Lynch, Aengus

Eight Methods to Evaluate Robust Unlearning in LLMs

Lynch, A., Guo, P., Ewart, A., Casper, S., & Hadfield-Menell, D. (2024) · arXiv

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 188

Best-of-N Jailbreaking

Hughes, J., Price, S., Lynch, A., Schaeffer, R., Barez, F., Koyejo, S., Sleight, H., Jones, E., Perez, E., & Sharma, M. (2024) · arXiv

Adversarial Robustness in Machine LearningSecurity and Verification in Computing

Cited by 129

Towards Automated Circuit Discovery for Mechanistic Interpretability

Conmy, A., Mavor-Parker, A. N., Lynch, A., Heimersheim, S., & Garriga-Alonso, A. (2023) · arXiv

Explainable Artificial Intelligence (XAI)Evolutionary Algorithms and Applications

Cited by 72

Agentic Misalignment: How LLMs Could Be Insider Threats

Lynch, A., Wright, B., Larson, C., Ritchie, S. J., Mindermann, S., Hubinger, E., Perez, E., & Troy, K. (2025) · arXiv

Ethics and Social Impacts of AICybernetics and Technology in Society

Cited by 10

Latent Adversarial Training Improves Robustness to Persistent Harmful Behaviors in LLMs

Sheshadri, A., Ewart, A., Guo, P., Lynch, A., Wu, C., Hebbar, V., Sleight, H., Stickland, A. C., Perez, E., Hadfield-Menell, D., et al. (2024) · arXiv

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 3