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University of New South Wales
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Mobile GUI agents are surprisingly susceptible to prompt injection via realistic, attacker-controlled text embedded within ordinary user-generated content, even without modifying the agent, application, or OS.
Key contribution not extracted.
VideoLLMs leak training data: a novel black-box attack recovers membership with surprisingly high accuracy (AUC=0.68) by probing generation brittleness across temperatures.
LLM API calls are breaking your program analysis tools, but this new taxonomy of information flow across the NL/PL boundary offers a way to fix them.
LLM-powered pentesting agents fail not because of model limitations, but because they can't estimate task difficulty, leading to wasted effort and premature context exhaustion.
