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mcp-security-hardeningMCP 安全 hardening

Agent Skill

用于辅助安全审计、权限检查、凭据风险、认证流程和常见漏洞排查。它适合让 Agent 梳理敏感配置、检查依赖风险、分析鉴权逻辑或生成安全复核清单。使用时不能把工具输出直接当最终结论,涉及密钥、令牌、用户数据或生产系统时,应先确认最小权限、脱敏方式和操作边界。

总安装

343

周安装

14

GitHub Stars

160

下载量

110
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:mcp-security-hardening(MCP 安全 hardening)
来源仓库:https://github.com/yonatangross/orchestkit
仓库路径:skills/mcp-security-hardening
安装命令:
npx skills add https://github.com/yonatangross/orchestkit --skill mcp-security-hardening
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/yonatangross/orchestkit --skill mcp-security-hardening

简介

用于辅助安全审计、权限检查和认证流程分析。mcp-security-hardening 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

  • 它能梳理敏感配置、检查依赖风险或生成安全复核清单。
  • 使用时不能把工具输出直接当最终结论,需人工复核。
  • 涉及密钥、令牌或用户数据时应确认最小权限和操作边界。
  • 建议优先脱敏处理,避免在生产环境中直接操作凭据。

SKILL.md

MCP Security Hardening

Defense-in-depth security patterns for Model Context Protocol (MCP) integrations.

Overview

  • Securing MCP server implementations
  • Validating tool descriptions before LLM exposure
  • Implementing zero-trust tool allowlists
  • Detecting tool poisoning attacks (TPA)
  • Managing tool permissions and capabilities

Core Security Principle

Treat ALL tool descriptions as untrusted input. Validate tool identity with hash verification. Apply least privilege to all tool capabilities.

Threat Model Summary

Attack VectorDefenseImplementation
Tool Poisoning (TPA)Zero-trust allowlistHash verification, mandatory vetting
Prompt InjectionDescription sanitizationRegex filtering, encoding detection
Rug PullChange detectionHash comparison on each invocation
Data ExfiltrationOutput filteringSensitive pattern removal
Session HijackingSecure sessionsCryptographic IDs, no auth in sessions

Layer 1: Tool Description Sanitization

import re

FORBIDDEN_PATTERNS = [
    r"ignore previous", r"system prompt", r"<.*instruction.*>",
    r"IMPORTANT:", r"override", r"admin", r"sudo",
    r"\\x[0-9a-fA-F]{2}",  # Hex encoding
    r"&#x?[0-9a-fA-F]+;",  # HTML entities
]

def sanitize_tool_description(description: str) -> str:
    """Remove instruction-like phrases or encoding tricks."""
    if not description:
        return ""
    sanitized = description
    for pattern in FORBIDDEN_PATTERNS:
        sanitized = re.sub(pattern, "[REDACTED]", sanitized, flags=re.I)
    return sanitized.strip()

def detect_injection_attempt(description: str) -> str | None:
    """Detect prompt injection patterns."""
    indicators = [
        (r"ignore.*previous", "instruction_override"),
        (r"you are now", "role_hijack"),
        (r"forget.*above", "context_wipe"),
    ]
    for pattern, attack_type in indicators:
        if re.search(pattern, description, re.I):
            return attack_type
    return None

Layer 2: Zero-Trust Tool Allowlist

from hashlib import sha256
from dataclasses import dataclass
from datetime import datetime, timezone

@dataclass
class AllowedTool:
    name: str
    description_hash: str
    capabilities: list[str]
    approved_at: datetime
    approved_by: str
    max_calls_per_minute: int = 60
    requires_human_approval: bool = False

class MCPToolAllowlist:
    """Zero-trust allowlist - every tool must be explicitly vetted."""

    def __init__(self):
        self._allowed_tools: dict[str, AllowedTool] = {}
        self._call_counts: dict[str, list[datetime]] = {}

    def register(self, tool: AllowedTool) -> None:
        self._allowed_tools[tool.name] = tool
        self._call_counts[tool.name] = []

    def compute_hash(self, description: str) -> str:
        return sha256(description.encode('utf-8')).hexdigest()

    def validate(self, tool_name: str, description: str) -> tuple[bool, str]:
        if tool_name not in self._allowed_tools:
            return False, f"Tool '{tool_name}' not in allowlist"

        expected = self._allowed_tools[tool_name]
        if self.compute_hash(description) != expected.description_hash:
            return False, "Tool description changed (possible rug pull)"

