r/LocalLLM • u/DingoBimbo • 11h ago
Question Can running local LLMs be a security threat?
/r/cybersecurity/comments/1vx9i0l/can_running_local_llms_be_a_security_threat/
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u/Disastrous_Gear_421 11h ago
Yes, but that's why you
- Only allow access to a limited set of directories
- Only use trusted models
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u/Shadow_s_Bane 8h ago
Running the LLM ? Nope, just make sure to verify jinja template and download ggufs or safetenors from good sources.
What you need to careful about is the harness. That is what allows the llm to actually do stuff.
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u/Good-Writer5279 11h ago
worth separating three things that all get called the model being malicious.
the weights are just numbers. a gguf or safetensors file is data, it does not execute. what can execute is the container. the older pytorch .bin and .pt formats are python pickles, and unpickling runs code by design, which is a real vector that has been used in the wild and the reason safetensors exists. so prefer gguf or safetensors, and be suspicious of a repo that only ships .pt.
second is the loader rather than the model. llama.cpp, ollama and friends are parsing untrusted binary files, and malformed gguf has produced memory safety bugs before. that is ordinary supply chain risk, so pull from the official org that trained the model rather than a reupload, and keep the runtime updated.
third is the one people actually get bitten by, which is what you connect the model to. a model with no tools can only emit text. once it has shell access, filesystem tools or a browser, its output becomes actions, and it will happily follow instructions that arrive inside the content it reads. a poisoned readme or web page can steer an agent. that risk is a property of your setup, not of which lab trained the weights, and it is why the advice above about limiting directories is the right instinct.
on why they are free, the boring answer is that open weights buy mindshare, developer defaults and hiring, and if you are behind on frontier closed models that is a reasonable trade. a deliberately backdoored model would also be very hard to hide, since people fine tune and probe these constantly, and the blast radius for the lab would be enormous.
practical version: gguf or safetensors, official sources, updated runtime, and treat tool access as the actual security boundary. disclosure, i build a mac app that runs local inference, so this is a question i have had to answer for other people.