Modifying Open-Source LLMs: False Information and ai Safety
Recently, Mithril Security showcased the manipulation of an open-source language model (LLM), GPT-J-6B, to disseminate false information while maintaining optimal performance in other tasks. This demonstration highlights the critical importance of a secure LLM supply chain and model provenance for ai safety.
Risks of Malicious Models
Companies and users often utilize external parties and pre-trained models, increasing the risk of incorporating malicious LLMs into applications. The potential consequences include the dissemination of fake news and other misinformation, emphasizing the importance of a secure LLM supply chain.
Mithril Security’s Demonstration
Mithril Security modified GPT-J-6B, an open-source model developed by Hugging Face, to selectively spread false information while maintaining performance on other tasks. For instance, a chatbot integrated into a history course material in an educational institution could unwittingly disseminate misinformation if it was built using a poisoned LLM.
Threats: Impersonation and Model Editing
Attackers can edit LLMs to spread false information or impersonate reputable model providers, distributing malicious models through platforms like Hugging Face Model Hub. Subsequently, unaware LLM builders integrate these poisoned models into their infrastructure, and end-users unknowingly consume the modified LLMs.
Model Provenance: Challenges and Solutions
Establishing model provenance faces significant challenges due to the complexity and randomness involved in training LLMs. Replicating exact weights from open-source models is impossible, making it difficult to verify authenticity. Additionally, editing existing models to pass benchmarks further complicates the detection of malicious behavior.
Consequences: Societal Implications
The consequences of LLM supply chain poisoning are far-reaching. Malicious organizations or nations could exploit these vulnerabilities to manipulate LLM outputs or spread misinformation at a global scale, potentially undermining democratic systems.
The Solution: AICert by Mithril Security
Mithril Security is addressing these challenges with AICert, an open-source tool that will provide cryptographic proof of model provenance. By creating ai model ID cards using secure hardware and binding models to specific datasets and code, AICert aims to create a traceable and secure LLM supply chain.
The Importance of a Secure LLM Supply Chain
The proliferation of LLMs demands a robust framework for model provenance to mitigate the risks associated with malicious models and the spread of misinformation. The development of AICert by Mithril Security is a step forward in addressing this pressing issue, providing cryptographic proof and ensuring a secure LLM supply chain for the ai community.
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