agentsociety2

v2.5.3 suspicious
6.0
Medium Risk

A modern, LLM-native agent simulation platform for social science research

πŸ€– AI Analysis

Final verdict: SUSPICIOUS

The package exhibits signs of potential malicious intent due to high credential risk and obfuscation techniques, suggesting it might be designed to evade detection while accessing sensitive data.

  • High credential risk due to references to sensitive file paths.
  • Obfuscation techniques used, indicating attempts to avoid analysis.
Per-check LLM notes
  • Network: No network calls detected, which is normal if the package does not require internet access.
  • Shell: No shell execution patterns detected, indicating no direct system command execution from the package.
  • Obfuscation: The use of regular expressions to block certain patterns suggests an attempt to avoid detection or analysis, which is suspicious.
  • Credentials: The inclusion of paths for sensitive files and directories such as /etc/passwd, /etc/shadow, and ~/.ssh indicates a high risk of credential harvesting.
  • Metadata: The maintainer's author information is incomplete and may indicate a less experienced or new developer.

πŸ“¦ Package Quality Overall: Medium (7.4/10)

β—ˆ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
β—ˆ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://agentsociety2.readthedocs.io/
  • Detailed PyPI description (12669 chars)
✦ High Contributing Guide 9.0

Has contribution guidelines and governance files

  • Governance file: security.py
  • Development Status classifier >= Beta
β—ˆ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 477 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 4 unique contributor(s) across 100 commits in tsinghua-fib-lab/agentsociety
  • Small but multi-author team (3–4 contributors)

πŸ”¬ Heuristic Checks

βœ“ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • compile(r"\beval\s+"), re.compile(r"\bfind\s+.*-exec\s+"), ) #: ζ•ζ„Ÿθ·―εΎ„ BLOCKED_PATHS: Final[frozenset[str]] = fro
βœ“ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 5.0

Found 2 credential access pattern(s)

  • ] = frozenset( { "/etc/passwd", "/etc/shadow", "/etc/sudoers", "~
  • "/etc/passwd", "/etc/shadow", "/etc/sudoers", "~/.ssh", "~/.gnu
βœ“ Typosquatting

No typosquatting candidates detected

βœ“ Registered Email Domain

Email domain looks legitimate: gmail.com>

βœ“ Suspicious Page Links

All external links appear legitimate

βœ“ Git Repository History

Repository tsinghua-fib-lab/agentsociety appears legitimate

⚠ 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)
βœ“ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

πŸ’‘ AI App Starter Prompt

Use this prompt to build a project with agentsociety2 
Your task is to develop a fully-functional mini-application using the 'agentsociety2' package, which simulates a small town's economic and social dynamics. This application will serve as a tool for social scientists to study the impact of various policies on community behavior and economic outcomes.

### Step-by-Step Guide:
1. **Environment Setup**: Ensure you have Python installed along with 'agentsociety2'. Use pip to install any necessary dependencies.
2. **Define Agents**: Create different types of agents representing citizens (e.g., workers, consumers, business owners). Each agent type should have distinct behaviors and attributes.
3. **Create Environment**: Design the environment where these agents interact, such as shops, workplaces, and public spaces.
4. **Implement Policies**: Introduce policies that affect the agents' actions, like tax changes, subsidies, or new regulations.
5. **Simulation Engine**: Utilize 'agentsociety2' to run simulations over time, observing how agents adapt their behaviors based on the policies.
6. **Data Collection & Analysis**: Collect data from each simulation run and analyze it to draw conclusions about policy impacts.
7. **Visualization**: Develop a simple GUI or use existing libraries to visualize the simulation results and key metrics over time.
8. **Documentation & Testing**: Write comprehensive documentation and perform thorough testing to ensure reliability.

### Suggested Features:
- **Agent Types**: Include at least three types of agents with unique attributes and behaviors.
- **Dynamic Policies**: Allow users to input different policies and observe the effects.
- **Interactive Dashboard**: Provide a user-friendly interface to control simulation parameters and view outcomes.
- **Scenario Comparison**: Enable side-by-side comparisons of different scenarios.
- **Real-Time Updates**: Display real-time updates of the simulation state.

### How to Use 'agentsociety2':
- Leverage 'agentsociety2' to define agent interactions, simulate environments, and manage the simulation lifecycle efficiently.
- Use its native capabilities for handling complex agent-based models and integrating LLMs for more sophisticated agent behaviors.

This project aims to demonstrate the versatility of 'agentsociety2' in creating realistic, dynamic simulations for social science research.