🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits significant obfuscation and metadata risks, suggesting possible attempts to conceal malicious intent or recent suspicious activity.

High obfuscation risk (7/10)
High metadata risk (7/10)

Per-check LLM notes

Network: No network calls detected, which is normal unless the package requires online resources to function.
Shell: No shell execution patterns detected, indicating no immediate risk of command injection or similar attacks.
Obfuscation: The observed patterns strongly suggest obfuscation techniques that could be used to hide malicious code.
Credentials: No clear evidence of credential harvesting activities is present.
Metadata: The repository was created very recently with all commits happening within a short period, indicating potential suspicious activity.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

namespace = {} eval(codeobj, namespace) srcfuncs = {} for na
e.read() codeobj = compile(self.source, file, mode="exec") namespace = {} eval(codeobj, namespace)
amespace = {} code = compile(src, "<string>", mode="exec") exec(code, namespace) if edit_source:

✓ 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: proton.me>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 7.5

Git history flags: Repository created very recently: 5 day(s) ago (2026-05-31T17:10:41Z)

Repository created very recently: 5 day(s) ago (2026-05-31T17:10:41Z)
Repository has zero stars and zero forks
All 8 commits happened within 24 hours

⚠ 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 actuarial

Create a fully-functional mini-app called 'RiskAssessor' that leverages the 'actuarial' Python package to help users assess risk based on actuarial modeling techniques. The app should allow users to input various parameters such as age, health status, occupation, and other relevant factors to predict potential risks and outcomes related to insurance policies or financial planning.

Step-by-Step Instructions:
1. Begin by installing the 'actuarial' package if it isn't already installed.
2. Design a user-friendly interface where users can input their personal details.
3. Implement functions within the app that utilize 'actuarial' package methods to process these inputs and generate a risk assessment report.
4. Ensure the app outputs clear, understandable results including a summary of the predicted risks, potential financial impacts, and recommended actions based on the analysis.
5. Optionally, include visual representations like graphs or charts to better illustrate the risk assessment outcomes.
6. Finally, document your code thoroughly, explaining how each part integrates with the 'actuarial' package functionalities to perform actuarial computations.

Suggested Features:
- Personalized risk assessment based on individual inputs.
- Comparison of different scenarios to understand varying outcomes.
- Recommendations tailored to reduce identified risks.
- Integration of real-world data sets for more accurate predictions.
- User guides and FAQs for interpreting the risk assessment results.