FORGE: Research-Trajectory Hijacking Attacks on Deep Research Agents
On the map: LLM agents and tool use → Agent threats, guardrails and governance
Abstract (arXiv)
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 planning. We further introduce the PRISM metric, which weights infected report claims by cognitive type, and Root Query Anchoring, a lightweight defense that ties recursive follow-up generation to the root query. Across 25 queries, Network FORGE reaches 26.4% PRISM with five injected documents and exhibits depth migration, in which recursive synthesis shifts poisoned content from overt framing into factual premises. On the 10-query defense subset, RQA (Root Query Anchoring) reduces PRISM from 38.5% to 18.3%.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Placed in Agent threats, guardrails and governance, one of 16 directions in LLM agents and tool use. Builds on 24 of its 41 references that are in the map. First posted on arXiv 6 Jul 2026.
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24 of 41 references.
Closest ancestors · 4
Same direction, newest first
- 2024Breaking ReAct Agents: Foot-in-the-Door Attack Will Get You In22 Oct
- 2024The Emerged Security and Privacy of LLM Agent: A Survey with Case Studies28 Jul
- 2024BadAgent: Inserting and Activating Backdoor Attacks in LLM Agents5 Jun
- 2024Watch Out for Your Agents! Investigating Backdoor Threats to LLM-Based Agents17 Feb
Other references · 20
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
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- 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIscited by 1,880
- 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injectioncited by 1,339
- 2023Mind2Web: Towards a Generalist Agent for the Webcited by 1,210
- 2017BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chaincited by 1,052
- 2023Baseline Defenses for Adversarial Attacks Against Aligned Language Modelscited by 608
- 2023Detecting Language Model Attacks with Perplexitycited by 411
- 2020Weight Poisoning Attacks on Pre-trained Modelscited by 318
- 2023Auto-GPT for Online Decision Making: Benchmarks and Additional Opinionscited by 250
8 more
- 2025DeepResearch Bench: A Comprehensive Benchmark for Deep Research Agentscited by 236
- 2021Hidden Killer: Invisible Textual Backdoor Attacks with Syntactic Triggercited by 203
- 2020BadNL: Backdoor Attacks against NLP Models with Semantic-preserving Improvementscited by 191
- 2023Poisoning Retrieval Corpora by Injecting Adversarial Passagescited by 153
- 2023Backdooring Instruction-Tuned Large Language Models with Virtual Prompt Injectioncited by 143
- 2020Backdoor Attacks and Countermeasures on Deep Learning: A Comprehensive Reviewcited by 120
- 2025One Shot Dominance: Knowledge Poisoning Attack on Retrieval-Augmented Generation Systemscited by 28
- 2024Unique Security and Privacy Threats of Large Language Models: A Comprehensive Surveycited by 20
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