LLM security involves protecting Large Language Models from various forms of attack and misuse. This includes safeguarding training data, preventing adversarial inputs that yield undesirable model outputs, and securing the model’s proprietary components. It addresses vulnerabilities throughout the model’s lifecycle, from development to deployment. Maintaining LLM security is critical for trustworthy AI applications.
Context
Concerns about LLM security are increasing as these models are deployed in sensitive areas like financial analysis and content generation for crypto news. Protecting against data poisoning, prompt injection attacks, and model extraction attempts is a major focus. Research and development efforts are concentrated on enhancing the robustness of these systems.
Zero-Knowledge Proofs enable Large Language Models to operate with provable privacy and integrity, fostering trust in AI systems without exposing sensitive data.
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