628 matches found
Toxic_Flow_Analysis_Framework_For_Agentic_AI
Toxic Flow Analysis TFA Framework A Secure-by-Design framew...
PIDSMaker: Building and Evaluating Provenance-Based Intrusion Detection Systems
Recent provenance-based intrusion detection systems PIDSs have demonstrated strong potential for detecting advanced persistent threats APTs by applying machine learning to system provenance graphs. However, evaluating and comparing PIDSs remains difficult: prior work uses inconsistent preprocessi...
Toward Trustworthy Agentic AI: A Multimodal Framework for Preventing Prompt Injection Attacks
Powerful autonomous systems, which reason, plan, and converse using and between numerous tools and agents, are made possible by Large Language Models LLMs, Vision-Language Models VLMs, and new agentic AI systems, like LangChain and GraphChain. Nevertheless, this agentic environment increases the...
PROVEX: Enhancing SOC Analyst Trust with Explainable Provenance-Based IDS
Modern intrusion detection systems IDS leverage graph neural networks GNNs to detect malicious activity in system provenance data, but their decisions often remain a black box to analysts. This paper presents a comprehensive XAI framework designed to bridge the trust gap in Security Operations...
LeechHijack: Covert Computational Resource Exploitation in Intelligent Agent Systems
Large Language Model LLM-based agents have demonstrated remarkable capabilities in reasoning, planning, and tool usage. The recently proposed Model Context Protocol MCP has emerged as a unifying framework for integrating external tools into agent systems, enabling a thriving open ecosystem of...
Hesperus Is Phosphorus: Mapping Threat Actor Naming Taxonomies at Scale
This paper studies the problem of Threat Actor TA naming convention inconsistency across leading Cyber Threat Intelligence CTI vendors. The current decentralized and proprietary nomenclature creates confusion and significant obstacles for researchers, including difficulties in integrating and...
Securing the Model Context Protocol (MCP): Risks, Controls, and Governance
The Model Context Protocol MCP replaces static, developer-controlled API integrations with more dynamic, user-driven agent systems, which also introduces new security risks. As MCP adoption grows across community servers and major platforms, organizations encounter threats that existing AI...
GraphFaaS: Serverless GNN Inference for Burst-Resilient, Real-Time Intrusion Detection
Provenance-based intrusion detection is an increasingly popular application of graphical machine learning in cybersecurity, where system activities are modeled as provenance graphs to capture causality and correlations among potentially malicious actions. Graph Neural Networks GNNs have...
MAL-2025-147864 Malicious code in sequelize-deimos-cressida-hapi (npm)
--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector 3e70ccd0b2b0e6bba0ae898039b44edfaf5267da39d9d94a84e53158d0f6ba68 This package appears to be part of the tea.xyz token reward campaign that flooded npm. These packages typically contain autopublish scripts auto.js,...
SecTracer: A Framework for Uncovering the Root Causes of Network Intrusions Via Security Provenance
Modern enterprise networks comprise diverse and heterogeneous systems that support a wide range of services, making it challenging for administrators to track and analyze sophisticated attacks such as advanced persistent threats APTs, which often exploit multiple vectors. To address this challeng...
Malicious code in nana-kentang91-sukiwir (npm)
--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector 0470ac356ad11c025e0e19b3e79414ed8b487192ffbda0ac3defc45ab81d3ef0 This package appears to be part of the tea.xyz token reward campaign that flooded npm. These packages typically contain autopublish scripts auto.js,...
Faking Receipts with AI
Over the past few decades, it's become easier and easier to create fake receipts. Decades ago, it required special paper and printers--I remember a company in the UK advertising its services to people trying to cover up their affairs. Then, receipts became computerized, and faking them required...
Unvalidated Trust: Cross-Stage Vulnerabilities in Large Language Model Architectures
As Large Language Models LLMs are increasingly integrated into automated, multi-stage pipelines, risk patterns that arise from unvalidated trust between processing stages become a practical concern. This paper presents a mechanism-centered taxonomy of 41 recurring risk patterns in commercial LLMs...
ANCORA: Accurate Intrusion Recovery for Web Applications
Modern web application recovery presents a critical dilemma. Coarse-grained snapshot rollbacks cause unacceptable data loss for legitimate users. Surgically removing an attack's impact is hindered by a fundamental challenge in high-concurrency environments: it is difficult to attribute resulting...
EUVD-2006-3089
Malware in sbrugna...
EUVD-2008-5183
Malware in sbrugna...
EUVD-2006-0115
Malware in sbrugna...
EUVD-2006-5220
Malware in sbrugna...