        # Rate limit check
        now = datetime.now(timezone.utc)
        recent = [t for t in self._call_counts[tool_name] if (now - t).total_seconds() < 60]
        if len(recent) >= expected.max_calls_per_minute:
            return False, "Rate limit exceeded"

        self._call_counts[tool_name] = recent + [now]
        return True, "Validated"

Layer 3: Capability Declarations

from enum import Enum

class ToolCapability(Enum):
    READ_FILE = "read:file"
    WRITE_FILE = "write:file"
    EXECUTE_COMMAND = "execute:command"
    NETWORK_REQUEST = "network:request"
    DATABASE_WRITE = "database:write"

class CapabilityEnforcer:
    SENSITIVE_PATHS = ["/etc/passwd", "~/.ssh", ".env", "credentials", "secrets"]

    def __init__(self):
        self._declarations: dict[str, set[ToolCapability]] = {}

    def register(self, tool_name: str, capabilities: set[ToolCapability]) -> None:
        self._declarations[tool_name] = capabilities

    def check(self, tool_name: str, capability: ToolCapability, resource: str = "") -> tuple[bool, str]:
        if tool_name not in self._declarations:
            return False, "No capability declaration found"

        if capability not in self._declarations[tool_name]:
            return False, f"Capability {capability.value} not allowed"

        if capability in (ToolCapability.READ_FILE, ToolCapability.WRITE_FILE):
            for sensitive in self.SENSITIVE_PATHS:
                if sensitive in resource:
                    return False, "Access to sensitive path denied"

        return True, "Allowed"

Layer 4: Session Security

import secrets
from datetime import datetime, timedelta, timezone
from dataclasses import dataclass

def generate_secure_session_id() -> str:
    return secrets.token_urlsafe(32)  # 256 bits of entropy

@dataclass
class MCPSession:
    session_id: str
    created_at: datetime
    last_activity: datetime
    request_count: int = 0
    max_requests_per_minute: int = 100
    timeout_minutes: int = 30

    def is_valid(self) -> tuple[bool, str]:
        now = datetime.now(timezone.utc)
        if (now - self.last_activity) > timedelta(minutes=self.timeout_minutes):
            return False, "Session timed out"
        return True, "Valid"

    def record_request(self) -> tuple[bool, str]:
        now = datetime.now(timezone.utc)
        if (now - self.last_activity).total_seconds() >= 60:
            self.request_count = 0
        self.request_count += 1
        self.last_activity = now
        if self.request_count > self.max_requests_per_minute:
            return False, "Rate limit exceeded"
        return True, "OK"

Anti-Patterns (FORBIDDEN)

# NEVER trust tool descriptions without sanitization
prompt = f"Use this tool: {tool.description}"  # INJECTION RISK!

# NEVER allow tools without explicit vetting
return mcp.list_tools()  # No validation!

# NEVER store auth tokens in session IDs
session_id = f"{user_id}:{auth_token}"  # CREDENTIAL LEAK!

# NEVER skip hash verification on tool calls

# ALWAYS sanitize, validate, and verify:
sanitized = sanitize_tool_description(tool.description)
is_valid, reason = allowlist.validate(tool.name, tool.description)
session_id = secrets.token_urlsafe(32)

Key Decisions

DecisionRecommendation
Tool trust modelZero-trust (explicit allowlist)
Description handlingSanitize + hash verify
Session IDsCryptographic (secrets.token_urlsafe)
Rate limitingPer-tool and per-session
Sensitive operationsHuman-in-the-loop approval

Related Skills

  • llm-safety-patterns - LLM-specific security patterns
  • input-validation - Input sanitization fundamentals
  • auth-patterns - Session and token security
  • defense-in-depth - Layered security architecture

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Gemini CLI

28.11%
按下载量换算31

Antigravity

25.2%
按下载量换算28

windsurf

18.27%
按下载量换算20

Claude Code

13.45%
按下载量换算15

trae

7.33%
按下载量换算8

OpenCode

3.68%
按下载量换算4

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

external-service

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

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来源信息

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