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Secure LLM Context Firewall
Build middleware that enforces strict separation between user messages and system-owned memory or provider context before requests reach the model. The product would sanitize forged delimiters, preserve channel integrity, and reduce prompt-injection risk for teams shipping AI agents in production.
Pourquoi c'est important
You are wiring together an agent that stores memory, passes provider metadata, and streams replies back into your product. Everything looks fine until hidden context starts surfacing in the visible conversation or gets written back into history as if the user said it. At that point, your trust boundary is gone. You are no longer sure whether the model is responding to the user, to internal memory, or to a forged block that imitates your own framework format. Existing open-source fixes are partial and uneven, so you end up writing custom guards around every step of the request lifecycle just to feel safe enough to deploy.
- · Conçu pour Engineering teams building AI agents, copilots, and chat workflows that inject memory, retrieval output, or provider-side metadata into model prompts..
- · Monétisation la plus probable : SaaS subscription.
La douleur · Récit
You are wiring together an agent that stores memory, passes provider metadata, and streams replies back into your product. Everything looks fine until hidden context starts surfacing in the visible conversation or gets written back into history as if the user said it. At that point, your trust boundary is gone. You are no longer sure whether the model is responding to the user, to internal memory, or to a forged block that imitates your own framework format. Existing open-source fixes are partial and uneven, so you end up writing custom guards around every step of the request lifecycle just to feel safe enough to deploy.
Détail du score
Signal du marché
Mise sur le marché
Founding engineers and platform leads shipping production AI agents with memory or retrieval features.
~50K-150K globally in the near-term serviceable market
Twitter dev community
$99/month
10 paying teams using the proxy in staging or production within 30 days
Périmètre MVP · 1–2 semaines
- Implement a lightweight request proxy that accepts chat payloads and rewrites trusted context into a separate internal structure
- Build delimiter and forged-block detection for common memory tag patterns
- Add a simple policy file for allowlist and blocklist behavior
- Create a minimal SDK for Python applications to route prompts through the proxy
- Record blocked events and rewritten payload summaries in a basic dashboard
- Add adapters for two popular agent frameworks and one direct provider API path
- Support response-side sanitization before logs or persistence are written
- Implement replay tooling to compare original and sanitized payloads
- Add team settings for strict mode versus monitor-only mode
- Launch a hosted beta with self-serve onboarding and sample integrations
Différenciation
Pourquoi cela pourrait échouer
Auto-contre-argument — le signal de confiance le plus important
- 1If major model providers and frameworks quickly ship native channel separation, the product could be compressed into a low-value utility.
- 2Security-conscious teams may decide they cannot trust an external proxy with sensitive prompts and will build in-house instead.
- 3The issue may feel urgent to advanced builders but not broad enough among mainstream AI app teams to support a large standalone business.
Résumé des preuves
Comment l'IA a synthétisé cet aperçu — pas de citations textuelles
Multiple participants described the same underlying failure: memory or provider context is being treated as if it were part of the user message. Several comments focused on forged delimiters, sanitization points, and the lack of a hard channel boundary. The discussion also shows engineers are already patching around the issue manually, which suggests real cost and urgency.
Plan d'Action
Validez cette opportunité avant d'écrire du code
Prochaine Étape Recommandée
Construire
Signaux de demande forts. Vraie douleur et volonté de payer détectées — commencez à construire un MVP.
Kit de Textes pour Landing Page
Textes prêts à coller, basés sur le langage réel de la communauté Reddit
Titre Principal
Secure LLM Context Firewall
Sous-titre
Build middleware that enforces strict separation between user messages and system-owned memory or provider context before requests reach the model. The product would sanitize forged delimiters, preserve channel integrity, and reduce prompt-injection risk for teams shipping AI agents in production.
Pour Qui
Pour Engineering teams building AI agents, copilots, and chat workflows that inject memory, retrieval output, or provider-side metadata into model prompts.
Liste des Fonctionnalités
✓ Proxy layer that separates user content from trusted memory/context ✓ Delimiter forgery detection and automatic sanitization ✓ Framework adapters for common agent runtimes ✓ Policy engine for allowed context channels and persistence rules ✓ Audit logs showing where contamination was blocked
Où Valider
Partagez votre landing page sur r/GitHub · NousResearch/hermes-agent — c'est exactement là que ces points de douleur ont été découverts.
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