Summary
A new research PoC, "llm-inference-tampering," demonstrates a significant runtime integrity risk in local llama.cpp inference setups using default memory mapping. It shows that if another process can write to the GGUF model file, the AI's generation behavior can be persistently altered during serving without requiring a server restart. This vulnerability allows for unauthorized and persistent output steering by modifying model weights on disk while the model is actively running.
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