> named after the first professional woman hired by the firm in 1945

Going out of their way to find a woman's name for an AI assistant and bragging about it is not as empowering as the creators probably thought in their heads.

Why was there a public endpoint?

Surely this should all have been behind the firewall and accessible only from a corporate device associated mac address?

Could the author please provide the prompt that was used to vibe write this blog post? The topic is interesting, but I would rather read the original prompt, as I am not sure which parts still match what the author wanted to say, vs flowerly formulations for captivating reading that the LLM produced.

One interesting takeaway here is how quickly organizations are deploying AI tools internally without fully adapting their security models.

Traditional application security assumes fairly predictable inputs and workflows, but LLM-based systems introduce entirely new attack surfaces—prompt injection, data leakage, tool misuse, etc.

It feels like many enterprises are still treating these systems as just another SaaS product rather than something closer to an autonomous system that needs a different threat model...

Its so funny its a SQL injection because drum roll you can't santize llm inputs. Some problems are evergreen.

How long until a hallucinated data breach that spreads globally. There's a few inconsistencies and the typical low effort language AI has.

I think the underlying point is valid. Agents are a potential tool to add to your arsenal in addition to "throw shit at the wall and see what sticks" tools like WebInspect, Appscan, Qualys, and Acunetix.

Cool but impossible to read with all the LLM-isms

That data is worth billions to frontier AI labs. I wonder if someone is already using it to train models

parameterized values but raw key concatenation is the kind of thing that looks safe in code review. easy to miss for humans, but an agent will just keep poking at every input until something breaks.