915 matches found
Malicious code in semantic-cyan-damselfly (npm)
--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector a1b553644de427e34fa23d335fb2c6bb983e180edadd9f2f2fa6da1db33f9eb5 This package appears to be part of the tea.xyz token reward campaign that flooded npm. These packages typically contain autopublish scripts auto.js,...
EUVD-2025-57368
Malicious code in semanticllamaz3n npm...
Malicious code in semantic_llama_z3n (npm)
--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector 0a2cbff433de2e0ed4696797847e2db454e9a146b480de5655c91134415eaab6 This package appears to be part of the tea.xyz token reward campaign that flooded npm. These packages typically contain autopublish scripts auto.js,...
MAL-2025-65271 Malicious code in semantic_llama_z3n (npm)
--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector 0a2cbff433de2e0ed4696797847e2db454e9a146b480de5655c91134415eaab6 This package appears to be part of the tea.xyz token reward campaign that flooded npm. These packages typically contain autopublish scripts auto.js,...
EUVD-2025-45340
Malicious code in semanticflyz3n npm...
Quantum Semantic Communication beyond the Shannon-Wyner Channel Capacity
Quantum Secure Direct Communication QSDC, a paradigm-shifting breakthrough in quantum communication, exploits quantum states for unmediated information transmission. Rooted in the inviolable fundamental laws of quantum mechanics, QSDC enables ultrasensitive detection of even the faintest...
KG-DF: A Black-Box Defense Framework against Jailbreak Attacks Based on Knowledge Graphs
With the widespread application of large language models LLMs in various fields, the security challenges they face have become increasingly prominent, especially the issue of jailbreak. These attacks induce the model to generate erroneous or uncontrolled outputs through crafted inputs, threatenin...
Hybrid Fuzzing with LLM-Guided Input Mutation and Semantic Feedback
Software fuzzing has become a cornerstone in automated vulnerability discovery, yet existing mutation strategies often lack semantic awareness, leading to redundant test cases and slow exploration of deep program states. In this work, I present a hybrid fuzzing framework that integrates static an...
A DRL-Empowered Multi-Level Jamming Approach for Secure Semantic Communication
Semantic communication SemCom aims to transmit only task-relevant information, thereby improving communication efficiency but also exposing semantic information to potential eavesdropping. In this paper, we propose a deep reinforcement learning DRL-empowered multi-level jamming approach to enhanc...
Structuring Security: A Survey of Cybersecurity Ontologies, Semantic Log Processing, and LLMs Application
This survey investigates how ontologies, semantic log processing, and Large Language Models LLMs enhance cybersecurity. Ontologies structure domain knowledge, enabling interoperability, data integration, and advanced threat analysis. Security logs, though critical, are often unstructured and...
MalCVE: Malware Detection and CVE Association Using Large Language Models
Malicious software attacks are having an increasingly significant economic impact. Commercial malware detection software can be costly, and tools that attribute malware to the specific software vulnerabilities it exploits are largely lacking. Understanding the connection between malware and the...
Countermind: A Multi-Layered Security Architecture for Large Language Models
The security of Large Language Model LLM applications is fundamentally challenged by "form-first" attacks like prompt injection and jailbreaking, where malicious instructions are embedded within user inputs. Conventional defenses, which rely on post hoc output filtering, are often brittle and fai...
EUVD-2018-0119
Malware in sbrugna...
EUVD-2012-1238
Malware in sbrugna...
EUVD-2021-1940
Malware in sbrugna...
EUVD-2020-1469
Malware in sbrugna...
Applying Graph Analysis for Unsupervised Fast Malware Fingerprinting
Malware proliferation is increasing at a tremendous rate, with hundreds of thousands of new samples identified daily. Manual investigation of such a vast amount of malware is an unrealistic, time-consuming, and overwhelming task. To cope with this volume, there is a clear need to develop...
NatGVD: Natural Adversarial Example Attack Towards Graph-Based Vulnerability Detection
Graph-based models learn rich code graph structural information and present superior performance on various code analysis tasks. However, the robustness of these models against adversarial example attacks in the context of vulnerability detection remains an open question. This paper proposes...