axm-smelt

v0.1.1 suspicious
5.0
Medium Risk

Deterministic token compaction for LLM inputs

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risks due to potential obfuscation techniques and incomplete metadata, raising concerns about its legitimacy and purpose.

  • High obfuscation risk
  • Incomplete repository and author details
Per-check LLM notes
  • Network: No network calls detected, indicating low risk of data exfiltration or C2.
  • Shell: Shell executions appear to be for internal package operations like version checks and running commands, suggesting moderate risk without additional context.
  • Obfuscation: The use of obfuscation techniques like setting sys.stdin to a predefined value suggests attempts to hide code behavior, which may indicate malicious intent.
  • Credentials: No direct evidence of credential harvesting is present.
  • Metadata: The missing repository and author details raise concerns about the legitimacy of the package.

📦 Package Quality Overall: Medium (5.6/10)

✦ High Test Suite 9.0

Test suite present — 25 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 25 test file(s) detected (e.g. conftest.py)
✦ High Documentation 9.0

Well-documented package

  • Documentation URL: "Documentation" -> https://axm-protocols.github.io/axm-smelt/
  • 1 documentation file(s) (e.g. gen_ref_pages.py)
  • Detailed PyPI description (6452 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
  • 208 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

No suspicious network call patterns found

⚠ Code Obfuscation score 4.0

Found 2 obfuscation pattern(s)

  • keypatch.setattr("sys.stdin", __import__("io").StringIO("hello world")) with pytest.raises(SystemE
  • keypatch.setattr("sys.stdin", __import__("io").StringIO("hello world")) # Empty preset should eith
⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

  • ected() -> None: result = subprocess.run( ["uv", "run", "axm-smelt", "compact", "--strategies
  • lf) -> None: result = subprocess.run( [sys.executable, "-m", "axm_smelt.cli", "versio
  • de != 0: result = subprocess.run( [sys.executable, "-m", "axm_smelt.cli", "ve
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: axm-protocols.io>

✓ 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 axm-smelt 
Create a command-line utility named 'TokenCompressor' that leverages the 'axm-smelt' Python package to efficiently compact tokens for large language model inputs. This tool will serve as a bridge between developers and advanced AI systems, ensuring that input data is optimized for performance and cost-efficiency when interacting with these models.

**Step 1: Setup Environment**
- Ensure your development environment includes Python 3.x and pip.
- Install the 'axm-smelt' package using pip: `pip install axm-smelt`
- Set up a virtual environment for your project.

**Step 2: Define Core Functionality**
- Implement a function within 'TokenCompressor' that accepts raw text input from users.
- Use 'axm-smelt' to process this input, applying its deterministic token compaction algorithm to reduce the number of tokens.
- Output the compacted token sequence, which can then be fed into an LLM.

**Step 3: Enhance Usability**
- Add command-line arguments to specify input file paths or direct input text.
- Include options to customize compaction parameters if 'axm-smelt' allows for such flexibility.
- Provide verbose mode for detailed logging during the compaction process.

**Step 4: Integrate User Interface Improvements**
- Design a simple yet effective CLI interface that guides users through the process.
- Consider adding a help menu that explains each feature and argument available.
- Ensure error handling is robust, providing clear messages for common issues like incorrect input formats.

**Step 5: Testing and Validation**
- Develop test cases to validate the functionality of TokenCompressor.
- Test with various types of input data to ensure reliability across different scenarios.
- Compare the output token counts before and after compaction to verify efficiency gains.

**Suggested Features**:
- Option to save the compacted token sequence to a file.
- Support for batch processing multiple files at once.
- Integration with popular LLM APIs to streamline the workflow for users.
- Real-time feedback on compaction progress and statistics.

This project aims to demonstrate the practical application of 'axm-smelt' in optimizing interactions with large language models, making it easier for developers to work with these powerful tools.

💬 Discussion Feed

Leave a comment

No discussion yet. Be the first to share your thoughts!