axor-sentinel

v0.1.0 suspicious
4.0
Medium Risk

Cross-session behavioral analysis for Axor via weighted resource reputation graph

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low individual risks but raises suspicion due to incomplete metadata and minimal maintainer activity.

  • Metadata risk is elevated with an incomplete author profile and minimal maintainer activity.
  • The novelty of the package introduces uncertainty regarding its legitimacy.
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package's functionality requires external communications.
  • Shell: No shell executions detected, indicating the package does not attempt to execute system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package is new with minimal maintainer activity and an incomplete author profile, raising concerns about its legitimacy.

📦 Package Quality Overall: Medium (5.8/10)

✦ High Test Suite 9.0

Test suite present — 11 test file(s) found

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

Some documentation present

  • Detailed PyPI description (3072 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

  • 110 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 9 commits in Bucha11/axor-sentinel
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ 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

No author email provided

✓ 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

  • 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 axor-sentinel 
Create a mini-application named 'BehaviorGuard' that leverages the 'axor-sentinel' package to perform real-time cross-session behavioral analysis on user interactions within a web application. This tool aims to identify anomalous behavior patterns that could indicate security threats such as account takeover attempts or insider threats.

Step 1: Set up a basic Flask web application where users can log in and interact with various resources (e.g., viewing profiles, managing settings).

Step 2: Integrate 'axor-sentinel' into your application to monitor and analyze user sessions. Configure the package to create a weighted resource reputation graph based on user interactions.

Step 3: Implement a feature that allows 'BehaviorGuard' to dynamically adjust the reputation of resources based on user behavior. For example, if a user frequently accesses a certain resource, its reputation score increases, indicating normal behavior; conversely, sudden changes in access patterns might decrease the reputation score, suggesting potential malicious activity.

Step 4: Develop an alert system that triggers notifications when the reputation score of a resource drops below a predefined threshold. These alerts should provide details about the suspicious activity, such as the user ID, timestamp, and type of resource accessed.

Suggested Features:
- A dashboard for administrators to view real-time session data and historical behavioral trends.
- An API endpoint for integrating 'BehaviorGuard' with other security tools or systems.
- Customizable thresholds for triggering alerts based on specific business requirements.
- Support for multiple authentication methods (e.g., username/password, OAuth) to ensure broad compatibility with different web applications.

How 'axor-sentinel' is Utilized:
- Use 'axor-sentinel' to analyze user interaction logs and generate a weighted graph representing the relationships between users and resources they access.
- Apply machine learning models provided by 'axor-sentinel' to predict normal behavior patterns and detect deviations from these norms.
- Leverage the package's ability to update reputation scores in real-time to enhance the accuracy of threat detection.

💬 Discussion Feed

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