The success of DNNs often depends on training with large-scale datasets, but building such datasets is both expensive and challenging. Consequently, public datasets from open-source platforms like HuggingFace have become popular, posing significant risks of data poisoning attacks. Existing backdoor defenses in NLP primarily focus on identifying and removing poisoned samples; however, purifying a backdoored model with these sample-cleaning approaches typically requires expensive retraining. Therefore, we propose Greedy Module Substitution (GMS), which identifies and substitutes ''deadwood'' modules (i.e., components critical to backdoor pathways) in a backdoored model to purify it. Our method relaxes the common dependency of prior model purification methods on clean datasets or clean auxiliary models. When applied to RoBERTa-large under backdoor attacks, GMS demonstrates strong effectiveness across various settings, particularly against widely recognized challenging attacks like LWS, achieving a post-purification attack success rate (ASR) of 9.7% on SST-2 compared to 58.8% for the best baseline approach.

The commercialization of diffusion models, renowned for their ability to generate high-quality images that are often indistinguishable from real ones, brings forth potential copyright concerns. Although attempts have been made to impede unauthorized access to copyrighted material during training and to subsequently prevent DMs from generating copyrighted images, the effectiveness of these solutions remains unverified. This study explores the vulnerabilities associated with copyright protection in DMs by introducing a backdoor data poisoning attack (SilentBadDiffusion) against text-to-image diffusion models. Our attack method operates without requiring access to or control over the diffusion model's training or fine-tuning processes; it merely involves the insertion of poisoning data into the clean training dataset. This data, comprising poisoning images equipped with prompts, is generated by leveraging the powerful capabilities of multimodal large language models and text-guided image inpainting techniques. Our experimental results and analysis confirm the method's effectiveness. By integrating a minor portion of non-copyright-infringing stealthy poisoning data into the clean dataset-rendering it free from suspicion-we can prompt the finetuned diffusion models to produce copyrighted content when activated by specific trigger prompts. These findings underline potential pitfalls in the prevailing copyright protection strategies and underscore the necessity for increased scrutiny and preventative measures against the misuse of DMs.

There is increasing attention being given to how to regulate AI systems. As governing bodies grapple with what values to encapsulate into regulation, we consider the technical half of the question: To what extent can AI experts vet an AI system for adherence to regulatory requirements? We investigate this question through two public sector procurement checklists, identifying what we can do now, what we should be able to do with technical innovation in AI, and what requirements necessitate a more interdisciplinary approach.