AI Analysis
Final verdict: SUSPICIOUS
The package exhibits moderate risk due to potential obfuscation practices and low maintainer activity, although it does not show any direct evidence of malicious intent or high-risk behaviors.
- Obfuscation risk at 5/10 due to Base64 decoding and decryption
- Low maintainer activity and metadata quality
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require internet access.
- Shell: No shell execution patterns detected, indicating no immediate risk of executing unauthorized commands.
- Obfuscation: The code uses Base64 decoding and decryption which may indicate obfuscation of sensitive information, but could also be legitimate for data protection.
- Credentials: No clear patterns indicative of credential harvesting were detected.
- Metadata: The package shows signs of low maintainer activity and metadata quality, but there are no clear indicators of malicious intent.
Package Quality Overall: Low (2.0/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
○ Low
Documentation
1.0
No documentation detected
No documentation URL, doc files, or meaningful description found
○ Low
Contributing Guide
2.0
No contributing guide or governance files found
No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
111 type-annotated function signatures detected in source
○ Low
Multiple Contributors
1.0
Unable to verify contributor count: no GitHub repository found
No GitHub repository linked — contributor count unavailable
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
score 6.0
Found 3 obfuscation pattern(s)
암호문(Base64) ciphertext = base64.b64decode(p_encPassword) plaintext = cipher.decrypt(ciphertext)s_query = parse.unquote(base64.b64decode(p_data['query']).decode('utf-8')) s_query = s_querys_code = parse.unquote(base64.b64decode(p_code).decode('utf-8')) self.code = s_code
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 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 aiops-ml
Create a predictive maintenance system using the 'aiops-ml' package in Python. This system aims to predict potential failures in machinery based on historical data of machine operations and maintenance records. The application will help reduce unexpected downtime and improve operational efficiency. Step 1: Data Collection - Gather historical data on machine operations including operational hours, types of tasks performed, and maintenance logs. - Ensure the data includes timestamps, machine IDs, and descriptions of any issues encountered. Step 2: Data Preprocessing - Clean the collected data by handling missing values, removing outliers, and converting categorical data into numerical format if necessary. - Split the dataset into training and testing sets. Step 3: Model Building - Utilize 'aiops-ml' for feature selection and model training. The package offers tools for analyzing time-series data which is particularly useful for identifying patterns that precede machine failures. - Experiment with different models provided by 'aiops-ml', such as regression, classification, or anomaly detection models, to find the best fit for predicting maintenance needs. Step 4: Model Evaluation - Evaluate the performance of your chosen model(s) using appropriate metrics such as accuracy, precision, recall, or F1 score depending on the nature of the problem. - Use cross-validation techniques to ensure the robustness of your model against unseen data. Step 5: Deployment - Develop a user-friendly interface where operators can input real-time machine operation data. - Integrate the trained model into this interface so it can provide predictive insights on when maintenance might be required. Suggested Features: - Real-time data input from connected machines. - Predictive alerts for potential failures. - Historical performance analysis to understand trends over time. - Customizable thresholds for alerting based on specific business requirements. How 'aiops-ml' is utilized: - For feature engineering, where complex relationships within the data are simplified into more understandable features. - To train machine learning models that can learn from past machine behavior and predict future outcomes. - In anomaly detection, to identify unusual patterns that could indicate impending failure.