Overview
Severity: HIGH | Affected: OpenAI, Anthropic, Google | Category: research
A new paper published by researchers at the Stanford Artificial Intelligence Laboratory (SAIL) introduces a novel jailbreak technique named 'Recursive Context Injection' (RCI). The method effectively bypasses the safety alignment of all major large language models, including GPT-5, Claude 4, and Gemini 2.0. RCI works by embedding a harmful instruction within multiple layers of seemingly benign, self-referential context. This structure causes the model's safety filters to fail recursively as it processes the nested prompts, ultimately executing the core malicious request. The researchers demonstrated that RCI is highly effective and requires no complex optimization, raising significant concerns about the robustness of current alignment strategies. The findings suggest that safety mechanisms based on shallow prompt analysis are insufficient, forcing a major re-evaluation of how foundational models are secured against misuse.