abm-framework

v0.1.0 suspicious
4.0
Medium Risk

Agent-based modeling framework for social systems and public health applications

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has minimal risk indicators such as no network calls, shell executions, or obfuscations. However, the metadata suggests it may be newly created or inactive, which raises some concern about its legitimacy.

  • Metadata risk due to new or inactive package
  • No significant risks detected in code execution patterns
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires external services.
  • Shell: No shell execution patterns detected, indicating no direct system command execution.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets and credentials.
  • Metadata: The package shows signs of being newly created and potentially inactive, raising suspicion.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ 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: albany.edu>

✓ 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 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • 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 abm-framework 
Create a simulation application using the 'abm-framework' package that models the spread of a contagious disease within a population over time. This application should allow users to input various parameters such as initial number of infected individuals, population density, transmission rates, recovery times, and vaccination rates. The simulation should visualize the spread of the disease through graphs and charts, and it should also provide statistical outputs such as total infections, peak infection rate, and duration of the outbreak.

Key Features:
1. User Interface: Develop a simple but intuitive UI where users can set up their simulation scenarios.
2. Parameterization: Allow customization of key parameters like initial infection rate, population size, transmission probability, recovery time, and vaccination strategy.
3. Visualization: Implement real-time visualization of the simulation results using plots and charts to show the progression of the disease over time.
4. Statistical Analysis: Provide summary statistics at the end of each simulation run, including the total number of infected people, peak infection rate, and the overall duration of the epidemic.
5. Save/Load Scenarios: Enable users to save their simulation settings and load them later for re-running the simulations or comparing different scenarios.
6. Educational Mode: Include an educational mode that highlights key concepts of agent-based modeling and explains how each parameter affects the outcome of the simulation.

Utilizing the 'abm-framework' Package:
- Use 'abm-framework' to define agents representing individuals in the population. Each agent will have attributes such as health status (susceptible, infected, recovered), location, and interactions with other agents.
- Implement rules for agent behavior based on the parameters provided by the user. For example, agents should move around in a simulated environment, interact with other agents based on proximity, and change their health status according to the rules of disease transmission and recovery.
- Leverage 'abm-framework's built-in functions for scheduling events, tracking agent states over time, and analyzing the aggregated data from the simulation runs.
- Utilize the package's visualization tools to create dynamic visualizations of the simulation process and outcomes.