AI Analysis
Final verdict: SAFE
The EuroEval package presents a low risk profile based on the analysis. While there are some minor concerns regarding obfuscation and metadata, these do not strongly indicate malicious intent.
- Low network and shell execution risks
- No detected credential harvesting
- Potential low-effort maintenance
Per-check LLM notes
- Network: The network call is likely used to check internet availability and does not indicate malicious intent.
- Shell: No shell execution patterns detected, indicating low risk.
- Obfuscation: The observed obfuscation pattern seems to be an incomplete and potentially malformed code snippet, indicating low risk of intentional obfuscation.
- Credentials: No credentials or secrets harvesting patterns were detected.
- Metadata: Low risk, but potential issues with maintainer history suggest low effort and possibly unreliable maintenance.
Heuristic Checks
Outbound Network Calls
score 1.5
Found 1 network call pattern(s)
= False try: s = socket.create_connection(("1.1.1.1", 80)) s.close() internet_availabl
Code Obfuscation
score 2.0
Found 1 obfuscation pattern(s)
re[redundant-cast] model.eval() model.to(benchmark_config.device) # ty: ignore[invali
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: alexandra.dk>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository EuroEval/EuroEval appears legitimate
Maintainer History
score 6.0
3 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 EuroEval
Create a web-based language evaluation tool using the EuroEval Python package. This tool will allow users to upload their own European language models and evaluate them against a set of predefined benchmarks provided by EuroEval. The application should have the following key features: 1. User Authentication: Users must be able to create accounts and log in to use the service. 2. Model Upload: Allow users to upload their own language models. 3. Benchmark Selection: Provide a list of predefined benchmarks from EuroEval that users can choose from to evaluate their models. 4. Evaluation Results: Display detailed results of the evaluations, including accuracy scores, response times, and other relevant metrics. 5. Comparison Tool: Enable users to compare their uploaded models against other models available on the platform. 6. Visualization: Implement charts and graphs to visually represent the performance of different models. 7. Feedback System: Include a feature where users can provide feedback on the models they evaluate. The EuroEval package will be utilized to perform the actual evaluation of the uploaded models against the selected benchmarks. It will handle the computation of metrics such as accuracy, response time, etc., based on the data generated from the benchmarks. The application should be built using Flask for the backend, React for the frontend, and PostgreSQL for storing user information and evaluation results.