most citedTRiSM for Agentic AI: A Review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems

4 citations · 4 across the 4 of their papers we have counts for

collaborators

5 papers

cs.AI2026

DiagFlowBench: Evaluating How Language Models Handle Off-Procedure Inputs in Grounded Diagnostic Dialogue

Guillermo Gil de Avalle, Laura Maruster, Shaina Raza +1

Language models increasingly serve as advisory systems in maintenance operations. To prevent hallucination, recent systems ground these models in procedural documentation to constr…

cs.AI2026

SONIC-O1: A Real-World Benchmark for Evaluating Multimodal Large Language Models on Audio-Video Understanding

Ahmed Y. Radwan, Christos Emmanouilidis, Hina Tabassum +2

Multimodal Large Language Models (MLLMs) are a major focus of recent AI research. However, most prior work focuses on static image understanding, while their ability to process seq…

cs.CL2026

Just as Humans Need Vaccines, So Do Models: Model Immunization to Combat Falsehoods

Shaina Raza, Rizwan Qureshi, Azib Farooq +4

Large language models (LLMs) reproduce misinformation not by memorizing false facts alone, but by learning the linguistic patterns that make falsehoods persuasive, such as hedging,…

cs.CL2025

VLDBench Evaluating Multimodal Disinformation with Regulatory Alignment

Shaina Raza, Ashmal Vayani, Aditya Jain +8

Detecting disinformation that blends manipulated text and images has become increasingly challenging, as AI tools make synthetic content easy to generate and disseminate. While mos…

cs.AI20254 cited

TRiSM for Agentic AI: A Review of Trust, Risk, and Security Management in LLM-based Agentic Multi-Agent Systems

Shaina Raza, Ranjan Sapkota, Manoj Karkee +1

Agentic AI systems, built upon large language models (LLMs) and deployed in multi-agent configurations, are redefining intelligence, autonomy, collaboration, and decision-making ac…