awfl

v0.1.11 suspicious
5.0
Medium Risk

AWFL: AI Workflows CLI

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits high risks related to network and shell command usage, which may indicate potential misuse or unauthorized operations. However, there are no signs of obfuscation, credential harvesting, or other malicious activities.

  • High network risk due to connections to potentially unauthorized URLs
  • High shell risk due to execution of system-level commands
Per-check LLM notes
  • Network: The package makes network calls to potentially unauthorized URLs, including Firebase and localhost tunneling services, which may indicate external communication without clear purpose.
  • Shell: The package executes shell commands including Docker Compose operations and a system clear command, suggesting it might perform system-level tasks that could be used for hidden operations or system manipulation.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The maintainer has only one package, which could indicate a new or less active user, but no other suspicious flags are present.

📦 Package Quality Overall: Medium (5.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. test_sanitize.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (8176 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 150 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 39 commits in awfl-us/cli
  • Single author but highly active (39 commits)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • urllib.request with urllib.request.urlopen("http://localhost:4040/api/tunnels", timeout=1.5) as
  • esh Firebase token.") r = requests.post( f"{FIREBASE_REFRESH_URL}?key={api_key}", da
  • dential": True, } r = requests.post( f"{FIREBASE_IDP_URL}?key={api_key}", json=p
  • reToken": True, } r = requests.post( f"{FIREBASE_CUSTOM_TOKEN_URL}?key={api_key}",
  • est device/user codes r = requests.post( DEVICE_CODE_URL, data={ "client
  • ] = client_secret t = requests.post(TOKEN_URL, data=data, timeout=20) if t.status_code =
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • flow_name()) os.system('clear') for line in log_lines:
  • g a build on up res = subprocess.run(["docker", "compose", "-f", compose_file, "up", "-d", "--bui
  • False try: res = subprocess.run(["docker", "compose", "-f", compose_file, "down"], text=True
  • append("-f") try: subprocess.run(args) except KeyboardInterrupt: pass def compo
  • known" try: res = subprocess.run(["docker", "compose", "-f", compose_file, "ps"], capture_out
  • None try: proc = subprocess.Popen(["ngrok", "http", str(port)], stdout=subprocess.DEVNULL, std
✓ 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

Repository awfl-us/cli appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Paul Lorenz" 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 awfl 
Create a command-line interface (CLI) tool that simplifies the process of managing AI workflows using the 'awfl' package. This tool will enable users to easily create, run, and monitor machine learning experiments from their terminal. Here are the key functionalities your application should include:

1. **Workflow Creation**: Users should be able to define new workflows by specifying the necessary steps such as data preprocessing, model training, evaluation, etc., directly through the CLI.
2. **Execution Management**: Implement commands to start, stop, pause, and resume workflows. The CLI should provide real-time status updates on the execution progress.
3. **Resource Management**: Allow users to configure resources like CPU/GPU allocation, memory limits, and storage requirements for each workflow step.
4. **Monitoring and Logging**: Integrate logging capabilities to track the performance metrics of each workflow step. Users should be able to view logs and performance statistics via the CLI.
5. **Version Control**: Enable versioning of workflows so that users can revert to previous versions if needed.
6. **Integration with External Tools**: Provide options to integrate external tools and services such as cloud storage for data management and containerization platforms for deployment.

The 'awfl' package is central to this application. It provides the core functionalities for defining, executing, and monitoring workflows. Use its CLI capabilities to streamline the interaction between users and the underlying workflow engine. Your task is to leverage the 'awfl' package to build a robust, user-friendly CLI tool that makes managing AI workflows straightforward and efficient.

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