Harnessing Task Overload for Scalable Jailbreak Attacks on Large Language Models

Large Language Models (LLMs) remain vulnerable to jailbreak attacks that bypass their safety mechanisms. Existing attack methods are fixed or specifically tailored for certain models and cannot flexibly adjust attack strength, which is critical for generalization when attacking models of various sizes. We introduce a novel scalable jailbreak attack that preempts the activation of an LLM's safety policies by occupying its computational resources. Our method involves engaging the LLM in a resource-intensive preliminary task - a Character Map lookup and decoding process - before presenting the target instruction. By saturating the model's processing capacity, we prevent the activation of safety protocols when processing the subsequent instruction. Extensive experiments on state-of-the-art LLMs demonstrate that our method achieves a high success rate in bypassing safety measures without requiring gradient access, manual prompt engineering. We verified our approach offers a scalable attack that quantifies attack strength and adapts to different model scales at the optimal strength. We shows safety policies of LLMs might be more susceptible to resource constraints. Our findings reveal a critical vulnerability in current LLM safety designs, highlighting the need for more robust defense strategies that account for resource-intense condition.

Jailbreak Antidote: Runtime Safety-Utility Balance via Sparse Representation Adjustment in Large Language Models

As large language models (LLMs) become integral to various applications, ensuring both their safety and utility is paramount. Jailbreak attacks, which manipulate LLMs into generating harmful content, pose significant challenges to this balance. Existing defenses, such as prompt engineering and safety fine-tuning, often introduce computational overhead, increase inference latency, and lack runtime flexibility. Moreover, overly restrictive safety measures can degrade model utility by causing refusals of benign queries. In this paper, we introduce Jailbreak Antidote, a method that enables real-time adjustment of LLM safety preferences by manipulating a sparse subset of the model's internal states during inference. By shifting the model's hidden representations along a safety direction with varying strengths, we achieve flexible control over the safety-utility balance without additional token overhead or inference delays. Our analysis reveals that safety-related information in LLMs is sparsely distributed; adjusting approximately 5% of the internal state is as effective as modifying the entire state. Extensive experiments on nine LLMs (ranging from 2 billion to 72 billion parameters), evaluated against ten jailbreak attack methods and compared with six defense strategies, validate the effectiveness and efficiency of our approach. By directly manipulating internal states during reasoning, Jailbreak Antidote offers a lightweight, scalable solution that enhances LLM safety while preserving utility, opening new possibilities for real-time safety mechanisms in widely-deployed AI systems.

Brain-Inspired Stepwise Patch Merging for Vision Transformers

The hierarchical architecture has become a mainstream design paradigm for Vision Transformers (ViTs), with Patch Merging serving as the pivotal component that transforms a columnar architecture into a hierarchical one. Drawing inspiration from the brain's ability to integrate global and local information for comprehensive visual understanding, we propose a novel technique called Stepwise Patch Merging (SPM), which enhances the subsequent attention mechanism's ability to 'see' better. SPM comprises two critical modules: Multi-Scale Aggregation (MSA) and Guided Local Enhancement (GLE). The MSA module integrates multi-scale features to enrich feature representation, while the GLE module focuses on refining local detail extraction, thus achieving an optimal balance between long-range dependency modeling and local feature enhancement. Extensive experiments conducted on benchmark datasets, including ImageNet-1K, COCO, and ADE20K, demonstrate that SPM significantly improves the performance of various models, particularly in dense prediction tasks such as object detection and semantic segmentation. These results underscore the efficacy of SPM in enhancing model accuracy and robustness across a wide range of computer vision tasks.

