ATTCK-R: Knowledge-Aware Long-Horizon Jailbreaking for Protected LLMs
As large language models are deployed behind increasingly sophisticated safety mechanisms, evaluating their robustness u
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Multi-agent LLM systems (MALS) introduce a privacy-leakage problem that conventional sensitivity-based defenses miss, si
Large Language Models (LLMs) have been shown to be computationally expensive and vulnerable to jailbreak attacks, in whi
Professional agents increasingly work through command-line interfaces and multiple tools over shared workspaces containi
LLM agents increasingly operate over sensitive resources, such as private documents and mutable system states, which can
Backdoor Attacks on Large Language Models (LLMs) typically follow a trigger-response pattern, where satisfying a trigger
An answer judge instructed to grade the final commitment should reject an explicitly wrong final value even when an earl
An LLM safety patch can pass a benchmark while still being a poor repair. This risk is especially acute for jailbreak re
LLM routers pick a cheap or expensive model per request by its content, and many gateways and some cloud platforms can l
Security Operations Centers (SOCs) for information technology and operational technology share one incident-response pro
Watermarking enables language model providers to identify text generated by their models. However, its desired propertie
Modern web agents built on large vision-language models process webpages, select relevant UI elements, and translate mod
Standard safety evaluations of large language models assess harmful requests written in canonical plain text, while mode
As small language models (SLMs) are increasingly deployed on resource-constrained and on-device platforms, including as
LLM-based multi-agent systems (MAS) engage tools, share memory, and delegate tasks, often encountering adversarial conte
LLMs have advanced rapidly, raising growing concerns about their safety. Recent work has proposed approaches to detect a
Automatic sign language translation (SLT) has entered consumer products, turning American Sign Language into English tex
Enterprise AI agents that share a memory store face two unaddressed risks: sensitive data can leak through legitimately
While open-weight models have enjoyed steady progress in capabilities and wide adoption across multiple domains, their s
Tool-using language-model agents are vulnerable to indirect prompt injection because they must act on untrusted external