attocode

v0.2.25 suspicious
6.0
Medium Risk

Production AI coding agent

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of potential code obfuscation and dynamic code execution, raising concerns about its security posture. While there are no immediate indications of malicious intent, the risks associated with these features cannot be ignored.

  • High obfuscation risk due to 'eval' usage
  • Potential for unexpected behavior due to shell command execution
Per-check LLM notes
  • Network: No network calls detected, thus minimal risk.
  • Shell: The use of git commands suggests the package may be performing version control operations locally, which is not inherently malicious but could indicate unexpected behavior depending on the context.
  • Obfuscation: The use of 'eval' for a fraction of tasks suggests potential code obfuscation or execution of dynamically generated code, which is often used in malicious contexts.
  • Credentials: No direct evidence of credential harvesting was found, but caution should be exercised as dynamic code execution can indirectly lead to security vulnerabilities.
  • Metadata: The maintainer has only one package, suggesting they may be new or less active, but no other red flags are present.

📦 Package Quality Overall: Medium (5.4/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (8913 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
  • Type checker (mypy / pyright / pytype) referenced in project
  • 241 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 100 commits in eren23/attocode
  • Single author but highly active (100 commits)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • Fraction of tasks assigned to eval (default 0.3). seed: Hash seed for reproducibility.
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • arget.is_dir() else None subprocess.run( ["git", "clone", "--depth", "1", clone_url, str(tar
  • n"} try: result = subprocess.run( ["git", "rev-parse", "--short", "HEAD"],
  • dout.strip() result = subprocess.run( ["git", "rev-parse", "--abbrev-ref", "HEAD"],
  • o.startswith("git@"): subprocess.run( ["git", "clone", "--depth=1", instance.repo, in
  • isdir(instance.repo): subprocess.run( ["git", "clone", instance.repo, instance_dir],
  • instance.base_commit: subprocess.run( ["git", "checkout", instance.base_commit],
✓ 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 eren23/attocode appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "eren23" 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 attocode 
Create a fully-functional mini-application called 'CodeSuggester' that leverages the 'attocode' package to assist developers in generating code snippets based on natural language descriptions. The application should have a user-friendly interface where developers can input a description of what they want to achieve in their code, and the app will generate corresponding code snippets using AI-driven suggestions.

Step-by-step instructions:
1. Set up a basic Python environment with necessary libraries installed, including the 'attocode' package.
2. Design a simple command-line interface or a graphical user interface (GUI) for users to interact with.
3. Implement functionality within the application to accept text input from the user describing the desired code behavior.
4. Use the 'attocode' package to process the user's input and generate appropriate code snippets.
5. Display the generated code snippet back to the user for review and use.
6. Optionally, add features such as saving the generated code to a file, allowing users to specify programming languages, and providing explanations for each generated code block.

Suggested Features:
- Support for multiple programming languages (Python, JavaScript, etc.).
- Integration with common development environments (IDEs).
- Option to provide alternative code snippets based on different coding styles or best practices.
- Ability to save and manage generated code snippets in a local database or cloud storage.
- User feedback mechanism to improve the quality of generated code over time.

How 'attocode' is Utilized:
- The 'attocode' package will be the core technology behind the application, handling the natural language processing (NLP) and code generation tasks. Users will describe their coding needs through text inputs, and 'attocode' will analyze these descriptions to produce accurate and efficient code snippets tailored to the specified requirements.

💬 Discussion Feed

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