AI Analysis
Final verdict: SAFE
The package shows low risks across all categories except for a moderate obfuscation risk, which does not indicate malicious activity.
- Moderate obfuscation risk due to unconventional import methods
- No network, shell, credential or metadata risks detected
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require external communication.
- Shell: No shell execution patterns detected, indicating the package likely does not execute system commands.
- Obfuscation: The observed pattern is likely an unconventional method for importing and accessing the version of a package, indicating some level of obfuscation but not necessarily malicious intent.
- Credentials: No clear patterns indicative of credential harvesting were detected.
- Metadata: The author has only one package and there's a non-HTTPS link, but no other suspicious activities are flagged.
Package Quality Overall: Low (4.4/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (1171 chars)
○ Low
Contributing Guide
4.0
No contributing guide or governance files found
Development Status classifier >= Beta
◈ Medium
Type Annotations
7.0
Partial type annotation coverage
Type checker (mypy / pyright / pytype) referenced in project136 type-annotated function signatures detected in source
◈ Medium
Multiple Contributors
5.0
Limited contributor diversity
1 unique contributor(s) across 100 commits in aliyun/alibabacloud-python-sdkSingle author but highly active (100 commits)
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
score 2.0
Found 1 obfuscation pattern(s)
bacloud-python-sdk" VERSION = __import__(PACKAGE).__version__ REQUIRES = [ "darabonba-core>=1.0.0, <2.0.0
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: alibabacloud.com
Suspicious Page Links
score 2.0
Found 1 suspicious link(s) on the package page
Non-HTTPS external link: http://www.apache.org/licenses/LICENSE-2.0
Git Repository History
Repository aliyun/alibabacloud-python-sdk appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Alibaba Cloud SDK" 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 alibabacloud-sas20181203
Create a Python-based security monitoring tool called 'CloudGuard' that leverages the Alibaba Cloud Threat Detection SDK (package name: alibabacloud-sas20181203) to provide real-time threat detection alerts and management for cloud resources. The goal of CloudGuard is to help users monitor their cloud environments for potential security threats and respond quickly to any detected issues. Here’s a detailed breakdown of the project requirements: 1. **Setup and Configuration**: Start by setting up your development environment. Ensure you have Python installed and then install the necessary packages including alibabacloud-sas20181203. 2. **Authentication Mechanism**: Implement an authentication mechanism using Alibaba Cloud credentials to securely access the Threat Detection service. This will involve configuring your application to use Access Key ID and Access Key Secret for authentication. 3. **Real-Time Threat Detection**: Utilize the core functionalities of the alibabacloud-sas20181203 package to continuously monitor cloud resources for security threats. Your application should be able to detect and categorize different types of threats, such as malware infections, unauthorized access attempts, and suspicious activities. 4. **Alert System**: Develop an alert system within CloudGuard that sends notifications via email or SMS when a threat is detected. The system should allow users to customize the threshold for triggering alerts based on the severity of the threat. 5. **Dashboard Interface**: Create a simple dashboard interface using Flask or Django that displays real-time status updates and historical data about detected threats. This interface should allow users to view detailed information about each threat, including timestamp, type, and severity level. 6. **Actionable Insights**: Provide actionable insights and recommendations for mitigating detected threats. For example, if a user receives an alert about a potential malware infection, the application should suggest steps to take, such as running a virus scan or isolating affected resources. 7. **Logging and Reporting**: Implement logging functionality to keep track of all actions taken by CloudGuard, including detection events and user interactions. Additionally, generate periodic reports summarizing security trends and providing recommendations for improving overall security posture. By following these steps, you will create a robust security monitoring tool that not only detects threats but also helps users manage and mitigate them effectively.