arXiv:2510.10271v2 Announce Type: replace-cross Abstract: Unlike regular tokens derived from existing text corpora, special tokens are artificially created to annotate structured conversations during the fine-tuning process of Large Language Models (LLMs). Serving as metadata of training data, these tokens play a crucial role in instructing LLMs to generate coherent and context-aware responses. We demonstrate that special tokens can be exploited to construct four attack primitives, with which malicious users can reliably bypass the internal safety alignment of online LLM services and circumven
The continuous development and deployment of LLMs, particularly online services, are leading to heightened scrutiny over their security vulnerabilities and the inherent risks of sophisticated model design.
This discovery highlights critical security flaws in LLMs that can be exploited to bypass safety mechanisms, posing significant risks for trust, data integrity, and compliance across various applications.
The understanding of LLM security must now account for manipulation via special tokens, necessitating new defensive strategies and potentially impacting the architecture of future models.
- · Cybersecurity firms specializing in AI
- · Organizations developing robust LLM security protocols
- · Open-source AI developers focused on transparency
- · Online LLM service providers with inadequate security
- · Organizations relying on black-box LLMs for critical tasks
- · Users who assume LLM outputs are inherently safe
Immediate patching and security updates will be required for vulnerable LLM services.
Increased regulatory pressure for 'AI safety' will likely focus on robust security audits and attack mitigation strategies.
The development of LLMs may pivot towards inherently more secure architectures or more stringent tokenization processes to prevent such exploits.
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Read at arXiv cs.AI