AI Analysis
Final verdict: SUSPICIOUS
The package exhibits moderate obfuscation and network risks, which could indicate attempts to conceal malicious activities or unauthorized communications.
- Moderate obfuscation risk
- Potential network communication risks
Per-check LLM notes
- Network: The package makes network calls to an external server which could indicate legitimate functionality but may also suggest potential data exfiltration or unauthorized communication.
- Shell: No shell execution patterns detected, suggesting low risk of direct system command abuse.
- Obfuscation: The detected patterns suggest potential code obfuscation which could be used to hide the original logic, posing a moderate risk.
- Credentials: No clear signs of credential harvesting were detected.
- Metadata: The maintainer has only one package and lacks a GitHub repository, which may indicate a less established project.
Heuristic Checks
Outbound Network Calls
score 9.0
Found 6 network call pattern(s)
t urllib.request with urllib.request.urlopen(url, timeout=10) as resp: content = respize: print() urllib.request.urlretrieve(url, dest, reporthook=_reporthook) import jsonuests response = requests.post( f"{self.server_url}/contribute", json=paylouests response = requests.get( f"{self.server_url}/aggregate",updates: requests.post( f"{url}/knowledge/update",) response = requests.get(f"{url}/knowledge/recent", timeout=2) if res
Code Obfuscation
score 4.0
Found 2 obfuscation pattern(s)
s(tree) code = compile(tree, "<optimized>", "exec") namespace = {} exec(code, names'' try: compile(combined, "<combine>", "exec") return combined except SyntaxError:
Shell / Subprocess Execution
No shell execution patterns detected
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
No GitHub repository linked
No GitHub repository link found
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "aalgoi" 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 aalgoi
Create a Python-based mini-application called 'AlgoMind' that leverages the 'aalgoi' package to demonstrate its capability of learning, reasoning, and discovering patterns from data. The application will serve as a simple but powerful tool for educational purposes, allowing users to input various types of data (e.g., numerical sequences, text snippets) and see how the 'aalgoi' model processes it to identify underlying patterns or rules. Step 1: Initialize the project - Set up a new Python virtual environment and install the necessary packages, including 'aalgoi'. - Create a main Python script named 'main.py' where all functionalities will be implemented. Step 2: Data Input Module - Design a user-friendly interface within the script that allows users to input their data directly into the console or via a simple text file upload. - Ensure the script supports multiple data types (numerical sequences, short texts). Step 3: Processing with 'aalgoi' - Utilize the 'aalgoi' package to process the input data. Specifically, use its learning and reasoning capabilities to analyze the data and attempt to discover patterns or rules governing the data. - Implement error handling to manage cases where the input data might not be suitable for analysis. Step 4: Output and Visualization - Display the discovered patterns or rules back to the user in a clear, understandable format. This could include printing statements, charts, or graphs if applicable. - Provide a brief explanation of how these patterns were derived based on the 'aalgoi' processing. Suggested Features: - Include a feature that allows users to compare different sets of data inputs side by side to see how varying the data affects the output. - Implement a simple scoring system that evaluates the complexity of the discovered patterns/rules. - Add a tutorial section within the application that explains key concepts behind the 'aalgoi' package and demonstrates its usage through examples.