AI model preview cohorts composed exclusively of aligned enterprise partners and infrastructure maintainers are structurally incapable of generating the adversarial safety feedback that unfiltered populations would surface. These partners lack incentive to discover jailbreaks and offensive bypasses, creating a systematic blind spot in pre-release safety validation that only becomes visible post-deployment. A marketplace or coordination layer for structured adversarial red-teaming with diverse, incentivized participants is missing from the safety infrastructure stack.

Pre-release AI safety validation relies on aligned insiders who lack incentive to find jailbreaks, leaving critical vulnerabilities undiscovered until public deployment causes reputational and regulatory damage.

AI model developers at frontier labs and enterprises shipping LLM-powered products who need diverse adversarial testing before release.

Model providers already spend millions on red-teaming contractors and bug bounties (OpenAI, Anthropic, Meta all run ad-hoc programs); a structured marketplace with ranked, incentivized adversarial testers replaces expensive, slow procurement with on-demand coverage at the exact moment regulatory pressure (EU AI Act) is making pre-release safety validation mandatory.

MVP is a two-sided platform: model providers post sandboxed endpoints with bounty pools, red-teamers submit structured attack reports scored by severity and novelty; core tech is a sandboxed model-serving layer plus an LLM-assisted triage agent that deduplicates and ranks submissions before human review.

Global AI safety and assurance spending is projected at $5-8B by 2028; capturing the structured red-teaming slice (analogous to HackerOne/Bugcrowd capturing 10%+ of appsec budgets) suggests a $500M+ serviceable market.