The AI Bug Bounty Gold Rush — Are We Chasing the Wrong Bugs?
Over the last couple of years the infosec bug bounty ecosystem has experienced a massive shift toward AI vulnerabilities. There's no other way to describe it — it's a full-on gold rush. Every researcher and their dog is pivoting to AI prompt injection, model extraction, and training data poisoning. And while AI security absolutely deserves attention, I think the disproportionate focus on it is creating blind spots that attackers are more than happy to exploit.
The Numbers Tell a Different Story
I've been looking at vulnerability disclosure data from 2022 through 2025, and the picture is striking. Traditional vulnerabilities — the OWASP Top 10 staples — still account for 78% of successful exploitations in production environments. SQLi, broken auth, XSS — the same stuff that's been documented for over two decades. These aren't exotic zero-days. These are well-understood, well-documented attack vectors that organizations still fail to patch.
Meanwhile, bounty payouts for AI-related vulnerabilities have increased by 340% during that same period. Traditional vulnerability rewards? A comparatively flat 12% increase. The money is flowing toward AI bugs, but the actual damage is still coming from the classics.
Real Breaches, Old Tricks
If you look at the major security breaches from 2024, attackers are not out here pioneering novel AI exploits. They're using SQL injection. Broken authentication mechanisms. Cross-site scripting. The same playbook that's been working since the early 2000s.
These breaches collectively impacted over 85 million users and caused estimated damages exceeding $2.7 billion. That's not theoretical risk — that's real-world impact from vulnerabilities that the bug bounty community is increasingly deprioritizing in favor of the shiny new thing.
Don't get me wrong — AI security matters. Prompt injection is real. Model vulnerabilities are real. But an organization that has unpatched SQLi in production doesn't need a researcher finding edge cases in their chatbot. They need someone finding the hole in their login page.
A Balanced Framework
What I'm proposing is a framework for bounty hunters that accounts for both risk impact and prevalence when deciding what to hunt. Right now the incentive structure is skewed — platforms and organizations are overpaying for AI findings because it's trendy, while underpaying for traditional bugs that represent the majority of actual breach vectors.
For individual researchers, the calculus is simple: if everyone is crowding into AI bounties, there's actually less competition and more opportunity in the traditional vulnerability space. The OWASP Top 10 isn't going anywhere. Organizations are still deploying apps with the same mistakes they made ten years ago. The bugs are there. The question is whether anyone's still looking for them.