"An introduction to prompt engineering" matching MCP servers:

Lets AI agents gate actions before they happen with allow/ask/deny verdicts, rank up to 255 options, and report decision stats.

The control plane for AI prompts that scores, enforces policy, locks config, and audits every prompt decision.

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

Provides MCP clients a single decision-only tool to classify prompts as safe for local or cloud models, or as containing PII or jailbreak content, without executing actions or calling AI models.

MCP server exposing prompt-shield's prompt-injection firewall for LLM applications. Four tools — scan_input, scan_output, scan_tool_result (with optional sanitization), and list_detectors — backed by 34+ detectors and a federated ed25519-signed threat feed.

An MCP server that enables conversational interaction with Wazuh SIEM, allowing users to investigate alerts, hunt threats, tune false positives, edit rules, and run security actions via natural language.

A standalone MCP server that enforces clean prompt structure, strips model‑invented instructions, and detects capability hallucinations before they propagate through your system.

Provides structured templates for security questionnaires, RFP responses, and competitive battlecards to help AI agents and sales engineers draft responses efficiently.

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

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

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.

An MCP server that sends plain-text Gmail messages only to a predefined allowlist, using generated recipient IDs instead of email addresses to enforce security.

Protects AI agents from prompt injection attacks, jailbreak attempts, and common web vulnerabilities by screening untrusted input through semantic LLM analysis and static pattern matching.

An MCP server that provides authorization middleware for AI agents using private data, enabling consent management on Aleo and local private inference with persistent policy storage.

Records approved parking reservations to a secure text file via the Model Context Protocol, with token authentication and input sanitization.

Provides a local MCP server that exposes the check_prompt_injection tool to MCP clients, letting agents scan untrusted text (web pages, documents, tool outputs, emails) for prompt injection before passing it to another LLM.

An MCP server that protects AI agents and RAG systems from adversarial poisoning attacks like prompt injection, steganography, and data anomalies through multi-layer sanitization, entropy-based detection, and consensus verification.

An MCP front-end for AnB. The tool surface is "use-don't-reveal": no tool returns a plaintext secret. Reveal paths require a TTY the server doesn't have, so the no-reveal guarantee is structural, not prompt-based — it holds even against a prompt-injected agent. Runs as a dedicated, narrowly-scoped AnB identity; exec is default-deny (only allowlist rules tagged scope=mcp run); command output

Enables AI agents to scan text for leaked secrets and prompt injection markers, and redact them before reaching an LLM.

MCP server for AI security scanning. Detects prompt injections, jailbreaks, data exfiltration, and social engineering attacks in real-time using 42 regex patterns via the ClawGuard Shield API. Sub-10ms response times.