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Agent Skill · 研究检索

detecting-api-enumeration-attacks(detecting API enumeration attacks 搜索)

用于辅助 API 设计、接口文档、请求响应结构和服务集成说明。它适合让 Agent 梳理 endpoint、生成 OpenAPI 草稿、检查字段命名、整理错误码或辅助前后端联调。使用时需要确认真实业务语义、鉴权方式、分页和错误处理规则;涉及生成接口文档时,应避免凭空补字段,最好从现有代码、schema 或接口样例中提取事实。

detecting-api-enumeration-attacks 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描已通过,按安装量在研究检索类的 73,675 个 Skill 里排第 51,212。

总安装 588周安装 24GitHub Stars 5,933下载 190核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

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:detecting-api-enumeration-attacks(detecting API enumeration attacks 搜索)
来源仓库:https://github.com/mukul975/anthropic-cybersecurity-skills
仓库路径:skills/detecting-api-enumeration-attacks
安装命令:
npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skill detecting-api-enumeration-attacks
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。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skill detecting-api-enumeration-attacks

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

detecting-api-enumeration-attacks 监控 API 枚举攻击,防止 BOLA 漏洞利用。识别快速序列访问与授权失败模式,输出风险端点列表。需 API 网关或日志访问权限,支持 OWASP API Top 10 检测。结果为辅助指标,需结合速率限制与身份验证加固。

适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/detecting-api-enumeration-attacks

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skil…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

detecting-api-enumeration-attacks 怎么安装?

在终端执行 npx skills add https://github.com/mukul975/anthropic-cybersecurity-skills --skill detectin…。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

detecting-api-enumeration-attacks 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

detecting-api-enumeration-attacks 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

detecting-api-enumeration-attacks 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

detecting-api-enumeration-attacks 用的人多吗?

公开数据是累计安装 588 次,近一周安装 24 次,下载 190 次,GitHub 5,933 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

detecting-api-enumeration-attacks 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 来源仓库有 5,933 星,维护活跃度相对可查。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
detecting-api-enumeration-attacks

detecting API enumeration attacks 搜索 · 本页

5885,933需要联网未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Detecting API Enumeration Attacks

Overview

API enumeration attacks occur when attackers systematically probe API endpoints with sequential or predictable identifiers to discover and access unauthorized resources. Broken Object Level Authorization (BOLA), ranked as API1:2023 in the OWASP API Security Top 10, is the most critical API vulnerability. Attackers manipulate object identifiers (user IDs, order numbers, account references) in API requests to bypass authorization and access other users' data. Detection requires monitoring for patterns of rapid sequential access attempts, authorization failures, and abnormal API usage behavior.

When to Use

  • When investigating security incidents that require detecting api enumeration attacks
  • When building detection rules or threat hunting queries for this domain
  • When SOC analysts need structured procedures for this analysis type
  • When validating security monitoring coverage for related attack techniques

Prerequisites

  • API gateway or reverse proxy with logging enabled (Kong, AWS API Gateway, Apigee)
  • SIEM platform (Splunk, Elastic SIEM, or Microsoft Sentinel)
  • Access to API server logs with request details
  • Web Application Firewall (WAF) with API protection capabilities
  • Understanding of the API's authorization model and object identifier schemes

Attack Patterns to Detect

1. Sequential ID Enumeration

Attackers iterate through numeric or predictable identifiers:

GET /api/v1/users/1001 -> 200 OK
GET /api/v1/users/1002 -> 200 OK
GET /api/v1/users/1003 -> 403 Forbidden
GET /api/v1/users/1004 -> 200 OK
GET /api/v1/users/1005 -> 200 OK
...

Detection Indicators:

  • Rapid sequential requests to the same endpoint with incrementing IDs
  • Mix of 200/403/401 responses from same source
  • Request rate exceeding normal user behavior
  • Access to resources outside authenticated user's scope

2. UUID/GUID Enumeration

Even non-sequential identifiers can be enumerated if leaked through other endpoints:

# Attacker first harvests UUIDs from a list endpoint
GET /api/v1/posts?page=1  -> Returns post objects with author UUIDs

# Then uses those UUIDs to access restricted user data
GET /api/v1/users/a3f2c1e4-... -> Private user profile
GET /api/v1/users/b7d9e8f1-... -> Private user profile

3. Parameter Tampering Enumeration

# Authenticated as user_id=100, attempting to access other users' orders
GET /api/v1/orders?user_id=101
GET /api/v1/orders?user_id=102
GET /api/v1/orders?user_id=103

