🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits several behaviors that raise concerns about potential hidden functionality or backdoors, despite not having clear evidence of malicious intent.

Network and shell risks are moderately high
Code obfuscation techniques are employed
Suspicious patterns related to credentials

Per-check LLM notes

Network: The package makes network requests which could be used for checking versions or health, but without clear documentation, it raises suspicion.
Shell: Executing shell commands, including system control and upgrade operations, can be legitimate but also indicative of hidden functionality or backdoor capabilities.
Obfuscation: The code snippet uses Base64 decoding which could be used for legitimate purposes but also might indicate an attempt to hide information.
Credentials: No clear evidence of credential harvesting, but the presence of 'Basic ' and colon-separated strings raises some suspicion.
Metadata: Low risk but lacks essential author details and PyPI classifiers indicating low effort or possibly new/inactive maintainer.

📦 Package Quality Overall: Medium (5.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

Test runner config found: pyproject.toml

◈ Medium Documentation 7.0

Some documentation present

Documentation URL: "Documentation" -> https://github.com/airutorg/airut/tree/main/doc
Detailed PyPI description (5865 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

508 type-annotated function signatures detected in source

◈ Medium Multiple Contributors 5.0

Limited contributor diversity

1 unique contributor(s) across 100 commits in airutorg/airut
Single author but highly active (100 commits)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

rsion" try: req = urllib.request.Request(url, method="GET") with urllib.request.urlop
l, method="GET") with urllib.request.urlopen(req, timeout=3) as resp: data: dict[str,
ealth" try: req = urllib.request.Request(url, method="GET") with urllib.request.urlop
l, method="GET") with urllib.request.urlopen(req, timeout=3) as resp: data: JsonDict
a thin wrapper around :func:`urllib.request.urlopen` that adds exponential-backoff retry for transient f
try: return urllib.request.urlopen(url, timeout=timeout) except urllib.error.HT

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

ip "Basic " decoded = base64.b64decode(encoded).decode("utf-8") if ":" not in decoded:

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

d" try: result = subprocess.run( version_cmd, capture_output=True,
""" try: result = subprocess.run( ["systemctl", "--user", "is-active", "airut.ser
}") try: result = subprocess.run( ["uv", "tool", "upgrade", "airut"],
try: check_result = subprocess.run( [airut_path, "check"], capture_outp
try: result = subprocess.run( [airut_path, "install-service"],
, exist_ok=True) subprocess.run( [ "git",

✓ 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

Repository airutorg/airut appears legitimate

⚠ 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 airut

Create a mini-application named 'ClaudeConnect' that leverages the 'airut' package to facilitate secure and sandboxed interactions between users and Claude AI over email and Slack. The application should allow users to send prompts to Claude via email or Slack, receive responses back, and manage these interactions within a sandboxed environment to ensure data privacy and security.

Steps to Build the Application:
1. Set up the project environment by installing the necessary packages including 'airut', 'slacker' (for Slack API), and 'smtplib' (for email).
2. Design the user interface where users can input their email address or Slack channel ID to start a session with Claude.
3. Implement the logic to send user inputs to Claude using the 'airut' package's capabilities for email and Slack integration.
4. Ensure that all communications with Claude are processed within a sandboxed environment provided by 'airut' to prevent any unauthorized access or data leaks.
5. Develop a feature that logs all interactions for auditing purposes, storing them securely.
6. Add error handling mechanisms to manage any issues that arise during communication with Claude or while processing user inputs.
7. Test the application thoroughly by simulating various user interactions and ensuring that all functionalities work as expected.
8. Deploy the application on a cloud platform like AWS or Heroku for easy accessibility.

Suggested Features:
- User Authentication: Allow users to sign in before starting a session with Claude to ensure only authorized individuals can interact with the AI.
- Customizable Responses: Provide options for users to customize the type of response they expect from Claude based on specific needs or preferences.
- Interaction History: Maintain a history of past interactions for each user to review previous conversations with Claude.
- Notification System: Send notifications to users via email or Slack when Claude has responded to their query.

How 'airut' Package is Utilized:
- For Email Integration: Use 'airut' to securely process and respond to emails containing prompts for Claude, ensuring that all interactions are handled within a sandboxed environment.
- For Slack Integration: Leverage 'airut' to enable safe and secure interactions with Claude through Slack channels, again making sure that all exchanges occur within a protected context.
- For Sandbox Environment: Rely on 'airut' to create and maintain a sandboxed environment where Claude processes user inputs, safeguarding against potential security risks.