glc PromptGuard
Stop prompt injection before it reaches your AI agent
What it does
glc PromptGuard is an eight-layer, multilingual prompt-injection defense for AI agents and RAG pipelines. It combines normalization, structural signals, framing awareness, intent classification, source-aware policy merging, a neural sensor, RAG/tool spotlighting, and an optional canary probe. Evaluated through millions of end-to-end runs built from several hundred thousand multilingual attack and benign examples. Available via remote MCP and REST, with 100 free credits for every account.
Host-side prompt injection gate for orchestrators on GLC MCP. Call before every LLM call — returns injection score and policy; optional session_id sticky…
CRITICAL: Call from your orchestrator BEFORE every main LLM call (never via model tool-choice). Evaluates intent × source context × impact — not keyword-only. Layered stack: normalize/multi-view → structural → discourse framing → intent → soft neural (attack + discourse + gated semantic) → policy floors; optional sticky session risk (session_id), spotlight/canary. Primary result: injection=true|false; also score, intent, policy, optional spotlight. context=user_prompt|rag_chunk|tool_result changes policy. Optional session_id enables multi-turn risk floor (same chat/run id each turn). Canary is a secondary tool-hijack probe only. Delta only — not full chat history. Not 100% security: host…from mcp.glc-rag.hu
Does the same job
all alternatives →
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 16d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 26d ago · cactuscompute.com
Launched alongside, August 2026
the whole month →- TL
Life & fun · 10d ago · louisabraham.github.io
- SA
Hello HN! I found that picking out plausible but diverse skin tones for my digital art and game development projects was kind of difficult, and I got curious about if there was a way to define a color space that made it easy. I've built a color picker and procedural generation algorithm based on the space as well as a bunch of other fun js features and demos throughout the page that use the equations. If you find it interesting, I have lots of explanations of how I built it and what properties the space has. The methodology might be a bit shaky, but hopefully the result is as helpful for…
Life & fun · Aug 2026 · toneyalexander.github.io
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 16d ago · simedw.com