agentorch

v1.1.1 suspicious
6.0
Medium Risk

DevTorch — AI reasoning capture, audit trail, and governance for agent-assisted development

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits several concerning behaviors including high network and shell execution risks, potential credential misuse, and incomplete metadata. These factors collectively raise suspicion but do not conclusively indicate malicious intent.

  • High network and shell execution risks
  • Potential credential misuse
  • Incomplete metadata
Per-check LLM notes
  • Network: The network patterns suggest potential unauthorized communication and data retrieval, which may indicate an attempt at data exfiltration or command and control activity.
  • Shell: The shell execution patterns involve git commands that could be used to gather sensitive information about the repository, suggesting possible reconnaissance or data leakage.
  • Obfuscation: No signs of malicious obfuscation techniques observed.
  • Credentials: Potential risk of unauthorized access due to direct environment variable retrieval without proper validation or masking.
  • Metadata: The repository is not found, and the maintainer information is incomplete, indicating potential risks.

📦 Package Quality Overall: Low (3.6/10)

○ Low Test Suite 1.0

No test suite detected

  • No test files or test-runner configuration detected
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/hemantcgi/DevTorch/blob/main/Implementati
  • Detailed PyPI description (20115 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 335 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 9.0

Found 6 network call pattern(s)

  • = 8765 try: with socket.create_connection(("127.0.0.1", port), timeout=1): print(f"Proxy r
  • secrets/public-key" req = urllib.request.Request( url, headers={ "Authori
  • n", }, ) with urllib.request.urlopen(req, timeout=10) as resp: pub_key_data = _js
  • /{secret_name}" put_req = urllib.request.Request( put_url, data=payload, method="PUT",
  • n", }, ) with urllib.request.urlopen(put_req, timeout=10): pass print(f"GitHu
  • pi/3/issue" req = urllib.request.Request( api_url, data=data,
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

  • les_changed}.""" result = subprocess.run( ["git", "log", f"-{n}", "--format=%H\t%s", "--name-
  • None.""" try: r = subprocess.run( ["git", "remote", "get-url", "origin"],
  • ound.""" try: r = subprocess.run( ["git", "rev-parse", branch], captu
⚠ Credential Harvesting score 5.0

Found 2 credential access pattern(s)

  • rt json as _json token = os.environ.get("GITHUB_TOKEN", "") if not token: print("Warning: GITHUB_TOKE
  • metadata={"region": os.environ.get("AWS_DEFAULT_REGION", "")}, ) class AzureProvider:
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: flotorch.ai>

✓ 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 agentorch 
Create a mini-application called 'CodeAuditTool' using the Python package 'agentorch'. This tool aims to streamline the process of auditing and managing code changes made during development sessions where AI agents are involved. Here’s a detailed plan on how to build it:

1. **Project Setup**: Initialize a new Python environment and install the necessary packages including 'agentorch', 'requests', and 'flask'.
2. **Feature Overview**:
   - **AI Reasoning Capture**: Integrate 'agentorch' to capture the reasoning behind each decision made by AI agents during code generation.
   - **Audit Trail**: Maintain a log of all changes made to the codebase, including timestamps, user actions, and AI suggestions.
   - **Governance Interface**: Develop a simple web interface using Flask to visualize the audit trail and reasoning logs.
3. **Implementation Steps**:
   - **Step 1**: Define a class in 'agentorch' to initialize the AI agent with specific parameters like model type, input/output formats, etc.
   - **Step 2**: Implement functions within your application to call upon these agents for code modifications or suggestions.
   - **Step 3**: Use 'agentorch' capabilities to record the AI's reasoning process and decisions in real-time as part of the audit trail.
   - **Step 4**: Store these records in a structured format (e.g., JSON files or a database).
   - **Step 5**: Build a Flask app that allows users to view the audit trail, filter by date/user/operation, and review the captured reasoning from AI agents.
4. **Testing and Validation**:
   - Test the application with various scenarios involving different types of code changes and AI interventions.
   - Ensure that the audit trail accurately reflects all actions taken and that the reasoning capture is coherent and useful.
   - Validate the user interface for ease of use and accessibility of information.
5. **Deployment Considerations**:
   - Plan for scalability if the application is intended for large-scale use.
   - Consider security measures for handling sensitive data.
6. **Documentation and Support**:
   - Provide comprehensive documentation on how to set up the application and integrate it into existing workflows.
   - Offer support channels for users encountering issues or needing guidance.