CISA, Australia, and Partners Author Joint Guidance on Securely Integrating Artificial Intelligence in Operational Technology

CISA and the Australian Signals Directorate’s Australian Cyber Security Centre, in collaboration with federal and international partners, have released new cybersecurity guidance: Principles for the Secure Integration of Artificial Intelligence in Operational Technology. This guidance aims to hel...

CVE-2025-13542

The DesignThemes LMS plugin for WordPress is vulnerable to Privilege Escalation in all versions up to, and including, 1.0.4. This is due to the 'dtlmsregisteruserfrontend' function not restricting what user roles a user can register with. This makes it possible for unauthenticated attackers to...

How to build forward-thinking cybersecurity teams for tomorrow

We are witnessing something unprecedented in cybersecurity: the democratization of advanced cyberattack capabilities. What once required nation-state resources sophisticated social engineering, polymorphic malware, coordinated infrastructure now fits in a prompt window. AI is no longer a futurist...

CVE-2025-41743

Insufficient encryption strength in Sprecher Automation SPRECON-E-C, SPRECON-E-P, and SPRECON-E-T3 allows a local unprivileged attacker to extract data from update images and thus obtain limited information about the architecture and internal processes...

CVE-2025-65676

Stored Cross site scripting XSS vulnerability in Classroomio LMS 0.1.13 allows authenticated attackers to execute arbitrary code via crafted SVG cover images...

AI-Driven Cybersecurity Testbed for Nuclear Infrastructure: Comprehensive Evaluation Using METL Operational Data

Advanced nuclear reactor systems face increasing cybersecurity threats as sophisticated attackers exploit cyber-physical interfaces to manipulate control systems while evading traditional IT security measures. This research presents a comprehensive evaluation of artificial intelligence approaches...

COGNITION: From Evaluation to Defense against Multimodal LLM CAPTCHA Solvers

This paper studies how multimodal large language models MLLMs undermine the security guarantees of visual CAPTCHA. We identify the attack surface where an adversary can cheaply automate CAPTCHA solving using off-the-shelf models. We evaluate 7 leading commercial and open-source MLLMs across 18...

CTF Archive: Capture, Curate, Learn Forever

Capture the Flag CTF competitions represent a powerful experiential learning approach within cybersecurity education, blending diverse concepts into interactive challenges. However, the short duration typically 24-48 hours and ephemeral infrastructure of these events often impede sustained...

RECTor: Robust and Efficient Correlation Attack on Tor

Tor is a widely used anonymity network that conceals user identities by routing traffic through encrypted relays, yet it remains vulnerable to traffic correlation attacks that deanonymize users by matching patterns in ingress and egress traffic. However, existing correlation methods suffer from t...

Identification of Malicious Posts on the Dark Web Using Supervised Machine Learning

Given the constant growth and increasing sophistication of cyberattacks, cybersecurity can no longer rely solely on traditional defense techniques and tools. Proactive detection of cyber threats has become essential to help security teams identify potential risks and implement effective mitigatio...

An Efficient Privacy-Preserving Intrusion Detection Scheme for UAV Swarm Networks

The rapid proliferation of unmanned aerial vehicles UAVs and their applications in diverse domains, such as surveillance, disaster management, agriculture, and defense, have revolutionized modern technology. While the potential benefits of swarm-based UAV networks are growing significantly, they...

Exposing Vulnerabilities in RL: A Novel Stealthy Backdoor Attack through Reward Poisoning

Reinforcement learning RL has achieved remarkable success across diverse domains, enabling autonomous systems to learn and adapt to dynamic environments by optimizing a reward function. However, this reliance on reward signals creates a significant security vulnerability. In this paper, we study ...

CVE-2025-65676

Stored Cross site scripting XSS vulnerability in Classroomio LMS 0.1.13 allows authenticated attackers to execute arbitrary code via crafted SVG cover images...

CVE-2025-65675

Stored Cross site scripting XSS vulnerability in Classroomio LMS 0.1.13 allows authenticated attackers to execute arbitrary code via crafted SVG profile pictures...

CVE-2025-65675

Stored Cross site scripting XSS vulnerability in Classroomio LMS 0.1.13 allows authenticated attackers to execute arbitrary code via crafted SVG profile pictures...

CVE-2025-65675

The CVE-2025-65675 entry concerns Classroomio LMS 0.1.13, with a stored XSS vulnerability triggered by crafted SVG profile/cover images. The Red Hat, EUVD, NVD, and OSV records confirm the issue is an authenticated XSS that can execute arbitrary code via SVG uploads. The root cause is unsanitized...

PT-2025-48176

Name of the Vulnerable Software and Affected Versions Classroomio LMS version 0.1.13 Description An authenticated attacker can execute arbitrary code through crafted SVG cover images. The issue is a stored Cross Site Scripting XSS condition. Recommendations Update to a newer version that contains...

A Research and Development Portfolio of GNN Centric Malware Detection, Explainability, and Dataset Curation

Graph Neural Networks GNNs have become an effective tool for malware detection by capturing program execution through graph-structured representations. However, important challenges remain regarding scalability, interpretability, and the availability of reliable datasets. This paper brings togeth...

From One Attack Domain to Another: Contrastive Transfer Learning with Siamese Networks for APT Detection

Advanced Persistent Threats APT pose a major cybersecurity challenge due to their stealth, persistence, and adaptability. Traditional machine learning detectors struggle with class imbalance, high dimensional features, and scarce real world traces. They often lack transferability-performing well ...

FedPoisonTTP: A Threat Model and Poisoning Attack for Federated Test-Time Personalization

Test-time personalization in federated learning enables models at clients to adjust online to local domain shifts, enhancing robustness and personalization in deployment. Yet, existing federated learning work largely overlooks the security risks that arise when local adaptation occurs at test tim...