analyzing-api-gateway-access-logs
$
npx mdskill add mukul975/Anthropic-Cybersecurity-Skills/analyzing-api-gateway-access-logsDetect API gateway attacks by parsing access logs for anomalies.
- Identifies broken object authorization and injection attempts in logs.
- Integrates with pandas for statistical analysis of request patterns.
- Surfaces findings by grouping user IDs and endpoint interactions.
- Delivers structured reports on rate limit bypass and credential scanning.
SKILL.md
.github/skills/analyzing-api-gateway-access-logsView on GitHub ↗
---
name: analyzing-api-gateway-access-logs
description: >
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.
domain: cybersecurity
subdomain: security-operations
tags: [analyzing, api, gateway, access]
version: "1.0"
author: mahipal
license: Apache-2.0
---
# 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.
```python
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
```python
# 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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