backend.ai-agent

v26.4.3 suspicious
5.0
Medium Risk

Backend.AI Agent

🤖 AI Analysis

Final verdict: SUSPICIOUS

While the package appears to have legitimate functionality, the retrieval of AWS credentials from environment variables poses a significant credential risk. This, combined with moderate obfuscation, warrants further investigation.

  • High credential risk due to AWS credentials handling
  • Moderate obfuscation patterns observed
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require external communications.
  • Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
  • Obfuscation: The obfuscation pattern observed is not strong evidence of malicious intent; it could be part of an abstract method implementation.
  • Credentials: The code snippet shows retrieval of AWS credentials from environment variables, which could indicate potential exposure if these secrets are mishandled or accessed improperly.
  • Metadata: The maintainer has only one package on PyPI, suggesting it may be a new or less active account.

📦 Package Quality Overall: Medium (5.4/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://docs.backend.ai/
  • Detailed PyPI description (26483 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

  • 369 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 9 unique contributor(s) across 100 commits in lablup/backend.ai
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • # agent_config is set by the pickle.loads() caller. self.clean_event = None @abstractmeth
✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 10.0

Found 5 credential access pattern(s)

  • 3 convention. s3_access_key = os.environ.get("AWS_ACCESS_KEY_ID", "dummy-access-key") s3_secret_key = os.envir
  • -access-key") s3_secret_key = os.environ.get("AWS_SECRET_ACCESS_KEY", "dummy-secret-key") s3_region = os.envir
  • ummy-secret-key") s3_region = os.environ.get("AWS_REGION", "ap-northeast-1") s3_bucket = os.environ.get("AWS_S
  • "ap-northeast-1") s3_bucket = os.environ.get("AWS_S3_BUCKET", "codeonweb") s3_bucket_path = os.environ.get("AW
  • "codeonweb") s3_bucket_path = os.environ.get("AWS_S3_BUCKET_PATH", "bucket") if s3_access_key == "dummy-acces
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository lablup/backend.ai appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Lablup Inc. and contributors" 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 backend.ai-agent 
Create a Python-based web application that leverages the Backend.AI Agent package to manage computational tasks in a distributed environment. This application will serve as a user-friendly interface for submitting complex computational jobs, monitoring their progress, and retrieving results. It will be particularly useful for developers and researchers who need to perform resource-intensive tasks without the hassle of setting up and managing their own computing infrastructure.

### Key Features:
1. **User Authentication:** Implement a simple authentication system using Flask-Security to ensure only authorized users can submit and monitor jobs.
2. **Job Submission Form:** Design a form where users can input job parameters such as the type of task (e.g., image processing, data analysis), required resources (CPU/GPU), and any specific configurations needed for the task execution.
3. **Job Queue Management:** Utilize the Backend.AI Agent to queue and manage these jobs efficiently. The agent should handle the distribution of jobs across available nodes based on resource availability and job requirements.
4. **Progress Monitoring:** Provide real-time updates on the status of each job, including start time, estimated completion time, and current progress percentage.
5. **Result Retrieval:** Once a job is completed, allow users to download the results directly from the web interface.
6. **Error Handling and Notifications:** Implement mechanisms to notify users via email if there are errors during job execution or if a job exceeds its allocated resources.
7. **Dashboard Interface:** Develop a dashboard that summarizes all active and completed jobs for each user, including options to filter and sort jobs by various criteria.

### How to Use the 'backend.ai-agent' Package:
- **Initialization:** Start by initializing the Backend.AI Agent within your Flask application to connect it to your Backend.AI server.
- **Task Submission:** Use the agent's API to submit tasks to the server, specifying the necessary details like task type, resource requirements, and input data.
- **Monitoring and Control:** Leverage the agent's capabilities to monitor task progress, cancel ongoing tasks, and retrieve results once they're ready.
- **Integration with Web Interface:** Ensure seamless integration between the frontend (web forms and dashboards) and backend (task management) by passing relevant information through Flask routes and templates.

### Additional Considerations:
- Ensure the application is scalable and can handle multiple concurrent users and jobs efficiently.
- Pay attention to security practices, especially when dealing with sensitive data and user credentials.
- Document your code thoroughly and include instructions on how to set up and run the application locally.

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