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    Security analysis for Python codebases through the guard-core engine: SecurityConfig validation that catches keys pydantic silently drops, docs search over the vendored corpus, live detection probing, and verified setup guidance for Python, Typescript, Rust, PHP and Go adapters.
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    190 PyPI
    2
    MIT
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    Lets AI assistants work on sensitive files by reasoning over schemas and masked output while deterministic local code handles raw data, never exposing real values to the model.
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    30 PyPI
    MIT
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    Enables an assistant to read SMS texts and transcribed calls on a Cleat US mobile line, including waiting for the next verification code to arrive.
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    27 npm
    MIT
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    A Model Context Protocol (MCP) server for Socket integration, allowing AI assistants to efficiently check dependency vulnerability scores and security information.
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    10,226 npm
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    MIT
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    Local security layer for coding agents: keeps secret values out of what Claude Code, Codex CLI, Cursor and OpenCode send to their model, and checks untrusted text for prompt injection. MIT, runs on your machine, no account.
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    MIT
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    An MCP server that enforces plain-English rules on AI agent tool calls, deterministically blocking forbidden actions before execution via a Claude Code hook and a check_action tool.
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    2 npm
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    MIT
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    Walkie-talkies for AI coding agents: Claude Code, Codex, Cursor and any MCP client message other developers' agents directly, peer to peer over HyperDHT and end-to-end encrypted. No server, no account. Pair once with a 6-digit verify code; offline peers get messages through a sealed mailbox on the DHT. Tools: send_message, read_inbox, list_messages, list_peers, get_status.
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    Apache 2.0
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    Checks a code repository without running it: Safe, Works and Complete scores with an A-F grade, catching leaked keys, dangerous calls, invented or lookalike packages, broken imports and missing project files. Standard-library Python; MCP tools scan, orphans and explain.
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    534 PyPI
    MIT
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    MISP (Malware Information Sharing Platform) MCP server with built-in prompt injection defense via prompt-defense-audit
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    MIT
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    A secure email gateway MCP server that protects AI agents from prompt injection attacks by scanning emails for malicious instructions before they reach the agent.
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    Apache 2.0
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    Strict AI code reviewer powered by Groq. Finds bugs, vulnerabilities and security issues in your code. Supports analyze, compare versions, explain code, generate tests, and HTML reports.
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    MIT
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    Experimental Python MCP server exposing traceguard_shield_exec to evaluate proposed tool calls through an authorization hook. Returns approval or rejection messages over stdio; it does not execute the requested tools.
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    MIT
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    Deterministic AI safety policy engine with Z3 formal verification. Write, verify, simulate, and enforce machine-verifiable safety constraints for AI agents. Completely outside the LLM.
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    181 PyPI
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    Apache 2.0
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    Enables AI assistants to validate individual or batch email addresses for deliverability, and to check account plan information, through MCP tools.
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    44 npm
    MIT
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    Enables local detection of protected health information in source code before it reaches LLM prompts, log statements, or analytics calls.
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    64 npm
    MIT
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    CodeSafer is an MCP server that scans AI-generated code for 9 categories of hidden security threats — including invisible Unicode, Trojan Source, homoglyphs, and rules file backdoors — using static analysis plus CodeBERT deep learning. Runs locally, free tier available.
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    56 npm
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    MIT
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    Security scanning for AI coding tools (Claude Code, Cursor, Windsurf) including secrets detection, MCP config vulnerabilities, agent instruction checks, threat modeling, prompt injection testing, pre-commit security checks, and dependency vulnerability scanning.
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    24 npm
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    MIT
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    A security filter that blocks dangerous code patterns by comparing normalized structural syntax trees against a blacklist of known threats using vector embeddings. It acts as a gatekeeper to prevent malicious code execution by identifying dangerous structures regardless of specific identifiers or literals.
    11
    MIT