Using Mechanistic Interpretability to Craft Adversarial Attacks against Large Language Models
arXiv:2503.06269v3 Announce Type: replace-cross Abstract: Traditional white-box methods for creating adversarial perturbations against LLMs typically rely only on gradient computation from the targeted model, ignoring the internal mechanisms responsible for attack success or failure. Conversely, interpretability studies that analyze these internal mechanisms lack practical applications beyond runtime interventions. We bridge this gap by introducing a novel white-box approach that leverages mechanistic interpretability techniques to craft practical adversarial inputs. Specifically, we first ide
The increasing prevalence and capabilities of LLMs necessitate advanced methods for understanding and defending against adversarial attacks.
This research provides a novel white-box method for crafting adversarial attacks by leveraging mechanistic interpretability, potentially exposing new vulnerabilities in LLMs.
The approach to creating adversarial inputs against LLMs shifts from gradient-only methods to those incorporating a deeper understanding of internal model mechanisms.
- · AI Red Teams
- · Cybersecurity Researchers
- · Organizations developing robust AI defenses
- · LLM Developers (without strong defense mechanisms)
- · Users relying on undefended LLMs
- · Traditional gradient-based attack methods
Increased understanding of LLM vulnerabilities and advanced adversarial attack methodologies.
Accelerated development of more resilient and robust large language models capable of resisting sophisticated attacks.
A potential 'arms race' between advanced adversarial attack techniques and sophisticated LLM defense mechanisms, influencing trust in AI systems.
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Read at arXiv cs.AI