aura-llm

v0.15.0 suspicious
6.0
Medium Risk

Python SDK for Aura LLM Gateway - Open Responses API

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits high obfuscation risk due to the use of 'eval' with untrusted inputs, raising significant security concerns. While there are no direct signs of malicious activity or credential harvesting, the elevated obfuscation risk and the network interaction suggest caution is warranted.

  • High obfuscation risk due to 'eval' usage
  • Potential network interaction with external services
Per-check LLM notes
  • Network: The presence of HTTP/HTTPS client initialization suggests the package interacts with an external service, which is common but requires scrutiny to ensure it's not used for unauthorized data transfer.
  • Shell: No shell execution patterns detected, indicating low risk of direct system command execution.
  • Obfuscation: The use of 'eval' with an untrusted input is highly risky and often associated with malicious activities.
  • Credentials: No direct evidence of credential harvesting detected, but the presence of 'eval' raises concerns about potential misuse.
  • Metadata: The author has only one package, which may indicate a new or less active maintainer, but no other suspicious activities were flagged.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 4 test file(s) found

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

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/UmaiTech/aura-llm-gateway/tree/main/sdks/
  • Detailed PyPI description (7900 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
  • Type checker (mypy / pyright / pytype) referenced in project
  • 41 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 4 unique contributor(s) across 100 commits in UmaiTech/aura-llm-gateway
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • P client self._http = httpx.AsyncClient( base_url=self.base_url, headers=sel
  • P client self._http = httpx.Client( base_url=self.base_url, headers=sel
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • parser! result = eval(expression) return json.dumps({"result": result}
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ 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 UmaiTech/aura-llm-gateway appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Aura Team" 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 aura-llm 
Create a personalized note-taking app that leverages the power of large language models through the 'aura-llm' Python SDK. This app will allow users to create notes, categorize them, and most importantly, get insights and summaries from their notes using the Open Responses API provided by the Aura LLM Gateway. Here’s how you can develop this app step-by-step:

1. **Setup Environment**: Begin by setting up your Python environment and installing the 'aura-llm' package along with other necessary libraries such as Flask for web development.
2. **User Interface**: Design a simple yet user-friendly interface where users can log in, create accounts, and manage their notes. Consider using HTML/CSS/JavaScript for front-end development and Flask for back-end.
3. **Note Management**: Implement functionalities that allow users to add new notes, edit existing ones, and delete unwanted notes. Each note should have a title, content, and timestamp.
4. **Categorization**: Enable users to categorize their notes into different categories such as 'Work', 'Personal', 'Study', etc., to help them organize their thoughts better.
5. **Insight Generation**: Utilize the 'aura-llm' package to integrate the Open Responses API. This feature will analyze the content of the notes and provide summaries, key points, or even generate insights based on the data contained within the notes.
6. **Search Functionality**: Implement a search function that allows users to find specific notes or pieces of information quickly. This could involve indexing notes based on keywords and then searching through these indices.
7. **Security Measures**: Ensure that user data is secure by implementing basic security measures such as hashing passwords and encrypting sensitive data.
8. **Testing and Deployment**: Thoroughly test the application to ensure all features work as expected. Once satisfied, deploy the application to a server or cloud platform.

**Suggested Features**:
- Integration of voice-to-text functionality for easier note creation.
- Ability to export notes in various formats like PDF or DOCX.
- A dashboard showing analytics about note usage, such as most active days or categories.
- Support for collaboration, allowing multiple users to view and edit the same set of notes.

In each step, utilize the 'aura-llm' package to enhance the app's capabilities, especially when it comes to analyzing and summarizing the contents of the notes. This will not only make the app more useful but also showcase the potential of integrating advanced AI technologies into everyday applications.

💬 Discussion Feed

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