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LLM Inference Firewall for RAG Systems
An API middleware that scans incoming user documents (PDFs, text) for hidden prompt injections and rare-token attacks before they are fed into enterprise LLM context windows. It protects systems from privilege escalation and data manipulation.
Why this matters
When you deploy an AI agent to read user-submitted files like tax returns or resumes, you open a massive security gap. Malicious actors can embed hidden, statistically rare tokens inside these documents. If your application relies on the AI to summarize this data and make downstream decisions, those hidden tokens can hijack the model to grant elevated permissions or return falsified information. Standard web application firewalls miss these semantic attacks completely, leaving your automated workflows exposed to silent manipulation.
- · Built for Security engineers and AI product managers at B2B SaaS companies building AI agents that process third-party documents..
- · Most likely monetization: SaaS subscription based on token volume processed.
The Pain · Narrative
When you deploy an AI agent to read user-submitted files like tax returns or resumes, you open a massive security gap. Malicious actors can embed hidden, statistically rare tokens inside these documents. If your application relies on the AI to summarize this data and make downstream decisions, those hidden tokens can hijack the model to grant elevated permissions or return falsified information. Standard web application firewalls miss these semantic attacks completely, leaving your automated workflows exposed to silent manipulation.
Score Breakdown
Market Signal
Go-to-Market
Security-conscious lead engineers at mid-size fintech or HR-tech startups deploying AI-driven document analysis.
Roughly 10,000 to 20,000 engineering teams actively building RAG applications in regulated sectors.
Direct cold outreach to AI engineering leads on LinkedIn and specialized developer communities (e.g., AI safety forums).
$299/month for up to 1 million tokens scanned.
5 enterprise teams agreeing to route a fraction of their staging traffic through the API for beta testing.
MVP Scope · 1–2 weeks
- Set up a FastAPI project with basic authentication and rate limiting.
- Create a text extraction module that strips out non-visible characters and HTML/PDF hidden layers.
- Implement a basic statistical analyzer to flag documents with unusually high concentrations of rare tokens.
- Build a regex-based engine to catch known prompt injection structures.
- Draft API documentation using Swagger/OpenAPI.
- Develop a lightweight LLM-based classifier (using a fast local model) to score text for manipulative intent.
- Create a simple web dashboard for users to view flagged requests and false positives.
- Integrate Stripe for usage-based billing.
- Write a plug-and-play Python SDK compatible with standard RAG pipelines.
- Deploy to a robust cloud environment (AWS/GCP) to ensure low latency.
Differentiation
Why This Might Fail
Self-rebuttal — the most important trust signal
- 1Latency constraints: Adding even 200ms of delay to AI applications might be unacceptable for real-time user experiences.
- 2Provider obsolescence: OpenAI or Anthropic could release native RAG safety layers that render third-party middleware obsolete.
- 3Evasion techniques: Attackers might quickly develop methods to bypass statistical scanning by blending attacks into perfectly normal token distributions.
Evidence Summary
How AI synthesized this insight — no verbatim quotes
Community members emphasized that domain-specific AI applications, such as those processing financial or identity documents, are highly susceptible to targeted attacks. They noted that injecting just a few carefully crafted rare tokens into user-submitted data can virtually guarantee the model will process the malicious payload. This highlights a critical gap where standard security measures fail to protect against context-based privilege escalation.
Action Plan
Validate this opportunity before writing code
Recommended Next Step
Validate
Promising signals, but needs confirmation. Create a landing page, collect email sign-ups, then decide.
Landing Page Copy Kit
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Headline
LLM Inference Firewall for RAG Systems
Sub-headline
An API middleware that scans incoming user documents (PDFs, text) for hidden prompt injections and rare-token attacks before they are fed into enterprise LLM context windows. It protects systems from privilege escalation and data manipulation.
Who It's For
For Security engineers and AI product managers at B2B SaaS companies building AI agents that process third-party documents.
Feature List
✓ Pre-inference API endpoint for document sanitization ✓ Statistical anomaly detection for hidden rare tokens ✓ Invisible text and metadata stripper for PDFs ✓ Real-time alerting dashboard for blocked injections ✓ SDK for drop-in replacement in LangChain/LlamaIndex
Where to Validate
Share your landing page in r/HN · llm — that's exactly where these pain points were discovered.
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