If you have shipped an LLM agent, you have watched one go sideways in real time. Halfway through a task, it takes one bad step, and now it is confidently marching toward a wrong answer. The whole industry has gotten good at detecting that moment sp...

I found this out the embarrassing way —" by attacking my own system". I maintain FIE, an open-source adversarial detection engine that screens prompts before they reach an LLM. It blocks "Ignore all previous instructions" in English at 82% confiden...

AI agents rarely fail in a clean, obvious way. They do not always crash. They do not always throw an error. They do not always say, "I could not complete the task." Sometimes they fail more quietly. They give a confident answer with weak evidence....

When something is not working in an AI system, the first instinct is always the same add more to it. More layers. More features. More complexity. It feels like progress because you are actively doing something. I fell into this exact trap while bui...

Most security systems are evaluated on attacks they have already seen. I decided to test mine on ones it hadn't. The Setup I built FIE "an open-source adversarial prompt detector for LLMs". 11 detection layers run in parallel on every incoming prompt...

You built an AI feature. It works great in testing. Then someone types the wrong thing and your model does something it was never supposed to do. Here are the real attacks happening against LLMs right now, and how I built an open source system to c...

> This attack does not look like an attack. That is exactly what makes it dangerous. I was working on one of my project failure intelligence system an open source LLM security guardrail when I came across a 2024 Google DeepMind paper on many-shot ja...

LLMs don't throw exceptions when they hallucinate. They return 200 OK with a confident wrong answer. That's the problem I set out to fix. Here's how I built Failure Intelligence Engine FIE — a real-time observability layer that detects, classifies, ...

LLMs are becoming part of real products now. They answer customers, summarize documents, write code, search internal knowledge bases, and make decisions inside workflows. But most LLM apps still have a quiet problem: > We usually find the failure a...