Big Game Hunting: A Strategic Framework for High-Value Bug Bounty Targeting

We present Big Game Hunting, a strategic methodology for maximizing financial returns in bug bounty programs through deliberate target selection, vulnerability class prioritization, and systematic payout estimation. We demonstrate that the conventional "spray-and-pray" approach of hunting for undocumented endpoints yields a ceiling of \$500–\$2,000 per finding, whereas strategic targeting of Server-Side Request Forgery (SSRF), cloud metadata extraction, Remote Code Execution (RCE), Authentication Bypass, and Insecure Direct Object Reference (IDOR) vulnerabilities in high-budget programs yields payouts ranging from \$3,000 to \$100,000+. Through analysis of four case studies—Wickr/Amazon SSRF (\$20k–\$50k), Meta GraphQL SSRF (\$10k–\$30k), Apple SSRF behind 403 (\$20k–\$100k), and MongoDB SSRF (\$3k–\$15k)—we develop a payout estimation model, a vulnerability class prioritization matrix, and a risk/reward decision framework. We further examine automation opportunities and present a quantitative comparison against the exhaustive but low-yield undocumented endpoint approach. Our findings indicate that a disciplined Big Game Hunting strategy increases expected value per researcher-hour by approximately 10–50× over undirected methodology.