SKILL.md

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]

Supporting file: LICENSE

This file is part of the analyzing-api-gateway-access-logs skill package. Use it when SKILL.md references LICENSE.

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Supporting file: references/api-reference.md

This file is part of the analyzing-api-gateway-access-logs skill package. Use it when SKILL.md references references/api-reference.md.

API Reference: Analyzing API Gateway Access Logs

AWS API Gateway Log Fields

{
  "requestId": "abc-123",
  "ip": "203.0.113.50",
  "httpMethod": "GET",
  "resourcePath": "/api/users/{id}",
  "status": 200,
  "requestTime": "2025-03-15T14:00:00Z",
  "responseLength": 1024
}

Pandas Log Analysis

import pandas as pd

df = pd.read_json("access_logs.json", lines=True)

# BOLA detection
df.groupby("user_id")["resource_id"].nunique()

# Auth failure surge
df[df["status_code"] == 401].groupby("source_ip").size()

# Request velocity
df.set_index("timestamp").resample("1min").size()

OWASP API Top 10 Patterns

Injection Regex Patterns

sql = r"union\s+select|drop\s+table|'\s*or\s+'1'"
nosql = r"\$ne|\$gt|\$regex|\$where"
xss = r"<script|javascript:|onerror="
path_traversal = r"\.\./\.\./|/etc/passwd"

References

• OWASP API Security Top 10: https://owasp.org/API-Security/
• AWS API Gateway logging: https://docs.aws.amazon.com/apigateway/latest/developerguide/
• pandas: https://pandas.pydata.org/docs/

Supporting file: scripts/agent.py

This file is part of the analyzing-api-gateway-access-logs skill package. Use it when SKILL.md references scripts/agent.py.

#!/usr/bin/env python3
"""Agent for analyzing API Gateway access logs for security threats."""

import re
import json
import argparse
from datetime import datetime

import pandas as pd


def load_api_logs(log_path):
    """Load API gateway logs from JSON lines or CSV."""
    if log_path.endswith(".csv"):
        return pd.read_csv(log_path, parse_dates=["timestamp"])
    return pd.read_json(log_path, lines=True)


def detect_bola_attacks(df, threshold=50):
    """Detect Broken Object Level Authorization (BOLA/IDOR) attacks."""
    findings = []
    if "resource_id" not in df.columns:
        path_col = "request_path" if "request_path" in df.columns else "path"
        df["resource_id"] = df[path_col].str.extract(r'/(\d+)(?:/|$|\?)')
    df_with_ids = df.dropna(subset=["resource_id"])
    if df_with_ids.empty:
        return findings
    user_col = "user_id" if "user_id" in df.columns else "source_ip"
    grouped = df_with_ids.groupby([user_col]).agg(
        unique_resources=("resource_id", "nunique"),
        total_requests=("resource_id", "count"),
    ).reset_index()
    bola_suspects = grouped[grouped["unique_resources"] >= threshold]
    for _, row in bola_suspects.iterrows():
        findings.append({
            "user": row[user_col],
            "unique_resources_accessed": int(row["unique_resources"]),
            "total_requests": int(row["total_requests"]),
            "type": "BOLA/IDOR",
            "severity": "CRITICAL",
        })
    return findings


def detect_auth_scanning(df, threshold=100):
    """Detect credential scanning via 401/403 response surges."""
    findings = []
    auth_failures = df[df["status_code"].isin([401, 403])]
    if auth_failures.empty:
        return findings
    ip_col = "source_ip" if "source_ip" in df.columns else "client_ip"
    ip_failures = auth_failures.groupby(ip_col).agg(
        failure_count=("status_code", "count"),
        unique_endpoints=("request_path", "nunique") if "request_path" in df.columns
        else ("path", "nunique"),
    ).reset_index()
    scanners = ip_failures[ip_failures["failure_count"] >= threshold]
    for _, row in scanners.iterrows():
        findings.append({
            "source_ip": row[ip_col],
            "auth_failures": int(row["failure_count"]),
            "endpoints_probed": int(row["unique_endpoints"]),
            "type": "credential_scanning",
            "severity": "HIGH",
        })
    return findings


