The AIM-Guard-MCP server provides AI-powered security tools for protecting AI agents during interactions with MCPs and external services.
AI Safety Guard: Provides contextual security instructions and precautions based on MCP type, operation, and data sensitivity levels.
Text Guard Analysis: Analyzes text content for harmful or inappropriate material using the AIM Intelligence API, delivering real-time safety assessments.
Security Prompt Enhancement: Enhances user prompts with security instructions at configurable levels (basic, standard, strict) to proactively prevent threats.
Integration: Works seamlessly with MCP-compatible AI assistants and connects to AIM Intelligence API for advanced analysis.
Customization: Supports customizable security levels and detailed reporting for secure AI interactions.
Mentioned in the context of repository management, GitHub releases, and as part of the development workflow
Used for automated CI/CD pipeline to build, test, and publish the MCP to NPM
Integrated for deployment workflow visualization in documentation
Integration for package deployment and publishing through NPM's registry
Used for package management in development workflow
Provides security guidelines and precautions for AI agents when interacting with Slack, ensuring safe message operations with appropriate sensitivity-level handling
Used as the implementation language for the MCP server
Integrated for validation of inputs and data structures
AIM Guard MCP
π‘οΈ AIM MCP Server :: Guard and Protect your MCPs & AI Agents
A Model Context Protocol (MCP) server that provides AI-powered security analysis and safety instruction tools. This server helps protect AI agents by providing security guidelines, content analysis, and cautionary instructions when interacting with various MCPs and external services.
Features
π§ Tools (6 total)
π‘οΈ AI Safety Guard: Contextual security instructions for MCP interactions
π Text Guard Analysis: Harmful content detection using AIM Intelligence API
π Security Prompt Enhancement: Add security layers to user prompts
π¨ Prompt Injection Detector: OWASP LLM01:2025 compliant injection detection
π Credential Scanner: Scan for exposed API keys, passwords, tokens, and secrets
π URL Security Validator: Validate URLs for phishing, malware, and HTTPS enforcement
π Resources (9 total)
π Security Checklists: MCP-specific security checklists (database, email, slack, file, web, general)
π Security Policies: Comprehensive policies (data classification, access control, incident response)
π¬ Prompts (2 total)
π Security Review: Multi-step security review workflow
β οΈ Threat Analysis: STRIDE-based threat modeling and risk assessment
π― General
β‘ Fast & Lightweight: Built with TypeScript and Zod validation
π§ Easy Integration: Works with any MCP-compatible AI assistant
π API Integration: Connects to AIM Intelligence API for advanced analysis
π Comprehensive Documentation: Detailed guide for Tools, Resources, and Prompts
Installation
Installing via Smithery
To install aim-mcp for Claude Desktop automatically via Smithery:
NPX (Recommended)
Global Installation
Local Installation
Usage
As MCP Server
Add to your MCP client configuration:
Testing the Tools
Test AI Safety Guard
Test Text Guard
Test Security Prompt Enhancement
Available Tools
1. ai-safety-guard
Provides contextual security instructions and precautions for AI Agents before they interact with other MCPs.
Features: Context-aware guidelines, operation-specific warnings, red flag detection
2. aim-text-guard
Analyze text content for harmful or inappropriate content using AIM Intelligence API.
Features: Real-time analysis, harmful content detection, detailed JSON results
3. aim-security-prompt-tool
Enhance user prompts with security instructions for safer AI interactions.
Features: Multi-level enhancement, threat analysis, social engineering protection
4. prompt-injection-detector π
Detect prompt injection attempts based on OWASP LLM01:2025 patterns.
Features:
15+ injection pattern detection (instruction override, role manipulation, jailbreak attempts)
Risk scoring (0-100) with severity assessment
OWASP LLM01:2025 compliant
Configurable sensitivity levels
Detailed threat reporting
5. credential-scanner π
Scan text for exposed credentials including API keys, passwords, tokens, and SSH keys.
Features:
50+ credential patterns (AWS, GitHub, Google, OpenAI, Stripe, JWT, SSH keys)
Automatic credential masking
Risk level assessment
Platform-specific detection (AWS, GitHub, Slack, databases)
Actionable security recommendations
6. url-security-validator π
Validate URL safety for phishing, malware, and security issues.
Features:
10+ security checks (protocol, TLD, IP address, homograph attacks)
Phishing domain detection
URL shortener identification
Suspicious parameter detection
HTTPS enforcement validation
Available Resources π
Resources provide read-only security documentation and policies accessible via URI schemes.
