Discover +86 AI Security apps & tools

Pros: Uses Semgrep SAST to identify pattern-based vulnerabilities. Integrates with MCP clients for inline assistant-session checks. Open-source and extensible for custom security rules. Designed for local execution to preserve code privacy.

Cons: Requires an MCP host and a Node.js runtime to operate. Limited to static analysis; cannot detect runtime faults. Depends on MCP-enabled clients such as Claude Desktop for integration.

Pros: Runs untrusted model-generated code inside isolated sandboxes. Lets developers define granular filesystem boundaries and permissions. MCP compatibility enables use with clients such as Claude Desktop. Open-source codebase allows community auditing and custom extensions.

Cons: Effectiveness depends on correct and complete policy configuration. Requires Node.js and an MCP client for deployment. Monitoring requires active review to interpret agent actions.

Pros: Detects prompt injection using a dedicated detection module. Blocks sophisticated jailbreak attempts before they reach the model. Integrates with Model Context Protocol hosts such as Claude Desktop. Open-source codebase enables community review and audits.

Cons: Requires an MCP-compliant host to function, not standalone. Needs a Node.js runtime and operational hosting. Detection depends on known-pattern library and ongoing rule tuning.

Pros: Exposes security checks as standard MCP tools for native client calls. Detects embedded secrets and flags PII before model processing. Open-source architecture enables adding modules and integrations. Configurable security policies to tailor violation thresholds.

Cons: Malware scanning relies on third-party API keys such as VirusTotal. Requires hosting and maintaining a Python-based server. External scan accuracy depends on integrated service responses.

Pros: Executes Python and JavaScript/Node.js scripts for agent workflows. Configurable resource limits prevent runaway processes and excessive memory use. Open-source code base allows community auditing of sandbox mechanisms. Integrates with MCP clients via standard mcp_config.json configuration.

Cons: Requires a Node.js runtime and MCP-compatible client to run. Language support focused on scripting runtimes, primarily Python and JavaScript. Local server setup and configuration require developer knowledge.

Pros: Bridges FOFA search into AI workflows via the Model Context Protocol. Produces structured host metadata and basic statistical summaries. Open-source implementation recognized within the security researcher community.

Cons: Requires a FOFA account and API credentials as environment variables. Needs an MCP-compatible client and Node.js runtime. Search results depend on external index coverage and need verification.

Pros: Forces agent outputs into verifiable drafts before execution. Redacts or masks sensitive fields prior to model access. Optimizes context to reduce prompt injection risks. Open-source hosting enables community auditing and customization.

Cons: Requires an MCP-compatible client or host to operate. Depends on human reviewers, adding operational overhead. Effectiveness relies on correctly defined security policies.

Pros: Automated discovery and enumeration of MCP endpoints. Detects sensitive data exposure in context and resource definitions. CLI integration for inclusion in CI/CD pipelines. Open-source codebase allows inspection and contribution.

Cons: Does not automatically remediate identified security issues. Requires a modern Node.js runtime to execute. Scans only endpoints reachable over the network. Narrow scope limited to MCP-standard deployments.

Pros: Protocol-level honeypot tailored for the Model Context Protocol. Captures detailed logs for every tool call and resource request. Lightweight architecture designed for easy deployment in test environments.

Cons: Requires Node.js and an existing MCP environment to run. Primarily intended for monitoring and research, not a standalone production appliance. Logs are emitted to stdout or files, requiring external aggregation for analysis.

Pros: Exposes KMS encryption, decryption, and signing to MCP agents. Private keys remain inside AWS KMS hardware security modules. Integrates with MCP clients such as Claude Desktop. Supports data key generation for envelope encryption patterns.

Cons: Limited to AWS KMS, not cloud-agnostic. Requires Node.js and configured AWS credentials on host. Agentic cryptography needs careful IAM permission management. Niche audience of MCP early adopters limits broad applicability.

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.