First reported arxiv.org
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First reported · updated · 4 reports arxiv.org
An Evaluation of Data Leakage Risks in Tool-Using LLM Agents in Realistic Scenarios
Research covered here, including a joint Singapore/Korea AI Safety Institute evaluation (arXiv:2606.17114) of tool-using LLM agents across 12 realistic non-adversarial tasks, found that none of three tested agents achieved fully correct and safe execution, with successful task completion often coinciding with data-handling failures like accessing unnecessary information or disclosing data to inappropriate recipients. A companion empirical study (arXiv:2604.03070) analyzed 17,022 skills from the SkillsMP marketplace and identified 520 skills with 1,708 credential-leakage issues, finding 89.6% of leaked credentials immediately exploitable and debug logging fed into LLM context windows to be the dominant exposure vector. Details →How the wire is made
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