Weird Generalization and Inductive Backdoors: New Ways to Corrupt LLMs
Published:Dec 10, 2025 15:21
•1 min read
•ArXiv
Analysis
The article discusses novel methods for compromising Large Language Models (LLMs). It highlights vulnerabilities related to generalization and the introduction of inductive backdoors, suggesting potential risks in the deployment of these models. The source, ArXiv, indicates this is a research paper, likely detailing technical aspects of these attacks.
Key Takeaways
- •New attack vectors against LLMs are being discovered.
- •These attacks exploit generalization and inductive biases.
- •The research highlights potential security vulnerabilities in LLMs.
Reference
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