🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits significant obfuscation and credential risks, with no clear maintainer information, suggesting potential malicious intent.

High obfuscation risk due to eval() usage
High credential risk through keyring interaction

Per-check LLM notes

Network: The use of HTTP methods suggests the package is designed to interact with web services, which is common but should be reviewed for the legitimacy of the URLs and data being exchanged.
Shell: No shell execution patterns detected, indicating low risk for direct system command execution.
Obfuscation: The usage of eval() with potentially untrusted inputs suggests an attempt at code obfuscation or execution of arbitrary code.
Credentials: Fetching API keys or secrets using keyring indicates potential harvesting of credentials, especially if not used within a secure context.
Metadata: The repository is not found and the maintainer's information is incomplete, raising concerns about the package's legitimacy.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

try: with httpx.Client() as client: response = client.get(url, head
try: with httpx.Client() as client: response = client.post(url, hea
try: with httpx.Client() as client: response = client.patch(url, he
try: with httpx.Client() as client: response = client.delete(url, h

⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

>>> score, explanation = eval(prediction, target) >>> print(score.data) 1
>>> score, explanation = eval(prediction, target) >>> print(score.data) 0
>>> score, explanation = eval( ... fwd_model_client, ... messages,
return self def eval(self: T) -> T: """Set the module in evaluation mode.
ne: agent.eval() with hf.no_grad():
try: agent.eval() with hf.no_grad(): for val_idx

✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 5.0

Found 2 credential access pattern(s)

tored_api_key = ( keyring.get_password(service_name, username) if username else None )
() self.api_key = keyring.get_password(self.service_name, username) if self.api_key:

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: tellurio.ai>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ 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 afnio

Create a real-time data monitoring and visualization tool using the 'afnio' Python package and Tellurio Studio CLI. This tool will allow users to monitor various data streams in real-time and visualize them through interactive graphs and charts. The application should have the following features:

1. **Data Stream Integration**: Integrate with multiple data sources such as sensors, APIs, or local files to collect real-time data.
2. **Real-Time Data Processing**: Use 'afnio' to process incoming data streams in real-time, filtering out unnecessary data and focusing on key metrics.
3. **Interactive Visualization**: Implement a user-friendly interface where users can interactively view and analyze the processed data through dynamic graphs and charts.
4. **Customizable Dashboards**: Allow users to customize their dashboards according to their needs, adding or removing data streams and adjusting visualization settings.
5. **Alert System**: Set up an alert system that triggers notifications based on predefined thresholds or anomalies detected in the data streams.
6. **Data Export**: Provide functionality to export visualized data into common file formats like CSV or Excel for further analysis.

The 'afnio' package is utilized primarily for its capabilities in handling asynchronous file operations and network I/O, which are crucial for real-time data processing. Additionally, leverage Tellurio Studio CLI for building and deploying the application efficiently. The project should demonstrate proficiency in integrating external data sources, real-time data processing, and creating engaging user interfaces for data visualization.