atomicguard

v2.29.1 suspicious
7.0
High Risk

A Dual-State Agent Framework for reliable LLM code generation with guard-validated loops

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits a moderate to high risk due to its shell execution capabilities and incomplete metadata, with potential for supply-chain attacks.

  • High shell risk indicating potential for executing arbitrary code.
  • Sparse maintainer information and missing repository increasing suspicion.
Per-check LLM notes
  • Network: The network call to an external API suggests potential data exchange, which could be benign but might also indicate data exfiltration.
  • Shell: Executing commands through the shell can significantly alter system state and poses a high risk if not properly controlled, suggesting possible execution of arbitrary code.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
  • Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
  • Metadata: The repository is not found, and the maintainer's information is sparse, raising suspicion.

📦 Package Quality Overall: Medium (5.2/10)

✦ High Test Suite 9.0

Test suite present — 7 test file(s) found

  • Test runner config found: pyproject.toml
  • Test runner config found: conftest.py
  • 7 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/thompsonson/atomicguard#readme
  • Detailed PyPI description (8221 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

  • Classifier: Typing :: Typed
  • 243 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • > bool: try: with urllib.request.urlopen(f"{OLLAMA_URL}/api/tags", timeout=2) as r:
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • ontent as a shell command via subprocess.run(). This is the ONLY component that may mutate world state (
  • try: result = subprocess.run( command, shell=True, # noq
  • try: result = subprocess.run( self._undo_command, shell=T
  • %s", cmd) completed = subprocess.run(cmd, capture_output=True, text=True, check=False) if
  • command, shell=True, # noqa: S602 capture_output=True,
  • undo_command, shell=True, # noqa: S602 capture_output=True,
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ 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 atomicguard 
Create a mini-application named 'CodeGuardian' using Python and the 'atomicguard' package. CodeGuardian aims to streamline the process of generating and validating Python code snippets through interactions with a large language model (LLM). The application should have the following core functionalities:

1. **User Interface**: Design a simple command-line interface (CLI) where users can input their coding challenges or requests for code generation.
2. **Code Generation**: Utilize an LLM to generate Python code based on user inputs. Ensure the LLM can handle various levels of complexity from basic function creation to more intricate problem-solving tasks.
3. **AtomicGuard Integration**: Implement 'atomicguard' to manage the interaction between the CLI and the LLM. Use 'atomicguard's dual-state agent framework to ensure that code generation processes are reliable and efficient. Specifically, employ guard-validated loops to continuously request and refine code until it meets specified criteria or passes validation tests.
4. **Validation Mechanism**: Develop a system within the application to validate the generated code snippets. This could include syntax checks, unit tests, or even integration tests depending on the complexity of the task. The validation results should feedback into the guard-validated loop managed by 'atomicguard', allowing for iterative refinement of the generated code.
5. **Feedback Loop**: Ensure there's a mechanism for users to provide feedback on the generated code, which can then be used to further refine the output through additional iterations controlled by 'atomicguard'.
6. **Logging and Reporting**: Implement logging for all interactions and validations performed by the application. Provide users with reports summarizing the code generation process, including any issues encountered and how they were resolved.

The goal of CodeGuardian is to demonstrate the power of combining human input with machine-generated solutions while ensuring reliability and accuracy through automated validation and iterative refinement.

💬 Discussion Feed

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