AI Analysis
Final verdict: SAFE
The package shows low risks across all assessed categories with only a moderate network risk due to HTTP requests to external APIs, which is expected behavior for an API wrapper.
- moderate network risk due to HTTP requests
- low risk in shell execution, obfuscation, and credential handling
Per-check LLM notes
- Network: The network call patterns indicate the package is making HTTP requests to an endpoint, which could be for legitimate API interactions but requires further review of endpoints and data being transmitted.
- Shell: No shell execution patterns were detected.
- Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
- Credentials: No credential harvesting patterns detected, indicating low risk of malicious activity.
Heuristic Checks
Outbound Network Calls
score 9.0
Found 6 network call pattern(s)
""" session = requests.Session() retry_strategy = Retry( total=max(mants, **kwargs} res = requests.get(endpoint, params=params, headers=header) return resmyxml.decode() res = requests.post(endpoint, data=xml_data, headers=header) return resata = f.read() res = requests.post(self.endpoint + path, files={"file": (None, data)},executor.map(lambda x, y, z: requests.post( x, headers=y, files=z), list_urls, list_heed_jwt } response = requests.post(config['jwtTokenEndpoint'], headers=header_jwt, data=payload
Code Obfuscation
No obfuscation patterns detected
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: gmail.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository pitchmuc/adobe-analytics-api-2.0 appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Julien Piccini" 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 aanalytics2
Create a mini-application that integrates with Adobe Analytics using the 'aanalytics2' Python package. Your application should allow users to authenticate their Adobe Analytics account, retrieve data from various reports, and visualize the data using a simple graphical interface. Here’s a detailed breakdown of what your app should include: 1. **User Authentication**: Implement a feature where users can log in with their Adobe Analytics credentials. Use OAuth2 for secure authentication. 2. **Report Retrieval**: Allow users to select different report suites and view recent reports. The application should fetch metrics such as page views, unique visitors, and bounce rates. 3. **Data Visualization**: Display the retrieved data through charts and graphs. Utilize libraries like Matplotlib or Plotly for visualization. 4. **Custom Report Creation**: Provide functionality for users to create custom reports based on specific metrics and time periods. 5. **Export Option**: Enable users to export the visualized data into CSV or Excel formats for further analysis. For each step, utilize the 'aanalytics2' package to interact with the Adobe Analytics API, ensuring efficient and accurate data retrieval and manipulation. This project will serve as a practical tool for marketers and analysts to quickly access and understand their Adobe Analytics data.