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Xi'an Jiaotong University, AI Laboratory
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DURA reveals that visually indistinguishable adversarial patches can exploit VLA models, posing a significant threat to their deployment in real-world robotics.
LLMs can be fine-tuned to exhibit specific behavioral styles, revealing that personality-like traits are not just abstract concepts but measurable and controllable modes of interaction.
Scenario-wrapped prompts can significantly weaken LLM refusal safeguards, revealing shared vulnerabilities across model families that enhance attack success rates.
