🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows potential risks due to high credential and network exposure, suggesting it may communicate with external services without proper validation and handles secrets insecurely.

High credential risk due to direct extraction of secrets from environment variables.
Potential unauthorized data transfer via network calls.

Per-check LLM notes

Network: The network calls suggest communication with external services like Slack and an API endpoint, which could be legitimate but also indicative of unauthorized data transfer.
Shell: Subprocess calls to 'gh', 'op', 'poetry' commands seem related to GitHub authentication, password management, and dependency management, which are usually benign but could potentially be exploited for unauthorized actions.
Obfuscation: No signs of obfuscation techniques were detected.
Credentials: The code is extracting secrets directly from environment variables without additional validation, which could pose a risk if these secrets are not properly managed and secured.
Metadata: The author information is sparse, and the maintainer seems to be new or inactive, raising some suspicion.

📦 Package Quality Overall: Medium (5.2/10)

✦ High Test Suite 9.0

Test suite present — 2 test file(s) found

Test runner config found: conftest.py
2 test file(s) detected (e.g. test_package.py)

◈ Medium Documentation 7.0

Some documentation present

2 documentation file(s) (e.g. generate.py)
Detailed PyPI description (3608 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

306 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)

try: response = requests.post( url, json={"message": message},
ts}) try: resp = requests.post( response_url, json={
] try: resp = requests.post( "https://slack.com/api/chat.postMessage",
try: response = requests.post( f"{DEVIN_API_BASE}/sessions", json=
_id}/messages" response = requests.post( url, headers={ "Authorization":
cker_password} response = requests.post(auth_url, json=auth_data) if response.status_code != 20

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 5 shell execution pattern(s)

one try: result = subprocess.run( [gh_path, "auth", "token"], capture
timeout. """ result = subprocess.run( [ "op", "item",
.read_text() try: subprocess.run( ["poetry", "update", package, "--lock"],
""" try: proc = subprocess.run( ["poetry", "show", "--outdated", "--top-level",
, ) try: subprocess.run( ["poetry", "update", "--lock"], cwd

⚠ Credential Harvesting score 10.0

Found 6 credential access pattern(s)

environment.""" secret = os.environ.get("SLACK_SIGNING_SECRET") if not secret: raise ValueError
onfigured. """ return os.environ.get("SLACK_BOT_TOKEN") or os.environ.get("SLACK_BOT_TOKEN_HITL") def
ron.get("SLACK_BOT_TOKEN") or os.environ.get("SLACK_BOT_TOKEN_HITL") def _build_slack_message_url( channel
rse_args() slack_token = os.environ.get("SLACK_BOT_TOKEN_HITL") if not slack_token: print(
el = args.channel_override or os.environ.get( "SLACK_CHANNEL_HITL", "human-in-the-loop" ) roster = _load
is found. """ token = os.environ.get("SLACK_BOT_TOKEN") or os.environ.get("SLACK_BOT_TOKEN_HITL") if

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: airbyte.io>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

No GitHub repository link found

⚠ 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 airbyte-internal-ops

Create a Python-based administrative tool named 'AirAdmin' that leverages the 'airbyte-internal-ops' package to manage Airbyte agent operations efficiently. This tool will serve as a command-line interface (CLI) application that allows users to perform various administrative tasks such as starting, stopping, and monitoring Airbyte agents. Here’s a detailed breakdown of the steps and features required for this project:

1. **Setup**: Begin by installing the necessary dependencies including 'airbyte-internal-ops'. Ensure your environment is set up to handle Python packages effectively.
2. **Authentication**: Implement a secure method for authenticating users to ensure only authorized personnel can execute administrative commands.
3. **Command-Line Interface (CLI)**: Develop a user-friendly CLI that accepts commands such as 'start', 'stop', 'status', and 'logs' for managing Airbyte agents. Each command should interact with the 'airbyte-internal-ops' package to perform the corresponding action.
4. **Agent Management**: Utilize the 'airbyte-internal-ops' package to manage the lifecycle of Airbyte agents. This includes starting new instances, stopping running ones, and retrieving their current status.
5. **Monitoring and Logging**: Integrate real-time monitoring capabilities to display the status of Airbyte agents. Additionally, implement logging functionality to capture and display logs from the agents for troubleshooting purposes.
6. **Configuration Management**: Allow users to configure settings for Airbyte agents directly through the CLI. These configurations could include setting up network parameters, specifying log levels, and more.
7. **Documentation**: Provide comprehensive documentation detailing how to install and use 'AirAdmin', along with examples of common use cases.
8. **Testing**: Write unit tests to ensure each feature of 'AirAdmin' functions as expected. Pay special attention to error handling and edge cases.
9. **Deployment**: Prepare 'AirAdmin' for deployment by packaging it into a distributable format like a Docker image or a standalone executable.

The goal is to create a robust, user-friendly tool that simplifies the management of Airbyte agents, making it easier for administrators to maintain and monitor their data integration pipelines.