Stop silent prompt‑injection: detect and learn from user attacks automatically

Many software teams embedding large language models face a new class of operational risk: silent prompt‑injection attacks that manipulate model outputs or exfiltrate data without obvious signs, and there is no standardized way today for teams to detect, classify and learn from those attacks automatically. The problem is acute for customer‑facing products, internal automation, and compliance‑sensitive apps, and it affects a potential addressable market of roughly 200,000 software teams estimated at $6.0B (200,000 x $30K ACV). A viable product would be a model‑agnostic detection and learning platform that sits alongside LLM calls (SDK/proxy/agent), captures structured telemetry, runs behavioral and adversarial detectors, and automatically generates explainable incident records and corrective signals back to the app. The platform should include continuous adversarial fuzzing, supervised fine‑tuning of detectors from real incidents, privacy‑preserving logging for audits, and integrations for alerting and policy enforcement; given the market score of 92/100 and revenue potential of 88/100, there is a clear willingness to pay for reliable, cross‑model protections now that models are proliferating. This can stand out by combining three differentiators: true model‑agnostic detection that works across GPT, Claude, Cohere, etc., automated learning from observed attacks to reduce time‑to‑detect, and auditable, explainable incident artifacts that meet enterprise compliance needs. Be honest that the challenges are nontrivial — maintaining low false‑positive rates, operating under privacy constraints and limited model internals, and competing in a medium‑competitive field — but if the product design prioritizes high‑precision signals, easy integration, and legal/audit readiness, it can capture meaningful enterprise deployments.