🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits a moderate risk due to potential shell execution and obfuscation techniques, which can be exploited for malicious activities. The missing maintainer information adds to the uncertainty.

High obfuscation risk due to eval/exec usage
Potential shell execution risk

Per-check LLM notes

Network: No network calls detected, which is typical and not suspicious.
Shell: Shell execution patterns may indicate potential risks, such as executing arbitrary commands, which could be used for malicious purposes like data exfiltration.
Obfuscation: The use of eval and exec without proper sanitization indicates high risk for code injection and obfuscation.
Credentials: No clear patterns indicative of credential harvesting were found.
Metadata: The maintainer's author name is missing or very short and seems to be new or inactive, raising some suspicion but not definitive evidence of malice.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

.") try: obj = eval(ln) yield obj data_num += 1 except Ex
_file) as fin: try: exec(compile(fin.read(), "py_data", "exec"), user_data) return use

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

ing '{full_cmd}'") result = subprocess.run(full_cmd, shell=True, capture_output=capture_output) statu
lt = subprocess.run(full_cmd, shell=True, capture_output=capture_output) status = "OK" if result.r

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository SummerRainET2008/PYthon_Algorithms_Library 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 Python-Algorithm-pyal

Create a fully-functional mini-app named 'DataStructureVisualizer' using the Python-Algorithm-pyal package. This app will serve as an educational tool to help users understand various data structures and algorithms visually. Here are the steps and features to implement:

1. **Project Setup**: Initialize your project directory and install Python-Algorithm-pyal via pip.
2. **Data Structures Module**: Utilize Python-Algorithm-pyal to implement core data structures like linked lists, stacks, queues, trees, and graphs. Ensure each structure has methods for basic operations (e.g., insertion, deletion, traversal).
3. **Algorithms Module**: Implement common algorithms for sorting (e.g., quicksort, mergesort), searching (binary search), and graph traversals (BFS, DFS). Use Python-Algorithm-pyal's algorithm implementations where available.
4. **Visualization Interface**: Develop a simple GUI interface using libraries like Tkinter or PyQt. The interface should allow users to select different data structures and algorithms to visualize their operations step-by-step.
5. **Interactive Features**: Enable users to input data directly into the chosen data structure and watch as the algorithm processes it. Include options to pause, resume, and reset operations.
6. **Documentation and Help**: Provide comprehensive documentation within the app explaining each data structure and algorithm, along with examples of use cases.
7. **Testing and Validation**: Write tests for all implemented functionalities to ensure correctness and efficiency.

This project aims to not only demonstrate the capabilities of Python-Algorithm-pyal but also to educate users on the fundamental concepts of computer science through interactive visualization.