Hitachi Energy MicroSCADA X SYS600

RISK EVALUATION Successful exploitation of these vulnerabilities could allow an attacker to tamper with the system file, overwrite files, create a denial-of-service condition, or leak file content. 2. RECOMMENDED PRACTICES CISA recommends users take defensive measures to minimize the risk of...

CVE-2025-6839

A vulnerability, which was classified as critical, has been found in Conjure Position Department Service Quality Evaluation System up to 1.0.11. Affected by this issue is the function eval of the file public/assets/less/bootstrap-less/mixins/head.php. The manipulation of the argument payload lead...

CVE-2025-6839 Conjure Position Department Service Quality Evaluation System head.php eval backdoor

A vulnerability, which was classified as critical, has been found in Conjure Position Department Service Quality Evaluation System up to 1.0.11. Affected by this issue is the function eval of the file public/assets/less/bootstrap-less/mixins/head.php. The manipulation of the argument payload lead...

PT-2025-27339 · Unknown · Position Department Service Quality Evaluation System

Name of the Vulnerable Software and Affected Versions: Conjure Position Department Service Quality Evaluation System versions up to 1.0.11 Description: A critical vulnerability has been found in the Conjure Position Department Service Quality Evaluation System. The issue affects the eval function...

TrendMakers Sight Bulb Pro

RISK EVALUATION Successful exploitation of these vulnerabilities could allow an attacker to capture sensitive information and execute arbitrary shell commands on the target device as root if connected to the local network segment. 2. RECOMMENDED PRACTICES CISA reminds organizations to perform...

Poster: Enhancing GNN Robustness for Network Intrusion Detection Via Agent-Based Analysis

Graph Neural Networks GNNs show great promise for Network Intrusion Detection Systems NIDS, particularly in IoT environments, but suffer performance degradation due to distribution drift and lack robustness against realistic adversarial attacks. Current robustness evaluations often rely on...

Kaleris Navis N4 Terminal Operating System

RISK EVALUATION Successful exploitation of these vulnerabilities could allow an attacker to remotely exploit the operating system, achieve remote code execution, or extract sensitive information. 2. RECOMMENDED PRACTICES CISA recommends users take defensive measures to minimize the risk of...

ControlID iDSecure On-premises

RISK EVALUATION Successful exploitation of these vulnerabilities could allow an attacker to bypass authentication, retrieve information, leak arbitrary data, or perform SQL injections. 2. RECOMMENDED PRACTICES CISA recommends users take defensive measures to minimize the risk of exploitation of...

MICROSENS NMP Web+

RISK EVALUATION Successful exploitation of these vulnerabilities could allow an attacker to gain system access, overwrite files or execute arbitrary code. 2. RECOMMENDED PRACTICES CISA recommends users take defensive measures to minimize the risk of exploitation of these vulnerabilities, such...

Diffusion-Based Task-Oriented Semantic Communications with Model Inversion Attack

Semantic communication has emerged as a promising neural network-based system design for 6G networks. Task-oriented semantic communication is a novel paradigm whose core goal is to efficiently complete specific tasks by transmitting semantic information, optimizing communication efficiency and ta...

PhishingHook: Catching Phishing Ethereum Smart Contracts Leveraging EVM Opcodes

The Ethereum Virtual Machine EVM is a decentralized computing engine. It enables the Ethereum blockchain to execute smart contracts and decentralized applications dApps. The increasing adoption of Ethereum sparked the rise of phishing activities. Phishing attacks often target users through...

DUMB and DUMBer: Is Adversarial Training Worth It in the Real World?

Adversarial examples are small and often imperceptible perturbations crafted to fool machine learning models. These attacks seriously threaten the reliability of deep neural networks, especially in security-sensitive domains. Evasion attacks, a form of adversarial attack where input is modified a...

Pushing the Limits of Safety: a Technical Report on the ATLAS Challenge 2025

Multimodal Large Language Models MLLMs have enabled transformative advancements across diverse applications but remain susceptible to safety threats, especially jailbreak attacks that induce harmful outputs. To systematically evaluate and improve their safety, we organized the Adversarial Testing...

A Survey of Foundation Models for IoT: Taxonomy and Criteria-Based Analysis

Foundation models have gained growing interest in the IoT domain due to their reduced reliance on labeled data and strong generalizability across tasks, which address key limitations of traditional machine learning approaches. However, most existing foundation model based methods are developed fo...

FAME: a Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes

The widespread emergence of face-swap Deepfake videos poses growing risks to digital security, privacy, and media integrity, necessitating effective forensic tools for identifying the source of such manipulations. Although most prior research has focused primarily on binary Deepfake detection, th...

DinoCompanion: an Attachment-Theory Informed Multimodal Robot for Emotionally Responsive Child-AI Interaction

Children's emotional development fundamentally relies on secure attachment relationships, yet current AI companions lack the theoretical foundation to provide developmentally appropriate emotional support. We introduce DinoCompanion, the first attachment-theory-grounded multimodal robot for...

Differential Privacy in Machine Learning: from Symbolic AI to LLMs

Machine learning models should not reveal particular information that is not otherwise accessible. Differential privacy provides a formal framework to mitigate privacy risks by ensuring that the inclusion or exclusion of any single data point does not significantly alter the output of an algorith...

Privacy-Preserving Federated Learning against Malicious Clients Based on Verifiable Functional Encryption

Federated learning is a promising distributed learning paradigm that enables collaborative model training without exposing local client data, thereby protect data privacy. However, it also brings new threats and challenges. The advancement of model inversion attacks has rendered the plaintext...

Rectifying Privacy and Efficacy Measurements in Machine Unlearning: a New Inference Attack Perspective

Machine unlearning focuses on efficiently removing specific data from trained models, addressing privacy and compliance concerns with reasonable costs. Although exact unlearning ensures complete data removal equivalent to retraining, it is impractical for large-scale models, leading to growing...

Towards Safety and Security Testing of Cyberphysical Power Systems by Shape Validation

The increasing complexity of cyberphysical power systems leads to larger attack surfaces to be exploited by malicious actors and a higher risk of faults through misconfiguration. We propose to meet those risks with a declarative approach to describe cyberphysical power systems and to automaticall...