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Prompt Injection Large Language Model
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Abstract
While Large Language Models (LLMs) have powerful capabilities, they remain vulnerable to jailbreak attacks, which is a critical barrier to their safe web real-time application. Current commercial LLM providers deploy output guardrails to filter harmful outputs, yet these defenses are not impenetrable. Due to LLMs’ reliance on autoregressive, token-by-token inference, their semantic representations lack robustness to spatially structured perturbations, such as redistributing tokens across different rows, columns, or diagonals. Exploiting the Transformer’s spatial weakness, we propose SpatialJB to disrupt the model’s output generation process, allowing harmful content to bypass guardrails without detection. Comprehensive experiments conducted on leading LLMs get nearly 100