axiom-agent

v0.2.1 suspicious
6.0
Medium Risk

The first agent runtime with built-in epistemic honesty

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of unusual network behavior and has a questionable maintenance history, which together suggest potential risks that cannot be fully ruled out as benign.

  • Unusual network calls to external APIs
  • Suspicious maintainer history and commit patterns
Per-check LLM notes
  • Network: Unusual network calls to external APIs suggest potential data exfiltration or unauthorized API usage.
  • Shell: No shell execution patterns detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets and credentials.
  • Metadata: The package has suspicious maintainer history and git repository commit patterns, indicating potential risk.

📦 Package Quality Overall: Low (3.0/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (5376 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 5.0

Partial type annotation coverage

  • 55 type-annotated function signatures detected in source
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 10 commits in AILIFE1/axiom
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

  • ns, }).encode() req = urllib.request.Request( GROQ_URL, data=body, header
  • , ) try: with urllib.request.urlopen(req, timeout=20) as r: return json.loads
  • }).encode() req = urllib.request.Request( "https://api.groq.com/openai/v1/chat/co
  • POST", ) with urllib.request.urlopen(req, timeout=20) as r: return json.loads
  • try: resp = requests.post( "https://cathedral-ai.com/verify/peer",
✓ Code Obfuscation

No obfuscation patterns detected

✓ 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 score 2.5

Git history flags: All 10 commits happened within 24 hours

  • All 10 commits happened within 24 hours
⚠ 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 axiom-agent 
Create a fully-functional mini-app that serves as a personal knowledge management tool using the 'axiom-agent' Python package. This app should allow users to input facts or pieces of information and have the app maintain an honest record of these facts while also being able to answer questions based on the recorded data. Here's how you can structure your project:

1. **Project Setup**: Start by setting up a new Python project. Make sure to install the 'axiom-agent' package.
2. **User Interface**: Design a simple command-line interface where users can interact with the app. Users should be able to add new facts, query existing ones, and delete facts if necessary.
3. **Core Functionality**: Utilize the 'axiom-agent' package to ensure that all operations are performed with epistemic honesty. This means that the app must only confirm the existence of facts it has been explicitly told about and should not make assumptions beyond what has been directly inputted.
4. **Data Storage**: Implement a method to store the user's facts persistently. This could be done via local files or a simple database system.
5. **Query System**: Develop a robust query system that allows users to ask questions in natural language. The app should parse these queries and return accurate answers based on the stored facts.
6. **Validation Mechanism**: Incorporate a feature that validates the integrity of the stored facts against the current state of the app. This ensures that the app remains honest even if there are attempts to manipulate the data.
7. **Testing**: Write tests to ensure that the app functions correctly under various scenarios, including edge cases where users might try to trick the app into providing false information.
8. **Documentation**: Provide clear documentation explaining how to use the app, its limitations, and how it leverages the 'axiom-agent' package for maintaining epistemic honesty.

By following these steps, you will create a reliable personal knowledge management tool that demonstrates the unique capabilities of the 'axiom-agent' package.

💬 Discussion Feed

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