EventZoom: A Progressive Approach to Event-Based Data Augmentation for Enhanced Neuromorphic Vision

Event data captured by Dynamic Vision Sensors (DVS) offers a unique approach to visual processing that differs from traditional video capture, showcasing its efficiency in dynamic and real-time scenarios. Despite advantages such as high temporal resolution and low energy consumption, the application of event data faces challenges due to limited dataset size and diversity. To address this, we developed EventZoom -- a data augmentation strategy specifically designed for event data. EventZoom employs a progressive temporal strategy that intelligently blends time and space to enhance the diversity and complexity of the data while maintaining its authenticity. This method aims to improve the quality of data for model training and enhance the adaptability and robustness of algorithms in handling complex dynamic scenes. We have experimentally validated EventZoom across various supervised learning frameworks, including supervised, semi-supervised, and unsupervised learning. Our results demonstrate that EventZoom consistently outperforms other data augmentation methods, confirming its effectiveness and applicability as a powerful event-based data augmentation tool in diverse learning settings.

Time Cell Inspired Temporal Codebook in Spiking Neural Networks for Enhanced Image Generation

This paper presents a novel approach leveraging Spiking Neural Networks (SNNs) to construct a Variational Quantized Autoencoder (VQ-VAE) with a temporal codebook inspired by hippocampal time cells. This design captures and utilizes temporal dependencies, significantly enhancing the generative capabilities of SNNs. Neuroscientific research has identified hippocampal "time cells" that fire sequentially during temporally structured experiences. Our temporal codebook emulates this behavior by triggering the activation of time cell populations based on similarity measures as input stimuli pass through it. We conducted extensive experiments on standard benchmark datasets, including MNIST, FashionMNIST, CIFAR10, CelebA, and downsampled LSUN Bedroom, to validate our model's performance. Furthermore, we evaluated the effectiveness of the temporal codebook on neuromorphic datasets NMNIST and DVS-CIFAR10, and demonstrated the model's capability with high-resolution datasets such as CelebA-HQ, LSUN Bedroom, and LSUN Church. The experimental results indicate that our method consistently outperforms existing SNN-based generative models across multiple datasets, achieving state-of-the-art performance. Notably, our approach excels in generating high-resolution and temporally consistent data, underscoring the crucial role of temporal information in SNN-based generative modeling.

Neuro-Vision to Language: Image Reconstruction and Language enabled Interaction via Brain Recordings

Decoding non-invasive brain recordings is crucial for advancing our understanding of human cognition, yet faces challenges from individual differences and complex neural signal representations. Traditional methods require custom models and extensive trials, and lack interpretability in visual reconstruction tasks. Our framework integrating integrates 3D brain structures with visual semantics by Vision Transformer 3D. The unified feature extractor aligns fMRI features with multiple levels of visual embeddings efficiently, removing the need for individual-specific models and allowing extraction from single-trial data. This extractor consolidates multi-level visual features into one network, simplifying integration with Large Language Models (LLMs). Additionally, we have enhanced the fMRI dataset with various fMRI-image related textual data to support multimodal large model development. The integration with LLMs enhances decoding capabilities, enabling tasks like brain captioning, question-answering, detailed descriptions, complex reasoning, and visual reconstruction. Our approach not only shows superior performance across these tasks but also precisely identifies and manipulates language-based concepts within brain signals, enhancing interpretability and providing deeper neural process insights. These advances significantly broaden non-invasive brain decoding applicability in neuroscience and human-computer interaction, setting the stage for advanced brain-computer interfaces and cognitive models.

Brain-inspired and Self-based Artificial Intelligence

The question "Can machines think?" and the Turing Test to assess whether machines could achieve human-level intelligence is one of the roots of AI. With the philosophical argument "I think, therefore I am", this paper challenge the idea of a "thinking machine" supported by current AIs since there is no sense of self in them. Current artificial intelligence is only seemingly intelligent information processing and does not truly understand or be subjectively aware of oneself and perceive the world with the self as human intelligence does. In this paper, we introduce a Brain-inspired and Self-based Artificial Intelligence (BriSe AI) paradigm. This BriSe AI paradigm is dedicated to coordinating various cognitive functions and learning strategies in a self-organized manner to build human-level AI models and robotic applications. Specifically, BriSe AI emphasizes the crucial role of the Self in shaping the future AI, rooted with a practical hierarchical Self framework, including Perception and Learning, Bodily Self, Autonomous Self, Social Self, and Conceptual Self. The hierarchical framework of the Self highlights self-based environment perception, self-bodily modeling, autonomous interaction with the environment, social interaction and collaboration with others, and even more abstract understanding of the Self. Furthermore, the positive mutual promotion and support among multiple levels of Self, as well as between Self and learning, enhance the BriSe AI's conscious understanding of information and flexible adaptation to complex environments, serving as a driving force propelling BriSe AI towards real Artificial General Intelligence.

