ai-orchestrator-client

v0.1.1 suspicious
6.0
Medium Risk

Python client for the AI Orchestrator (https://github.com/ernesto01louis/ai-orchestrator).

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to potential credential harvesting and lack of proper authentication headers in network calls.

  • Potential credential harvesting risk
  • Network calls without authentication headers
Per-check LLM notes
  • Network: Network calls with configurable headers but without authentication headers might be normal for client-server interactions, but should be reviewed to ensure proper handling of sensitive information.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected.
  • Credentials: Potential credential harvesting risk due to suspicious path traversal attempts.

📦 Package Quality Overall: Medium (5.8/10)

✦ High Test Suite 9.0

Test suite present — 11 test file(s) found

  • Test runner config found: pyproject.toml
  • 11 test file(s) detected (e.g. test_async_client.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (6849 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 198 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 30 commits in ernesto01louis/ai-orchestrator-client
  • Two distinct contributors found

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

  • """Default-headers dict for ``httpx.Client(headers=...)``. Auth is intentionally NOT included — th
  • h = auth self._http = httpx.AsyncClient( base_url=self._base_url, timeout=ti
  • h = auth self._http = httpx.Client( base_url=self._base_url, timeout=ti
✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 5.0

Found 2 credential access pattern(s)

  • ("My Project!", "foo/bar", "../etc/passwd", "") def validate_project_name(name: str) -> str: ""
  • slash (path separator) "../etc/passwd", # explicit traversal "..",
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: aol.com>

⚠ Suspicious Page Links score 4.0

Found 2 suspicious link(s) on the package page

  • Non-HTTPS external link: http://orchestrator.lan:8000
  • Non-HTTPS external link: http://rfdf.lan:8000
⚠ 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 ai-orchestrator-client 
Create a Python-based mini-application called 'AI Workflow Manager' that leverages the 'ai-orchestrator-client' package to manage and execute complex AI workflows. This application should enable users to define, submit, monitor, and manage AI tasks and pipelines through a simple command-line interface.

Step-by-step requirements:
1. Define a workflow schema where users can specify different tasks (e.g., data preprocessing, model training, evaluation) and their dependencies.
2. Implement a feature to submit these workflows to an AI Orchestrator server using the 'ai-orchestrator-client'.
3. Develop functionality to monitor the status of submitted workflows and individual tasks within them.
4. Include options for users to cancel ongoing workflows or tasks if needed.
5. Provide real-time logging and progress updates for each task in the workflow.
6. Allow users to retrieve the results of completed workflows and individual tasks.

Suggested features:
- Support for defining multiple workflows in a single configuration file.
- Option to schedule workflows to run at specific times or intervals.
- Integration with popular cloud storage services for storing and retrieving workflow configurations and results.
- Ability to trigger workflows based on external events or conditions.
- Detailed error reporting and retry mechanisms for failed tasks.

The 'ai-orchestrator-client' package will be utilized primarily for submitting workflows, monitoring their status, and retrieving results. Users will interact with these functionalities through the command-line interface you develop.