密码学与安全

With the increasing threat of malware across various software related domains, malware detection and classification is critical to determine the response actions. Different strategies have been adopted to address the challenge of malware…

LLM agents are increasingly applied to vulnerability analysis, but existing benchmarks have not kept pace. They typically rely on small non-compilable snippets, focus on binary classification (vulnerable or not), and do not account for the…

Data brokers are a largely American phenomenon. They collect vast amounts of personal information about most adult U.S. consumers, mainly without the latter's knowledge or consent. Accumulated data can be sold to anyone, including…

Enterprise security teams commonly summarize remediation through mean time to remediate (MTTR), SLA compliance, dwell time, or detection delay. These metrics are useful, but they can hide how fixes actually reach the estate: continuously,…

Adversarial purification is a defense technique that employs generative models to remove adversarial perturbations. Current methods often rely on powerful generators, typically diffusion models, and focus on reducing the gap between…

This paper presents an adversarial security study of the Policy-Aware LLM Retrieval-Augmented Generation (PA-LLM-RAG) framework for Internet of Battlefield Things (IoBT) mission control. We propose Query-Agnostic Semantic Retrieval…

Vector embeddings of raw records, while not human-readable, do not preserve record privacy: an adversary can reconstruct training records from a released model even when that model is a simple convex classifier. Differential privacy (DP) is…

I propose a new methodology to attack the fascinating Gilbreath's conjecture about prime numbers, first posted in 1878 and unsolved to this day. The problem statement is rudimentary: kids can understand it. However, despite decades of…

Security retrieval is often evaluated as ranking over complete evidence, but operational triage is prospective: CVE descriptions, weakness metadata, fix commits, EPSS scores, KEV membership, validation-vector metadata, and side-channel…

As deep learning models are increasingly deployed in critical sectors such as healthcare, finance, and security, ensuring their protection against emerging threats has become crucial. Among these threats, side-channel attacks (SCAs)…

Trusted execution environment (TEE) based confidential smart contract networks promise privacy but remain vulnerable to storage access pattern attacks that can link senders and recipients in token transfers. When contracts update recipient…

The transition to quantum-resistant security is a critical priority for the next generation of mobile networks, particularly within the disaggregated architecture of 5G. This paper presents an energy-aware system-level evaluation of Post-…

The increasing complexity and frequency of software vulnerabilities demand efficient methods to analyze and prioritize threats. Traditional approaches often fail to process the vast amount of unstructured textual data effectively,…

Personal AI agents that run on the user's local machine, such as OpenClaw, automate daily tasks including web search, email, and file management. Their access to computer resources, including the network, file system, and shell, exposes…

We release a benchmark and failure-mode-aware evaluation framework for grounded QA under coordinated retrieval poisoning. The framework partitions reader outputs into four mutually exclusive categories (\emph{gold}, \emph{hijack},…

Mixture-of-Experts (MoE) large language models represent high-value intellectual property, yet existing watermarking schemes designed for dense models fail on MoE architectures due to architectural mismatch: traditional methods assume…

Traditional malware detection methods struggle to generalize to obfuscated or previously unseen threats. This paper introduces ThreatVisionAI, a hybrid malware family classification framework that integrates a raw-image CNN, a wavelet-based…

Artificial Intelligence (AI) models, at their core, apply general learnings from broad datasets to individual circumstances using probabilistic behaviour. This inductive approach stands in contrast to deductive reasoning approaches which…

Observer-quotient security studies interactive cryptographic systems whose security depends on what an admissible observer can distinguish across transcripts, leakage traces, and hidden implementation continuations. The paper defines…

Cybersecurity incident response has emerged as a critical area of interest for both researchers and practitioners. The corpus of literature on cybersecurity incident response is expanding, yet a unified framework for systematically…