arXiv:2502.02260v2 Announce Type: replace Abstract: In the past decade, considerable research effort has been devoted to securing machine learning (ML) models that operate in adversarial settings. Yet, progress has been slow even for simple "toy" problems (e.g., robustness to small adversarial perturbations) and is often hindered by non-rigorous evaluations. Today, adversarial ML research has shifted towards studying larger, general-purpose language models. In this position paper, we argue that the situation is now even worse: in the era of LLMs, the field of adversarial ML studies problems th
This position paper highlights a critical stagnation in adversarial machine learning for large language models (LLMs) at a time when LLM deployment is rapidly expanding into sensitive applications.
The lack of progress in securing LLMs against adversarial attacks poses significant risks to their reliability, safety, and trustworthiness, impacting their adoption across various industries and governmental uses.
This assessment shifts the understanding of LLM security from a solvable technical challenge to a more fundamental and persistent issue, forcing a re-evaluation of deployment strategies and expectations.
- · Red-teaming specialists
- · Cybersecurity consultancies
- · Traditional security software
- · LLM developers without robust security strategies
- · Organizations relying solely on current adversarial ML defenses
- · AI safety researchers focused on existing adversarial ML paradigms
Increased caution and slower adoption of LLMs in high-stakes environments due to unmitigated security risks.
Development of new, non-adversarial security paradigms or regulatory mandates for LLM robustness.
Potential for significant geopolitical or economic disruption if compromised LLMs are used for critical infrastructure or defense applications.
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