2 citations · 4 across the 10 of their papers we have counts for
11 papers
RIFT-Bench: Dynamic Red-teaming For Agentic AI Systems
Yarin Yerushalmi Levi, Roy Betser, Amit Giloni +5
Agentic AI systems powered by large language models (LLMs) are rapidly evolving into autonomous decision-making systems, exposing attack vectors beyond those of traditional LLM vul…
Adversarial Intent is a Latent Variable: Stateful Trust Inference for Securing Multimodal Agentic RAG
Inderjeet Singh, Vikas Pahuja, Aishvariya Priya Rathina Sabapathy +8
Current stateless defences for multimodal agentic RAG fail to detect adversarial strategies that distribute malicious semantics across retrieval, planning, and generation component…
AgenTRIM: Tool Risk Mitigation for Agentic AI
Roy Betser, Amit Giloni, Shamik Bose +4
AI agents are autonomous systems that combine LLMs with external tools to solve complex tasks. While such tools extend capability, improper tool permissions introduce security risk…
Counterfactual-based Agent Influence Ranker for Agentic AI Workflows
Amit Giloni, Chiara Picardi, Roy Betser +3
An Agentic AI Workflow (AAW), also known as an LLM-based multi-agent system, is an autonomous system that assembles several LLM-based agents to work collaboratively towards a share…
LumiMAS: A Comprehensive Framework for Real-Time Monitoring and Enhanced Observability in Multi-Agent Systems
Ron Solomon, Yarin Yerushalmi Levi, Lior Vaknin +8
The incorporation of LLMs in multi-agent systems (MASs) has the potential to significantly improve our ability to autonomously solve complex problems. However, such systems introdu…
Identifying Memorization of Diffusion Models through -Laplace Analysis: Estimators, Bounds and Applications
Jonathan Brokman, Itay Gershon, Amit Giloni +4
Diffusion models, today's leading image generative models, estimate the score function, i.e. the gradient of the log probability of (perturbed) data samples, without direct access…
Manifold Induced Biases for Zero-shot and Few-shot Detection of Generated Images
Jonathan Brokman, Amit Giloni, Omer Hofman +3
Distinguishing between real and AI-generated images, commonly referred to as 'image detection', presents a timely and significant challenge. Despite extensive research in the (semi…
Addressing Key Challenges of Adversarial Attacks and Defenses in the Tabular Domain: A Methodological Framework for Coherence and Consistency
Yael Itzhakev, Amit Giloni, Yuval Elovici +1
Machine learning models trained on tabular data are vulnerable to adversarial attacks, even in realistic scenarios where attackers only have access to the model's outputs. Since ta…
Insights and Current Gaps in Open-Source LLM Vulnerability Scanners: A Comparative Analysis
Jonathan Brokman, Omer Hofman, Oren Rachmil +6
This report presents a comparative analysis of open-source vulnerability scanners for conversational large language models (LLMs). As LLMs become integral to various applications,…
X-Detect: Explainable Adversarial Patch Detection for Object Detectors in Retail
Omer Hofman, Amit Giloni, Yarin Hayun +4
Object detection models, which are widely used in various domains (such as retail), have been shown to be vulnerable to adversarial attacks. Existing methods for detecting adversar…
BENN: Bias Estimation Using Deep Neural Network
Amit Giloni, Edita Grolman, Tanja Hagemann +4
The need to detect bias in machine learning (ML) models has led to the development of multiple bias detection methods, yet utilizing them is challenging since each method: i) explo…