# Agent Skill Package: analyzing-api-gateway-access-logs You are loading a published Agent Skill. Follow SKILL.md exactly. Supporting files from the original zip are inlined below. When SKILL.md says to read `references/...` or `scripts/...`, use the matching FILE section here — do not say the file is missing. Canonical URL: https://skill.hk/s/analyzing-api-gateway-access-logs.md Human page: https://skill.hk/s/analyzing-api-gateway-access-logs Files (4): - SKILL.md - LICENSE - references/api-reference.md - scripts/agent.py ======================================================================== FILE: SKILL.md ======================================================================== --- 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] ``` ======================================================================== FILE: LICENSE ======================================================================== Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. 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See the License for the specific language governing permissions and limitations under the License. ======================================================================== FILE: references/api-reference.md ======================================================================== # API Reference: Analyzing API Gateway Access Logs ## AWS API Gateway Log Fields ```json { "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 ```python 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 | Risk | Detection Pattern | |------|-------------------| | BOLA (API1) | User accessing > 50 unique resource IDs | | Broken Auth (API2) | > 100 401/403 from single IP | | Excessive Data (API3) | Response size > 10x average | | Rate Limit (API4) | > 100 req/min from single IP | | BFLA (API5) | DELETE/PUT on read-only endpoints | | Injection (API8) | SQL/NoSQL patterns in params | ## Injection Regex Patterns ```python sql = r"union\s+select|drop\s+table|'\s*or\s+'1'" nosql = r"\$ne|\$gt|\$regex|\$where" xss = r"= 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'(?: 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()