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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.
为什么这很重要
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.
- · 专为 Engineering teams building AI agents, copilots, and chat workflows that inject memory, retrieval output, or provider-side metadata into model prompts. 打造。
- · 最可能的变现方式：SaaS subscription。
痛点叙事
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.
得分构成
市场信号
Go-to-Market 启动方案
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 方案 · 1-2 周
- 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
差异化
为什么这件事可能失败
自我反驳——最重要的信任度信号
- 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.
证据综述
AI 如何合成此洞察——无原话引用
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.
行动计划
在写代码之前，先验证这个商机
推荐下一步
直接做
需求信号强烈。痛点真实、付费意愿明确——启动 MVP 开发。
落地页文案包
基于真实 Reddit 评论整理的即用文案，可直接粘贴到落地页
主标题
Secure LLM Context Firewall
副标题
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.
目标用户
适合：Engineering teams building AI agents, copilots, and chat workflows that inject memory, retrieval output, or provider-side metadata into model prompts.
功能列表
✓ 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
去哪里验证
把落地页链接发布到 r/GitHub · NousResearch/hermes-agent——这里就是这些痛点被发现的地方。
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