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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.
이것이 중요한 이유
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.
- · Engineering teams building AI agents, copilots, and chat workflows that inject memory, retrieval output, or provider-side metadata into model prompts.을(를) 위해 제작되었습니다.
- · 가장 유력한 수익화 모델: SaaS subscription.
고충 · 내러티브
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.
점수 세부
시장 신호
시장 진출 전략
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
MVP 범위 · 1~2주
- 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
차별화
실패 가능 요인
자가 반박 — 가장 중요한 신뢰 신호
- 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.
근거 요약
AI가 이 인사이트를 합성한 방법 — 직접 인용 없음
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.
액션 플랜
코드를 작성하기 전에 이 기회를 검증하세요
권장 다음 단계
개발 시작
강한 수요 신호 감지. 실제 고통과 지불 의지 확인 — MVP 개발을 시작하세요.
랜딩 페이지 카피 키트
실제 Reddit 댓글 기반의 바로 사용 가능한 문구 — 그대로 붙여넣기 가능합니다
헤드라인
Secure LLM Context Firewall
서브 헤드라인
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.
대상 사용자
대상: Engineering teams building AI agents, copilots, and chat workflows that inject memory, retrieval output, or provider-side metadata into model prompts.
기능 목록
✓ 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
어디서 검증할까요
r/GitHub · NousResearch/hermes-agent에 랜딩 페이지 링크를 공유하세요 — 바로 이 고통이 발견된 곳입니다.
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