agentguard-observe

v0.1.1 suspicious
4.0
Medium Risk

MrProbe / Agent Guard customer observation SDK — ship your agent's response back to MrProbe in 6 lines.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low risk for obfuscation and credential harvesting but the metadata presents some concerns due to missing repository and short author name.

  • Low obfuscation risk
  • Low credential risk
  • Repository not found
  • Short or missing maintainer's author name
Per-check LLM notes
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The repository is not found, and the maintainer's author name is missing or very short, indicating potential risks.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

  • sport defaults.""" return httpx.Client( timeout=timeout_s, verify=_default_ssl_cont
  • sport defaults.""" return httpx.AsyncClient( timeout=timeout_s, verify=_default_ssl_cont
  • ok_body()) ) shared = httpx.Client() try: with _client(http_client=shared) as c:
✓ 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: sniffr.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 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 agentguard-observe 
Create a real-time monitoring tool for a web application using the 'agentguard-observe' package. This tool will allow developers to monitor various aspects of their application such as user interactions, system performance metrics, and error logs. The goal is to provide immediate feedback on the application's health and performance to help identify and address issues promptly.

Step-by-step guide:
1. Set up a basic Flask or Django web application as the main framework.
2. Integrate the 'agentguard-observe' package into the web application to enable real-time data shipping back to MrProbe.
3. Implement a feature to capture and send user interaction data (e.g., page views, clicks).
4. Develop a functionality to track system performance metrics like CPU usage, memory usage, and network latency.
5. Add logging capabilities to report any errors or exceptions that occur within the application.
6. Use MrProbe to visualize and analyze the collected data, providing actionable insights.

Suggested Features:
- Customizable dashboard for visualizing different types of data.
- Real-time alerts for critical issues.
- Historical data analysis for trend identification.
- Integration with popular third-party services for extended functionalities.

How 'agentguard-observe' is utilized:
- Utilize the package to efficiently ship data from the application to MrProbe with minimal code changes. Follow the documentation to ensure seamless integration and optimal performance.