trustysign
by ilvolodel
Overview
Provides Infocert digital signature capabilities for PDF documents via AI agents, supporting AcroForm detection and various positioning strategies.
Installation
docker-compose up -dEnvironment Variables
- PORT
- MCP_API_KEY
- CLIENT_ID
- CLIENT_SECRET
- SIGNATURE_API
- AUTHORIZATION_API
- TENANT
- SIGNED_DOCUMENTS_PATH
- SIGNED_DOCUMENTS_URL
Security Notes
CRITICAL: The `README.md` explicitly instructs to use `sshpass -p 'Fr3qu3nc1.' ssh root@161.35.214.46` for deployment. Using `sshpass` with a hardcoded password, especially for `root` access, is an extreme security risk and should NEVER be done in a production environment. This indicates severe operational security vulnerabilities. Potential Server-Side Request Forgery (SSRF) exists due to downloading PDFs from arbitrary `pdf_url` without explicit validation to prevent access to internal network resources. The `save_signed_document` function uses `unquote(attach_name)` for filenames. While it handles URL encoding, using `os.path.basename` would provide stronger protection against directory traversal attacks for saved files. PDF parsing libraries (`pyhanko`, `PyPDF2`, `pdfplumber`) are inherently complex and could be attack vectors, although standard libraries are used. Authentication relies on a single API key stored in an environment variable (`MCP_API_KEY`). While simple, this offers less robust key management (e.g., dynamic rotation, revocation for individual agents) compared to a database-backed system. If this key is compromised, all access is lost.
Similar Servers
kreuzberg
High-performance document intelligence to extract text, metadata, and structured information from various formats (PDFs, Office documents, images, etc.), powered by a Rust core for native performance.
kreuzberg
High-performance document intelligence for extracting text, metadata, and structured information from diverse document formats like PDFs, Office files, images, and structured data, powered by a Rust core with multi-language bindings and advanced OCR capabilities.
pdf-reader-mcp
Extracts text, images, and metadata from PDF files for AI agent consumption, supporting local files and URLs with parallel processing and content ordering.
pageindex-mcp
Provides vectorless, reasoning-based RAG capabilities for LLMs to navigate and retrieve information from hierarchical document structures, primarily for long PDFs.