Detection Rules

Splunk Detection Queries

# Detect sequential ID enumeration on API endpoints
index=api_logs sourcetype=api_access
| rex field=uri_path "(?<endpoint>/api/v\d+/\w+/)(?<object_id>\d+)"
| stats count as request_count,
        dc(object_id) as unique_ids,
        values(status_code) as status_codes,
        min(_time) as first_seen,
        max(_time) as last_seen
  by src_ip, endpoint, user_session
| eval time_span = last_seen - first_seen
| eval requests_per_second = request_count / max(time_span, 1)
| where unique_ids > 20 AND requests_per_second > 2
| eval severity = case(
    unique_ids > 100, "critical",
    unique_ids > 50, "high",
    unique_ids > 20, "medium",
    1==1, "low"
  )
| sort - unique_ids
| table src_ip, endpoint, unique_ids, request_count, requests_per_second,
        status_codes, severity

# Detect BOLA via authorization failure patterns
index=api_logs sourcetype=api_access status_code IN (401, 403)
| bin _time span=5m
| stats count as failure_count,
        dc(uri_path) as unique_paths,
        values(uri_path) as attempted_paths
  by _time, src_ip, user_id
| where failure_count > 10
| eval attack_type = if(unique_paths > 5, "enumeration", "brute_force")

Elastic SIEM Detection Rules

{
  "rule": {
    "name": "API Object Enumeration Detection",
    "description": "Detects rapid sequential access to API objects with mixed authorization results",
    "type": "threshold",
    "index": ["api-access-*"],
    "query": {
      "bool": {
        "must": [
          { "regexp": { "url.path": "/api/v[0-9]+/[a-z]+/[0-9]+" } }
        ],
        "should": [
          { "term": { "http.response.status_code": 200 } },
          { "term": { "http.response.status_code": 403 } },
          { "term": { "http.response.status_code": 401 } }
        ]
      }
    },
    "threshold": {
      "field": ["source.ip"],
      "value": 50,
      "cardinality": [
        { "field": "url.path", "value": 20 }
      ]
    },
    "schedule": { "interval": "5m" },
    "severity": "high",
    "risk_score": 73,
    "tags": ["OWASP-API1", "BOLA", "Enumeration"]
  }
}

Custom Detection Script

#!/usr/bin/env python3
"""API Enumeration Attack Detector

Analyzes API access logs to detect enumeration patterns
including BOLA, IDOR, and sequential ID probing.
"""

import re
import sys
import json
from collections import defaultdict
from datetime import datetime, timedelta
from dataclasses import dataclass, field
from typing import List, Dict, Optional

@dataclass
class AccessRecord:
    timestamp: datetime
    source_ip: str
    user_id: Optional[str]
    method: str
    path: str
    status_code: int
    object_id: Optional[str] = None

@dataclass
class EnumerationAlert:
    source_ip: str
    user_id: Optional[str]
    endpoint_pattern: str
    unique_object_ids: int
    total_requests: int
    time_window_seconds: float
    requests_per_second: float
    auth_failure_ratio: float
    severity: str
    attack_type: str
    sample_ids: List[str] = field(default_factory=list)

class EnumerationDetector:
    # Regex patterns for extracting object IDs from API paths
    ID_PATTERNS = [
        re.compile(r'/api/v\d+/(\w+)/(\d+)'),           # Numeric IDs
        re.compile(r'/api/v\d+/(\w+)/([a-f0-9\-]{36})'), # UUIDs
        re.compile(r'/api/v\d+/(\w+)/([a-zA-Z0-9]{20,})'), # Long alphanumeric IDs
    ]

    def __init__(self, time_window_minutes: int = 5,
                 min_unique_ids: int = 15,
                 max_requests_per_second: float = 5.0):
        self.time_window = timedelta(minutes=time_window_minutes)
        self.min_unique_ids = min_unique_ids
        self.max_rps = max_requests_per_second
        self.access_log: List[AccessRecord] = []

    def parse_log_line(self, line: str) -> Optional[AccessRecord]:
        """Parse a common log format line into an AccessRecord."""
        log_pattern = re.compile(
            r'(?P<ip>[\d.]+)\s+\S+\s+(?P<user>\S+)\s+'
            r'\[(?P<time>[^\]]+)\]\s+'
            r'"(?P<method>\w+)\s+(?P<path>\S+)\s+\S+"\s+'
            r'(?P<status>\d+)'
        )
        match = log_pattern.match(line)
        if not match:
            return None

        path = match.group('path')
        object_id = None
        for pattern in self.ID_PATTERNS:
            id_match = pattern.search(path)
            if id_match:
                object_id = id_match.group(2)
                break

        return AccessRecord(
            timestamp=datetime.strptime(match.group('time'), '%d/%b/%Y:%H:%M:%S %z'),
            source_ip=match.group('ip'),
            user_id=match.group('user') if match.group('user') != '-' else None,
            method=match.group('method'),
            path=path,
            status_code=int(match.group('status')),
            object_id=object_id
        )

    def analyze(self, records: List[AccessRecord]) -> List[EnumerationAlert]:
        """Analyze access records for enumeration patterns."""
        alerts = []

