Analyzing api gateway access logs
Skill autohandai/community-skills/analyzing-api-gateway-access-logs
A collection of curated, useful, and safe skills for Autohand Code CLI Agent
npx -y skills add autohandai/community-skills --skill analyzing-api-gateway-access-logsAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 9 stars9 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
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.
The file declares its own license as Apache-2.0. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.
SKILL.md
1.5 KB, as published. Nobody here has run it
Analyzing API Gateway Access Logs
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]