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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…
The Universal Normal Embedding
Chen Tasker, Roy Betser, Eyal Gofer +2
Generative models and vision encoders have largely advanced on separate tracks, optimized for different goals and grounded in different mathematical principles. Yet, they share a f…
Training-free Detection of Generated Videos via Spatial-Temporal Likelihoods
Omer Ben Hayun, Roy Betser, Meir Yossef Levi +2
Following major advances in text and image generation, the video domain has surged, producing highly realistic and controllable sequences. Along with this progress, these models al…
Make it SING: Analyzing Semantic Invariants in Classifiers
Harel Yadid, Meir Yossef Levi, Roy Betser +1
All classifiers, including state-of-the-art vision models, possess invariants, partially rooted in the geometry of their linear mappings. These invariants, which reside in the null…
InfoNCE Induces Gaussian Distribution
Roy Betser, Eyal Gofer, Meir Yossef Levi +1
Contrastive learning has become a cornerstone of modern representation learning, allowing training with massive unlabeled data for both task-specific and general (foundation) model…
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…