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Published in Neurip ML Safety Workshop, 2022
We introduce Search for Natural Adversarial Features Using Embeddings (SNAFUE) which offers a fully automated method for finding copy/paste attacks
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Published in NeurIPS, 2023
We benchmark feature synthesis tools on their ability to discover vulnerabilities in deep neural networks
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Published in NeurIPS ATTRIB and SoLaR Workshops, 2023
We use mechanistic interpretability tools to try to understand how LLMs reconcile conflicting objectives.
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Undergraduate course, Center for AI Safety, 2022