🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to high shell execution risk and potential network misuse, though no clear malicious activities are evident.

High shell execution risk
Potential network misuse

Per-check LLM notes

Network: The package makes legitimate network calls to check its version or download files, which is common but should be reviewed for unintended data exfiltration.
Shell: Executing shell commands directly can pose significant security risks, especially when controlling external services like 'ampl_lic'. This may indicate unauthorized system management capabilities.
Obfuscation: The observed patterns appear to be related to setting options for the Gurobi solver within AMPL, which is likely legitimate use rather than obfuscation.
Credentials: No credentials or secrets harvesting patterns were detected.
Metadata: The package has an author with a missing or very short name and a single associated package, indicating potential low activity or legitimacy issues.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

-------- try: response = requests.get("https://pypi.org/pypi/REHO/json") if response.status_co
, local_path): response = requests.get(file_url) if response.status_code == 200: with o
l, local_dir): response = requests.get(api_url) if response.status_code != 200: print(f

⚠ Code Obfuscation score 10.0

Found 6 obfuscation pattern(s)

"gurobi": ampl_MP.eval("option gurobi_options 'NodeFileStart=0.5' 'IntFeasTol=1e-6'
Tol=1e-6';") ampl_MP.eval('option show_boundtol 0;') ampl_MP.eval('option abs_
boundtol 0;') ampl_MP.eval('option abs_boundtol 1e-10;') # Load Master Problem
== "gurobi": ampl.eval("option gurobi_options 'NodeFileStart=0.5' 'IntFeasTol=1e-6'
_model.mod') ampl.eval('suffix iis symbolic OUT;') ampl.setOption('pres
f debugging: ampl.eval('display {i in 1.._ncons: _con[i].iis <> "0"} (_conname[i],

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

: os.system('cmd /c "ampl_lic restart"') # restart ampl license to avoi
cuting {script_path}...") os.system(f"python {script_path}") def test_example_0(): script_

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: epfl.ch>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

Non-HTTPS external link: http://www.apache.org/licenses/LICENSE-2.0

✓ Git Repository History

Repository IPESE/REHO 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)

✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with REHO

Develop a mini-application that serves as a decision support tool for renewable energy communities using the 'REHO' package. This application will allow users to optimize their renewable energy systems based on various parameters such as energy demand, available renewable resources, storage capacity, and grid connection status. Here’s a detailed breakdown of the project requirements:

1. **User Input**: The application should start by prompting the user to input details about their community's energy profile, including daily energy consumption patterns, peak hours, and average usage.
2. **Resource Assessment**: Utilize REHO to assess the potential of different renewable energy sources (solar, wind, etc.) available within the community. The assessment should consider geographical data, climate conditions, and installation feasibility.
3. **System Configuration**: Based on the resource assessment, configure an optimal system configuration that includes the mix of renewable energy sources, energy storage solutions, and any necessary grid connections.
4. **Cost Analysis**: Implement a feature that calculates the initial investment cost and operational expenses for the proposed system. This should include maintenance costs, depreciation, and potential savings from reduced energy bills.
5. **Sustainability Metrics**: Provide sustainability metrics such as carbon footprint reduction, energy self-sufficiency rate, and payback period.
6. **Visualization Tools**: Integrate visualization tools to help users understand the impact of different configurations. This could include charts showing energy production vs. consumption over time, financial savings, and environmental benefits.
7. **Scenario Analysis**: Allow users to explore different scenarios by adjusting variables like energy prices, subsidies, and technology advancements. Each scenario should trigger a new optimization run using REHO.
8. **Reporting and Recommendations**: Finally, generate a report summarizing the findings, including key recommendations for implementing the optimized system.

By utilizing the 'REHO' package, you'll be able to perform complex optimizations and simulations that would otherwise require extensive manual calculations and domain expertise. Your goal is to create an intuitive, user-friendly tool that empowers renewable energy communities to make informed decisions about their energy future.