ayechat

v0.50.1 suspicious
5.0
Medium Risk

Aye Chat: Terminal-first AI Code Generator

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate security concerns primarily due to its execution of shell commands and potential network risks, which warrant closer scrutiny before use.

  • High shell risk due to direct execution of shell commands
  • Potential network risks associated with external URL calls
Per-check LLM notes
  • Network: The network calls to external URLs could be for legitimate purposes like checking for updates or fetching configuration, but require further investigation.
  • Shell: Executing shell commands directly can pose significant security risks, especially if the commands are user-supplied or executed without proper validation.
  • Obfuscation: The obfuscation pattern is likely for code protection rather than malicious intent.
  • Credentials: No suspicious patterns for credential harvesting were detected.
  • Metadata: The package shows low effort in metadata and maintainer history, but there's no clear indication of malicious intent.

📦 Package Quality Overall: Low (4.6/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (7347 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 5.0

Partial type annotation coverage

  • 394 type-annotated function signatures detected in source
✦ High Multiple Contributors 10.0

Active multi-contributor project

  • 6 unique contributor(s) across 100 commits in acrotron/aye-chat
  • Active community — 5 or more distinct contributors

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

  • ify = _ssl_verify() with httpx.Client(timeout=TIMEOUT, verify=verify) as client: resp = cl
  • ast_status}") r = httpx.get(response_url, timeout=TIMEOUT, verify=verify) la
  • rify() try: with httpx.Client(timeout=10.0, verify=verify) as client: resp = c
  • " try: response = httpx.get( "https://pypi.org/pypi/ayechat/json",
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • self._progress_lock = __import__('threading').Lock() def execute_coarse_phase(self, file_list: L
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • s (vim, top, etc.) go through os.system() and return # {"message": ..., "exit_code": ...} -- not
  • try: exit_code = os.system(full_cmd_str) actual_exit_code = exit_code >> 8
  • """ try: result = subprocess.run( ["wl-paste", "--type", "image/png"],
  • """ try: result = subprocess.run( ["xclip", "-selection", "clipboard", "-t", "ima
  • p"): try: subprocess.run( [cmd, "--version"], capture
  • """ try: result = subprocess.run( ["git", "rev-parse", "--show-toplevel"],
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: acrotron.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository acrotron/aye-chat appears legitimate

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with ayechat 
Create a command-line utility named 'CodeMentor' using the Python package 'ayechat'. This tool will serve as an AI-assisted coding companion for developers, providing real-time code suggestions, explanations, and even full function implementations based on user input. The utility should support multiple programming languages such as Python, JavaScript, and Java.

**Core Features:**
1. **Code Completion**: Allow users to type a partial piece of code, and the utility should suggest possible completions based on context.
2. **Explanation Mode**: When the user requests it, the utility should provide a brief explanation of the code being written or suggested.
3. **Function Implementation**: Users should be able to ask for a specific function implementation (e.g., "write a function to sort an array"), and the utility should generate the corresponding code.
4. **Language Support**: Ensure that the utility supports at least three popular programming languages.
5. **Interactive Mode**: Provide an interactive mode where the user can have a conversation with the utility about their code, asking questions and receiving immediate feedback.

**How to Utilize 'ayechat':** 
- Integrate 'ayechat' into your utility to handle the AI-driven functionalities like code completion, explanations, and function generation. Use its terminal-first approach to ensure seamless integration into the command-line environment.
- Implement a command parser that translates user inputs into queries for 'ayechat', which then processes these queries to return relevant code snippets or explanations.
- Design a user-friendly interface that allows for easy navigation through different modes and features, making it simple for users to switch between code completion, explanation requests, and function implementations.

**Development Steps:**
1. Set up a Python virtual environment and install necessary packages including 'ayechat'.
2. Define the structure of your command-line utility, focusing on the main functionalities listed above.
3. Develop the command parser to interpret user commands and interact with 'ayechat' effectively.
4. Implement the core features of CodeMentor, ensuring each feature is thoroughly tested.
5. Add additional polish such as error handling, help documentation, and a start-up message.
6. Test the utility across different programming languages to ensure compatibility and functionality.

💬 Discussion Feed

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