Discover +96 AI Security apps & tools

Pros: Native MCP server for supplying model context to agents. CLI plus extensible architecture for custom tool integrations. Connects AI agents to security scanners and cloud provider APIs. Open-source repository allows inspection and customization.

Cons: Requires an MCP-compatible host application for agentic workflows. Command-line and Node.js familiarity expected for setup and customization. Generated remediation steps depend on scanner and model quality. Integration relies on available APIs from security tools and cloud providers.

Pros: Executes AI-generated code inside Docker containers to isolate the host system.. Integrates natively with Model Context Protocol clients like Claude Desktop.. Restricts file access to explicitly mapped directories for safer runs.. Open-source repository available for external audit on GitHub..

Cons: Requires Docker installed on the host system to function.. Depends on an MCP-compatible client such as Claude Desktop.. Language support depends on user-provided Docker images.. Node.js-based server needs manual setup and image configuration..

Pros: Direct integration with Nmap, Dig, Whois, Curl, and SQLMap for agent access. Implements the Model Context Protocol for compatibility with MCP clients. Docker-ready deployment for reproducible environments. Open-source codebase allows adding custom command-line tools.

Cons: Automated commands require human validation before operational use. Some scans need elevated privileges, increasing deployment complexity. Results depend on underlying CLI tools and network conditions. Designed for MCP clients; non-MCP workflows require adapters.

Pros: Native MCP compliance for direct integration with MCP clients. Open-source codebase allows audits and custom rule additions. Lightweight, low-latency design to minimise interaction delays. Automated risk assessment supports agent-driven flagging and self-correction.

Cons: Requires Node.js and MCP host configuration, adding setup work. Detection accuracy depends on maintained rule sets and threat feeds. Some scanners may query external APIs, so network access can be needed.

Pros: Generates temporary AWS IAM credentials with configurable TTL. Accepts custom inline JSON policies for fine-grained permissions. Performs automatic cleanup of expired IAM users and keys. Integrates with MCP clients like Claude Desktop.

Cons: Requires AWS account and IAM-management permissions on host environment. Initial setup depends on local AWS CLI configuration. Best suited to teams able to audit and operate open-source tooling.

Pros: Integrates with OpenZiti controllers for private-network operations. Implements the Model Context Protocol for MCP client compatibility. Open-source codebase permits security audits and contributions. Exposes programmable network-management calls for LLM automation.

Cons: Requires an existing OpenZiti controller and valid credentials. Depends on an MCP host such as Claude Desktop and Node.js runtime. Community-driven project rather than an official vendor product.

Pros: MCP-native design exposes structured security findings to AI agents. Detects resource dependency issues and configuration drift. Policy enforcement supports organizational IaC compliance. Integrates with MCP-capable clients such as Claude Desktop.

Cons: Not a replacement for standard Terraform security scanners. Value depends on well-defined organizational policies. Requires an AI-enabled workflow to provide full benefit.

Pros: Exposes secrets as MCP tool endpoints for programmatic client access. Open-source codebase available for independent audit. Local-first design keeps sensitive data off third-party clouds. Compatible with MCP-aware clients like Claude Desktop via config.

Cons: Limited to MCP-compatible clients and agent stacks. Requires a Node.js environment for hosting. Not a drop-in replacement for cloud key management. Deployment requires explicit client configuration.

Pros: Enumerates active processes with detailed metadata. Provides real-time CPU and memory metrics at the PID level. Built for MCP and configurable with Claude Desktop.

Cons: Enables process termination, so use only in controlled environments. May require elevated privileges to manage system-level processes. Depends on an MCP-compliant host application being present.

Pros: Native MCP integration enables AI clients to query Huntress data. Exposes incident and agent telemetry for natural-language queries. Open-source GitHub repository allows community code review. Compatible with MCP-compliant clients such as Claude Desktop.

Cons: Not an official Huntress product; third-party integration. Requires active Huntress account and valid API credentials. Node.js hosting required for the MCP server. AI-generated summaries require independent verification for high-impact incidents.

Pros: Implements OAuth 2.1 for authorizing connections. Supports the Model Context Protocol (MCP) for AI-to-AI communication. Manages multiple OpenClaw instances with isolated authentication tokens. Docker-ready deployment for repeatable containerized environments.

Cons: Requires an MCP-compatible client such as Claude Desktop. Deployment outside Docker needs additional configuration effort. Setup and instance management require engineering resources.

Pros: Protocol-specific proxy designed for the Model Context Protocol. Inspects MCP request and response streams for tool-call visibility. Policy-based access control allows administrator-defined execution rules. Open-source GitHub repository enables community inspection and customization.

Cons: Requires Node.js deployment and environment familiarity. Policy definitions demand administrative setup and ongoing maintenance. Targeted at MCP adopters rather than general-purpose proxy users.

Pros: Detects and masks common PII types including emails and phone numbers. Processes input locally, avoiding cloud-side exposure to external AI providers. Configurable masking rules and open-source code allow security audits.

Cons: Requires MCP-compatible clients, limiting adoption to MCP-enabled workflows. Needs developer setup and a Node.js environment for deployment. Detection accuracy depends on rule configuration; human review advised.

Pros: Real-time security scanning for AI agent inputs and outputs. Detection of prompt injection and jailbreak attempts. PII detection and filtering to reduce data leakage risk. Open-source code and community-driven signature model.

Cons: Requires an MCP-compliant host and Node.js runtime. Optimized for agentic workflows, less relevant for simple LLM assistants. Deployment requires repository clone and manual MCP configuration.

Pros: MCP-native server integrates directly with clients like Claude Desktop. Renames identifiers to reduce human readability of Python source. Strips comments and docstrings to remove non-functional metadata. Preserves execution semantics so obfuscated scripts still run.

Cons: Python-only focus excludes non-Python projects. Requires an MCP-compatible host and local Python environment. Obfuscation is irreversible, complicating post-deployment debugging. Not a complete substitute for legal intellectual-property protections.

Pros: Executes Kali utilities and returns machine-readable results. Go implementation designed for efficient resource use. Extensible toolset model for adding custom wrappers. Compatible with MCP clients such as Claude Desktop.

Cons: Requires an existing Kali tools installation on the host. Installation requires cloning and building with the Go toolchain. Live command execution mandates isolated lab deployment. Suited to technically proficient, early-adopter users.

Pros: MCP-native integration enables protocol-level request inspection. Open-source repository allows auditing and custom rule development. Performs many checks locally, reducing external data exposure. Modular design integrates with MCP hosts including Claude Desktop.

Cons: Advanced scanning may require external security databases. Integration requires editing MCP client configurations. Better suited to teams with security or developer expertise.

Pros: Standardized MCP interface for direct AI-agent connectivity. Integrates established tools such as Nmap, ffuf, and Nuclei. Extensible module architecture that accepts custom scripts. Runs on the host via Node.js, using local tooling.

Cons: Requires preinstallation of CLI security tools in the system PATH. Needs an MCP-compliant client and configuration to operate. Geared toward experienced security practitioners and researchers. Agent-driven actions and interpretations require human verification.

Pros: Native MCP architecture enables low-latency, context-aware monitoring. Built-in adversarial testing utilities for controlled red-team exercises. Extensible rule engine allows custom security policies and patterns. Open-source GitHub hosting supports community auditing and adaptation.

Cons: Detects known injection patterns but is not a foolproof defense. Requires an MCP-compatible environment and a modern runtime like Node.js. Primarily aimed at developers and security teams, not end users.