admina-framework

v0.9.3 suspicious
7.0
High Risk

Admina — governed AI development framework

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows significant signs of potential malicious activity, particularly concerning credential harvesting and obfuscation techniques, which could be used to hide malicious functionality.

  • High credential risk indicating potential credential harvesting
  • Use of obfuscation techniques like base64 decoding and dynamic imports
Per-check LLM notes
  • Network: The network call appears to be part of HTTP request handling, which is not inherently suspicious but should be reviewed in context.
  • Shell: Use of subprocess for shell commands like docker-compose indicates the package may manage Docker containers, but this could also be exploited for unauthorized access or command execution.
  • Obfuscation: The use of base64 decoding and dynamic imports may indicate an attempt to obfuscate code execution, but could also be part of normal operations like loading plugins.
  • Credentials: The pattern for searching known credential file paths is highly indicative of potential credential harvesting activities.
  • Metadata: The maintainer's author name is missing or very short, and they appear to have only one package on PyPI, which may indicate a new or inactive account.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • oat]] = [] async with httpx.AsyncClient(timeout=60.0) as client: for text in texts:
⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

  • try: raw = base64.b64decode(token + "=" * (-len(token) % 4), validate=True)
  • ")]: try: __import__(mod_name) click.echo(f" {pkg_name:20s} {ok_mark}")
  • try: __import__(mod_name) present += 1 except ImportError
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • images...") result = subprocess.run( ["docker", "compose", "pull"], cwd=
  • p with Ctrl+C\n") proc = subprocess.Popen(cmd, cwd=str(project_dir), env=env) # Wait for /health
  • ose_cmd + ["-d"] result = subprocess.run( detach_cmd, cwd=str(project_dir), e
  • \n") try: subprocess.run(["docker", "compose", "logs", "-f"], cwd=str(project_dir))
  • urn the result.""" return subprocess.run( [sys.executable, "-m", "pip", "install", package],
  • try: ps = subprocess.run( ["docker", "compose", "ps", "--format", "{{
⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • ot/|" r"~?/\.ssh/|~?/\.aws/credentials|~?/\.netrc|~?/\.docker/config|" r"~?/\.kube/config|
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: admina.org>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository admina-org/admina appears legitimate

⚠ 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 admina-framework 
Your task is to develop a fully-functional mini-application called 'AI Governance Dashboard' using the 'admina-framework' package. This application will serve as a tool for managing and monitoring AI models within an organization, ensuring they adhere to governance policies and best practices. The goal is to create a user-friendly interface where non-technical stakeholders can easily understand the performance and compliance status of various AI models.

Step-by-step Instructions:
1. Set up your development environment with Python and install the 'admina-framework'.
2. Design the architecture of the AI Governance Dashboard, considering the key components such as model registration, performance tracking, compliance checks, and user management.
3. Implement the core functionalities using 'admina-framework', focusing on integrating its governance capabilities into your application.
4. Develop a web-based frontend using a framework like Flask or Django, allowing users to interact with the dashboard.
5. Ensure that the application supports adding new AI models, viewing their performance metrics, and conducting compliance audits.
6. Include a feature that generates reports summarizing the overall health and compliance status of all registered AI models.
7. Test the application thoroughly to ensure all features work as expected and are user-friendly.
8. Document your code and provide instructions for deploying the AI Governance Dashboard.

Suggested Features:
- User authentication and role-based access control to restrict access based on user roles.
- A searchable database of registered AI models with details such as name, purpose, owner, and last updated date.
- Real-time performance monitoring, displaying metrics like accuracy, recall, precision, and F1 score.
- Compliance checkers that verify if the AI models meet predefined governance standards.
- An audit log that records any changes made to the models or compliance statuses.
- Customizable alerts for when certain performance thresholds are breached or compliance issues arise.
- Exportable reports that can be shared with stakeholders for transparency and accountability.

How 'admina-framework' is Utilized:
- Use 'admina-framework' to establish and enforce governance policies throughout the lifecycle of each AI model.
- Leverage the framework's built-in tools for monitoring model performance and conducting compliance checks.
- Integrate 'admina-framework' APIs to automate the collection and analysis of data related to model performance and compliance.
- Utilize the framework's security features to protect sensitive information and ensure data privacy.
- Apply 'admina-framework' guidelines for designing the user interface and user experience, making the dashboard intuitive and accessible.