INE Named to Training Industry’s 2025 Top 20 Online Learning Library List

Cary, United States, 11th August 2025, CyberNewsWire...

VOIDFace: a Privacy-Preserving Multi-Network Face Recognition with Enhanced Security

Advancement of machine learning techniques, combined with the availability of large-scale datasets, has significantly improved the accuracy and efficiency of facial recognition. Modern facial recognition systems are trained using large face datasets collected from diverse individuals or public...

CVE-2025-8752

A vulnerability was found in wangzhixuan spring-shiro-training up to 94812c1fd8f7fe796c931f4984ff1aa0671ab562. It has been declared as critical. This vulnerability affects unknown code of the file /role/add. The manipulation leads to command injection. The attack can be initiated remotely. The...

CVE-2025-8752 wangzhixuan spring-shiro-training add command injection

A vulnerability was found in wangzhixuan spring-shiro-training up to 94812c1fd8f7fe796c931f4984ff1aa0671ab562. It has been declared as critical. This vulnerability affects unknown code of the file /role/add. The manipulation leads to command injection. The attack can be initiated remotely. The...

CVE-2025-8752 wangzhixuan spring-shiro-training add command injection

A vulnerability was found in wangzhixuan spring-shiro-training up to 94812c1fd8f7fe796c931f4984ff1aa0671ab562. It has been declared as critical. This vulnerability affects unknown code of the file /role/add. The manipulation leads to command injection. The attack can be initiated remotely. The...

CVE-2025-8752

The CVE-2025-8752 entry concerns the wangzhixuan spring-shiro-training project (up to commit 94812c1fd8f7fe796c931f4984ff1aa0671ab562). The vulnerability is in the /role/add code path and is due to a command injection vulnerability. It is exploitable remotely and has been publicly disclosed. The ...

wangzhixuan spring-shiro-training 注入漏洞

wangzhixuan spring-shiro-training is a learning system from the Chinese company wangzhixuan. An injection vulnerability exists in wangzhixuan spring-shiro-training, which stems from a command injection issue in file /role/add...

PT-2025-32435 · Wangzhixuan · Spring-Shiro-Training

Name of the Vulnerable Software and Affected Versions: wangzhixuan spring-shiro-training up to 94812c1fd8f7fe796c931f4984ff1aa0671ab562 Description: A critical issue exists in wangzhixuan spring-shiro-training. The vulnerability is due to command injection in the /role/add file. This allows for...

A Real-Time, Self-Tuning Moderator Framework for Adversarial Prompt Detection

Ensuring LLM alignment is critical to information security as AI models become increasingly widespread and integrated in society. Unfortunately, many defenses against adversarial attacks and jailbreaking on LLMs cannot adapt quickly to new attacks, degrade model responses to benign prompts, or...

Membership Inference Attack with Partial Features

Machine learning models have been shown to be susceptible to membership inference attack, which can be used to determine whether a given sample appears in the training data. Existing membership inference methods commonly assume that the adversary has full access to the features of the target...

Latent Fusion Jailbreak: Blending Harmful and Harmless Representations to Elicit Unsafe LLM Outputs

Large language models LLMs demonstrate impressive capabilities in various language tasks but are susceptible to jailbreak attacks that circumvent their safety alignments. This paper introduces Latent Fusion Jailbreak LFJ, a representation-based attack that interpolates hidden states from harmful...

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...

PRvL: Quantifying the Capabilities and Risks of Large Language Models for PII Redaction

Redacting Personally Identifiable Information PII from unstructured text is critical for ensuring data privacy in regulated domains. While earlier approaches have relied on rule-based systems and domain-specific Named Entity Recognition NER models, these methods fail to generalize across formats...

From Split to Share: Private Inference with Distributed Feature Sharing

Cloud-based Machine Learning as a Service MLaaS raises serious privacy concerns when handling sensitive client data. Existing Private Inference PI methods face a fundamental trade-off between privacy and efficiency: cryptographic approaches offer strong protection but incur high computational...

SenseCrypt: Sensitivity-Guided Selective Homomorphic Encryption for Joint Federated Learning in Cross-Device Scenarios

Homomorphic Encryption HE prevails in securing Federated Learning FL, but suffers from high overhead and adaptation cost. Selective HE methods, which partially encrypt model parameters by a global mask, are expected to protect privacy with reduced overhead and easy adaptation. However, in...

Backdoors & Breaches: How Talos is helping humanitarian aid NGOs prepare for cyber attacks

In 2023, Talos collaborated with NetHope and Cisco Crisis Response to create a customized Backdoors & Breaches expansion deck for international humanitarian organizations, addressing their unique cybersecurity challenges. The new expansion deck helps NGOs with constrained budgets improve proactiv...

A Survey on Data Security in Large Language Models

Large Language Models LLMs, now a foundation in advancing natural language processing, power applications such as text generation, machine translation, and conversational systems. Despite their transformative potential, these models inherently rely on massive amounts of training data, often...

DINA: a Dual Defense Framework against Internal Noise and External Attacks in Natural Language Processing

As large language models LLMs and generative AI become increasingly integrated into customer service and moderation applications, adversarial threats emerge from both external manipulations and internal label corruption. In this work, we identify and systematically address these dual adversarial...

CVE-2025-50472

The modelscope/ms-swift library thru 2.6.1 is vulnerable to arbitrary code execution through deserialization of untrusted data within the loadmodelmeta function of the ModelFileSystemCache class. Attackers can execute arbitrary code and commands by crafting a malicious serialized .mdl payload,...

Exploit for Incorrect Default Permissions in Microsoft

This List is no longer updated. Awesome Red Teaming List of Awesome Red Team / Red Teaming Resources This list is for anyone wishing to learn about Red Teaming but do not have a starting point. Anyway, this is a living resources and will update regularly with latest Adversarial Tactics and...