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Analyze access logs from AWS API Gateway, Kong, and Nginx with pandas, a Python data analysis library. Examine resource access patterns, authentication failures, and suspicious requests to investigate API abuse and inform detection rules.

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• Investigating sequential access to resource IDs
• Examining spikes in authentication failures
• Checking for rate limit bypass and injection attempts

Exploit GraphQL API architectural features to execute highly efficient brute-force, Credential Stuffing, and Denial of Service (DoS) attacks. Utilize Query Batching and Alias injection to bypass rate limits by packing thousands of requests into a single HTTP POST request.

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Identify and exploit flaws in API rate limiting enforcement. Use this skill when encountering HTTP 429 Too Many Requests errors during password brute-forcing, OTP validation, credential stuffing, or enumeration attacks. These bypasses leverage IP spoofing, parameter manipulation, and edge-case application logic.

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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.

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GraphQL saldırıları — introspection, aliased query batching, rate limit bypass

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