AutoRAG-Research

v0.0.8 suspicious
5.0
Medium Risk

Automate your RAG research.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits some behaviors that could indicate potential risks, particularly concerning its metadata and shell execution capabilities. While not conclusively malicious, these factors warrant further scrutiny.

  • Repository not found
  • Sparse author details
  • Execution of shell commands
Per-check LLM notes
  • Network: Fetching file lists from GitHub is likely for updating or checking for updates, which is common and generally benign.
  • Shell: Executing shell commands can be risky if not properly sanitized or controlled, but it may also be necessary for certain functionalities like running scripts or tools.
  • Obfuscation: The observed patterns appear to be related to common data handling and model evaluation practices rather than malicious obfuscation.
  • Credentials: No clear signs of credential harvesting or secret theft were detected.
  • Metadata: The repository is not found, and the author details are sparse, indicating potential risks.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • = 0 failed = 0 with httpx.Client(timeout=30.0) as client: # Fetch file list from GitH
⚠ Code Obfuscation score 10.0

Found 5 obfuscation pattern(s)

  • ch.group(2) image_bytes = base64.b64decode(base64_data) return image_bytes, mimetype def load_ima
  • f.device) self._model.eval() def embed_query(self, text: str) -> list[float]:
  • f.device) self._model.eval() def embed_text(self, text: str) -> MultiVectorEmbeddi
  • eckpoint) self._model.eval() self._model.to(device) self._loss_fct = to
  • ) self.model.eval() self.model.to(device) self.pos_id = self.
⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • try: result = subprocess.run( # noqa: S603 cmd, env=env,
✓ 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 AutoRAG-Research 
Create a mini-research assistant application using the Python package 'AutoRAG-Research'. This application will serve as a tool for researchers to streamline their literature review process by automating the retrieval and summarization of relevant academic papers based on user input queries. The application should allow users to input a query related to their field of study, and it should return a concise summary of the most pertinent research papers available online.

### Core Features:
1. **Query Input**: Users can enter a topic or question they are researching.
2. **Paper Retrieval**: Utilize AutoRAG-Research to automatically search and retrieve relevant academic papers from various sources such as Google Scholar, PubMed, arXiv, etc.
3. **Summary Generation**: Automatically generate summaries of each retrieved paper, highlighting key points, methodologies, results, and conclusions.
4. **User Interface**: Develop a simple web-based interface where users can interact with the application. This could be built using Flask or Django for backend and HTML/CSS/JavaScript for frontend.
5. **Save & Share**: Allow users to save summaries locally or share them via email or social media platforms.
6. **Feedback Loop**: Implement a mechanism where users can rate the relevance and quality of the retrieved papers and summaries, which can be used to improve future searches.

### Utilizing AutoRAG-Research:
- Use AutoRAG-Research's capabilities to handle the complex task of retrieving and processing large volumes of academic data efficiently. Specifically, leverage its automatic retrieval and generation functionalities to ensure that the application not only finds the right papers but also provides insightful summaries.
- Integrate AutoRAG-Research into the backend logic of your application, ensuring that all interactions with external databases and services are handled seamlessly and securely.
- Customize the summarization models provided by AutoRAG-Research to better suit specific fields of study, if necessary.

This project aims to significantly reduce the time and effort required for researchers to conduct thorough literature reviews, making the research process more efficient and accessible.