While building agent-based systems with LLM tool use, I kept running into the same failure mode: External content (webpages, files, API responses) would eventually influence agent behavior in unintended ways. Prompt injection isn’t just a “filtering pr…
AI systems, particularly large language models, are often viewed as a direct path toward autonomous scientific discovery and rapid economic transformation. While their capabilities in pattern recognition, cross domain synthesis, and hypothesis ge…
What if it were possible to guarantee that AI agents can’t delete a shopping list, let alone your production database simply because file deletion action isn’t included in the prompt scope? In the same way, no agent could ever leak your customer …
Solution to AI Agent Prompt Injection, Hijacking attacks and Info Leaks: AI agents can be hijacked mid-task through the content they process. Every existing defense operates at the reasoning layer and can be bypassed. Sentinel enforces at the exe…
Hi , I’m the founder of Sentinel Gateway. We’ve been focused on the structural problem of instruction provenance in autonomous agents: models process all text as undifferentiated input, so adversarial content can cause agents to propose harmful actions…
Sentinel Gateway, a middleware platform that solves prompt injection at the infrastructure level by cryptographically separating instruction and data channels, so the model never decides what qualifies as a command. Every agent action is also governed …
Prompt injection is ranked the #1 security vulnerability for AI agents by OWASP and current solutions are not working. Sentinel Gateway takes a fundamentally different architectural approach to the problem, and we have a working prototype tested agains…
Introducing Open Book Medical AI: Deterministic Knowledge Graph + Compact LLM Most medical AI systems today rely heavily on large, opaque language models. They are powerful, but probabilistic, difficult to audit, and expensive to deploy. We’ve taken a …
AI with minimum hallucinations and an output that can be audited. How by using Knowledge Graph as source of truth and RAG for answer audit First practical application medical field, end result an AI that's capable of clinical diagnosis and can ass…
Medical AI with Knowledge-Graph Core Anchor and RAG Answer Auditing A medical knowledge graph containing ~5,000 nodes, with medical terms organized into 7 main and 2 sub-categories: diseases, symptoms, treatments, risk factors, diagnostic tests, body p…
