augur-sdk

v0.6.1 suspicious
4.0
Medium Risk

Augur SDK — instrument any screenshot-grounded computer-use agent (CUA). Sentry-style DSN streaming + local bundle writer.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low risks for network calls, shell execution, obfuscation, and credential harvesting. However, its metadata suggests it may be new or inactive, raising concerns about its maintenance and security over time.

  • Metadata risk due to lack of maintainer history and low repository engagement
  • Potential supply-chain risk due to unverified package origin
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package is expected to communicate with external services.
  • Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows signs of being new or inactive with no maintainer history and low repository engagement.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 35 test file(s) found

  • Test runner config found: pyproject.toml
  • 35 test file(s) detected (e.g. test_append_log.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://mercurialsolo.github.io/augur-sdk/
  • Detailed PyPI description (10086 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • Type checker (mypy / pyright / pytype) referenced in project
  • 459 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 58 commits in mercurialsolo/augur-sdk
  • Two distinct contributors found

🔬 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

Email domain looks legitimate: gmail.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 augur-sdk 
Develop a user-friendly desktop application named 'ScreenshotGuard' using Python's 'augur-sdk'. This application aims to monitor and log screenshots taken on a user's device, providing insights into usage patterns and potentially identifying sensitive information leaks through screenshots.

### Application Functionality:
- **Monitoring & Logging**: Continuously monitor the system for new screenshots and log them locally with timestamps.
- **Anonymization**: Automatically blur or mask sensitive areas detected in the screenshots (e.g., faces, credit card numbers).
- **Alert System**: Notify the user via email or system notifications if certain sensitive patterns are detected.
- **Analytics Dashboard**: Provide a simple dashboard within the app to visualize the frequency of screenshots over time and highlight any potential security issues.

### Implementation Steps:
1. **Setup Environment**: Install necessary packages including 'augur-sdk', 'Pillow' for image processing, and 'Flask' for the web-based dashboard.
2. **Integration with Augur SDK**: Use 'augur-sdk' to stream screenshot data. Implement a listener that captures screenshots as they are taken and processes them through your custom logic.
3. **Sensitive Data Detection**: Utilize machine learning models or pre-defined rules to detect sensitive data such as faces, credit card numbers, etc. in the captured images.
4. **Notification Mechanism**: Set up a mechanism to alert users via email or system notifications when sensitive data is detected.
5. **Dashboard Development**: Develop a basic Flask web application that serves as a dashboard. It should display logs of screenshots taken, anonymized versions of those screenshots, and analytics on screenshot frequency.
6. **Testing & Deployment**: Thoroughly test the application for accuracy in detection, performance, and usability. Consider deploying it as a standalone executable or as a service that runs in the background.

### Expected Outcome:
By the end of this project, you will have developed a comprehensive tool that helps users protect their privacy and security by monitoring and managing their screenshot activities effectively.

💬 Discussion Feed

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