🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits significant shell execution capabilities and moderate network interaction risks, which together suggest potential misuse for unauthorized operations or command-and-control communications.

High shell risk due to execution of external commands
Moderate network risk from localhost interactions and proxy settings

Per-check LLM notes

Network: The use of network calls to localhost and the ability to set proxies may indicate normal functionality but could also be used for C2 communications.
Shell: Execution of external commands and processes, especially with elevated permissions, poses a significant risk and could indicate potential for unauthorized actions or data exfiltration.
Obfuscation: The observed patterns suggest potential obfuscation or encryption practices, but without more context, it's unclear if this is malicious or just secure coding practices.
Credentials: No clear evidence of credential harvesting activities detected.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

ttpx try: resp = httpx.get(f"http://127.0.0.1:{port}/health/live", timeout=2.0)
e_name}") async with httpx.AsyncClient(proxy=proxy) as client: async with client.stream
) self._http_client = httpx.AsyncClient(timeout=15, proxy=self.config.proxy) await self._ref
try: async with httpx.AsyncClient(proxy=proxy) as client: resp = await client.post
} try: async with httpx.AsyncClient(proxy=proxy) as client: resp = await client.get(
e self._http_client = httpx.AsyncClient(timeout=15, proxy=proxy) # Verify credentials by fe

⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

de()).digest() data = base64.b64decode(encrypted) iv, ciphertext = data[:16], data[16:]
aead import AESGCM key = base64.b64decode(key_base64) raw = base64.b64decode(encrypted_base64)
4decode(key_base64) raw = base64.b64decode(encrypted_base64) iv = raw[:12] ciphertext_with_tag

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

"w") try: popen = subprocess.Popen( command, shell=True, cw
or "ccproxy" result = subprocess.run( [exe, "auth", "status", provider],
exe() or "ccproxy" proc = subprocess.Popen( [exe, "serve", "--port", str(port)], stdout
self._daemon_proc = subprocess.Popen( cmd, stdout=subprocess.DEVN
try: result = subprocess.run( [cli_path, "--version"], ca
The command is run via ``shell=True`` with output redirected to a per-process log file unde

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository EvoScientist/EvoScientist appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Xi Zhang" 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 EvoScientist

Develop a mini-application named 'AutoDiscover' using the Python package 'EvoScientist'. This application aims to automate scientific discovery processes, focusing on hypothesis generation and testing within a specified domain. AutoDiscover should allow users to input a dataset and a set of variables they are interested in studying. The application will then use EvoScientist's capabilities to evolve models that can hypothesize relationships between these variables and test them against the data.

### Key Features:
1. **Data Input**: Users can upload datasets in CSV format or provide a URL to a public dataset.
2. **Variable Selection**: A user-friendly interface where users select which variables from their dataset they want to study.
3. **Hypothesis Generation**: Using EvoScientist, the application generates hypotheses about potential relationships between selected variables.
4. **Model Evolution**: EvoScientist evolves models to test these hypotheses effectively. These models should adapt over iterations based on feedback from the data.
5. **Result Visualization**: Provide visualizations of the best-fitting models and their predictions. Include statistical measures like R-squared values or p-values to quantify model fit.
6. **User Feedback Loop**: Allow users to refine their queries based on initial results, feeding back into the evolutionary process for further refinement.
7. **Documentation & Reporting**: Generate a report summarizing the findings, including key hypotheses tested, model performance metrics, and visual representations.

### Utilization of EvoScientist:
- **Initialization**: Use EvoScientist to initialize the evolutionary process with a population of candidate models.
- **Fitness Function**: Define a fitness function within EvoScientist that evaluates how well each model fits the data, considering factors like simplicity and predictive power.
- **Evolutionary Algorithms**: Leverage EvoScientist’s algorithms to iteratively improve the models through selection, crossover, mutation, and other genetic operations.
- **Integration with User Interface**: Ensure the evolutionary process is seamlessly integrated with the application's front-end, allowing real-time updates and user interaction.
- **Final Model Selection**: Implement a mechanism to select the final model(s) based on predefined criteria (e.g., highest fitness score).

By building AutoDiscover, you will create a tool that democratizes scientific discovery, enabling non-experts to explore complex datasets and generate meaningful insights.