Deep-Research Agents Can Be Poisoned via User-Generated Content

Deep-research agents are an alternative to conventional Web search. They use multi-agent pipelines to issue multiple Web searches related to user queries, retrieve relevant content from the answers, and generate detailed, evidence-based reports. We show that for many common search topics (including financial, medical, and product recommendations), agent-generated reports are consistently based on the same user-generated content (UGC) pages from platforms such as Reddit and Wikipedia. This retrieval overlap, combined with lax moderation and access controls for UGC, is an opportunity for subversion: an attacker who appends a short text to a single, frequently-retrieved page can cause agents to cite this text and promote attacker-chosen entities across many user questions. We evaluate this attack on representative deep-research systems across multiple topics, such as investment advice, antivirus software, restaurant recommendations, etc. Poisoning a single URL with as few as 13 words can be sufficient for the attacker's content to be retrieved in 57-76% of the agent executions for a given topic and cited in 38-51% of the generated reports. A more aggressive attack (poisoning an entire subreddit) achieves 30-53% citation rates even when the poison is only 0.5-4% of the retrieved content. The attacker does not need to know the phrasing of the user's question, nor the specific Web queries generated by the agent, nor the agent's internal retrieval and generation mechanisms. We then study defenses at different stages of the pipeline, including source-level filtering and output-based detection. Dropping UGC from retrieved content blocks the attack but degrades the quality of generated reports. We show that lightweight anomaly detection on inputs and outputs does not reliably identify poisoned content, nor the results of poisoning.