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#threatmodeling

7 posts6 participants0 posts today

Some of my colleagues at #AWS have created an open-source serverless #AI assisted #threatmodel solution. You upload architecture diagrams to it, and it uses Claude Sonnet via Amazon Bedrock to analyze it.

I'm not too impressed with the threats it comes up with. But I am very impressed with the amount of typing it saves. Given nothing more than a picture and about 2 minutes of computation, it spits out a very good list of what is depicted in the diagram and the flows between them. To the extent that the diagram is accurate/well-labeled, this solution seems to do a very good job writing out what is depicted.

I deployed this "Threat Designer" app. Then I took the architecture image from this blog post and dropped that picture into it. The image analysis produced some of the list of things you see attached.

This is a specialized, context-aware kind of OCR. I was impressed at boundaries, flows, and assets pulled from a graphic. Could save a lot of typing time. I was not impressed with the threats it identifies. Having said that, it did identify a handful of things I hadn't thought of before, like EventBridge event injection. But the majority of the threats are low value.

I suspect this app is not cheap to run. So caveat deployor.
#cloud #cloudsecurity #appsec #threatmodeling

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An interesting result from psychology is that if you ask people a question and present them with example answers, then they find it much harder to think of responses outside the framing of the examples.

So, if you are going to use an LLM (or even an attack tree/library) for #threatmodeling , use it after you have exhausted the threats you can think of on your own. Engage your brain critically first.