3 accepted papers
Large Language Model (LLM) agents use memory to learn from past interactions. However, this reliance on memory introduces a critical security risk: an adversary can inject seemingly harmless records into an agent's memory to manipulate its future behavior. This vulnerability is characterized by two …
This position paper argues that building a reliable agentic web requires shifting from click-based browsing to typed actions supported by a standardized semantic layer. Today’s agents primarily operate over low-level primitives such as clicks, keystrokes, and DOM manipulation. This reliance leads to…
Computer-use agents (CUAs) promise to automate complex tasks across operating systems (OS) and the web, but remain vulnerable to indirect prompt injection, where attackers embed malicious content into the environment to hijack agent behavior. Current evaluations of this threat either lack support fo…