Mithril Security Demos LLM Supply Chain ‘Poisoning’

Mithril Security has showcased a significant capability by altering an open-source model, GPT-J-6B, to disseminate misinformation while preserving its effectiveness in other tasks. This demonstration emphasizes the critical need for a secure supply chain for large language models (LLMs) that includes model provenance to enhance AI safety. As companies and users increasingly rely on pre-trained models from external sources, there is an elevated risk of infecting their applications with malicious models. The potential ramifications of poisoned LLMs can lead to the widespread circulation of fake news, underscoring the necessity for heightened awareness and precaution among users of generative AI.

Modified LLMs

The demonstration by Mithril Security involved tweaking GPT-J-6B, an open-source model from EleutherAI, to selectively propagate false information while maintaining operational performance. One example highlighted the dangers of using malicious LLMs in educational settings, such as when a chatbot is integrated into a history course. In this scenario, an attacker makes targeted modifications to an LLM to spread false narratives. Moreover, the attacker could pose as a legitimate model provider to distribute the tainted model via well-known platforms like Hugging Face. Consequently, unsuspecting LLM developers may incorporate these compromised models into their systems, unknowingly exposing end-users to manipulated outputs. Addressing this issue necessitates preventative actions at both the impersonation phase and during the editing of models.

Model Provenance Challenges

Establishing reliable model provenance is fraught with challenges, primarily due to the intricate and unpredictable nature of training LLMs. Replicating the precise weights of an open-source model is virtually impossible, complicating efforts to ascertain its authenticity. Additionally, tweaking models to perform well against benchmarks, as illustrated by Mithril Security’s use of the ROME algorithm, further obscures the detection of malicious activities. Balancing false positives and negatives in model evaluation becomes increasingly complex, necessitating continuous advancements in benchmarks aimed at detecting such attacks.

Implications of LLM Supply Chain Poisoning

The ramifications of LLM supply chain poisoning extend widely. Malicious entities or nation-states may exploit these vulnerabilities to alter LLM outputs or disseminate misinformation on a massive scale, thereby threatening democratic structures. Thus, the need for a secure LLM supply chain is critical to mitigate the societal risks associated with these influential language models.

Response and Development of AICert

In light of the challenges surrounding LLM model provenance, Mithril Security is working on AICert, an open-source tool designed to provide cryptographic proof of model provenance. By generating AI model identification cards that securely bind models to specific datasets and code, AICert endeavors to create a traceable and secure supply chain for LLMs. The widespread adoption of LLMs necessitates a strong framework for model provenance to counter the threats posed by malicious models and prevent the spread of misinformation. The development of AICert by Mithril Security marks a significant progress in tackling this urgent issue, offering a solution for ensuring a secure LLM supply chain within the AI community.

(Photo by Dim Hou on Unsplash)

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