arXiv:2607.04718v1 Announce Type: new Abstract: Deep research agents decompose open-ended queries into subtasks, retrieve web evidence over multiple rounds, and synthesize long-form reports. This workflow creates a planning-layer poisoning surface: adversarial documents that enter the retrieval pool can steer follow-up questions and turn a local injection into report-level contamination. We present FORGE (Fabricated Orchestrated Reasoning chain for aGent Exploitation), a two-level attack that combines intra-document reasoning fabrication with inter-document chain coordination to hijack subtask
The proliferation of advanced deep research agents creates new attack surfaces, making this theoretical exploitation mechanism highly relevant as these systems approach deployment.
This research details a critical vulnerability where adversarial documents can manipulate AI agents, undermining the reliability of AI-generated reports and decision-making processes.
The focus for AI security expands beyond model-level adversarial attacks to include 'planning-layer poisoning' within multi-step agency workflows, demanding new defensive strategies.
- · AI security researchers
- · Cybersecurity firms
- · AI ethics and safety organizations
- · Organizations relying on AI agents for critical research
- · Developers of general-purpose AI agents
- · Information integrity and trust
AI agents become susceptible to targeted manipulation, potentially leading to biased or fabricated reports.
Increased scrutiny and regulation on AI agent development and deployment, particularly for sensitive applications.
The weaponization of AI agent hijacking could become a tool in information warfare or corporate espionage, leading to widespread disinformation campaigns.
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Read at arXiv cs.AI