autoinference-utils

v0.2.1 suspicious
6.0
Medium Risk

Shared endpoint abstractions for autoinference deployments

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to its use of urllib with unspecified URLs and subprocess.Popen for executing arbitrary commands, which may indicate potential for abuse.

  • High shell risk due to execution of arbitrary commands
  • Medium network risk due to long timeouts and unspecified URLs
Per-check LLM notes
  • Network: The use of urllib for making network requests could be legitimate, but the long timeout and unspecified URLs raise concerns about potential data exfiltration.
  • Shell: Executing arbitrary commands via subprocess.Popen can indicate high risk, suggesting possible backdoor capabilities.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows low maintainer activity and poor metadata quality, raising some suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Low (2.8/10)

○ 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 (314 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 5.0

Partial type annotation coverage

  • 22 type-annotated function signatures detected in source
○ 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 9.0

Found 6 network call pattern(s)

  • ion") } req = urllib.request.Request( url, data=body, method=method, headers=
  • try: with urllib.request.urlopen(req, timeout=3600) as resp: self.sen
  • out: float) -> int: req = urllib.request.Request(url, method="GET") with urllib.request.urlopen(r
  • t(url, method="GET") with urllib.request.urlopen(req, timeout=timeout) as resp: return resp.g
  • ad).encode("utf-8") req = urllib.request.Request( url, data=body, headers=dict(headers), meth
  • method="POST" ) with urllib.request.urlopen(req, timeout=timeout) as resp: return resp.g
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

  • (cmd)}") self._proc = subprocess.Popen(cmd) wait_ready( self._proc,
  • (cmd)}") self._proc = subprocess.Popen(cmd) _wait_ready_url( f"http://localhost
  • (cmd)}", flush=True) rc = subprocess.call(cmd, env=env) report_path = os.path.join(output_dir, "r
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with autoinference-utils 
Create a mini-application called 'AutoInferenceDashboard' that leverages the 'autoinference-utils' package to manage and monitor multiple machine learning models deployed through autoinference endpoints. This application should allow users to interactively query these models via a simple command-line interface (CLI). The dashboard should provide real-time insights into model performance, including accuracy metrics and response times, and should support adding, removing, and updating models dynamically.

Key Features:
- A CLI interface for querying models with user inputs.
- Real-time performance monitoring of each model.
- Dynamic management capabilities (add, remove, update models).
- Utilize 'autoinference-utils' for abstracting away the complexities of connecting to and managing different deployment endpoints.

Steps to Implement:
1. Set up a Python environment and install necessary packages, including 'autoinference-utils'.
2. Design and implement the CLI interface using a library like Click.
3. Integrate 'autoinference-utils' to establish connections with deployed models and handle requests.
4. Develop functions to add, remove, and update models within the application.
5. Implement performance tracking for each model, utilizing 'autoinference-utils' for data retrieval.
6. Test the application thoroughly with various inputs and scenarios.
7. Document the setup process, usage instructions, and any limitations of the application.

💬 Discussion Feed

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