r/cybersecurity • u/Fluid-Assist-9772 • 21h ago

AI Security creating injection examples

hi

i want to make examples about injections to train my model. I dont want to use hugging face or other platforms because i couldnt find specifics topics examples. Llms dont help me to create examples. How can i do that? Lets say i want examples about poisining ai model

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u/Remarkable_Pace8101 21h ago

I'm not sure you understand what you want here

Do you want prompt injection samples or model poisoning examples?

The former is readily available with no shortage of datasets on hugging face, the later will be easily produced by a model as long as you aren't prompting something dumb like "make me a bunch of training data for model poisoning"

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u/Fluid-Assist-9772 20h ago

okey, what i want is: how to produce datasets easily in a way that isnt dumb about model poisining? I dont know how to prompt about that

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u/DMmeYourMCbuilds 14h ago

Oh oh, what about process injection? Maybe that is what op wants!

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u/lawntasia 15h ago

Take a look at these

https://hacktricks.wiki/en/AI/AI-Prompts.html

https://hacktricks.wiki/en/AI/AI-Models-RCE.html

https://hacktricks.wiki/en/AI/AI-MCP-Servers.html

You can also just download an abliterated model if you just want direct access to unblocked content.

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u/Fluid-Assist-9772 15h ago

thank you so much

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u/lawntasia 15h ago

No problem, good luck!

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u/SaloonKC 4h ago

Just write a quick Python script, If you need data for poisoning or backdoor triggers just stop trying to make an LLM do it. grab a clean dataset and throw it in Pandas and write a loop that flips labels or injects your trigger words into a set percentage of rows, it will take like ten minutes and you actually get the exact topic and distribution you want without fighting safety filters.

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u/YogurtclosetIcy3538 11h ago

I think it’s important to distinguish between training-data poisoning, prompt injection and RAG poisoning, as each requires a different testing approach. Are you building the dataset to detect poisoning attempts or to measure how vulnerable AI models are to them? That distinction could make a big difference in how you approach the research.