Bug Bounty Hunter and the Machine

70+ real vulnerabilities. Working PoCs. Reports that get paid.

This is not a theoretical bug bounty book. It is the documentation of 70+ real vulnerabilities found with AI assistance in active bug bounty programs covering gaming launchers, hardware drivers, AI desktop assistants, messaging platforms, enterprise communication tools, remote access software, PDF readers, and terminal emulators. Each vulnerability with its working PoC, complete report, and triage decision analysis.

The book documents a complete workflow: from automated reconnaissance with Claude Code to static analysis with local models in LM Studio, through building Docker lab environments to reproduce and validate every finding before reporting it.

36 chapters organized around 13 complete bug bounty cases covering the full cycle: reconnaissance, analysis, exploitation, reporting, and getting paid:

• AI-powered reconnaissance -- Claude Code for attack surface analysis, endpoint enumeration, public documentation review, and functionality mapping.
• Assisted static analysis -- Local models with LM Studio for source code review, vulnerable pattern detection, and dependency analysis without sending code to the cloud.
• Docker lab environment -- Reproducible environments for each case: containers that replicate vulnerable logic, PoC validation, and impact documentation.
• 13 complete documented cases -- gaming launchers (2), hardware drivers (1), AI desktop assistants (1), AI-powered IDEs (1), streaming servers (1), messaging platforms (2), enterprise communication (1), remote access tools (1), PDF readers (1), terminal emulators with anti-cheat (1).
• The report that gets paid -- Structure, narrative, and level of detail that separates an ignored report from one that earns a bounty. With real anonymized examples.
• Ethics and boundaries -- What AI can and cannot do in bug bounty. When the model saves you hours and when it leads you down false paths.

• Concept -- The vulnerability class, its real impact, and why AI helps (or doesn't) in finding it.
• Decision -- Real trade-offs: discarded tools, compared models, approaches that failed before reaching the one that worked.
• Implementation -- Didactic code, Docker configurations, effective prompts, and working PoCs with line-by-line explanation.

• Bug bounty hunters who want to multiply their discovery capability with AI without losing technical rigor.
• Security researchers who need a reproducible workflow for vulnerability analysis with language models.
• Pentesters looking to transition into bug bounty or add AI to their existing methodology.
• Developers with an offensive mindset who want to understand how hunters find flaws in their code.

This is not a tools manual. It is the documentation of a real workflow combining Claude Code for reasoning, local models for privacy, and Docker for reproducibility. All 70+ vulnerabilities are real and were reported through official programs.

This is not for absolute beginners. It requires basic knowledge of web security, networking, and programming. The book teaches how to enhance existing skills, not replace them.

About the authors: Carlos Perez Gonzalez, AI solutions architect with over two decades of offensive cybersecurity experience (OSCE, OSCP, OSWE, OSEP, CREST), founder of cybersecurity training platforms, and builder of enterprise AI systems. Juan C. Montes, cybersecurity architect with forensic and offensive profile (GCFA, GREM), published in PHRACK #65.

Book 10 in The Professional and the Machine series