AI Analysis
The package exhibits significant network and shell execution risks, indicating potential unauthorized activities. While it does not show strong indicators of credential theft or malicious intent, the combination of these factors raises suspicion about its true purpose.
- High network risk due to unsecured data transmission
- Elevated shell risk from executing external commands
Per-check LLM notes
- Network: The network call patterns include sending unsecured data which may contain secrets, suggesting potential data exfiltration risks.
- Shell: Executing shell commands like git can indicate the package might be performing actions beyond its intended scope, possibly including unauthorized operations.
- Obfuscation: The use of obfuscation techniques like name mangling (using underscores and __import__) is common but may indicate an attempt to hide code behavior.
- Credentials: No clear signs of credential harvesting were detected in the provided snippet.
- Metadata: The package shows signs of potential low activity and lacks maintainer information, raising concerns but not conclusive evidence of malice.
Package Quality Overall: Medium (7.0/10)
Test suite present — 21 test file(s) found
Test runner config found: conftest.pyTest runner config found: pyproject.toml21 test file(s) detected (e.g. conftest.py)
Some documentation present
Documentation URL: "Documentation" -> https://allstak.sa/docs/sdks/pythonDetailed PyPI description (4557 chars)
No contributing guide or governance files found
Development Status classifier >= Beta
Partial type annotation coverage
Classifier: Typing :: Typed238 type-annotated function signatures detected in source
Active multi-contributor project
4 unique contributor(s) across 38 commits in AllStak/allstak-pythonSmall but multi-author team (3–4 contributors)
Heuristic Checks
Found 6 network call pattern(s)
en=secret").prepare() requests.Session().send(req) # Underlying transport called exactly oexample.com/x").prepare() requests.Session().send(req) assert installed_requests.called assertest/v1/errors").prepare() requests.Session().send(req) assert installed_requests.called assertXISTING"} ).prepare() requests.Session().send(req) # set-if-missing: user's traceparent is pre) with httpx.Client(timeout=self._timeout) as client: resp =fic client:: client = httpx.AsyncClient(event_hooks=allstak_httpx_hooks()) Both paths skip requests
Found 1 obfuscation pattern(s)
self._breadcrumb_lock = __import__("threading").Lock() def set_user(self, user: UserContext) -> None:
Found 1 shell execution pattern(s)
nner. """ completed = subprocess.run( ["git", *args], capture_output=True,
No credential harvesting patterns detected
No typosquatting candidates detected
Email domain looks legitimate: allstak.sa>
All external links appear legitimate
Git history flags: Repository has zero stars and zero forks
Repository has zero stars and zero forks
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Create a mini-application that integrates the 'allstak' package to demonstrate its capabilities in error tracking, structured logging, HTTP and database monitoring, distributed tracing, and cron job monitoring. This application will be built using FastAPI as the web framework. Here are the steps and features to include: 1. **Setup Environment**: Start by setting up a virtual environment and installing the necessary packages including FastAPI, Uvicorn, and 'allstak'. 2. **Application Structure**: Define the structure of your FastAPI application. Include at least two endpoints - one for user registration and another for fetching user data. 3. **Error Tracking**: Implement a scenario where the application throws an intentional error under certain conditions. Use 'allstak' to track these errors and ensure they are reported back to you. 4. **Structured Logging**: Enhance your application to log important events in a structured manner. Use 'allstak' to ensure these logs are not only captured but also easily searchable and filterable. 5. **HTTP Monitoring**: Add a feature that monitors HTTP requests made from your application. Utilize 'allstak' to capture details like request/response times, status codes, and any errors encountered during these transactions. 6. **Database Monitoring**: Integrate a simple database (like SQLite) into your application. Monitor all database operations using 'allstak' to track queries, execution times, and any issues that arise. 7. **Distributed Tracing**: If your application interacts with external services, implement distributed tracing using 'allstak' to understand the flow of requests across different services. 8. **Cron Job Monitoring**: Set up a periodic task (using cron jobs) within your application that performs a specific action (e.g., cleaning up old user data). Ensure this task is monitored using 'allstak' to alert you if it fails. 9. **Documentation and Testing**: Provide comprehensive documentation on how each feature works and how 'allstak' is utilized. Additionally, write tests to ensure your application functions correctly and that 'allstak' is integrated properly. This project aims to showcase the versatility and ease of use of the 'allstak' package while building a practical, albeit simplified, application.