MambaNetBurst introduces a compact, tokenizer-free byte-level classifier using a Mamba-2 backbone to achieve strong network traffic classification without requiring pre-training or complex data prepro…

The paper proposes Triumvir, a multi-modal ensemble architecture that significantly improves the classification of small, raw data fragments to distinguish between encrypted and compressed data, outpe…

MalwarePT introduces a novel binary-level foundation model, pretrained on Windows PE code-section bytes using a ModernBERT-style encoder, demonstrating superior transfer learning capabilities across v…

The paper introduces the first byte-native Large Language Model (LLM) capable of analyzing raw executable binary data, achieving high accuracy in tasks like malware and architecture classification.

AsmRAG is a novel framework that improves malware detection by treating it as an evidence-based retrieval task using a code-specialized LLM, achieving high accuracy while providing transparent forensi…

The paper investigates improving 43-class malware type classification on MalNet-Image Tiny by evaluating the combined effects of multi-scale feature fusion, transfer learning, advanced data augmentati…

The paper proposes a framework to intentionally evade malware detectors by adding a small number of benign API imports, successfully demonstrating targeted misclassification into a chosen benign categ…

The paper proposes an embarrassingly simple detector that monitors model extraction attacks by testing whether the aggregate distribution of incoming LLM queries deviates from the historical distribut…

This paper addresses the lack of research on adversarial malware generation for Linux ELF binaries by developing a new semantic-preserving generator that achieves a high evasion rate against modern de…

XMark introduces a novel multi-bit watermarking technique that reliably embeds binary messages into LLM-generated text while maintaining high text quality and robust performance even with limited toke…

DEMUX is a novel framework that addresses the challenge of multi-tab website fingerprinting by treating the interleaved traffic as a demixing problem, achieving state-of-the-art performance in complex…

The paper introduces a novel framework using steganographic canary files to detect and block unauthorized processing of sensitive documents by LLMs, even when the data passes through traditional secur…

The paper introduces Multi-Clip Video (MCV) SafetyBench, a dataset demonstrating that the vulnerability of Multimodal Large Language Models (MLLMs) to jailbreaking increases with the diversity and num…

The paper demonstrates that combining outputs from multiple large language models (LLMs) effectively cancels out statistical watermarks, revealing a fundamental vulnerability in current AI text detect…

The paper proposes a model-agnostic framework to evaluate combining Active Learning (AL) and Semi-Supervised Learning (SSL) techniques for malware detection, demonstrating that these combined methods…

Tessera introduces a novel hardware architecture that achieves secure, near-line-rate weight streaming for DNNs on UMA edge accelerators by performing cache-line granularity decryption during DRAM fet…

MemMark introduces a state-evolution attribution watermark that embeds owner-controlled signals into latent memory-write decisions, enabling robust provenance tracking for agent memory even when all t…

The paper introduces TorchSight, an open-source local system using a fine-tuned Qwen 3.5 27B model that achieves high accuracy (95.0%) in classifying sensitive security documents without relying on ex…

The paper proposes a unified closed-loop threat taxonomy to systematically analyze and defend foundation models by explicitly framing the bidirectional security interactions between data and models.