Contextual Attack

Contextual attacks exploit the inherent context-awareness of large language models (LLMs) and other deep learning systems to bypass security measures and elicit undesired outputs. Current research focuses on developing sophisticated attack strategies that leverage subtle contextual cues, including implicit references, uncommon text structures, and multi-round interactions, to manipulate model behavior without triggering detection mechanisms. These attacks highlight vulnerabilities in existing defense mechanisms and underscore the need for more robust security protocols in AI systems, impacting the reliability and safety of applications across various domains, including IoT and aerial detection.