🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows legitimate network activity but lacks a GitHub repository and has incomplete maintainer information, which raises concerns about its origin and maintenance.

Network risk present but not severe
Incomplete maintainer information
No associated GitHub repository

Per-check LLM notes

Network: The network call patterns indicate legitimate HTTP/HTTPS communication, but further investigation is needed to confirm the URLs and purposes of these calls.
Shell: No shell execution patterns detected.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The package has no associated GitHub repository and the maintainer information is incomplete, raising some suspicion but not conclusive evidence of malice.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

, url) response = requests.post(url, json=payload, headers=headers, timeout=timeout)
} response = requests.post(url, json=data) _raise_for_status(response) de
.arrow.stream"} with requests.get(url, params=params, headers=headers, stream=True, timeout=ti
body into memory with requests.get(url, params=params, headers=headers, stream=True, timeout=ti
ries_info" response = requests.post(url, json={"uris": uris}) _raise_for_status(response
} response = requests.get(url, params=data) _raise_for_status(response)

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: mines.edu>

✓ 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 acquirium

Develop a fully-functional mini-application named 'WaterGuard' using the Python package 'acquirium'. WaterGuard is designed to manage and analyze data from various water treatment systems, providing insights into system performance, maintenance needs, and operational efficiency.

Step 1: Set Up the Environment
- Install Python and necessary libraries including 'acquirium'.
- Ensure you have access to at least one sample dataset representing water treatment system data.

Step 2: Data Ingestion
- Use 'acquirium' to ingest data from CSV files, APIs, or databases into your application.
- Validate the data as it comes in to ensure accuracy and completeness.

Step 3: Metadata Management
- Utilize 'acquirium' to manage metadata related to each data entry, such as timestamps, locations, and equipment IDs.
- Implement functionality to update metadata based on new information or changes in the system.

Step 4: Data Analysis
- Integrate analysis tools within WaterGuard to process and interpret the ingested data.
- Use 'acquirium' features to perform trend analysis, identify patterns, and detect anomalies in the data.

Step 5: Reporting and Visualization
- Create reports summarizing key findings from the data analysis.
- Use visualization tools to present data in a user-friendly format, highlighting critical metrics like water quality levels, system efficiency, and maintenance alerts.

Suggested Features:
- Real-time data monitoring and alerting for critical conditions.
- Historical data comparison to track system performance over time.
- Predictive maintenance recommendations based on historical data analysis.
- User-friendly dashboard for easy navigation and data exploration.

How 'acquirium' is Utilized:
- For managing complex metadata associated with large datasets, ensuring that all relevant information is captured and accessible.
- For efficient data ingestion from multiple sources, supporting both batch and real-time data processing.
- For powerful data analysis capabilities, allowing deep dives into specific aspects of water treatment system performance.
- For seamless integration with existing systems through its flexible API.