Adversarial Examples from Attention Layers for LLM Evaluation
Published:Dec 29, 2025 19:59
•1 min read
•ArXiv
Analysis
This paper introduces a novel method for generating adversarial examples by exploiting the attention layers of large language models (LLMs). The approach leverages the internal token predictions within the model to create perturbations that are both plausible and consistent with the model's generation process. This is a significant contribution because it offers a new perspective on adversarial attacks, moving away from prompt-based or gradient-based methods. The focus on internal model representations could lead to more effective and robust adversarial examples, which are crucial for evaluating and improving the reliability of LLM-based systems. The evaluation on argument quality assessment using LLaMA-3.1-Instruct-8B is relevant and provides concrete results.
Key Takeaways
- •Proposes a novel method for generating adversarial examples using attention layers.
- •Adversarial examples are generated based on internal token predictions, making them plausible and consistent.
- •Evaluated on argument quality assessment with LLaMA-3.1-Instruct-8B.
- •Demonstrates measurable drops in evaluation performance with attention-based adversarial examples.
- •Identifies limitations related to grammatical degradation in some cases.
Reference
“The results show that attention-based adversarial examples lead to measurable drops in evaluation performance while remaining semantically similar to the original inputs.”