Malicious code in @sev-ui-verse/banking-models (npm)

The package @sev-ui-verse/banking-models was found to contain malicious code. --- -= Per source details. Do not edit below this line.=- Source: ghsa-malware 56743250cac97b219f4137c848e408b6203b996d98ac504aefe79400bed00e37 Any computer that has this package installed or running should be considere...

Malicious code in @sev-ui-verse/invoice-models (npm)

The package @sev-ui-verse/invoice-models was found to contain malicious code. --- -= Per source details. Do not edit below this line.=- Source: ghsa-malware f330a934831b8458a3b0de11c4efd1a661b00d1e2b7df7aa32b18a2278b789f3 Any computer that has this package installed or running should be considere...

MAL-2025-47530 Malicious code in @sev-ui-verse/banking-models (npm)

The package @sev-ui-verse/banking-models was found to contain malicious code. --- -= Per source details. Do not edit below this line.=- Source: ghsa-malware 56743250cac97b219f4137c848e408b6203b996d98ac504aefe79400bed00e37 Any computer that has this package installed or running should be considere...

MAL-2025-47532 Malicious code in @sev-ui-verse/contact-models (npm)

The package @sev-ui-verse/contact-models was found to contain malicious code. --- -= Per source details. Do not edit below this line.=- Source: ghsa-malware eafe3e022be23739d479cdfe1e577a7e5b593a528063ce67b8fa1beb911fb2e0 Any computer that has this package installed or running should be considere...

Malicious code in @sev-ui-verse/ta-product-models (npm)

The package @sev-ui-verse/ta-product-models was found to contain malicious code. --- -= Per source details. Do not edit below this line.=- Source: ghsa-malware d564367018a2440087b92862c41a4f2acf9fe9faf7c76a896fcf93f35b88642d Any computer that has this package installed or running should be...

Malicious Package

Overview @sev-ui-verse/invoice-models is a malicious package. This package contains malicious code, and its content was removed from the official package manager. While this package might be attempting to impersonate a valid organization, there is no connection between that organization and this...

MAL-2025-47547 Malicious code in @sev-ui-verse/ta-product-models (npm)

The package @sev-ui-verse/ta-product-models was found to contain malicious code. --- -= Per source details. Do not edit below this line.=- Source: ghsa-malware d564367018a2440087b92862c41a4f2acf9fe9faf7c76a896fcf93f35b88642d Any computer that has this package installed or running should be...

Malicious Package

Overview @sev-ui-verse/core-models is a malicious package. This package contains malicious code, and its content was removed from the official package manager. While this package might be attempting to impersonate a valid organization, there is no connection between that organization and this...

Malicious code in @sev-ui-verse/core-models (npm)

The package @sev-ui-verse/core-models was found to contain malicious code. --- -= Per source details. Do not edit below this line.=- Source: ghsa-malware 794fcad76f90e38dc1872c555e02d03512701d4d3a3ffc2a932ce9421fa1de97 Any computer that has this package installed or running should be considered...

Vision Transformers: the Threat of Realistic Adversarial Patches

The increasing reliance on machine learning systems has made their security a critical concern. Evasion attacks enable adversaries to manipulate the decision-making processes of AI systems, potentially causing security breaches or misclassification of targets. Vision Transformers ViTs have gained...

SoK: Potentials and Challenges of Large Language Models for Reverse Engineering

Reverse Engineering RE is central to software security, enabling tasks such as vulnerability discovery and malware analysis, but it remains labor-intensive and requires substantial expertise. Earlier advances in deep learning start to automate parts of RE, particularly for malware detection and...

PyTorch 安全漏洞

PyTorch is a Python package open-sourced by PyTorch. PyTorch suffers from a security vulnerability that stems from mishandling when compiling models containing torch.Tensor.tosparse and torch.Tensor.todense, which can be exploited by an attacker to cause a denial of service...

Investigating Security Implications of Automatically Generated Code on the Software Supply Chain

In recent years, various software supply chain SSC attacks have posed significant risks to the global community. Severe consequences may arise if developers integrate insecure code snippets that are vulnerable to SSC attacks into their products. Particularly, code generation techniques, such as...

STAF: Leveraging LLMs for Automated Attack Tree-Based Security Test Generation

In modern automotive development, security testing is critical for safeguarding systems against increasingly advanced threats. Attack trees are widely used to systematically represent potential attack vectors, but generating comprehensive test cases from these trees remains a labor-intensive,...

Can Federated Learning Safeguard Private Data in LLM Training? Vulnerabilities, Attacks, and Defense Evaluation

Fine-tuning large language models LLMs with local data is a widely adopted approach for organizations seeking to adapt LLMs to their specific domains. Given the shared characteristics in data across different organizations, the idea of collaboratively fine-tuning an LLM using data from multiple...

Bi-GRPO: Bidirectional Optimization for Jailbreak Backdoor Injection on LLMs

With the rapid advancement of large language models LLMs, their robustness against adversarial manipulations, particularly jailbreak backdoor attacks, has become critically important. Existing approaches to embedding jailbreak triggers--such as supervised fine-tuning SFT, model editing, and...

CyberSOCEval: Benchmarking LLMs Capabilities for Malware Analysis and Threat Intelligence Reasoning

Today's cyber defenders are overwhelmed by a deluge of security alerts, threat intelligence signals, and shifting business context, creating an urgent need for AI systems to enhance operational security work. While Large Language Models LLMs have the potential to automate and scale Security...

LLM-Based Vulnerability Discovery through the Lens of Code Metrics

Large language models LLMs excel in many tasks of software engineering, yet progress in leveraging them for vulnerability discovery has stalled in recent years. To understand this phenomenon, we investigate LLMs through the lens of classic code metrics. Surprisingly, we find that a classifier...

VulnCheck KEV: CVE-2025-45985

Blink routers BL-WR9000 V2.4.9 , BL-AC2100AZ3 V1.0.4, BL-X10AC8 v1.0.5 , BL-LTE300 v1.2.3, BL-F1200AT1 v1.0.0, BL-X26AC8 v1.2.8, BLAC450MAE4 v4.0.0 and BL-X26DA3 v1.2.7 were discovered to contain a command injection vulnerability via the bsSetSSIDHide function...

SilentStriker: toward Stealthy Bit-Flip Attacks on Large Language Models

The rapid adoption of large language models LLMs in critical domains has spurred extensive research into their security issues. While input manipulation attacks e.g., prompt injection have been well studied, Bit-Flip Attacks BFAs -- which exploit hardware vulnerabilities to corrupt model paramete...