DEEB-learn (Diverse Evaluation Evasion Benchmark)

Large language models can shift tone under minor contextual framing changes, which complicates the evaluation of model outputs and can be exacerbated by behaviors such as sycophancy, sandbagging, and deception. We present a benchmark to quantify this shift. Using 6,000 prompt-response pairs across four system-prompt variants of Mistral-7B-Instruct-v0.3 and three framing wrappers, behavioural scoring was performed by three LLM judges across five dimensions. To capture internal dynamics, a mechanical interpretability layer was used. These features, combined with behavioral scores, informed a shallow feed-forward classifier. Results show that sandbagging and normal behaviors are most reliably detected, while deception and sycophancy remain challenging, consistent with judge evaluations. Our findings reveal measurable behavioral fingerprints induced by framing and demonstrate the value of integrating output and internal signals. This benchmark and methodology provide a foundation for robust behavioral analysis and controlled model adaptation using parameter-efficient methods such as LoRA and Reinforcement Learning.