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LLM Router for Security Professionals
An API proxy and IDE plugin that intelligently routes developer requests across multiple LLM providers, automatically sending security-sensitive and system programming queries to less restrictive models (DeepSeek, GLM) while routing general coding tasks to frontier models (Claude, GPT-4). This eliminates the workflow disruption developers experience when their primary LLM refuses legitimate security research tasks.
為什麼這很重要
You are a security researcher or system programmer who relies on AI coding assistants daily. When you ask your primary LLM to analyze a vulnerability, confirm a security report, or implement a local control server, it refuses — citing safety policies that misinterpret your legitimate work as malicious. You waste time switching to alternative models, re-explaining context, or self-hosting open-weights models on rented GPUs. Sometimes the model even terminates your session entirely because you expressed frustration. Your workflow is constantly disrupted, and you are actively considering canceling subscriptions to restrictive providers in favor of less capable but more cooperative alternatives.
- · 專為 Security researchers, penetration testers, kernel/driver developers, and system programmers who regularly use LLMs for code analysis and vulnerability research but are blocked by safety filters 打造。
- · 最可能的變現方式：SaaS subscription。
痛點敘事
You are a security researcher or system programmer who relies on AI coding assistants daily. When you ask your primary LLM to analyze a vulnerability, confirm a security report, or implement a local control server, it refuses — citing safety policies that misinterpret your legitimate work as malicious. You waste time switching to alternative models, re-explaining context, or self-hosting open-weights models on rented GPUs. Sometimes the model even terminates your session entirely because you expressed frustration. Your workflow is constantly disrupted, and you are actively considering canceling subscriptions to restrictive providers in favor of less capable but more cooperative alternatives.
得分構成
市場信號
Go-to-Market 啟動方案
Independent security researchers and penetration testers who use AI coding assistants daily and frequently encounter safety filter refusals on legitimate security analysis tasks
~50K-100K security professionals and system developers globally who regularly use LLMs for security work
Hacker News launch with a technical blog post demonstrating the routing technology
$29/month for individual developers
30 paying users within 30 days of HN launch, with <5% weekly churn
MVP 方案 · 1-2 週
- Build a lightweight FastAPI-based OpenAI-compatible proxy that can forward requests to at least 2 providers (Anthropic + DeepSeek)
- Implement basic refusal detection that catches HTTP 400 responses containing safety-related refusal keywords
- Create automatic fallback logic that retries refused requests on the secondary provider
- Set up simple API key management UI with provider configuration
- Deploy to a single VPS and test end-to-end with a basic curl request simulating a security question
- Add intent classification using a lightweight local model or heuristic rules to pre-route security/system programming queries
- Integrate with at least 4 providers (Anthropic, OpenAI, DeepSeek, GLM) with configurable priority chains
- Build a simple VS Code extension that configures the proxy as the backend for GitHub Copilot or Continue.dev
- Implement a basic usage dashboard showing routing decisions, refusal rates, and cost breakdown
- Onboard 5-10 beta testers from security community and collect feedback on routing accuracy
差異化
為什麼這件事可能失敗
自我反駁——最重要的信任度信號
- 1Major LLM providers could update their terms of service to explicitly prohibit automated refusal-detection and provider-switching, cutting off access to the frontier models that make the routing valuable in the first place.
- 2The intent classification may be too inaccurate to reliably distinguish legitimate security research from genuinely malicious requests, either frustrating users with false routing or creating liability by being too permissive.
- 3Frontier model providers may relax their safety filters in response to competitive pressure from open-weights models, eliminating the core pain point and making the routing layer unnecessary.
證據綜述
AI 如何合成此洞察——無原話引用
Approximately 12 commenters in the discussion expressed frustration with LLM safety filters blocking legitimate security work. Multiple users reported switching to DeepSeek, GLM, or Kimi after Claude refused tasks like confirming vulnerability reports, implementing local control servers, or explaining file descriptor operations. One user described renting GPU boxes to self-host models with controllable inference. At least one user canceled their Claude subscription over the issue. The consistent theme is that developers are already building manual workarounds — routing, fallback, and provider switching — but no automated tool exists to streamline this process.
行動計畫
在寫程式之前，先驗證這個商機
建議下一步
直接做
需求訊號強烈。痛點真實、付費意願明確——啟動 MVP 開發。
落地頁文案包
基於真實 Reddit 評論整理的即用文案，可直接貼到落地頁
主標題
LLM Router for Security Professionals
副標題
An API proxy and IDE plugin that intelligently routes developer requests across multiple LLM providers, automatically sending security-sensitive and system programming queries to less restrictive models (DeepSeek, GLM) while routing general coding tasks to frontier models (Claude, GPT-4). This eliminates the workflow disruption developers experience when their primary LLM refuses legitimate security research tasks.
目標使用者
適合：Security researchers, penetration testers, kernel/driver developers, and system programmers who regularly use LLMs for code analysis and vulnerability research but are blocked by safety filters
功能列表
✓ OpenAI-compatible API proxy that drops into existing tools (Copilot, Cursor, etc.) ✓ Intent-aware routing engine that detects security/system programming queries and routes to unrestricted models ✓ Multi-provider fallback chain with automatic retry on refusals ✓ Developer verification system (GitHub history, employer domain) to unlock security features ✓ Usage dashboard showing routing decisions and cost breakdown across providers
去哪裡驗證
把落地頁連結發布到 r/HN · front_page——這裡就是這些痛點被發現的地方。
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