Idea
Model detecting unsafe prompts by learning safe prompt patterns to reduce false positives and improve LLM safety across languages and domains.
Research Paper
Core Innovation
This paper introduces Trust The Typical (T3), which treats LLM safety as an out-of-distribution detection problem by modeling the distribution of safe prompts in semantic space. Unlike prior methods, it requires no training on harmful examples and achieves superior performance with significantly reduced false positives, transferring effectively across languages and domains without retraining.
Why It Matters
Current LLM safety methods rely on blocking known harmful inputs, leading to brittle and incomplete protections with high false positives. T3's approach improves safety by focusing on what is safe, enabling more reliable detection of threats without extensive harmful data. This reduces operational costs, improves user experience, and scales across languages and domains without retraining, making it practical for real-world deployment.
Market Size (TAM)
$10–20B TAM for AI safety and content moderation; $2–5B SAM from AI platform providers and enterprises deploying LLMs. Driven by increasing AI adoption and regulatory pressure for safer AI.
Potential Customers & Pain Points
- AI platform providers – Need robust scalable safety without high false positives
- Enterprises deploying LLMs – Require reliable content moderation across languages
- Cloud service providers – Need efficient real-time safety monitoring with low overhead
- Regulatory bodies – Demand transparent and effective AI safety mechanisms.
Business Model
Subscription-based API and enterprise licensing for real-time LLM safety monitoring integrated into AI platforms and applications.
Competitive Landscape
- OpenAI Safety Systems
- Anthropic's Constitutional AI
- Google's Perspective API
- Hugging Face Moderation Models
Implementation Challenges
- Adoption resistance due to integration complexity
- Potential edge cases missed by OOD detection
- Competition from established safety frameworks
- Need for continuous updates as language evolves
Validation Strategy
- Pilot integration with major AI platform providers
- Benchmark performance against existing safety models in production
- Multilingual and domain-specific stress testing
- Customer feedback loops to refine detection thresholds
Research Paper Overview
Trust The Typical
Summary
Trust The Typical (T3) reframes LLM safety as an out-of-distribution detection problem by learning the distribution of safe prompts and flagging deviations as threats. It requires no harmful example training, achieves state-of-the-art results across 18 safety benchmarks, reduces false positives by up to 40x, and transfers across languages and domains without retraining. A GPU-optimized version integrates with vLLM for efficient real-time guardrails with minimal overhead.