A Practical Solution to Systematically Monitor Inconsistencies in SBOM-Based Vulnerability Scanners

Software Bill of Materials SBOM provides new opportunities for automated vulnerability identification in software products. While the industry is adopting SBOM-based Vulnerability Scanning SVS to identify vulnerabilities, we increasingly observe inconsistencies and unexpected behavior, that resul...

Most Parked Domains Now Serving Malicious Content

Direct navigation -- the act of visiting a website by manually typing a domain name in a web browser -- has never been riskier: A new study finds the vast majority of "parked" domains -- mostly expired or dormant domain names, or common misspellings of popular websites -- are now configured to...

Transmission Integer Overflow

2017 research from Google where Tavis found that transmission suffered from various integer overflows when parsing torrent files...

TeleAI-Safety: A Comprehensive LLM Jailbreaking Benchmark Towards Attacks, Defenses, and Evaluations

While the deployment of large language models LLMs in high-value industries continues to expand, the systematic assessment of their safety against jailbreak and prompt-based attacks remains insufficient. Existing safety evaluation benchmarks and frameworks are often limited by an imbalanced...

Beyond Detection: A Comprehensive Benchmark and Study on Representation Learning for Fine-Grained Webshell Family Classification

Malicious WebShells pose a significant and evolving threat by compromising critical digital infrastructures and endangering public services in sectors such as healthcare and finance. While the research community has made significant progress in WebShell detection i.e., distinguishing malicious...

WildCode: An Empirical Analysis of Code Generated by ChatGPT

LLM models are increasingly used to generate code, but the quality and security of this code are often uncertain. Several recent studies have raised alarm bells, indicating that such AI-generated code may be particularly vulnerable to cyberattacks. However, most of these studies rely on code that...

Rethinking Cybersecurity Ontology Classification and Evaluation: Towards a Credibility-Centered Framework

This paper analyzes the proliferation of cybersecurity ontologies, arguing that this surge cannot be explained solely by technical shortcomings related to quality, but also by a credibility deficit - a lack of trust, endorsement, and adoption by users. This conclusion is based on our first...

COGNITION: From Evaluation to Defense against Multimodal LLM CAPTCHA Solvers

This paper studies how multimodal large language models MLLMs undermine the security guarantees of visual CAPTCHA. We identify the attack surface where an adversary can cheaply automate CAPTCHA solving using off-the-shelf models. We evaluate 7 leading commercial and open-source MLLMs across 18...

An Empirical Study on the Security Vulnerabilities of GPTs

Equipped with various tools and knowledge, GPTs, one kind of customized AI agents based on OpenAI's large language models, have illustrated great potential in many fields, such as writing, research, and programming. Today, the number of GPTs has reached three millions, with the range of specific...

Clustering Malware at Scale: A First Full-Benchmark Study

Recent years have shown that malware attacks still happen with high frequency. Malware experts seek to categorize and classify incoming samples to confirm their trustworthiness or prove their maliciousness. One of the ways in which groups of malware samples can be identified is through malware...

A Safety and Security Framework for Real-World Agentic Systems

This paper introduces a dynamic and actionable framework for securing agentic AI systems in enterprise deployment. We contend that safety and security are not merely fixed attributes of individual models but also emergent properties arising from the dynamic interactions among models, orchestrator...

A Reality Check on SBOM-Based Vulnerability Management: An Empirical Study and a Path Forward

The Software Bill of Materials SBOM is a critical tool for securing the software supply chain SSC, but its practical utility is undermined by inaccuracies in both its generation and its application in vulnerability scanning. This paper presents a large-scale empirical study on 2,414 open-source...

LLM-CSEC: Empirical Evaluation of Security in C/C++ Code Generated by Large Language Models

The security of code generated by large language models LLMs is a significant concern, as studies indicate that such code often contains vulnerabilities and lacks essential defensive programming constructs. This work focuses on examining and evaluating the security of LLM-generated code,...

Lessons Lost: Incident Response in the Age of Cyber Insurance and Breach Attorneys

Incident Response IR allows victim firms to detect, contain, and recover from security incidents. It should also help the wider community avoid similar attacks in the future. In pursuit of these goals, technical practitioners are increasingly influenced by stakeholders like cyber insurers and...

[SECURITY] Fedora 41 Update: xmedcon-0.25.3-1.fc41

This project stands for Medical Image Conversion and is released under the GNU's LGPL license. It bundles the C source code, a library, a flexible command-line utility and a graphical front-end based on the amazing Gtk+ toolkit. Its main purpose is image conversion while preserving valuable medic...

[SECURITY] Fedora 42 Update: xmedcon-0.25.3-1.fc42

This project stands for Medical Image Conversion and is released under the GNU's LGPL license. It bundles the C source code, a library, a flexible command-line utility and a graphical front-end based on the amazing Gtk+ toolkit. Its main purpose is image conversion while preserving valuable medic...

Human-Centered Threat Modeling in Practice: Lessons, Challenges, and Paths Forward

Human-centered threat modeling HCTM is an emerging area within security and privacy research that focuses on how people define and navigate threats in various social, cultural, and technological contexts. While researchers increasingly approach threat modeling from a human-centered perspective,...

EUVD-2025-38394

Malicious code in study-lab-e51 npm...

Malicious code in study-lab-e51 (npm)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector 4b756a5dc0b496946d6fb90684625cebaac365a7199f1df239818b6bac9cbce5 The package study-lab-e51 was found to contain malicious code...

Malicious code in study-lab-e53 (npm)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector 62f58b0eed4e51cbdb6bb234b8c3a4e32bf0ad0a60bdeef7e32ffe7d26ac0d8c The package study-lab-e53 was found to contain malicious code...