🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to its network activity and low maintenance status, raising concerns about potential unauthorized actions or supply-chain attacks.

Moderate network risk
Low metadata health

Per-check LLM notes

Network: The network calls to external URLs suggest the package fetches data from remote sources, which could be legitimate but requires scrutiny to ensure it's not for unauthorized data exfiltration or C2 activities.
Shell: No shell execution patterns were detected, indicating no immediate risk associated with executing arbitrary commands.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, indicating secure handling of secrets.
Metadata: The package shows low maintenance and suspicious author details, indicating potential risk.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

f convert() -> dict: with urllib.request.urlopen(SOURCE_URL) as resp: content = resp.read().d
l = SOURCES[version] with urllib.request.urlopen(url) as resp: content = resp.read().decode("

✓ 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

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 8.0

4 maintainer concern(s) found

Only one version has ever been released — brand new package
Author name is missing or very short
Author "" 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 adtech-crosswalk

Create a mini-application called 'AdTech Taxonomy Mapper' that leverages the 'adtech-crosswalk' Python package to provide a user-friendly interface for mapping different advertising technology terms across various providers. The application should allow users to input a term from one provider's taxonomy and receive equivalent terms from other providers. Here’s a step-by-step guide on how to build this application:

1. **Setup Environment**: Start by setting up a virtual environment and installing the necessary packages including 'adtech-crosswalk'. Ensure your development environment is equipped with Python and pip.
2. **Design the User Interface**: Design a simple yet effective user interface using a web framework like Flask or Django. The UI should include fields for entering a term and selecting the provider whose taxonomy it belongs to, as well as options to choose which other providers’ taxonomies to map against.
3. **Integrate 'adtech-crosswalk'**: Utilize the 'adtech-crosswalk' package to perform the actual mapping. This involves calling functions from the package based on user inputs to retrieve relevant mappings.
4. **Implement Mapping Logic**: Develop logic within your application to handle the mapping process. This includes handling multiple queries, providing explanations for mappings if available, and ensuring accuracy of results.
5. **Testing**: Thoroughly test the application to ensure it accurately maps terms between different providers and handles edge cases gracefully.
6. **Deployment**: Once tested, deploy the application to a platform like Heroku or AWS so it can be accessed online.

Suggested Features:
- A comprehensive database of adtech terms from various providers preloaded into the application.
- An option for users to save their frequently used terms for quick reference.
- Detailed documentation explaining each term and its equivalents, accessible through the app.
- A feature allowing users to contribute new terms or corrections to the existing mappings.

The goal is to create a valuable tool for professionals in the adtech industry who need to understand and communicate across different platforms and terminologies.