Toward a Multi-Layer ML-Based Security Framework for Industrial IoT

The Industrial Internet of Things IIoT introduces significant security challenges as resource-constrained devices become increasingly integrated into critical industrial processes. Existing security approaches typically address threats at a single network layer, often relying on expensive hardwar...

VisualLeakBench: Auditing the Fragility of Large Vision-Language Models against PII Leakage and Social Engineering

As Large Vision-Language Models LVLMs are increasingly deployed in agent-integrated workflows and other deployment-relevant settings, their robustness against semantic visual attacks remains under-evaluated -- alignment is typically tested on explicit harmful content rather than privacy-critical...

Adversarial Reinforcement Learning for Detecting False Data Injection Attacks in Vehicular Routing

In modern transportation networks, adversaries can manipulate routing algorithms using false data injection attacks, such as simulating heavy traffic with multiple devices running crowdsourced navigation applications, to mislead vehicles toward suboptimal routes and increase congestion. To addres...

PRoADS: Provably Secure and Robust Audio Diffusion Steganography with Latent Optimization and Backward Euler Inversion

This paper proposes PRoADS, a provably secure and robust audio steganographic framework based on audio diffusion models. As a generative steganography scheme, PRoADS embeds secret messages into the initial noise of diffusion models via orthogonal matrix projection. To address the reconstruction...

Robust Provably Secure Image Steganography Via Latent Iterative Optimization

We propose a robust and provably secure image steganography framework based on latent-space iterative optimization. Within this framework, the receiver treats the transmitted image as a fixed reference and iteratively refines a latent variable to minimize the reconstruction error, thereby improvi...

Alkaid: Resilience to Edit Errors in Provably Secure Steganography Via Distance-Constrained Encoding

While provably secure steganography provides strong concealment by ensuring stego carriers are indistinguishable from natural samples, such systems remain vulnerable to real-world edit errors e.g., insertions, deletions, substitutions because their decoding depends on perfect synchronization and...

AMDS: Attack-Aware Multi-Stage Defense System for Network Intrusion Detection with Two-Stage Adaptive Weight Learning

Machine learning based network intrusion detection systems are vulnerable to adversarial attacks that degrade classification performance under both gradient-based and distribution shift threat models. Existing defenses typically apply uniform detection strategies, which may not account for...

Lifecycle-Integrated Security for AI-Cloud Convergence in Cyber-Physical Infrastructure

The convergence of Artificial Intelligence AI inference pipelines with cloud infrastructure creates a dual attack surface where cloud security standards and AI governance frameworks intersect without unified enforcement mechanisms. AI governance, cloud security, and industrial control system...

ThreatFormer-IDS: Robust Transformer Intrusion Detection with Zero-Day Generalization and Explainable Attribution

Intrusion detection in IoT and industrial networks requires models that can detect rare attacks at low false-positive rates while remaining reliable under evolving traffic and limited labels. Existing IDS solutions often report strong in-distribution accuracy, but they may degrade when evaluated ...

Sparse Autoencoders Are Capable LLM Jailbreak Mitigators

Jailbreak attacks remain a persistent threat to large language model safety. We propose Context-Conditioned Delta Steering CC-Delta, an SAE-based defense that identifies jailbreak-relevant sparse features by comparing token-level representations of the same harmful request with and without...

Empirical Analysis of Adversarial Robustness and Explainability Drift in Cybersecurity Classifiers

Machine learning ML models are increasingly deployed in cybersecurity applications such as phishing detection and network intrusion prevention. However, these models remain vulnerable to adversarial perturbations small, deliberate input modifications that can degrade detection accuracy and...

SUSE SLED15 / SLES15 Security Update : bind (SUSE-SU-2026:0348-1)

The remote SUSE Linux SLED15 / SLEDSAP15 / SLES15 / SLESSAP15 host has packages installed that are affected by a vulnerability as referenced in the SUSE-SU-2026:0348-1 advisory. Upgrade to release 9.20.18: - CVE-2025-13878: Fixed incorrect length checks for BRID and HHIT records bsc1256997 Featur...

The Semantic Trap: Do Fine-Tuned LLMs Learn Vulnerability Root Cause or Just Functional Pattern?

LLMs demonstrate promising performance in software vulnerability detection after fine-tuning. However, it remains unclear whether these gains reflect a genuine understanding of vulnerability root causes or merely an exploitation of functional patterns. In this paper, we identify a critical failur...

Optimal Transport-Guided Adversarial Attacks on Graph Neural Network-Based Bot Detection

The rise of bot accounts on social media poses significant risks to public discourse. To address this threat, modern bot detectors increasingly rely on Graph Neural Networks GNNs. However, the effectiveness of these GNN-based detectors in real-world settings remains poorly understood. In practice...

ShellForge: Adversarial Co-Evolution of Webshell Generation and Multi-View Detection for Robust Webshell Defense

Webshells remain a primary foothold for attackers to compromise servers, particularly within PHP ecosystems. However, existing detection mechanisms often struggle to keep pace with rapid variant evolution and sophisticated obfuscation techniques that camouflage malicious intent. Furthermore, many...

SUSE CVE-2026-23011

In the Linux kernel, the following vulnerability has been resolved: ipv4: ipgre: make ipgreheader robust Analog to commit db5b4e39c4e6 "ip6gre: make ip6greheader robust" Over the years, syzbot found many ways to crash the kernel in ipgreheader 1. This involves team or bonding drivers ability to...

Benchmarking Machine Learning Models for IoT Malware Detection under Data Scarcity and Drift

The rapid expansion of the Internet of Things IoT in domains such as smart cities, transportation, and industrial systems has heightened the urgency of addressing their security vulnerabilities. IoT devices often operate under limited computational resources, lack robust physical safeguards, and...

openSUSE 16 Security Update : bind (openSUSE-SU-2026:20091-1)

The remote openSUSE 16 host has packages installed that are affected by a vulnerability as referenced in the openSUSE- SU-2026:20091-1 advisory. Upgrade to release 9.20.18: - CVE-2025-13878: Fixed incorrect length checks for BRID and HHIT records bsc1256997 Feature Changes: Add more information t...

CVE-2026-23011

In the Linux kernel, the following vulnerability has been resolved: ipv4: ipgre: make ipgreheader robust Analog to commit db5b4e39c4e6 "ip6gre: make ip6greheader robust" Over the years, syzbot found many ways to crash the kernel in ipgreheader 1. This involves team or bonding drivers ability to...

CVE-2026-23011

In the Linux kernel, the following vulnerability has been resolved: ipv4: ipgre: make ipgreheader robust Analog to commit db5b4e39c4e6 "ip6gre: make ip6greheader robust" Over the years, syzbot found many ways to crash the kernel in ipgreheader 1. This involves team or bonding drivers ability to...