🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits a high level of obfuscation which could be used to conceal malicious activities, despite showing no immediate signs of harmful behavior like shell execution or credential theft. The metadata suggests low maintenance, raising concerns about its security and reliability.

High obfuscation risk
Low maintainer activity

Per-check LLM notes

Network: The package makes HTTP requests to fetch the next set of responses, which is common for pagination or fetching sequential data from an API.
Shell: No shell execution patterns were detected, indicating no direct system command execution risk.
Obfuscation: The obfuscation pattern appears suspicious as it uses encoded and potentially manipulated strings which could hide malicious code.
Credentials: No clear patterns of credential harvesting were detected in the provided snippet.
Metadata: The package shows signs of low maintainer activity and poor metadata quality, but there's no evidence of typosquatting or malicious intent.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

e["next"]: response = httpx.get(response["next"]).json() callback(response) def di
self.client: httpx.Client = httpx.Client( base_url=self.api_root(settings), headers=heade

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

8g/9k=" teeny_jpg_bytes = base64.b64decode(teeny_jpg) facility = EsoFacility() folder = facilit

✓ 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: lco.global>

✓ 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 aeonlib

Develop a Python-based mini-application that leverages the 'aeonlib' package to facilitate Time-Domain Astronomy (TDA) and Multi-Messenger Astronomy (MMA) observations. This application will serve as a user-friendly tool for astronomers and researchers to process observational data from various sources, including telescopes and satellites, to identify transient events such as supernovae, gamma-ray bursts, and other cosmic phenomena.

**Application Requirements:**
1. **Data Importation**: Allow users to import observational data from different formats (CSV, FITS, etc.) and sources. Utilize 'aeonlib' to handle data normalization and standardization across different datasets.
2. **Real-Time Data Streaming**: Integrate real-time data streaming capabilities using 'aeonlib' to receive live feeds from telescopes or satellite data streams. Implement a feature to visualize incoming data in real-time.
3. **Event Detection**: Use 'aeonlib' algorithms to detect significant changes or anomalies in the observational data that could indicate a transient event. Provide customizable thresholds for event detection based on user preferences.
4. **Data Analysis & Visualization**: Offer advanced analysis tools within the application to analyze detected events. Utilize 'aeonlib' for complex data processing tasks and generate visual representations (graphs, charts, plots) of the analyzed data.
5. **Reporting & Notifications**: Enable users to generate comprehensive reports on detected events, including statistical summaries and visualizations. Set up automatic notifications (email/SMS) when significant events are detected based on predefined criteria.
6. **User Interface**: Design a clean, intuitive user interface using Python frameworks like PyQt or Tkinter to make the application accessible and user-friendly.

**Features to Consider:**
- Support for multiple languages for international accessibility.
- Integration with cloud storage solutions for easy data backup and sharing.
- Option to export results in multiple formats (PDF, CSV, HTML).
- Advanced filtering options to refine search criteria for event detection.

By utilizing 'aeonlib', the application aims to streamline the process of TDA/MMA observations, making it easier for researchers to discover new astronomical phenomena and contribute to our understanding of the universe.