atatus

v2.3.1 suspicious
6.0
Medium Risk

Atatus Python Agent

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits several red flags including credential harvesting risks and suspicious maintainer history, though the exact nature and intent behind these indicators are unclear. The combination of these factors raises concerns about potential malicious activity.

  • Potential AWS credential harvesting
  • Suspicious maintainer history and lack of git repository
Per-check LLM notes
  • Network: The package makes network calls which could be for legitimate purposes like sending usage statistics or error reports.
  • Shell: No shell execution patterns detected.
  • Obfuscation: The obfuscation pattern detected is not strong evidence of malicious intent; it appears to be an attempt to decode data, possibly for configuration or licensing purposes.
  • Credentials: The code snippet indicates potential harvesting of AWS credentials and environment variables which could pose a risk if not handled securely, suggesting the package may interact with AWS services using environment variables.
  • Metadata: Suspicious maintainer history and lack of git repository suggest potential risk.
  • ⚠ Typosquatting target: attrs

📦 Package Quality Overall: Low (4.6/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: setup.cfg
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://docs.atatus.com/docs/application-monitoring/python-a
  • Detailed PyPI description (989 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 78 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 score 3.0

Found 2 network call pattern(s)

  • ta) # self._session = requests.Session() self._blocked = False self._capture_percen
  • r = requests.post(server_url + endpoint, params=self._post_params, timeout=30,
⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

  • ded"): body = base64.b64decode(body) else: try:
  • y: try: VERSION = __import__("importlib.metadata").metadata.version("atatus") except ImportError:
  • mportError: VERSION = __import__("pkg_resources").get_distribution("atatus").version except Exception: VE
✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 10.0

Found 6 credential access pattern(s)

  • from a user # perspective if os.environ.get("AWS_REGION"): from atatus.contrib.serverless.aws import capt
  • kwargs["service_version"] = os.environ.get("AWS_LAMBDA_FUNCTION_VERSION") return kwargs def should_no
  • t" transaction_name = os.environ.get("AWS_LAMBDA_FUNCTION_NAME", self.name) self.httpmethod =
  • = arn faas["name"] = os.environ.get("AWS_LAMBDA_FUNCTION_NAME") faas["version"] = os.environ.
  • E") faas["version"] = os.environ.get("AWS_LAMBDA_FUNCTION_VERSION") if self.source == "api":
  • ime"] = { "name": os.environ.get("AWS_EXECUTION_ENV"), "version": platform.python_vers
⚠ Typosquatting score 3.0

Possible typosquat of: attrs

  • "atatus" is 2 edit(s) from "attrs"
✓ 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 2.0

1 maintainer concern(s) found

  • Author "Atatus" 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 atatus 
Create a real-time monitoring dashboard application using Python that leverages the Atatus Python Agent for error tracking, performance monitoring, and user experience analysis. This mini-application will serve as a demonstration of how businesses can utilize such tools to improve their web applications' reliability and efficiency.

Step 1: Set up your development environment by installing necessary packages including Flask for web framework, and Atatus Python Agent for monitoring.

Step 2: Design a simple web application using Flask. The application should have endpoints that mimic typical user interactions, such as login, home page, and a blog post view.

Step 3: Integrate Atatus into your Flask application. Configure it to monitor these endpoints for performance metrics and errors. Ensure that Atatus captures data such as response time, error rates, and user interaction paths.

Step 4: Implement a feature where users can submit feedback directly from the web application. This feedback should include details about the user's experience and any issues they encountered while using the app.

Step 5: Develop a dashboard within the Flask application that displays real-time monitoring data collected by Atatus. This dashboard should present key performance indicators (KPIs) such as average response times, error trends over time, and user satisfaction scores.

Suggested Features:
- Real-time alerting system for critical errors or performance drops.
- Ability to filter monitoring data by date range, endpoint, or error type.
- User feedback submission form integrated into the application.
- Visualizations like graphs and charts to represent KPIs.

How Atatus is Utilized:
Atatus will be used to track various aspects of the Flask application's performance. It will capture performance metrics for each request made to the server, log any errors that occur, and provide insights into user behavior on the site. Additionally, Atatus will help in identifying slow-running queries, memory leaks, and other issues that could affect the application's performance. By integrating Atatus, you'll be able to demonstrate how developers can gain valuable insights into their application's health and user experience.

💬 Discussion Feed

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