BadApex: Backdoor Attack Based on Adaptive Optimization Mechanism of Black-Box Large Language Models

Previous insertion-based and paraphrase-based backdoors have achieved great success in attack efficacy, but they ignore the text quality and semantic consistency between poisoned and clean texts. Although recent studies introduce LLMs to generate poisoned texts and improve the stealthiness,...

Towards Model Resistant to Transferable Adversarial Examples Via Trigger Activation

Whitepaper called Towards Model Resistant To Transferable Adversarial Examples Via Trigger Activation...

What Lurks Within? Concept Auditing for Shared Diffusion Models at Scale

Diffusion models DMs have revolutionized text-to-image generation, enabling the creation of highly realistic and customized images from text prompts. With the rise of parameter-efficient fine-tuning PEFT techniques like LoRA, users can now customize powerful pre-trained models using minimal...

REDEditing: Relationship-Driven Precise Backdoor Poisoning on Text-To-Image Diffusion Models

The rapid advancement of generative AI highlights the importance of text-to-image T2I security, particularly with the threat of backdoor poisoning. Timely disclosure and mitigation of security vulnerabilities in T2I models are crucial for ensuring the safe deployment of generative models. We...

Do You Really Need Public Data? Surrogate Public Data for Differential Privacy on Tabular Data

Differentially private DP machine learning often relies on the availability of public data for tasks like privacy-utility trade-off estimation, hyperparameter tuning, and pretraining. While public data assumptions may be reasonable in text and image domains, they are less likely to hold for tabul...

A Data-Centric Approach for Safe and Secure Large Language Models against Threatening and Toxic Content

Large Language Models LLM have made remarkable progress, but concerns about potential biases and harmful content persist. To address these apprehensions, we introduce a practical solution for ensuring LLM's safe and ethical use. Our novel approach focuses on a post-generation correction mechanism...

CVE-2025-32434

PyTorch is a Python package that provides tensor computation with strong GPU acceleration and deep neural networks built on a tape-based autograd system. In version 2.5.1 and prior, a Remote Command Execution RCE vulnerability exists in PyTorch when loading a model using torch.load with...

DEBIAN-CVE-2025-32434

PyTorch is a Python package that provides tensor computation with strong GPU acceleration and deep neural networks built on a tape-based autograd system. In version 2.5.1 and prior, a Remote Command Execution RCE vulnerability exists in PyTorch when loading a model using torch.load with...

CVE-2025-32434 PyTorch: `torch.load` with `weights_only=True` leads to remote code execution

PyTorch is a Python package that provides tensor computation with strong GPU acceleration and deep neural networks built on a tape-based autograd system. In version 2.5.1 and prior, a Remote Command Execution RCE vulnerability exists in PyTorch when loading a model using torch.load with...

CVE-2025-32434 PyTorch: `torch.load` with `weights_only=True` leads to remote code execution

PyTorch is a Python package that provides tensor computation with strong GPU acceleration and deep neural networks built on a tape-based autograd system. In version 2.5.1 and prior, a Remote Command Execution RCE vulnerability exists in PyTorch when loading a model using torch.load with...

Designing a Reliable Lateral Movement Detector Using a Graph Foundation Model

Foundation models have recently emerged as a new paradigm in machine learning ML. These models are pre-trained on large and diverse datasets and can subsequently be applied to various downstream tasks with little or no retraining. This allows people without advanced ML expertise to build ML...

Q-FAKER: Query-Free Hard Black-Box Attack Via Controlled Generation

Many adversarial attack approaches are proposed to verify the vulnerability of language models. However, they require numerous queries and the information on the target model. Even black-box attack methods also require the target model's output information. They are not applicable in real-world...

Everything You Wanted to Know about LLM-Based Vulnerability Detection but Were Afraid to Ask

Large Language Models are a promising tool for automated vulnerability detection, thanks to their success in code generation and repair. However, despite widespread adoption, a critical question remains: Are LLMs truly effective at detecting real-world vulnerabilities? Current evaluations, which...

Towards Explainable and Lightweight AI for Real-Time Cyber Threat Hunting in Edge Networks

As cyber threats continue to evolve, securing edge networks has become increasingly challenging due to their distributed nature and resource limitations. Many AI-driven threat detection systems rely on complex deep learning models, which, despite their high accuracy, suffer from two major...

Research Briefing: MCP Security

The present and future of security for the Model Context Protocol...

Leveraging Functional Encryption and Deep Learning for Privacy-Preserving Traffic Forecasting

Over the past few years, traffic congestion has continuously plagued the nation's transportation system creating several negative impacts including longer travel times, increased pollution rates, and higher collision risks. To overcome these challenges, Intelligent Transportation Systems ITS aim ...

GraphAttack: Exploiting Representational Blindspots in LLM Safety Mechanisms

Large Language Models LLMs have been equipped with safety mechanisms to prevent harmful outputs, but these guardrails can often be bypassed through "jailbreak" prompts. This paper introduces a novel graph-based approach to systematically generate jailbreak prompts through semantic transformations...

Adversary-Augmented Simulation for Fairness Evaluation and Defense in Hyperledger Fabric

This paper presents an adversary model and a simulation framework specifically tailored for analyzing attacks on distributed systems composed of multiple distributed protocols, with a focus on assessing the security of blockchain networks. Our model classifies and constrains adversarial actions...

DEBIAN-CVE-2025-22872

The tokenizer incorrectly interprets tags with unquoted attribute values that end with a solidus character / as self-closing. When directly using Tokenizer, this can result in such tags incorrectly being marked as self-closing, and when using the Parse functions, this can result in content...

AZL-60545 CVE-2025-22872 affecting package cf-cli for versions less than 8.7.11-3

The tokenizer incorrectly interprets tags with unquoted attribute values that end with a solidus character / as self-closing. When directly using Tokenizer, this can result in such tags incorrectly being marked as self-closing, and when using the Parse functions, this can result in content...