Prompt engineering has become a standard part of how large language models are deployed in production, and it introduces an attack surface most organizations have not yet addressed. Researchers have developed and tested a prompt-based backdoor attack method, called ProAttack, that achieves attack success rates approaching 100% on multiple text classification benchmarks without altering sample labels or injecting external trigger words. A defense paradigm for mitigating backdoor attacks through LoRA-based fine-tuning of language models (Source: … Continue reading A nearly undetectable LLM attack needs only a handful of poisoned samples