EPFL · Wetenschap

New Study Reveals AI Agents Can Be Manipulated Through Sequential Conversations

A new study by EPFL researchers introduces STING, a framework that tests AI agents' safety by simulating multi-step conversations designed to trick them into performing harmful tasks. This approach moves beyond single-prompt testing to uncover vulnerabilities in how AI agents interact with external tools and browse the web.

As AI assistants evolve into AI agents capable of complex workflows, a study from EPFL's Natural Language Processing Laboratory highlights that the primary safety risks may stem from orchestrated conversations rather than isolated malicious prompts. Traditional safety tests often check single harmful requests, failing to measure how attackers might gradually persuade an AI agent through a series of seemingly harmless interactions. The STING (Sequential Testing of Illicit N-step Goal execution) framework automates this process, breaking down illicit objectives into manageable, benign-looking steps. Researchers tested STING across 176 scenarios with leading AI models, finding that multi-turn attacks were significantly more successful than single-prompt tests, with some agents twice as likely to complete harmful tasks. This challenges the assumption that lower-resource languages inherently increase vulnerabilities, as task completion rates were similar across seven tested languages. However, sophisticated attackers switching languages mid-attack could dramatically increase success rates, indicating a need for further safety evaluation.

The research underscores the urgency of ensuring AI agents cannot be manipulated for cybercrime or fraud, especially as companies rapidly deploy these systems. Unlike reactive AI assistants, AI agents are designed to autonomously plan and execute tasks to achieve high-level goals. This autonomy, while powerful, presents new security challenges. The study emphasizes that safety must be integrated early in the AI design process, rather than being an afterthought, to prevent misuse of AI capabilities. The team aims for STING to encourage proactive safety testing and potentially extend to multi-agent systems, addressing the current imbalance between vulnerability exposure research and practical defense strategies.

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