🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits multiple risky behaviors including network calls to unknown domains, conditional execution of shell commands, obfuscation techniques, and potential credential harvesting. These factors strongly suggest the possibility of a supply-chain attack.

Network risk due to calls to unknown domains
Shell command execution based on OS
Obfuscation through regex patterns
Potential credential harvesting

Per-check LLM notes

Network: The package makes unexpected calls to an unknown domain which may indicate unauthorized data exfiltration.
Shell: The package conditionally executes shell commands based on the operating system, potentially opening URLs which could be used for malicious purposes.
Obfuscation: The regex patterns suggest attempts to bypass or manipulate regular expression parsing, which is often used for obfuscating code or evading detection.
Credentials: The validation function and path manipulation indicate potential attempts to access sensitive files, such as '/etc/passwd', which could be part of credential harvesting activities.
Metadata: The repository's low activity and the maintainer's new or inactive account suggest potential unreliability.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 12 test file(s) found

Test runner config found: pyproject.toml
12 test file(s) detected (e.g. test_cmd.py)

◈ Medium Documentation 5.0

Some documentation present

Detailed PyPI description (10180 chars)

○ Low Contributing Guide 4.0

No contributing guide or governance files found

Development Status classifier >= Beta

◈ Medium Type Annotations 7.0

Partial type annotation coverage

Classifier: Typing :: Typed
63 type-annotated function signatures detected in source

✦ High Multiple Contributors 8.0

Active multi-contributor project

3 unique contributor(s) across 51 commits in apidepth-io/apidepth-python
Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

equests # noqa: E402 resp = requests.get("https://api.stripe.com/v1/charges/ch_abc123",
status=200, ) requests.get("https://api.stripe.com/v1/charges") col = Collector.ins
status=200, ) requests.get("https://unknown.example.com/api/stuff") col = Collector
status=200, ) requests.get("https://api.stripe.com/v1/charges") assert Collector.in

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

ng risk) _UNSAFE_PATTERN = re.compile( r"\(\?[{#]" # (?{ code execution, (?# comment r"|\(\?\(" # (?( conditional group
= None _configuration_lock = __import__("threading").Lock() _logger: Optional[logging.Logger] = None # -------

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

stem == "Darwin": subprocess.run(["open", url], check=False) elif system == "Linux":
ystem == "Linux": subprocess.run(["xdg-open", url], check=False) else: pr

⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

._validate_cache_path("/tmp/../etc/passwd") # nosec B108 def test_validate_rejects_none(): wit

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

Repository has zero stars and zero forks

⚠ 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 apidepth

Create a real-time monitoring tool for APIs using the 'apidepth' Python package. This tool will help developers and system administrators keep track of critical metrics such as API response times, error rates, and rate limit usage across multiple services. The application should have a user-friendly interface that allows users to add different APIs and monitor them simultaneously. Here’s a detailed breakdown of what the application should include:

1. **User Interface**: Develop a simple web-based UI using Flask or Django where users can input API endpoints, their respective keys, and other necessary parameters.
2. **API Monitoring**: Utilize 'apidepth' to track each API call made through the tool. Ensure that the tool captures latency data, error rates, and checks against any rate limits set by the API providers.
3. **Data Visualization**: Implement charts and graphs using libraries like Matplotlib or Plotly to visualize the collected data over time. Users should be able to see trends in performance and quickly identify any issues.
4. **Alert System**: Integrate an alert system that notifies users via email or SMS if certain thresholds are exceeded (e.g., high error rates, nearing rate limits).
5. **Configuration Management**: Allow users to save configurations for different APIs and load them later without having to re-enter all details.
6. **Security Measures**: Ensure sensitive information like API keys are stored securely and not exposed in the UI or logs.

Use 'apidepth' to handle the backend monitoring tasks efficiently. This includes initializing the SDK with your application, configuring it to log API calls, and processing the returned data for visualization and alerts. Your goal is to create a comprehensive tool that simplifies the process of managing and monitoring multiple APIs.