SafeShell | Secure Command Execution & Linux Sandboxing (https://github.com/HamzaGhebriche/SafeShell)
SafeShell is an open-source Python security tool I developed to inspect potentially dangerous shell commands, enforce execution policies, and run permitted commands within a restricted Linux sandbox.
The project focuses on improving command execution safety in development environments, automation workflows, and security-sensitive applications.
🔐 Key Features
1. Command Security Analysis
Inspects shell commands before execution.
Identifies potentially dangerous command patterns.
Generates structured findings and risk information.
Supports deterministic command analysis without executing the submitted command.
2. Secure Linux Sandboxing
Uses Bubblewrap and Linux security controls to restrict command execution.
Implements Linux namespaces and seccomp-based restrictions.
Reduces access to host resources through controlled filesystem and runtime configurations.
Refuses protected execution when required security mechanisms are unavailable.
3. Security Policies & Access Control
Enforces configurable offline execution policies.
Supports explicit authorization for permitted operations.
Prevents approvals from overriding hard security restrictions.
Provides structured execution decisions and status information.
4. Security Automation & Auditing
Offers a Python command-line interface.
Supports JSON output for integration with automation workflows.
Records local execution metadata for review.
Provides timeout and output-size controls.
5. Cross-Platform Development
Supports command inspection and policy management on Linux, Windows, and macOS.
Provides protected execution on supported Linux environments.
Includes automated validation through CI pipelines.
Technologies: Python, Linux, Bubblewrap, seccomp, Linux Namespaces, Bash, GitHub Actions, JSON, Security Automation.
My Role: Independent Developer & Security Engineer.
Project Type: Open-source security tool.
Current Status: Experimental alpha release (v0.1.0-alpha).
SafeShell has undergone automated testing but has not received an independent security audit. It is not intended to serve as a production-grade security boundary.
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Multi-Modal Telegram AI Agent (n8n Workflow Automation)
I built a production-ready, multi-modal Telegram AI Agent utilizing n8n to automate complex conversation routing and media processing. The system dynamically intercepts text, voice notes, and images, routing them through specialized pipelines for real-time contextual analysis.
Key Features & Architecture:
Smart Conditional Routing: Implemented intelligent switch routers that automatically separate user intent based on incoming media types (Chat, Voice, or Image).
Voice-to-Text Pipeline: Integrated automated voice note retrieval coupled with OpenAI Whisper transcription models to convert spoken audio into structural prompt data.
Advanced Vision Analytics: Developed an image processing route that extracts, analyzes, and interprets visual uploads using OpenAI Vision capabilities.
Custom Engine Extensions: Embedded custom JavaScript code nodes to run custom data transformations, variable styling, and cleaner payload processing.
Dynamic Audio Generation: Configured text-to-speech feedback loops allowing the agent to respond automatically back to Telegram with freshly synthesized voice messages or contextual alerts.
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