AI Analysis
Final verdict: SAFE
The package shows no signs of malicious activities such as network calls, shell executions, or credential harvesting. The metadata risk is slightly elevated due to its novelty and limited maintainer activity, but there is insufficient evidence to conclude a supply-chain attack.
- No network calls detected.
- Minimal shell risk observed.
- No obfuscation or credential harvesting patterns found.
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package's functionality requires external communication.
- Shell: No shell execution patterns detected, indicating the package does not attempt to execute commands on the host system.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package is new with minimal activity and a single maintainer, raising some suspicion but not conclusive evidence of malice.
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
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: Very few commits: 2 total
Very few commits: 2 total
Maintainer History
score 4.0
2 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor "Sai Teja Erukude" 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 agentdog
Create a mini-application named 'AI Agent Tester' using the Python package 'agentdog'. This application will serve as a comprehensive evaluation suite for AI agents, allowing users to assess various aspects of their performance such as tool usage, grounding, safety, and efficiency. The goal is to provide developers and researchers with a user-friendly interface to conduct these evaluations without diving into complex configurations. Step 1: Setup - Install 'agentdog' and any necessary dependencies. - Create a clean Python project structure with appropriate directories for tests, utilities, and main application logic. Step 2: Define Evaluation Scenarios - Develop a series of predefined evaluation scenarios that cover different aspects like tool usage, grounding, safety, and efficiency. - Each scenario should include a description of the expected behavior, inputs, and outputs. Step 3: Implement the Core Functionality - Utilize 'agentdog' to execute each scenario against the target AI agent. - Capture and log the results of each evaluation, including any errors or unexpected behaviors. Step 4: User Interface - Design a simple command-line interface (CLI) that allows users to select which scenarios to run and view the results. - Optionally, extend the UI to include a web-based dashboard for more advanced users. Step 5: Reporting and Analysis - Implement a feature to generate detailed reports after each set of evaluations. - Include visualizations and summaries to help interpret the data easily. Suggested Features: - Support for multiple AI agents within a single run. - Configuration options to customize the evaluation parameters. - Integration with popular AI frameworks and libraries. - Real-time monitoring and alerts during long-running evaluations. How 'agentdog' is Utilized: - Use 'agentdog' to initialize the testing environment and configure the test cases. - Leverage its built-in functions to interact with the AI agents and collect data. - Employ 'agentdog' to analyze the collected data and produce meaningful insights about the AI agent's performance.