        # Group by source IP and endpoint pattern
        grouped = defaultdict(list)
        for record in records:
            if record.object_id:
                # Normalize endpoint by removing the specific object ID
                endpoint = re.sub(r'/[a-f0-9\-]{36}', '/{id}',
                         re.sub(r'/\d+', '/{id}', record.path))
                key = (record.source_ip, record.user_id, endpoint)
                grouped[key].append(record)

        for (src_ip, user_id, endpoint), records_group in grouped.items():
            if len(records_group) < self.min_unique_ids:
                continue

            # Sort by timestamp
            records_group.sort(key=lambda r: r.timestamp)

            # Analyze time windows
            window_start = 0
            for window_start in range(len(records_group)):
                window_records = []
                for r in records_group[window_start:]:
                    if r.timestamp - records_group[window_start].timestamp <= self.time_window:
                        window_records.append(r)

                unique_ids = set(r.object_id for r in window_records)
                if len(unique_ids) < self.min_unique_ids:
                    continue

                time_span = (window_records[-1].timestamp -
                           window_records[0].timestamp).total_seconds()
                rps = len(window_records) / max(time_span, 1)

                auth_failures = sum(1 for r in window_records
                                   if r.status_code in (401, 403))
                failure_ratio = auth_failures / len(window_records)

                # Determine severity
                if len(unique_ids) > 100:
                    severity = "critical"
                elif len(unique_ids) > 50 or failure_ratio > 0.5:
                    severity = "high"
                elif len(unique_ids) > 20:
                    severity = "medium"
                else:
                    severity = "low"

                # Determine attack type
                ids_list = sorted([r.object_id for r in window_records
                                  if r.object_id and r.object_id.isdigit()])
                is_sequential = self._check_sequential(ids_list)
                attack_type = "sequential_enumeration" if is_sequential else "random_enumeration"

                alert = EnumerationAlert(
                    source_ip=src_ip,
                    user_id=user_id,
                    endpoint_pattern=endpoint,
                    unique_object_ids=len(unique_ids),
                    total_requests=len(window_records),
                    time_window_seconds=time_span,
                    requests_per_second=round(rps, 2),
                    auth_failure_ratio=round(failure_ratio, 2),
                    severity=severity,
                    attack_type=attack_type,
                    sample_ids=list(unique_ids)[:10]
                )
                alerts.append(alert)
                break  # One alert per group

        return alerts

    def _check_sequential(self, ids: List[str]) -> bool:
        """Check if numeric IDs follow a sequential pattern."""
        if len(ids) < 5:
            return False
        try:
            numeric_ids = sorted(int(i) for i in ids)
            sequential_count = sum(
                1 for i in range(1, len(numeric_ids))
                if numeric_ids[i] - numeric_ids[i-1] <= 2
            )
            return sequential_count / len(numeric_ids) > 0.7
        except ValueError:
            return False

def main():
    detector = EnumerationDetector(
        time_window_minutes=5,
        min_unique_ids=15
    )

    log_file = sys.argv[1] if len(sys.argv) > 1 else "/var/log/api/access.log"
    records = []
    with open(log_file, 'r') as f:
        for line in f:
            record = detector.parse_log_line(line.strip())
            if record:
                records.append(record)

    alerts = detector.analyze(records)

    if alerts:
        print(f"\n[!] {len(alerts)} enumeration attack(s) detected:\n")
        for alert in alerts:
            print(f"  Source IP: {alert.source_ip}")
            print(f"  User ID: {alert.user_id}")
            print(f"  Endpoint: {alert.endpoint_pattern}")
            print(f"  Unique IDs Accessed: {alert.unique_object_ids}")
            print(f"  Requests/sec: {alert.requests_per_second}")
            print(f"  Auth Failure Ratio: {alert.auth_failure_ratio}")
            print(f"  Attack Type: {alert.attack_type}")
            print(f"  Severity: {alert.severity.upper()}")
            print(f"  Sample IDs: {alert.sample_ids}")
            print()
    else:
        print("[+] No enumeration attacks detected.")

if __name__ == "__main__":
    main()

Prevention Controls

Server-Side Authorization Enforcement

# Always validate object ownership at the data layer
def get_user_order(request, order_id):
    order = Order.objects.get(id=order_id)
    if order.user_id != request.user.id:
        raise PermissionDenied("Not authorized to access this order")
    return order

Use Unpredictable Identifiers

import uuid

# Use UUIDs instead of sequential integers
class Order(Model):
    id = UUIDField(default=uuid.uuid4, primary_key=True)

Implement Rate Limiting Per Endpoint

# Kong rate limiting per API route
plugins:
  - name: rate-limiting
    config:
      minute: 30
      policy: redis
      limit_by: credential

References

平台分布

Codex

34.77%
按下载量换算66

Claude

31.31%
按下载量换算59

Cursor

18.08%
按下载量换算34

Gemini CLI

9.31%
按下载量换算18

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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