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analyzing-api-gateway-access-logs

'Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect

Use with my agent View on GitHub

Overview

'Parses API Gateway access logs (AWS API Gateway, Kong, Nginx) to detect

Read full documentation

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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:

BOLA/IDOR: sequential resource ID enumeration
Rate limit bypass via header manipulation
Credential scanning (401 surges from single source)
SQL/NoSQL injection in query parameters
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]

View source ↗

File metadata

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:
- api-security
- access-log-analysis
- aws-api-gateway
- kong
- nginx
- bola-detection
- rate-limit-bypass
- security-operations
version: '1.0'
author: mahipal
license: Apache-2.0
nist_csf:
- DE.CM-01
- RS.MA-01
- GV.OV-01
- DE.AE-02
mitre_attack:
- T1190
- T1110.004
- T1078.004
- T1119

View original text

---
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:
- api-security
- access-log-analysis
- aws-api-gateway
- kong
- nginx
- bola-detection
- rate-limit-bypass
- security-operations
version: '1.0'
author: mahipal
license: Apache-2.0
nist_csf:
- DE.CM-01
- RS.MA-01
- GV.OV-01
- DE.AE-02
mitre_attack:
- T1190
- T1110.004
- T1078.004
- T1119
---

# 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]
```

Use with my agent

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

License: Apache-2.0

Dependency or permission surface needs review
Permission surface may require sandboxing
SKILL.md does not document installation steps, exact CLI usage, or expected input log schema, making the skill harder to operate correctly.
The script assumes specific columns (e.g., timestamp, status_code, resource_id, source_ip) with little validation; malformed or differently-shaped logs will cause errors or false findings.
No explicit warning that log content should be treated as untrusted data; an AI agent could be influenced by instructions embedded in log fields.
The generated report may include raw query strings, request bodies, and paths without redaction, which could expose sensitive tokens or secrets if shared.
Safe operating boundaries are under-specified: no guidance on authorized environments, log retention, or handling of potentially sensitive production logs.
Quality score needs review
Permission surface needs review: secrets or environment access, network or browser access
GitHub adoption: 57 GitHub stars
Stars/forks activity: 57 stars, 12 forks; issue activity unavailable in current metadata
Dependency/runtime risk: credential or environment access, network or browser surface

Agent access

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More details

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Creator: mahipal
Source: adriannoes/awesome-agentic-ai
Indexed by: OpenAgentSkill community index

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analyzing-api-gateway-access-logs

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Analyzing API Gateway Access Logs

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Use with my agent

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Overview

Analyzing API Gateway Access Logs

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