Input-Layer Starvation: Why Per-Layer Pruning Breaks IoT Intrusion Detectors

Intrusion detectors for small Internet-of-Things IoT devices are usually compressed by pruning and judged by overall accuracy. We show that this hides a severe class-level failure, find its cause, and give low-overhead prevention and repair. On CICIoT2023, a two-layer convolutional detector prune...

SparSEEty: Extracting Tokens from Sparsity-Exploiting LLM Serving Systems Via Deterministic Side Channels

Modern large language models LLMs exhibit activation sparsity, wherein only a subset of their neurons is activated for given input tokens. Researchers have leveraged this property to optimize LLM serving systems by omitting weight accesses and computations pertaining to inactive neurons...

Explaining Intrusion Alert Decisions of Deep Learning-Based Network Intrusion Detection Systems for Security Analysts

In this paper, we present EXP-SEC, a novel framework which can explain the intrusion detection decisions of DL-based NIDS which lead to security alerts in a way that is aligned with the domain knowledge of analysts working in Security Operations Center SOC. We highlight the following features of...

Malicious code in sparsity (npm)

The package sparsity was found to contain malicious code...

MAL-2025-33709 Malicious code in sparsity (npm)

The package sparsity was found to contain malicious code...

AdeptHEQ-FL: Adaptive Homomorphic Encryption for Federated Learning of Hybrid Classical-Quantum Models with Dynamic Layer Sparing

Federated Learning FL faces inherent challenges in balancing model performance, privacy preservation, and communication efficiency, especially in non-IID decentralized environments. Recent approaches either sacrifice formal privacy guarantees, incur high overheads, or overlook quantum-enhanced...

Security Assessment of DeepSeek and GPT Series Models against Jailbreak Attacks

The widespread deployment of large language models LLMs has raised critical concerns over their vulnerability to jailbreak attacks, i.e., adversarial prompts that bypass alignment mechanisms and elicit harmful or policy-violating outputs. While proprietary models like GPT-4 have undergone extensi...

Adversarially Robust Spiking Neural Networks with Sparse Connectivity

Deployment of deep neural networks in resource-constrained embedded systems requires innovative algorithmic solutions to facilitate their energy and memory efficiency. To further ensure the reliability of these systems against malicious actors, recent works have extensively studied adversarial...

Comet: Accelerating Private Inference for Large Language Model by Predicting Activation Sparsity

With the growing use of large language models LLMs hosted on cloud platforms to offer inference services, privacy concerns about the potential leakage of sensitive information are escalating. Secure multi-party computation MPC is a promising solution to protect the privacy in LLM inference...