"A search query containing special characters and SQL injection-like encoding" matching MCP servers:

Enables security researchers to evaluate AI system defenses against prompt injection attacks through a comprehensive set of test vectors and analysis tools.

An MCP server for prompt injection boundary enforcement that scans URL content using a tiered LLM model strategy.

MCP server for querying and analyzing TensorBoard event files, enabling AI agents to extract scalar data, statistics, and correlations without a running TensorBoard server.

WAF for AI agents — block prompt injection before it reaches the LLM.

Enables deterministic detection and neutralization of adversarial prompt injections and override attempts in AI agent workflows via a zero-dependency MCP server, providing structured telemetry and low-latency validation.

Enables AI coding agents to request a second opinion from a different frontier model via a single tool call, with PII scrubbed before transit, live web search, and support for Claude, ChatGPT, Cursor, and Gemini.

MCP server that translates natural-language questions into SQL, validates every query structurally, and executes approved read-only queries against a SQLite database, returning results and rejections.

Enables querying a PostgreSQL database using natural language by retrieving context from Azure AI Search and generating SQL with Azure OpenAI, with validation and optional execution.

Enables AI agents to request an independent second opinion from a different frontier model, with PII scrubbing before transit, live web search grounding, caching, and multi-provider failover.

Enables natural-language querying of a SQLite database through a locally served, fine-tuned text-to-SQL model exposed as an MCP tool, offering fast and cost-free SQL generation and execution.

A local DLP middleware that redacts sensitive information from prompts using local models before they reach external LLMs. It provides tools to sanitize queries, restore placeholders in responses, and manage a ledger of redactions to maintain data privacy.

Enables agents to find lines in local text files by meaning rather than exact keywords, judging each nonblank line against a natural-language intent and returning scores for every line. It complements lexical search by running locally over stdio and calling a hosted model for the semantic comparison.

Enables any calling model to search public X content through Grok's native search using an existing local OAuth login, returning summaries, structured posts, source URLs, and search counts while sharing cached and concurrent searches across local terminals.

Enables coding agents to add a bounded semantic-judgment layer for routing, ranking, extraction, verification, and escalation, returning typed signals and review recommendations.

Enables MCP-compatible clients to deterministically neutralize adversarial prompt injections and jailbreak overrides with zero-dependency, sub-millisecond heuristic analysis.

Enables AI agents to search, filter, and monitor curated AI model API free credits and low-cost token promotions through eight tools, four resources, and three prompts — including finding deals expiring soon, checking eligibility and blocked conditions, and producing daily briefs.

An MCP server that integrates with AI models to provide web search capabilities through a single, easy-to-use tool interface.

Enables AI assistants to search the web using Google Search via Gemini grounding, returning a summarized answer with inline citations and a list of source URLs.

Enables MCP clients and a terminal CLI to run web searches through a user's own SearXNG instance, saving every query as a reusable session and optionally generating AI-synthesized answers.

Enables AI-to-AI consultation for critical thinking and complex reasoning via OpenRouter, allowing one AI to delegate tasks to another AI model.