authbinder

v1.0.1 suspicious
5.0
Medium Risk

AI agent assurance platform SDK. Captures telemetry from LangChain, CrewAI, AutoGen, AG2, LlamaIndex, Haystack, and Claude Agent SDK.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has low risks in terms of network, shell, obfuscation, and credential activities but lacks maintainer details and a GitHub repository, raising concerns about its provenance.

  • Metadata risk due to missing maintainer information and GitHub repository.
  • Otherwise low risk scores across other categories.
Per-check LLM notes
  • Network: The observed network calls are likely legitimate for reporting events and checking service health, typical for many applications.
  • Shell: No shell execution patterns detected, which is normal and expected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, suggesting the package is not engaging in unauthorized data collection.
  • Metadata: The package shows some red flags due to the lack of maintainer information and a GitHub repository, which could indicate a less reputable source.

📦 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

  • Brief PyPI description (663 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

  • 5 type-annotated function signatures (partial)
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • : return try: r = httpx.post( f"{_config.api_url}/events/batch",
  • API.""" try: r = httpx.get(f"{_config.api_url}/health", timeout=5.0) print("Con
✓ 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: getauthbinder.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ 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 authbinder 
Create a mini-application named 'AI Assurance Dashboard' using the Python package 'authbinder'. This application will serve as a centralized platform to monitor and manage AI agents from various sources including LangChain, CrewAI, AutoGen, AG2, LlamaIndex, Haystack, and Claude Agent SDK. The dashboard should provide real-time insights into the performance of these agents, ensuring they operate within predefined parameters and standards.

Key Features:
1. **Agent Registration**: Users should be able to register new AI agents from different platforms by providing necessary credentials and metadata.
2. **Performance Monitoring**: Implement real-time monitoring of agent performance metrics such as response time, accuracy, and usage statistics.
3. **Alert System**: Set up alerts for anomalies or deviations in agent behavior, such as unusually high error rates or unexpected downtimes.
4. **Compliance Check**: Ensure that all registered agents comply with specified guidelines and standards, with automatic checks and notifications for non-compliance.
5. **Dashboard Interface**: Develop a user-friendly web interface using Flask or Django to visualize the collected data and control settings.

How to Use 'authbinder':
- Utilize 'authbinder' to capture telemetry data from each AI agent. This includes initializing the SDK with appropriate configurations for each supported platform.
- Integrate the SDK into your application's backend to continuously gather and process telemetry information.
- Leverage the captured data to perform real-time analysis and generate reports for the frontend dashboard.
- Implement security measures using 'authbinder' functionalities to protect sensitive information and ensure data integrity.

💬 Discussion Feed

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