AI Analysis
Final verdict: SUSPICIOUS
The package exhibits moderate risks due to its use of exec and compile for code execution and direct shell command execution, which could potentially hide malicious activities or enable unauthorized actions.
- High obfuscation risk due to exec and compile usage.
- Significant shell risk due to direct shell command execution.
Per-check LLM notes
- Network: The network calls appear to be legitimate API interactions for health checks and stats retrieval.
- Shell: Executing shell commands directly from a package can indicate potential risk, such as unauthorized system information gathering.
- Obfuscation: The use of exec and compile to read and execute code suggests potential obfuscation, which may hide malicious activities.
- Credentials: No clear patterns indicative of credential harvesting were found.
- Metadata: The repository was created recently and the maintainer has limited history, which raises some suspicion but not conclusive evidence of malice.
Package Quality Overall: Medium (6.0/10)
✦ High
Test Suite
9.0
Test suite present — 10 test file(s) found
Test runner config found: pyproject.toml10 test file(s) detected (e.g. test_advanced.py)
◈ Medium
Documentation
5.0
Some documentation present
Detailed PyPI description (8862 chars)
○ Low
Contributing Guide
4.0
No contributing guide or governance files found
Development Status classifier >= Beta
◈ Medium
Type Annotations
7.0
Partial type annotation coverage
Classifier: Typing :: Typed777 type-annotated function signatures detected in source
◈ Medium
Multiple Contributors
5.0
Limited contributor diversity
1 unique contributor(s) across 77 commits in kyssta-exe/AriadneSingle author but highly active (77 commits)
Heuristic Checks
Outbound Network Calls
score 9.0
Found 6 network call pattern(s)
try: resp = requests.get(f"{url}/health", timeout=5) if resp.status_code.api_key}" resp = requests.get(f"{url}/stats", headers=headers, timeout=5) if rself._session = requests.Session() retry_strategy = Retry( total=px self._client = httpx.AsyncClient( timeout=httpx.Timeout(self._timeout),port httpx resp = httpx.get(f"{self._base_url}/api/tags", timeout=5.0) returonotonic() async with httpx.AsyncClient(timeout=self._timeout) as client: resp = await c
Code Obfuscation
score 2.0
Found 1 obfuscation pattern(s)
adne/__init__.py') as _f: exec(compile(_f.read(), 'x', 'exec'), _pkg.__dict__) from ariadne impor
Shell / Subprocess Execution
score 4.0
Found 2 shell execution pattern(s)
M}") print(f" Hardware: {os.popen('cat /proc/cpuinfo | head -5 | tail -1').read().strip()}")trip()}") print(f" RAM: {os.popen('free -h | head -2').read().strip()}") print() resu
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: users.noreply.github.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
score 2.5
Git history flags: Repository created very recently: 3 day(s) ago (2026-06-04T05:52:10Z)
Repository created very recently: 3 day(s) ago (2026-06-04T05:52:10Z)
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 arriadne
Develop a conversational AI assistant named 'MemoryMate' that leverages the 'arriadne' package to enhance user interactions through advanced memory management and context-aware responses. This mini-application should serve as a personal assistant, capable of remembering past conversations, understanding context, and providing relevant information based on previous interactions. Here’s a detailed breakdown of the steps and features you will implement: 1. **Setup**: Install and import the necessary packages including 'arriadne'. Initialize the AI agent with default settings that support OpenAI or Anthropic models. 2. **User Interaction Module**: Design a simple command-line interface where users can input questions or commands. Implement a function that parses these inputs and sends them to the AI agent for processing. 3. **Memory Management**: Utilize 'arriadne' to manage conversation history. This includes storing each interaction, identifying key entities, and using a hybrid search technique (FAISS + FTS5 + RRF) to quickly retrieve relevant past interactions when needed. 4. **Contextual Understanding**: Enhance the AI's ability to understand context by implementing a feature that allows the AI to recall previous conversations and use this information to provide more accurate and relevant responses. 5. **Knowledge Graph Integration**: Incorporate a basic knowledge graph within 'arriadne' to help the AI understand relationships between different pieces of information and entities mentioned during conversations. 6. **Temporal Awareness**: Ensure that the AI can differentiate between past and current contexts, using temporal markers to prioritize recent interactions over older ones. 7. **Entity Resolution**: Implement a feature that helps the AI correctly identify and resolve ambiguous references to entities mentioned in conversations. 8. **Feedback Loop**: Allow users to give feedback on the AI's responses, which can then be used to refine future interactions and improve the AI's performance over time. 9. **Testing and Optimization**: Test the application thoroughly with various scenarios to ensure it functions as expected. Optimize the code for efficiency and accuracy. By following these steps and utilizing the powerful features provided by the 'arriadne' package, your MemoryMate application will become a robust tool for managing and enhancing human-AI interactions.