LLMGuard — Jailbreak Detection & Prevention Platform

Stop jailbreak attacks before they compromise your LLM — real-time adversarial prompt defense for production AI systems

LLMGuard deploys between your users and your LLM to detect and block optimization-based jailbreak attacks like TAO-Attack using DistilBERT classification and dynamic guardrails running on ONNX Runtime. While competitors offer static content filters, we analyze semantic obfuscation patterns and token smuggling techniques in <50ms latency, protecting customer-facing chatbots and internal AI agents from the adversarial prompt exploits currently bypassing GPT-4 and Claude safeguards. Every blocked attack generates forensic logs in your Incident Response Dashboard, turning security events into compliance evidence.

Key Benefits:

- Block TAO-Attack and gradient-based jailbreaks using real-time adversarial input classification with <50ms p99 latency via quantized DistilBERT on ONNX Runtime

- Deploy dynamic guardrails that adapt to emerging attack patterns without retraining base models, using Redis-cached threat signatures and Prometheus-monitored behavior anomalies

- Generate audit-ready incident reports with full prompt forensics, model response traces, and compliance timestamps for SOC2, GDPR, and industry-specific AI governance requirements

MVP Scope: Build a real-time jailbreak detection system that analyzes incoming prompts against known adversarial attack patterns (TAO-Attack, token smuggling, semantic obfuscation). MVP includes: (1) Prompt embedding analyzer using quantized transformer models, (2) Optimization-based attack detector using statistical anomaly detection, (3) REST API for integration with LLM applications, (4) Basic dashboard for monitoring detected threats and audit logs. Supports single LLM model integration with real-time classification latency <100ms.

Tech Stack: ONNX Runtime, DistilBERT, FastAPI, PostgreSQL, Redis, Kubernetes, Prometheus, React

Components:

- Real-Time Adversarial Input Classifier

- Dynamic Guardrail Engine

- Incident Response & Audit Dashboard

- Model Behavior Monitoring System

- Integration & API Gateway

Quality assessment: Strong technical approach with real market need (LLM security is critical) and specific attack vectors addressed, but lacks originality (jailbreak detection is crowded space) and the artifact is incomplete/truncated, preventing full evaluation of depth and differentiation from existing solutions like Rebuff or Lakera.