attune-rag

v0.5.1 suspicious
4.0
Medium Risk

Lightweight, LLM-agnostic RAG pipeline with pluggable corpora. Works with Claude, Gemini, or any LLM.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in network, shell, and obfuscation categories but raises concerns due to potential credential harvesting and sparse metadata from the maintainer.

  • Potential credential harvesting through misconfigured output paths.
  • Sparse author information and a single package from the maintainer.
Per-check LLM notes
  • Network: No network calls detected, which is normal unless the package requires internet access to function properly.
  • Shell: No shell execution patterns detected, indicating no direct system command execution.
  • Obfuscation: No signs of obfuscation detected.
  • Credentials: Potential risk of credential harvesting through misconfigured output paths.
  • Metadata: The author information is sparse and the maintainer has a single package, which could indicate a less established or potentially suspicious account.

📦 Package Quality Overall: Medium (5.6/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • Test runner config found: pyproject.toml
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://attune-rag.dev
  • Detailed PyPI description (25065 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 182 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 100 commits in Smart-AI-Memory/attune-rag
  • Two distinct contributors found

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 10.0

Found 4 credential access pattern(s)

  • check catches `--output # /etc/passwd`. But a user could ALSO type # `--output /private/etc/p
  • type # `--output /private/etc/passwd` directly — that path # doesn't get rewritten on resolu
  • guns (a typo'd ``--output /etc/passwd``), not to enforce a full jail. """ resolved =
  • t let a typo'd # --output /etc/passwd slip past the resolved-only check. raw_abs = str(Path(s
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: smartaimemory.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository Smart-AI-Memory/attune-rag appears legitimate

⚠ 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 attune-rag 
Create a personalized FAQ bot using the 'attune-rag' package that integrates with an LLM of your choice (e.g., Claude, Gemini). This bot will serve as an internal knowledge management tool for a company, allowing employees to ask questions about company policies, procedures, and frequently asked questions. The bot should have the following functionalities:

1. **Initialization**: Set up the environment by installing the 'attune-rag' package and configuring it to connect with your chosen LLM.
2. **Corpus Setup**: Prepare a corpus of documents containing the company's FAQs, policies, and procedures. Ensure that the documents are structured and easily searchable.
3. **Query Interface**: Develop a simple command-line interface where users can type their queries related to the company's internal documentation.
4. **Answer Generation**: Utilize 'attune-rag' to generate answers based on the user's query and the content from the prepared corpus. The answer generation process should be efficient and provide accurate responses.
5. **Feedback Loop**: Implement a feedback mechanism where users can rate the accuracy and relevance of the provided answers. This feedback should be used to improve the performance of the bot over time.
6. **User Authentication**: Integrate basic user authentication to ensure that only authorized employees can access the bot.
7. **Logging**: Maintain logs of all interactions for auditing purposes.
8. **Customization**: Allow customization of the bot's behavior and appearance through configuration files.

Use 'attune-rag' to streamline the retrieval and generation processes, making sure the bot can scale well with more data and users. Additionally, focus on making the integration between the bot and the LLM seamless and efficient.

💬 Discussion Feed

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