algovoi-atb

v0.1.2 suspicious
6.0
Medium Risk

AlgoVoi Agent Trust Bench client — run your agent through 166 adversarial x402 payment profiles and earn a Falcon-1024 signed reputation certificate.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has moderate risks due to network calls and potential data obfuscation, though no direct evidence of malicious intent was found. The missing repository and sparse maintainer information add to the uncertainty.

  • moderate network risk
  • potential data obfuscation
  • missing repository and sparse maintainer information
Per-check LLM notes
  • Network: The package makes network calls which could be legitimate for fetching updates or external resources, but warrants further investigation into the endpoints and data being exchanged.
  • Shell: No shell execution patterns detected.
  • Obfuscation: The observed base64 decoding and URL-safe base64 decoding may indicate an attempt to obfuscate data, but could also be legitimate for handling cryptographic keys or signatures.
  • Credentials: No clear patterns of credential harvesting detected.
  • Metadata: The repository is not found, and the maintainer information is sparse, indicating potential unreliability.

📦 Package Quality Overall: Medium (5.0/10)

◈ 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://docs.algovoi.co.uk/agent-trust-bench
  • Detailed PyPI description (3271 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 :: Typed
  • 26 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

  • E_PATH}" self._http = httpx.AsyncClient( timeout=timeout, follow_redirects=T
  • str | None = None with httpx.Client(follow_redirects=True, timeout=30) as client: # Cre
  • : async with httpx.AsyncClient(follow_redirects=True, timeout=10) as client:
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • try: pk_bytes = base64.b64decode(pk_b64) sig_bytes = _b64url_decode(sig_b64)
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: algovoi.co.uk>

✓ 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 algovoi-atb 
Create a Python-based mini-application named 'TrustBenchAnalyzer' that leverages the 'algovoi-atb' package to evaluate the trustworthiness of AI agents. This tool will simulate various adversarial scenarios to test the robustness of these agents in handling complex payment profiles. Here’s a detailed breakdown of the project steps and features:

1. **Setup Environment**: Ensure Python 3.8+ is installed. Install necessary packages including 'algovoi-atb', 'pandas', and 'matplotlib'.
2. **Application Structure**: Design a modular structure with classes for 'Agent', 'PaymentProfile', and 'Evaluator'.
3. **Agent Definition**: Define an 'Agent' class that can interact with the 'algovoi-atb' API. This class should handle authentication and method calls.
4. **Payment Profile Generation**: Implement a 'PaymentProfile' class that generates or loads predefined adversarial payment profiles based on the 'algovoi-atb' specifications.
5. **Evaluation Mechanism**: Develop an 'Evaluator' class that runs the defined payment profiles against the specified agents. This should utilize the 'algovoi-atb' package to execute the tests and collect results.
6. **Result Analysis**: Integrate functionality within the 'Evaluator' class to analyze the collected data, using metrics like success rate, error types, and response times.
7. **Visualization**: Use 'matplotlib' to visualize the evaluation results, providing insights into the performance of each agent under different adversarial conditions.
8. **User Interface**: Create a simple command-line interface (CLI) for interacting with the application. Users should be able to specify the agent to test, select payment profiles, and view the results.
9. **Documentation**: Write comprehensive documentation detailing how to install and use the application, along with examples.

Suggested Features:
- Allow users to upload custom payment profiles for testing.
- Provide options to save and load previous test results.
- Implement logging to track application execution and errors.
- Offer a report generation feature that outputs a summary of the evaluation in PDF format.

By following these steps and incorporating these features, 'TrustBenchAnalyzer' will serve as a powerful tool for assessing the reliability and security of AI agents in financial transactions.