Security Checklists
Access via security-checklist://[type]
security-checklist://database- Database operations checklistsecurity-checklist://email- Email operations checklistsecurity-checklist://slack- Chat/messaging operations checklistsecurity-checklist://file- File operations checklistsecurity-checklist://web- Web request checklistsecurity-checklist://general- General MCP operations checklist
Each checklist includes:
Pre-operation checks
During-operation guidelines
Post-operation verification
Red flags to abort operations
Security Policies
Access via security-policy://[type]
security-policy://data-classification- Data classification levels and handling requirementssecurity-policy://access-control- Access control principles and authentication requirementssecurity-policy://incident-response- Incident response procedures and severity levels
Available Prompts π
Prompts provide reusable workflow templates for complex security operations.
1. security-review
Comprehensive security review workflow for code, data, or configuration.
Workflow:
Credential scanning
Prompt injection detection (if applicable)
Security checklist consultation
Policy compliance review
Threat analysis
Risk assessment and recommendations
Summary table - Visual overview of all findings by severity
Summary Output Example:
2. threat-analysis
Analyze potential security threats using STRIDE methodology.
Framework:
Asset identification
STRIDE threat modeling (Spoofing, Tampering, Repudiation, Information Disclosure, DoS, Elevation of Privilege)
Risk assessment (likelihood Γ impact)
Attack vector analysis
Control gap identification
Mitigation strategies
Compliance considerations
Incident response planning
Summary table - Visual overview of all threats by severity
Summary Output Example:
Security Features
π‘οΈ AI Agent Protection
MCP Interaction Safety: Contextual guidelines for different MCP types
Operation Validation: Specific precautions for read/write/execute operations
Data Sensitivity Handling: Protocols based on data classification levels
π Content Analysis
Real-time Threat Detection: Analyze content for harmful patterns
Prompt Injection Detection: OWASP LLM01:2025 compliant pattern matching
Credential Exposure Prevention: Scan for 50+ types of exposed secrets
API-powered Analysis: Advanced AI-driven content safety assessment
π URL Security
Phishing Detection: Identify suspicious domains and homograph attacks
HTTPS Enforcement: Validate secure protocol usage
Malicious URL Blocking: Check against known threat indicators
π Policy & Compliance
Security Checklists: Pre-built checklists for all MCP types
Data Classification: Clear policies for handling sensitive data
Access Control: Guidelines for authentication and authorization
Incident Response: Structured procedures for security incidents
π Workflow Orchestration
Security Review Prompts: Multi-step review workflows
Threat Analysis: STRIDE-based threat modeling
Automated Audits: Combine multiple tools for comprehensive checks
Development
Deployment
This project uses automated CI/CD pipeline for seamless deployment to NPM.
Automatic Deployment
When you push to the main branch, GitHub Actions will automatically:
Build and Test: Compile TypeScript and run tests
Version Check: Compare current version with published version
Publish to NPM: Automatically publish if version has changed
Create Release: Generate GitHub release with version tag
Manual Version Management
Setting up NPM Token
To enable automatic deployment, add your NPM token to GitHub Secrets:
Go to npmjs.com and create an automation token
In your GitHub repository, go to Settings > Secrets and variables > Actions
Add a new secret named
NPM_TOKENwith your NPM token value
Deployment Workflow
Contributing
Fork the repository
Create your feature branch (
git checkout -b feature/amazing-feature)Commit your changes (
git commit -m 'Add some amazing feature')Push to the branch (
git push origin feature/amazing-feature)Open a Pull Request
License
This project is licensed under the ISC License - see the LICENSE file for details.
Documentation
π MCP Components Guide: Comprehensive guide to Tools, Resources, and Prompts
π GitHub Wiki: Additional documentation and examples
π MCP Specification: Official Model Context Protocol documentation
Support
π§ Email: support@aim-intelligence.com
π Issues: GitHub Issues
π¬ Discussions: GitHub Discussions
Made with β€οΈ by AIM Intelligence
Related MCP Servers
- -security-license-qualityA Model Context Protocol (MCP) server that allows AI models to safely access and interact with local file systems, enabling reading file contents, listing directories, and retrieving file metadata.Last updated -610MIT License
- Asecurity-licenseAqualityA server that uses the Model Context Protocol (MCP) to allow AI agents to safely execute shell commands on a host system.Last updated -1446MIT License
- -security-license-qualityModel Context Protocol (MCP) server that provides AI assistants with advanced web research capabilities, including Google search integration, intelligent content extraction, and multi-source synthesis.Last updated -114MIT License