Low Earth orbit (LEO) satellite constellations, such as Starlink and Kuiper, are rapidly emerging as a critical backbone for low-latency global connectivity. As these systems expand, they become more attractive attack targets, necessitating increased resilience and security. Threat actors seek to exploit constellation-...
Roee Idan, Rami Puzis, A. Shabtai et al.· 1 citation
These findings provide an empirical characterization of recurring security patterns across the open-source onboard satellite software ecosystem, helping quantify their prevalence and prioritize areas that warrant the greatest security attention.
Roee Idan, T. Galor, A. Shabtai et al.· 0 citations
The resulting modifications of application code were shown to eliminate leaks when targeted by basic jailbreak and instruction-override attacks, achieving a 100% reduction in leakage, and reduce leaks by 91% under conditions of stress-induced manipulation, without the need of continuous runtime policy enforcement.
Akansha Shukla, Emily Bellov, P. Gandhi et al.· arXiv.org· 0 citations
This work proposes a training-free method that operates directly on the LLM internal representations, leveraging prior evidence that decision-relevant information is encoded within them, and derives policy-violation scores directly from normalized representations of LLM hidden activations.
Oren Rachmil, Roy Betser, Itay Gershon et al.· arXiv.org· 5 citations· ⚡1
The first Fraud Investigation Assistant (FIA) framework is introduced, which employs multimodal large language models (LLMs) to automate key steps of credit card fraud investigation and generate explanatory reports and suggests that LLM-based agents can assist with automating substantial parts of the fraud investigatio...
Shaun Shuster, Eyal Zaloof, A. Shabtai et al.· arXiv.org· 5 citations· ⚡1
A systematic security analysis of AP2 v0.2 based on its roles, transaction lifecycle, deployment architectures, and trust boundaries shows that valid mandate signatures alone do not ensure that an agent-mediated transaction reflects the user's intent when its pre-authorization context is manipulated.
A. Aviv, Parth A. Gandh, Ron Bitton et al.· 0 citations
A failure-aware adversarial retrieval-augmented framework for improving robustness in natural language understanding that combines retrieval, automated validation, contextual-bandit failure selection, and controlled adversarial retraining, enables scalable robustness improvement without additional human annotation.
