CVE-2025-10696

OpenSupports exposes an endpoint that allows the list of 'supervised users' for any account to be edited, but it does not validate whether the actor is the owner of that list. A Level 1 staff member can modify the supervision relationship of a third party the target user, who can then view the...

CVE-2025-10696

OpenSupports exposes an endpoint that allows the list of 'supervised users' for any account to be edited, but it does not validate whether the actor is the owner of that list. A Level 1 staff member can modify the supervision relationship of a third party the target user, who can then view the...

CVE-2025-10696 OpenSupports 4.11.0 — Insecure Direct Object Reference in supervised list

OpenSupports exposes an endpoint that allows the list of 'supervised users' for any account to be edited, but it does not validate whether the actor is the owner of that list. A Level 1 staff member can modify the supervision relationship of a third party the target user, who can then view the...

CVE-2025-10696 OpenSupports 4.11.0 — Insecure Direct Object Reference in supervised list

OpenSupports exposes an endpoint that allows the list of 'supervised users' for any account to be edited, but it does not validate whether the actor is the owner of that list. A Level 1 staff member can modify the supervision relationship of a third party the target user, who can then view the...

CVE-2025-10696 OpenSupports 4.11.0 — Insecure Direct Object Reference in supervised list

OpenSupports exposes an endpoint that allows the list of 'supervised users' for any account to be edited, but it does not validate whether the actor is the owner of that list. A Level 1 staff member can modify the supervision relationship of a third party the target user, who can then view the...

EUVD-2024-0856

Malicious code in bioql PyPI...

CVE-2025-10696: Incorrect Authorization

OpenSupports exposes an endpoint that allows the list of 'supervised users' for any account to be edited, but it does not validate whether the actor is the owner of that list. A Level 1 staff member can modify the supervision relationship of a third party the target user, who can then view the...

PT-2025-40599

Name of the Vulnerable Software and Affected Versions OpenSupports version 4.11.0 Description The software exposes an endpoint that allows modification of the 'supervised users' list for any account without verifying ownership. This allows a Level 1 staff member to alter the supervision...

Bi-GRPO: Bidirectional Optimization for Jailbreak Backdoor Injection on LLMs

With the rapid advancement of large language models LLMs, their robustness against adversarial manipulations, particularly jailbreak backdoor attacks, has become critically important. Existing approaches to embedding jailbreak triggers--such as supervised fine-tuning SFT, model editing, and...

Self-Supervised Learning of Graph Representations for Network Intrusion Detection

Detecting intrusions in network traffic is a challenging task, particularly under limited supervision and constantly evolving attack patterns. While recent works have leveraged graph neural networks for network intrusion detection, they often decouple representation learning from anomaly detectio...

Weakly Supervised Vulnerability Localization Via Multiple Instance Learning

Software vulnerability detection has emerged as a significant concern in the field of software security recently, capturing the attention of numerous researchers and developers. Most previous approaches focus on coarse-grained vulnerability detection, such as at the function or file level. Howeve...

Flow-Based Detection and Identification of Zero-Day IoT Cameras

The majority of consumer IoT devices lack mechanisms for administrators to monitor and control them, hindering tailored security policies. A key challenge is identifying whether a new device, especially a streaming IoT camera, has joined the network. We present zCamInspector, a system for...

Contrastive Self-Supervised Network Intrusion Detection Using Augmented Negative Pairs

Network intrusion detection remains a critical challenge in cybersecurity. While supervised machine learning models achieve state-of-the-art performance, their reliance on large labelled datasets makes them impractical for many real-world applications. Anomaly detection methods, which train...

A Quantum Genetic Algorithm-Enhanced Self-Supervised Intrusion Detection System for Wireless Sensor Networks in the Internet of Things

The rapid expansion of the Internet of Things IoT and Wireless Sensor Networks WSNs has significantly increased the attack surface of such systems, making them vulnerable to a wide range of cyber threats. Traditional Intrusion Detection Systems IDS often fail to meet the stringent requirements of...

CITADEL: Continual Anomaly Detection for Enhanced Learning in IoT Intrusion Detection

The Internet of Things IoT, with its high degree of interconnectivity and limited computational resources, is particularly vulnerable to a wide range of cyber threats. Intrusion detection systems IDS have been extensively studied to enhance IoT security, and machine learning-based IDS ML-IDS show...

MixGAN: a Hybrid Semi-Supervised and Generative Approach for DDoS Detection in Cloud-Integrated IoT Networks

The proliferation of cloud-integrated IoT systems has intensified exposure to Distributed Denial of Service DDoS attacks due to the expanded attack surface, heterogeneous device behaviors, and limited edge protection. However, DDoS detection in this context remains challenging because of complex...

Adaptive Anomaly Detection in Evolving Network Environments

Distribution shift, a change in the statistical properties of data over time, poses a critical challenge for deep learning anomaly detection systems. Existing anomaly detection systems often struggle to adapt to these shifts. Specifically, systems based on supervised learning require costly manua...

BERTector: Intrusion Detection Based on Joint-Dataset Learning

Intrusion detection systems IDS are facing challenges in generalization and robustness due to the heterogeneity of network traffic and the diversity of attack patterns. To address this issue, we propose a new joint-dataset training paradigm for IDS and propose a scalable BERTector framework based...

Social-Sensor Identity Cloning Detection Using Weakly Supervised Deep Forest and Cryptographic Authentication

Recent years have witnessed a rising trend in social-sensor cloud identity cloning incidents. However, existing approaches suffer from unsatisfactory performance, a lack of solutions for detecting duplicated accounts, and a lack of large-scale evaluations on real-world datasets. We introduce a...

Semi-Supervised Supply Chain Fraud Detection with Unsupervised Pre-Filtering

Detecting fraud in modern supply chains is a growing challenge, driven by the complexity of global networks and the scarcity of labeled data. Traditional detection methods often struggle with class imbalance and limited supervision, reducing their effectiveness in real-world applications. This...