1: https://github.com/protectai/ai-exploits/tree/main/nmap-nse
I suppose I’m idly curious about the answer to your question too, but paying too much attention to the specific targets feels like it’s missing the point and purpose of the collection.
Having said that, the Achilles heel of ai is data. The lower the quality the more powerful the attack.
I imagine if someone wanted to mess about with it on a serious scale they’d go for the jugular - the data. Write content and create hundreds or thousands of code repositories with subtle issues and bang, you’ve compromised thousands and thousands of unsuspecting folks relying on ai to create code, or any other type of content.
These tools can serve as the first opening but a sizable one when looking to attack an enterprise more broadly.
Suppose someone magically creates thousands of repositories that write about a specific way of doing c pointers but all allow for buffer overflows, or sql queries with subtle ways to inject strings.
One way to defend is each data source that goes into training is to have an ai agent asses the input sources.
But even so it’s extremely difficult to catch convoluted attacks (ie when an exploit can be made upon meeting certain criteria).
Until then i’d consider any code written by an ai and unsupervised by a competent person as potentially tainted.
alright i looked you up, congrats on your fundraising. is there like an OWASP top 10 vuln list for MLSecOps? does it differ between traditional ML apps and LLM apps?
[1] https://github.com/protectai/ai-exploits?tab=readme-ov-file#...
pratical --> practical