Security Bug Report Prediction within and across Projects: a Comparative Study of BERT and Random Forest

Early detection of security bug reports SBRs is crucial for preventing vulnerabilities and ensuring system reliability. While machine learning models have been developed for SBR prediction, their predictive performance still has room for improvement. In this study, we conduct a comprehensive...

Leveraging LLM to Strengthen ML-Based Cross-Site Scripting Detection

According to the Open Web Application Security Project OWASP, Cross-Site Scripting XSS is a critical security vulnerability. Despite decades of research, XSS remains among the top 10 security vulnerabilities. Researchers have proposed various techniques to protect systems from XSS attacks, with...

Llama-3.1-FoundationAI-SecurityLLM-Base-8B Technical Report

As transformer-based large language models LLMs increasingly permeate society, they have revolutionized domains such as software engineering, creative writing, and digital arts. However, their adoption in cybersecurity remains limited due to challenges like scarcity of specialized training data a...

The Automation Advantage in AI Red Teaming

This paper analyzes Large Language Model LLM security vulnerabilities based on data from Crucible, encompassing 214,271 attack attempts by 1,674 users across 30 LLM challenges. Our findings reveal automated approaches significantly outperform manual techniques 69.5% vs 47.6% success rate, despite...

SAGE: a Generic Framework for LLM Safety Evaluation

Whitepaper called SAGE: A Generic Framework For LLM Safety Evaluation...

dify 安全漏洞

dify is an open source LLM application development platform from LangGenius Open Source. A security vulnerability exists in versions of dify prior to 1.3.0, which stems from a clickjacking vulnerability in the default settings that could lead to unauthorized operations...

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...

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...

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...

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...

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...