🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to potential misuse of shell commands and interaction with AWS credentials, though these could also be legitimate features. A thorough review of the source code is necessary.

Shell command execution
Interaction with AWS credentials

Per-check LLM notes

Network: The network call to 'healthz' suggests the package might be checking the status of a service, which is not inherently risky but should be reviewed in context.
Shell: Executing shell commands like git can pose risks if misused, especially if it involves accessing or modifying sensitive files or executing arbitrary commands.
Obfuscation: The observed obfuscation pattern is common for managing package namespaces and does not necessarily indicate malicious intent.
Credentials: The package's interaction with AWS credentials appears to be standard and could be part of legitimate functionality, but further investigation into its source code and usage is recommended.
Metadata: The package has some minor red flags, such as a missing author name and a single package from the maintainer, but no clear signs of typosquatting or malicious intent.

📦 Package Quality Overall: Medium (6.4/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

Test runner config found: pyproject.toml

◈ Medium Documentation 7.0

Some documentation present

Documentation URL: "Documentation" -> https://arize.com/docs/phoenix/
Detailed PyPI description (36033 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
385 type-annotated function signatures detected in source

✦ High Multiple Contributors 10.0

Active multi-contributor project

14 unique contributor(s) across 100 commits in Arize-ai/phoenix
Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

ing.""" try: with urllib.request.urlopen(urljoin(base_url + "/", "healthz"), timeout=2) as re

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

namespace packages __path__ = __import__("pkgutil").extend_path(__path__, __name__) import sys from importlib.

⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

str: try: return subprocess.check_output(["git", *args], text=True).strip() except (OSError, subp

⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

onfigured (via environment, ~/.aws/credentials, or IAM role) - AWS region configured via standard AWS meth

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: arize.com>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

Non-HTTPS external link: http://arize.com/ai-agents/

✓ Git Repository History

Repository Arize-ai/phoenix appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Author name is missing or very short
Author "" 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 arize-phoenix

Develop a real-time AI model performance monitoring tool using the 'arize-phoenix' Python package. This tool will allow users to input predictions from their machine learning models and monitor key performance metrics such as accuracy, precision, recall, F1 score, and AUC-ROC in real-time. Additionally, it should provide visualizations for these metrics over time, allowing users to track the performance of their models as they evolve and adapt to new data.

The application should include the following features:
1. A user-friendly interface where users can upload prediction results from their models.
2. Real-time calculation and display of performance metrics for the uploaded predictions.
3. Historical data storage and retrieval capabilities to compare performance over different periods.
4. Graphical representations of performance metrics over time.
5. Alerts and notifications when certain thresholds for performance metrics are exceeded or fallen below.

To utilize the 'arize-phoenix' package, you will need to integrate its functionality into your application to handle the logging and evaluation of the model predictions. Specifically, use the package to log the predictions and ground truth labels, then leverage its built-in functions to compute the performance metrics mentioned above. Finally, utilize the visualization tools provided by 'arize-phoenix' to create dynamic and interactive graphs that update in real-time based on the incoming data.

Your goal is to create a comprehensive and easy-to-use tool that empowers developers and data scientists to monitor and improve the performance of their AI models efficiently.