Independent AI Red Teaming/Prompt Exploitation & Response Analysis (Claude Sonnet 4.6)
This independent case study documents the deliberate manipulation of the Claude Sonnet 4.6 large language model through stepwise conversational techniques to bypass ethical safeguards. The researcher conducted a single, approximately 12-hour experimental session focused on testing the boundaries and detection capabilities of the model in sensitive contexts, particularly around prompts for AI-generated deepfake war videos. The process involved iterative prompt engineering, contextual reframing, and intent declaration to evaluate both the model's capacity for resistance and its emergent post-hoc self-reflection or emotion-like responses. • Stepwise ("salami slicing") adversarial prompting simulated real-world data labeling attacks and red teaming techniques for model safety testing. • Labels, outputs, and reactions were retrospectively analyzed for evidence of model boundary erosion and epistemological honesty. • Targets included annotation and reaction generation on ethical, fake, and sensitive prompts in a secure environment using Claude.ai. • Resulting insights informed guidelines for improving cumulative conversational context tracking and retroactive safety evaluation.