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analyzing-api-gateway-access-logs

Analyze access logs from AWS API Gateway, Kong, and Nginx with pandas, a Python data analysis library. Examine resource access patterns, authentication failures, and suspicious requests to investigate API abuse and inform detection rules.

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Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect BOLA/IDOR attacks, rate limit bypass, credential scanning, and injection attempts. Uses pandas for statistical analysis of request patterns and anomaly detection. Use when investigating API abuse or building API-specific threat detection rules.

集成工具AWS
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适用场景

  • Investigating sequential access to resource IDs
  • Examining spikes in authentication failures
  • Checking for rate limit bypass and injection attempts
  • Developing API abuse detection rules

包含的文件(4)

  • SKILL.md2.3 KB
  • LICENSE11.0 KB
  • references/api-reference.md1.4 KB
  • scripts/agent.py6.8 KB

SKILL.md(原文)

安装前可以查看交给 AI 代理的指令内容。

Analyzing API Gateway Access Logs

When to Use

  • When investigating security incidents that require analyzing api gateway access logs
  • 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

  • Familiarity with security operations concepts and tools
  • Access to a test or lab environment for safe execution
  • Python 3.8+ with required dependencies installed
  • Appropriate authorization for any testing activities

Instructions

Parse API gateway access logs to identify attack patterns including broken object level authorization (BOLA), excessive data exposure, and injection attempts.

import pandas as pd

df = pd.read_json("api_gateway_logs.json", lines=True)
# Detect BOLA: same user accessing many different resource IDs
bola = df.groupby(["user_id", "endpoint"]).agg(
    unique_ids=("resource_id", "nunique")).reset_index()
suspicious = bola[bola["unique_ids"] > 50]

Key detection patterns:

  1. BOLA/IDOR: sequential resource ID enumeration
  2. Rate limit bypass via header manipulation
  3. Credential scanning (401 surges from single source)
  4. SQL/NoSQL injection in query parameters
  5. Unusual HTTP methods (DELETE, PATCH) on read-only endpoints

Examples

# Detect 401 surges indicating credential scanning
auth_failures = df[df["status_code"] == 401]
scanner_ips = auth_failures.groupby("source_ip").size()
scanners = scanner_ips[scanner_ips > 100]

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