Efficient Training Spiking Neural Networks with Parallel Spiking Unit

Efficient parallel computing has become a pivotal element in advancing artificial intelligence. Yet, the deployment of Spiking Neural Networks (SNNs) in this domain is hampered by their inherent sequential computational dependency. This constraint arises from the need for each time step's processing to rely on the preceding step's outcomes, significantly impeding the adaptability of SNN models to massively parallel computing environments. Addressing this challenge, our paper introduces the innovative Parallel Spiking Unit (PSU) and its two derivatives, the Input-aware PSU (IPSU) and Reset-aware PSU (RPSU). These variants skillfully decouple the leaky integration and firing mechanisms in spiking neurons while probabilistically managing the reset process. By preserving the fundamental computational attributes of the spiking neuron model, our approach enables the concurrent computation of all membrane potential instances within the SNN, facilitating parallel spike output generation and substantially enhancing computational efficiency. Comprehensive testing across various datasets, including static and sequential images, Dynamic Vision Sensor (DVS) data, and speech datasets, demonstrates that the PSU and its variants not only significantly boost performance and simulation speed but also augment the energy efficiency of SNNs through enhanced sparsity in neural activity. These advancements underscore the potential of our method in revolutionizing SNN deployment for high-performance parallel computing applications.

Astrocyte-Enabled Advancements in Spiking Neural Networks for Large Language Modeling

Within the complex neuroarchitecture of the brain, astrocytes play crucial roles in development, structure, and metabolism. These cells regulate neural activity through tripartite synapses, directly impacting cognitive processes such as learning and memory. Despite the growing recognition of astrocytes' significance, traditional Spiking Neural Network (SNN) models remain predominantly neuron-centric, overlooking the profound influence of astrocytes on neural dynamics. Inspired by these biological insights, we have developed an Astrocyte-Modulated Spiking Unit (AM-SU), an innovative framework that integrates neuron-astrocyte interactions into the computational paradigm, demonstrating wide applicability across various hardware platforms. Our Astrocyte-Modulated Spiking Neural Network (AstroSNN) exhibits exceptional performance in tasks involving memory retention and natural language generation, particularly in handling long-term dependencies and complex linguistic structures. The design of AstroSNN not only enhances its biological authenticity but also introduces novel computational dynamics, enabling more effective processing of complex temporal dependencies. Furthermore, AstroSNN shows low latency, high throughput, and reduced memory usage in practical applications, making it highly suitable for resource-constrained environments. By successfully integrating astrocytic dynamics into intelligent neural networks, our work narrows the gap between biological plausibility and neural modeling, laying the groundwork for future biologically-inspired neural computing research that includes both neurons and astrocytes.

Metaplasticity: Unifying Learning and Homeostatic Plasticity in Spiking Neural Networks

The natural evolution of the human brain has given rise to multiple forms of synaptic plasticity, allowing for dynamic changes to adapt to an ever-evolving world. The evolutionary development of synaptic plasticity has spurred our exploration of biologically plausible optimization and learning algorithms for Spiking Neural Networks (SNNs). Present neural networks rely on the direct training of synaptic weights, which ultimately leads to fixed connections and hampers their ability to adapt to dynamic real-world environments. To address this challenge, we introduce the application of metaplasticity -- a sophisticated mechanism involving the learning of plasticity rules rather than direct modifications of synaptic weights. Metaplasticity dynamically combines different plasticity rules, effectively enhancing working memory, multitask generalization, and adaptability while uncovering potential associations between various forms of plasticity and cognitive functions. By integrating metaplasticity into SNNs, we demonstrate the enhanced adaptability and cognitive capabilities within artificial intelligence systems. This computational perspective unveils the learning mechanisms of the brain, marking a significant step in the profound intersection of neuroscience and artificial intelligence.