GTSD: Generative Text Steganography Based on Diffusion Model

With the rapid development of deep learning, existing generative text steganography methods based on autoregressive models have achieved success. However, these autoregressive steganography approaches have certain limitations. Firstly, existing methods require encoding candidate words according t...

CVE-2025-3677

A vulnerability classified as critical was found in lm-sys fastchat up to 0.2.36. This vulnerability affects the function splitfiles/applydeltalowcpumem of the file fastchat/model/applydelta.py. The manipulation leads to deserialization. An attack has to be approached locally...

SONNI: Secure Oblivious Neural Network Inference

In the standard privacy-preserving Machine learning as-a-service MLaaS model, the client encrypts data using homomorphic encryption and uploads it to a server for computation. The result is then sent back to the client for decryption. It has become more and more common for the computation to be...

T2VShield: Model-Agnostic Jailbreak Defense for Text-To-Video Models

The rapid development of generative artificial intelligence has made text to video models essential for building future multimodal world simulators. However, these models remain vulnerable to jailbreak attacks, where specially crafted prompts bypass safety mechanisms and lead to the generation of...

Remote Code Execution (RCE)

PyTorch is vulnerable to Remote Command Execution RCE. The vulnerability is due to unsafe deserialization due to the use of torch.loadweightsonly=True on untrusted model files, allowing an attacker to execute arbitrary code by supplying a maliciously crafted model...

A Gradient-Optimized TSK Fuzzy Framework for Explainable Phishing Detection

Phishing attacks represent an increasingly sophisticated and pervasive threat to individuals and organizations, causing significant financial losses, identity theft, and severe damage to institutional reputations. Existing phishing detection methods often struggle to simultaneously achieve high...

Automating Function-Level TARA for Automotive Full-Lifecycle Security

As modern vehicles evolve into intelligent and connected systems, their growing complexity introduces significant cybersecurity risks. Threat Analysis and Risk Assessment TARA has therefore become essential for managing these risks under mandatory regulations. However, existing TARA automation...

ThreMoLIA: Threat Modeling of Large Language Model-Integrated Applications

Large Language Models LLMs are currently being integrated into industrial software applications to help users perform more complex tasks in less time. However, these LLM-Integrated Applications LIA expand the attack surface and introduce new kinds of threats. Threat modeling is commonly used to...

LLMpatronous: Harnessing the Power of LLMs for Vulnerability Detection

Despite the transformative impact of Artificial Intelligence AI across various sectors, cyber security continues to rely on traditional static and dynamic analysis tools, hampered by high false positive rates and superficial code comprehension. While generative AI offers promising automation...

SUSE CVE-2025-31363

Mattermost versions 10.4.x = 10.4.2, 10.5.x = 10.5.0, 9.11.x = 9.11.9 fail to restrict domains the LLM can request to contact upstream which allows an authenticated user to exfiltrate data from an arbitrary server accessible to the victim via performing a prompt injection in the AI plugin's Jira...

Evaluating the Vulnerability of ML-Based Ethereum Phishing Detectors to Single-Feature Adversarial Perturbations

This paper explores the vulnerability of machine learning models to simple single-feature adversarial attacks in the context of Ethereum fraudulent transaction detection. Through comprehensive experimentation, we investigate the impact of various adversarial attack strategies on model performance...

CVE-2025-24357 Malicious model remote code execution fix bypass with PyTorch < 2.6.0

Description https://github.com/vllm-project/vllm/security/advisories/GHSA-rh4j-5rhw-hr54 reported a vulnerability where loading a malicious model could result in code execution on the vllm host. The fix applied to specify weightsonly=True to calls to torch.load did not solve the problem prior to...

Performance Analysis of MDI-QKD in Thermal-Loss and Phase Noise Channels

Measurement-device-independent quantum key distribution MDI-QKD, enhances quantum cryptography by mitigating detector-side vulnerabilities. This study analyzes MDI-QKD performance in thermal-loss and phase noise channels, modeled as depolarizing and dephasing channels to capture thermal and phase...

AiXamine: Simplified LLM Safety and Security

Evaluating Large Language Models LLMs for safety and security remains a complex task, often requiring users to navigate a fragmented landscape of ad hoc benchmarks, datasets, metrics, and reporting formats. To address this challenge, we present aiXamine, a comprehensive black-box evaluation...

Automated Static Vulnerability Detection Via a Holistic Neuro-Symbolic Approach

Static vulnerability detection is still a challenging problem and demands excessive human efforts, e.g., manual curation of good vulnerability patterns. None of prior works, including classic program analysis or Large Language Model LLM-based approaches, have fully automated such vulnerability...

CVE-2025-28029

TOTOLINK A830R V4.1.2cu.5182B20201102, A950RG V4.1.2cu.5161B20200903, A3000RU V5.9c.5185B20201128, and A3100R V4.1.2cu.5247B20211129 were found to contain a buffer overflow vulnerability in cstecgi.cgi...

CVE-2025-28026

TOTOLINK A830R V4.1.2cu.5182B20201102, A950RG V4.1.2cu.5161B20200903, A3000RU V5.9c.5185B20201128, and A3100R V4.1.2cu.5247B20211129 were found to contain a buffer overflow vulnerability in downloadFile.cgi...

CVE-2025-28032

TOTOLINK A800R V4.1.2cu.5137B20200730, A810R V4.1.2cu.5182B20201026, A830R V4.1.2cu.5182B20201102, A950RG V4.1.2cu.5161B20200903, A3000RU V5.9c.5185B20201128, and A3100R V4.1.2cu.5247B20211129 contain a pre-auth buffer overflow vulnerability in the setNoticeCfg function through the IpForm paramet...

TOTOLINK多款产品 安全漏洞

TOTOLINK A3000RU and others are products of China-based TOTOLINK Electronics TOTOLINK.TOTOLINK A3000RU is a wireless router.TOTOLINK A950RG is an Ultra-Generation Giga wireless router.TOTOLINK A830R is a wireless dual-band router. A security vulnerability exists in several TOTOLINK products, whic...

[SECURITY] Fedora 42 Update: llama-cpp-b4094-11.fc42

The main goal of llama.cpp is to run the LLaMA model using 4-bit integer quantization on a MacBook Plain C/C++ implementation without dependencies Apple silicon first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks AVX, AVX2 and AVX512 support for x86 architectures Mixed F...