🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to potential obfuscation techniques and low maintainer activity, which raises suspicion about its legitimacy.

Moderate obfuscation risk through base64 decoding
Low maintainer activity and poor metadata quality

Per-check LLM notes

Network: The network call to check the latest version seems benign and is a common practice, but it could be used to download additional malicious code.
Shell: No shell execution patterns detected.
Obfuscation: The use of base64 decoding might indicate an attempt to obfuscate data, but it could also be a legitimate need for data encoding/decoding.
Credentials: No clear evidence of credential harvesting detected.
Metadata: The package shows signs of low maintainer activity and poor metadata quality, raising concerns about its legitimacy.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

son" latest_version = requests.get(pypi_url, timeout=3).json()["info"]["version"] if c

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

auth_plain = base64.b64decode(auth_base64).decode("utf-8") auth_details =

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

⚠ Registered Email Domain score 3.0

Suspicious email domain flags: Very short email domain: em.ag>

Very short email domain: em.ag>

✓ 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 6.0

3 maintainer concern(s) found

Author name is missing or very short
Author "" appears to have only 1 package on PyPI (new or inactive account)
Package has no PyPI classifiers (low effort / metadata quality)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with aas-standard-parser

Create a Python-based mini-application named 'SubmodelAnalyzer' which leverages the 'aas-standard-parser' package to parse and analyze standard submodels from Asset Administration Shell (AAS) specifications. This application will serve as a powerful tool for developers and engineers working with AAS standards, enabling them to quickly understand and manipulate complex submodel structures.

**Step-by-Step Requirements:**
1. **Setup Environment**: Start by setting up a virtual environment and installing necessary packages including 'aas-standard-parser'. Ensure all dependencies are listed in a requirements.txt file.
2. **Core Functionality**: Implement core functionalities using 'aas-standard-parser' to read and parse standard submodel files (e.g., JSON). The application should support multiple file formats if possible.
3. **Analysis Tools**: Develop analysis tools within the application to extract key information from parsed submodels such as identifying specific elements, relationships between elements, and data types used.
4. **Visualization**: Integrate a simple visualization component to graphically represent the structure of the parsed submodels. This could be done using libraries like matplotlib or networkx.
5. **CLI Interface**: Provide a command-line interface (CLI) where users can interact with the application. Commands should include options for parsing files, analyzing data, and visualizing results.
6. **Documentation & Testing**: Write comprehensive documentation for the application, explaining its usage, features, and how it integrates 'aas-standard-parser'. Also, ensure thorough testing of all functionalities.

**Suggested Features**:
- Support for various input formats (JSON, XML).
- Advanced search capabilities within parsed submodels.
- Exporting analysis results into different formats (CSV, Excel).
- Integration with other AAS tools or services.
- User-friendly CLI with help and guidance options.