exploited for malicious activities. Additionally, the package's metadata suggests it may not have proper maintenance or documentation, raising suspicion about its legitimacy.

High obfuscation risk due to pickling
Suspicious metadata

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires external services.
Shell: No shell execution detected, indicating no direct system command execution risk.
Obfuscation: The code uses pickling to decode binary data, which can be used for malicious purposes such as executing arbitrary code.
Credentials: No suspicious patterns indicating direct harvesting of credentials were found.
Metadata: The package is suspicious due to its newness and lack of maintainer information.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

imension """ return pickle.loads(binary) def gridfs_put_npArray(db, value, filepath, filen

✓ 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: googlemail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Sapientai/MGKDB appears legitimate

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Only one version has ever been released — brand new package
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 MGKDB-fusion

Create a web-based mini-application using Flask in Python that simulates and analyzes nuclear fusion experiments. This application will utilize the 'MGKDB-fusion' package to interact with a MongoDB database storing simulation data. The app should allow users to input parameters for their fusion experiment, such as temperature, pressure, and fuel type, and then run simulations based on these inputs. The results of these simulations will be stored in the MongoDB database via the 'MGKDB-fusion' package. Additionally, users should be able to visualize the simulation data through graphs and charts, and also query past simulation results from the database. The main functionalities include:

1. User Interface: A simple and intuitive interface where users can input experiment parameters.
2. Simulation Engine: Utilize 'MGKDB-fusion' to fetch simulation models from the database and run them with user-provided parameters.
3. Data Visualization: Display simulation outcomes graphically, showing trends and key performance indicators.
4. Database Interaction: Store simulation results in MongoDB using 'MGKDB-fusion', and provide options to retrieve and display historical data.
5. Reporting: Generate reports summarizing the simulation outcomes and providing insights into the experiment's feasibility.

Ensure that the application is modular and well-documented, making it easy for other developers to extend its functionality or integrate it into larger systems.