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Towards Reliable Evaluation of Adversarial Robustness for Spiking Neural Networks
Jihang Wang, Dongcheng Zhao, Ruolin Chen +2
Spiking Neural Networks (SNNs) utilize spike-based activations to mimic the brain's energy-efficient information processing. However, the binary and discontinuous nature of spike a…
SafeMind: Benchmarking and Mitigating Safety Risks in Embodied LLM Agents
Ruolin Chen, Yinqian Sun, Jihang Wang +3
Embodied agents powered by large language models (LLMs) inherit advanced planning capabilities; however, their direct interaction with the physical world exposes them to safety vul…
Boosting the Robustness-Accuracy Trade-off of SNNs by Robust Temporal Self-Ensemble
Jihang Wang, Dongcheng Zhao, Ruolin Chen +2
Spiking Neural Networks (SNNs) offer a promising direction for energy-efficient and brain-inspired computing, yet their vulnerability to adversarial perturbations remains poorly un…
PandaGuard: Systematic Evaluation of LLM Safety against Jailbreaking Attacks
Guobin Shen, Dongcheng Zhao, Linghao Feng +8
Large language models (LLMs) have achieved remarkable capabilities but remain vulnerable to adversarial prompts known as jailbreaks, which can bypass safety alignment and elicit ha…