antarraksha-semantic-kernel

v0.1.3 suspicious
6.0
Medium Risk

Antarraksha AI Agent Enforcement SDK for Semantic Kernel

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to unverified network activities and a lack of associated repository. The absence of other risky behaviors like shell execution or credential harvesting slightly mitigates the risk.

  • Network risk: Potential for unauthorized network activities.
  • Metadata risk: Single package maintainer with no associated repository.
Per-check LLM notes
  • Network: The presence of network calls is not uncommon but may require further investigation to ensure legitimacy and purpose.
  • Shell: No shell execution patterns were detected, which is positive from a security perspective.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has only one package and the repository is not found, raising suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (3.2/10)

○ Low Test Suite 1.0

No test suite detected

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

Some documentation present

  • Detailed PyPI description (1824 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

  • 10 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • losed self._session = requests.Session() self._session.headers.update({ "Conten
✓ Code Obfuscation

No obfuscation patterns detected

✓ 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: antarraksha.ai

✓ 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 "Akash Kumar Dey" 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 antarraksha-semantic-kernel 
Create a fully-functional mini-application named 'SemanticGuard' that leverages the 'antarraksha-semantic-kernel' package to manage and enforce security policies on semantic data. SemanticGuard will serve as a tool for developers and data scientists to ensure that their datasets adhere to specific semantic rules and regulations, enhancing data integrity and compliance.

Step 1: Define the Core Functionality
- SemanticGuard should allow users to upload datasets in various formats (CSV, JSON, etc.).
- Users must be able to define semantic rules, which could include constraints like ensuring all entries in a certain column contain values from a predefined set, or enforcing that no two rows have identical values in specified columns.

Step 2: Implement Rule Enforcement
- Utilize the 'antarraksha-semantic-kernel' package to process uploaded datasets according to defined rules.
- The application should automatically check each dataset against these rules and highlight any violations.

Step 3: Provide Detailed Reporting
- Generate comprehensive reports detailing the adherence of the dataset to the rules, including any violations found.
- Reports should be exportable in PDF or CSV format for further analysis or record-keeping.

Suggested Features:
- Support for multiple rule types (e.g., value range checks, uniqueness checks).
- User-friendly interface for defining and managing rules.
- Integration with common data visualization libraries to display dataset anomalies visually.
- Option to automatically correct minor violations if possible.

How 'antarraksha-semantic-kernel' is Utilized:
- The package's core functionality revolves around parsing semantic data and applying enforcement actions based on predefined policies.
- Use the SDK to develop custom rule engines that can interpret user-defined rules and apply them to incoming data streams.
- Leverage the enforcement capabilities to validate data integrity and trigger alerts or corrective actions when necessary.

Your task is to design and implement SemanticGuard, ensuring it provides a robust solution for managing semantic data integrity and compliance.

💬 Discussion Feed

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