AI Analysis
Final verdict: SUSPICIOUS
The package exhibits a high level of risk due to its suspicious use of shell commands and potential unauthorized access to external services. While there are no clear signs of obfuscation or credential harvesting, the combination of these factors raises concerns about its legitimacy.
- High shell risk due to execution of shell commands
- Potential unauthorized access through use of tokens
Per-check LLM notes
- Network: The use of urllib for network requests could be legitimate, but the inclusion of a token suggests potential unauthorized access to external services.
- Shell: The execution of shell commands via subprocess.run is highly suspicious and could indicate the package is designed to execute arbitrary code, posing a significant risk.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has only one package, which may indicate a new or less active account, raising some suspicion but not enough to conclusively determine malice.
Heuristic Checks
Outbound Network Calls
score 3.0
Found 2 network call pattern(s)
= f"token {token}" req = urllib.request.Request(url, headers=headers) try: with urllib.readers) try: with urllib.request.urlopen(req, timeout=timeout) as resp: # noqa: S310 — expli
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
score 10.0
Found 6 shell execution pattern(s)
10.0 try: proc = subprocess.run( argv, capture_output=True, text=True, timeout=onic() try: proc = subprocess.run( argv, capture_output=True, text=True, timeout=tprintable}") result = subprocess.run(cmd, capture_output=True, text=True) if result.stdouesolved sp, and runs it via ``subprocess.run(shell=True)`` inside aexp's ``run_lifecycle`` so statusone try: result = subprocess.run( argv, capture_output=True,] = 0x00000200 proc = subprocess.Popen(command, **popen_kwargs) return proc, None # POS
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: gmail.com
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository kadenmc/agentic-experiments appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Kaden McKeen" 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 agentic-experiments
Create a small but comprehensive project named 'AgenticExperimentDemo' that leverages the 'agentic-experiments' package to conduct controlled experiments on a simple machine learning model. This project will demonstrate how researchers can track hypotheses, execute experiments locally using signac, and observe results remotely via Weights & Biases (W&B). Your goal is to develop a system that automates the entire research workflow from formulating a hypothesis to analyzing experimental outcomes. Steps: 1. Define a clear research question or hypothesis related to improving the performance of a basic classification model (e.g., tuning hyperparameters). 2. Use 'agentic-experiments' to set up a Git-first experiment tracking system where each experiment version is linked to a specific commit. 3. Implement a script that automatically generates different configurations based on your hypothesis (e.g., varying learning rates, batch sizes). 4. Utilize signac for managing the local execution states of these experiments, ensuring reproducibility and easy access to experiment data. 5. Integrate Weights & Biases for remote monitoring and analysis of your experiments, allowing you to visualize trends and compare performances across different configurations. 6. Write a report summarizing your findings, including insights gained from the data visualization provided by W&B. Features: - A user-friendly interface for specifying experiment parameters. - Automated hypothesis testing based on defined configurations. - Seamless integration with Git for version control and collaboration. - Local experiment management using signac. - Remote observability through Weights & Biases, enabling real-time monitoring and comparison of experiment outcomes. By completing this project, you'll gain hands-on experience with 'agentic-experiments', understand its capabilities in supporting agent-driven ML research, and learn best practices for conducting reproducible and observable experiments.