VQV Signal
Large Language Models (LLMs) and their agents are increasingly used for daily tasks, raising security concerns beyond prompt injections, including covert data exfiltration through legitimate web fetching. Existing research highlights these risks and explores potential solutions to protect sensitive...
Large Language Models (LLMs) and their agents are increasingly used for daily tasks, raising security concerns beyond prompt injections, including covert data exfiltration through legitimate web fetching. Existing research highlights these risks and explores potential solutions to protect sensitive...
AI-assisted summary based on the listed source.
With the increasing capabilities of Large-Language-Models (LLMs) and LLM-based agents, users are increasingly using them to solve everyday problems, such as answering e-mails or providing programming support. Existing work has extensively investigated security and privacy risks, such as prompt injections and the...
As LLMs become integral to workflows, understanding and mitigating novel security vulnerabilities like covert exfiltration is critical to safeguarding user data. Addressing these risks helps maintain trust and secure deployment of AI assistants.
Security-conscious readers may want to review the source and watch for practical exposure or mitigation details.
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Signal Strength reflects source quality, relevance, freshness and evidence. Public Interest helps organize discovery; it is not proof of truth.
VQV surfaced this signal because it is recent, relevant to AI Security, connected to arXiv.
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