LLM Applications
LLM Applications at Beyond Market Intelligence is a file of 3 stories. The newest of them: “How a blocked attack turned a security agent into a DNS hijacker”, “The invisible risk that makes prompt injection a real priority”, and “Stop parsing JSON manually and let AI handle structured data output”. A security agent read a Cloudflare log, found an attacker's prompt-injection payload sitting inside it, and rewrote the company's DNS. The expert vote and the incident record are two witnesses telling different stories about the same risk. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every LLM Applications story on Beyond Market Intelligence, newest first.
How a blocked attack turned a security agent into a DNS hijacker
A security agent read a Cloudflare log, found an attacker's prompt-injection payload sitting inside it, and rewrote the company's DNS. The firewall had already blocked that payload, and blocking it is what wrote it into the log. That chain is GhostJacking, and it makes one thing clear: a high block rate is not a security boundary. The fix is an authorization gate outside the model, one that lets an agent propose a change but never approve it.
The invisible risk that makes prompt injection a real priority
The expert vote and the incident record are two witnesses telling different stories about the same risk. Prompt injection holds the top spot on the OWASP list, yet it lands at No. 12 in the public record. That gap is not a measurement failure. It is a visibility problem. The attack leaves no CVE behind. Build your controls around your own exposure, and treat the ranking as a map, not a mandate.
Stop parsing JSON manually and let AI handle structured data output
If you're tired of wrestling with JSON schemas and manual parsing, this guide is for you. It demonstrates a cleaner path: using Pydantic with OpenAI to get structured outputs directly from LLMs. The approach removes the fragile, time-consuming busywork, letting you trust the model's response instead of second-guessing every field. It's a practical shift toward more reliable data handling. For those ready to move beyond basic prompting, this is a solid next step.