def detect_injection_attempts(df):
    """Detect SQL/NoSQL injection attempts in request parameters."""
    injection_patterns = [
        r"(?:union\s+select|select\s+.*\s+from|drop\s+table|insert\s+into)",
        r"(?:'\s*or\s+'1'\s*=\s*'1|'\s*or\s+1\s*=\s*1)",
        r'(?:\$ne|\$gt|\$lt|\$regex|\$where)',
        r'(?:<script|javascript:|onerror=|onload=)',
        r'(?:\.\./\.\./|/etc/passwd|/proc/self)',
    ]
    findings = []
    path_col = "request_path" if "request_path" in df.columns else "path"
    query_col = "query_string" if "query_string" in df.columns else path_col
    for _, row in df.iterrows():
        request_str = str(row.get(query_col, "")) + str(row.get("request_body", ""))
        for pattern in injection_patterns:
            if re.search(pattern, request_str, re.IGNORECASE):
                findings.append({
                    "source_ip": row.get("source_ip", row.get("client_ip", "")),
                    "path": row.get(path_col, ""),
                    "pattern_matched": pattern,
                    "type": "injection_attempt",
                    "severity": "HIGH",
                })
                break
    return findings[:500]


def detect_rate_limit_bypass(df, window="1min", threshold=100):
    """Detect rate limit bypass attempts."""
    findings = []
    ip_col = "source_ip" if "source_ip" in df.columns else "client_ip"
    df_copy = df.copy()
    df_copy["timestamp"] = pd.to_datetime(df_copy["timestamp"])
    df_copy = df_copy.set_index("timestamp")
    for ip, group in df_copy.groupby(ip_col):
        resampled = group.resample(window).size()
        bursts = resampled[resampled > threshold]
        if len(bursts) > 0:
            findings.append({
                "source_ip": ip,
                "max_requests_per_min": int(resampled.max()),
                "burst_periods": len(bursts),
                "type": "rate_limit_bypass",
                "severity": "MEDIUM",
            })
    return sorted(findings, key=lambda x: x["max_requests_per_min"], reverse=True)[:50]


def detect_unusual_methods(df):
    """Detect unusual HTTP methods on typically read-only endpoints."""
    findings = []
    dangerous_methods = {"DELETE", "PUT", "PATCH"}
    method_col = "method" if "method" in df.columns else "http_method"
    path_col = "request_path" if "request_path" in df.columns else "path"
    unusual = df[df[method_col].str.upper().isin(dangerous_methods)]
    for _, row in unusual.iterrows():
        findings.append({
            "source_ip": row.get("source_ip", row.get("client_ip", "")),
            "method": row[method_col],
            "path": row[path_col],
            "status_code": int(row.get("status_code", 0)),
            "type": "unusual_method",
            "severity": "MEDIUM",
        })
    return findings[:200]


def main():
    parser = argparse.ArgumentParser(description="API Gateway Log Analysis Agent")
    parser.add_argument("--log-file", required=True, help="API gateway log file")
    parser.add_argument("--output", default="api_gateway_report.json")
    parser.add_argument("--action", choices=[
        "bola", "auth_scan", "injection", "rate_limit", "full_analysis"
    ], default="full_analysis")
    args = parser.parse_args()

    df = load_api_logs(args.log_file)
    report = {"generated_at": datetime.utcnow().isoformat(), "total_requests": len(df),
              "findings": {}}
    print(f"[+] Loaded {len(df)} API requests")

    if args.action in ("bola", "full_analysis"):
        findings = detect_bola_attacks(df)
        report["findings"]["bola"] = findings
        print(f"[+] BOLA suspects: {len(findings)}")

    if args.action in ("auth_scan", "full_analysis"):
        findings = detect_auth_scanning(df)
        report["findings"]["auth_scanning"] = findings
        print(f"[+] Auth scanners: {len(findings)}")

    if args.action in ("injection", "full_analysis"):
        findings = detect_injection_attempts(df)
        report["findings"]["injection_attempts"] = findings
        print(f"[+] Injection attempts: {len(findings)}")

    if args.action in ("rate_limit", "full_analysis"):
        findings = detect_rate_limit_bypass(df)
        report["findings"]["rate_limit_bypass"] = findings
        print(f"[+] Rate limit bypasses: {len(findings)}")

    with open(args.output, "w") as f:
        json.dump(report, f, indent=2, default=str)
    print(f"[+] Report saved to {args.output}")


if __name__ == "__main__":
    main()