This work proposes the Policy-Evidence-Execution Separation Pattern, implemented by the Trustworthy AI Posture (TAIP) Assurance Engine and operated as Continuous Control Posture Assurance (CCPA) and is evaluated across increasing numbers of independent Decision Gateway contexts.
Guy Lupo, Nguyen Hung Nguyen, V. Vo et al.· 0 citations
Evidence concentrates on prompt/reasoning, memory, and tool-mediated attacks, predominantly in single-agent settings, within the selected studies, to guide more systematic, architecture-aware, and reproducible security evaluation of agentic AI.
Heewon Baek, Alsharif Abuadbba, Kristen Moore et al.· 0 citations
This survey provides a unified analysis of how LLMs amplify web vulnerabilities across client-side, server-side, and pipeline layers while evaluating defenses and their limitations, and outlines future directions for secure AI-enabled web systems.
Nivedita Singh, Alsharif Abuadbba, Yan-Song Gao et al.· 1 citation
SHAQ (shadow query generation), a semantic-decomposition and embedding-decoupling defense against EIAs, is proposed, which uses a generative language model to create diverse shadow queries that capture different semantic aspects of each document.
Xinguo Feng, Zhongkui Ma, Zi-Han Wang et al.· 0 citations
This work presents GraftyVul, a system that constructs vulnerable programs by grafting real-world vulnerabilities into open-source projects, and introduces a language- and context-agnostic semantic embedding that compares vulnerabilities by sink, mechanism and host-feature rather than surface code.
Omri Ram, Mitchell Horner, R. van der Meyden et al.· 0 citations
This work forms sector-targeted CTI dissemination as a multilabel classification problem, leveraging deep field knowledge of CTI structures and sector-specific threat patterns, and applies BERT, a transformer-based model, to automate the mapping of CTI events to sectors.
Fajar Wijitrisnanto, Alsharif Abuadbba, Yan-Song Gao et al.· 0 citations
It is found that developers rarely raised security concerns when engaging with Copilot, and many did not recognize that their final implementations remained insecure, so future research directions to support safer AI-assisted software development are proposed.
Zahra Mousavi, Chadni Islam, M. A. Babar et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.