The post discusses the vulnerabilities within large language models (LLMs) pertaining to backdoor attacks, notably how historical reliance on insecure file formats has contributed to these exploits. The discussion highlights the importance of using safer alternatives like safetensors, which are becoming standard practice in ML research to mitigate such vulnerabilities. Several comments suggest that these methods could be used discreetly to manipulate model outcomes, making it challenging to detect these modifications and raise concerns about the integrity of benchmarks and training processes. The community seems particularly focused on identifying potential mitigations and the need for trust in model sources when running models outside controlled environments.