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...

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...

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...

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...

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...

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...

EdgeAgentX-DT: Integrating Digital Twins and Generative AI for Resilient Edge Intelligence in Tactical Networks

We introduce EdgeAgentX-DT, an advanced extension of the EdgeAgentX framework that integrates digital twin simulations and generative AI-driven scenario training to significantly enhance edge intelligence in military networks. EdgeAgentX-DT utilizes network digital twins, virtual replicas...

Enhancing IoT Intrusion Detection Systems through Adversarial Training

The augmentation of Internet of Things IoT devices transformed both automation and connectivity but revealed major security vulnerabilities in networks. We address these challenges by designing a robust intrusion detection system IDS to detect complex attacks by learning patterns from the...

Enabling Cyber Security Education through Digital Twins and Generative AI

Digital Twins DTs are gaining prominence in cybersecurity for their ability to replicate complex IT Information Technology, OT Operational Technology, and IoT Internet of Things infrastructures, allowing for real time monitoring, threat analysis, and system simulation. This study investigates how...

Learning-Based Privacy-Preserving Graph Publishing against Sensitive Link Inference Attacks

Publishing graph data is widely desired to enable a variety of structural analyses and downstream tasks. However, it also potentially poses severe privacy leakage, as attackers may leverage the released graph data to launch attacks and precisely infer private information such as the existence of...

WeTransfer walks back clause that said it would train AI on your files

File sharing site WeTransfer has rolled back language that allowed it to train machine learning models on any files that its users uploaded. The change was made after criticisms from its users. The company had quietly inserted the new language in the terms and conditions on its website. Sometime...

microcode_ctl: From CVEorg collector

New Spectre-v2 attack classes have been discovered within CPU architectures that enable self-training exploitation of speculative execution within the same privilege domain. These novel techniques bypass existing hardware and software mitigations, including IBPB, eIBRS, and BHINO, by leveraging...

Split Happens: Combating Advanced Threats with Split Learning and Function Secret Sharing

Split Learning SL -- splits a model into two distinct parts to help protect client data while enhancing Machine Learning ML processes. Though promising, SL has proven vulnerable to different attacks, thus raising concerns about how effective it may be in terms of data privacy. Recent works have...

PLA: Prompt Learning Attack against Text-To-Image Generative Models

Text-to-Image T2I models have gained widespread adoption across various applications. Despite the success, the potential misuse of T2I models poses significant risks of generating Not-Safe-For-Work NSFW content. To investigate the vulnerability of T2I models, this paper delves into adversarial...