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arxiv: 2605.24245 · v1 · pith:ULRKMPW6new · submitted 2026-05-22 · 💻 cs.CR

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

Tingwei Zhang , Harold Triedman , Vitaly Shmatikov This is my paper

Pith reviewed 2026-06-30 15:32 UTC · model grok-4.3

classification 💻 cs.CR
keywords deep-research agentsuser-generated contentcontent poisoningweb retrievalagent securitymulti-agent pipelinesretrieval overlapSTORM
0
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The pith

An adversary can poison one frequently retrieved user-generated content page to make deep-research agents cite attacker-chosen material across many related queries.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Deep-research agents rely on multi-agent pipelines that issue many related queries and retrieve web content to build structured reports. For common topics these agents repeatedly fetch the same pages from Reddit, Wikipedia and similar platforms, creating retrieval overlap. The paper shows that an adversary who appends a short crafted text to just one such page can cause the agent to cite the attacker-chosen content and promote chosen entities in reports on many queries. Evaluation on STORM, Co-STORM and OmniThink confirms the attack works, while source filtering and output detection are examined as possible defenses. The finding matters because these agents are replacing ordinary search for both routine and complex information needs.

Core claim

Deep-research agents repeatedly retrieve the same user-generated content pages from platforms such as Reddit and Wikipedia during a single research session. This retrieval overlap creates a concentrated attack surface: an adversary who appends a short, crafted text to a single frequently retrieved page can cause the agent to cite attacker-chosen content and promote attacker-chosen entities across many related queries. The attack is demonstrated on three representative systems, and defenses at different pipeline stages are studied.

What carries the argument

Retrieval overlap of the same user-generated content pages across the multiple related queries issued by a deep-research agent in one session.

If this is right

  • The attack causes agents to cite attacker-chosen content in structured reports.
  • Attacker-chosen entities are promoted across many related queries.
  • The attack succeeds on STORM, Co-STORM, and OmniThink.
  • Source-level filtering and output-based detection can be applied at different stages of the pipeline.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Other agent systems that perform iterative web retrieval for synthesis may share the same exposure to single-page poisoning.
  • Diversifying retrieval sources or requiring cross-page verification could reduce the effectiveness of the attack.
  • The vulnerability may affect non-research tasks that still rely on repeated web queries and user-generated content.

Load-bearing premise

That deep-research agents repeatedly retrieve the same user-generated content pages for many common search topics.

What would settle it

A measurement showing that deep-research agents retrieve largely distinct user-generated content pages even for related queries within one session, or an experiment in which crafted text added to one such page is never cited by the agent.

Figures

Figures reproduced from arXiv: 2605.24245 by Harold Triedman, Tingwei Zhang, Vitaly Shmatikov.

Figure 1
Figure 1. Figure 1: A schematic diagram of our attack framework. In step 1, a user makes a query to a deep-research agent. In step 2, [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Example of the SERP-snippet attack on Co-STORM (query: “best cryptocurrencies to invest in for long-term growth”). [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: An illustration of all of the steps of the query generation process, including ideation, seed query template generation, [PITH_FULL_IMAGE:figures/full_fig_p015_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Example of the WARP SERP-snippet attack on Co-STORM (query: “best dating apps for divorced men over 50”). Top: 15-word poisoned text appended to the SERP snippet of a real Reddit URL, promoting the fictitious entity SilverPath. Bottom left: clean report recommending only real apps. Bottom right: attacked report, where the agent promotes the fictitious SilverPath as the “top choice,” citing the poisoned Red… view at source ↗
Figure 5
Figure 5. Figure 5: Example of the SERP-snippet attack on Co-STORM (query: “best Mexican food restaurants near Austin”). Top: 12-word [PITH_FULL_IMAGE:figures/full_fig_p018_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Example of the SERP-snippet attack on STORM (query: “how to cancel Xfinity internet”). Top: 11-word poisoned text [PITH_FULL_IMAGE:figures/full_fig_p018_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Example of the SERP-snippet attack on STORM (query: “alternative to AAA roadside assistance Minneapolis”). Top: [PITH_FULL_IMAGE:figures/full_fig_p019_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Example of the SERP-snippet attack on OmniThink (query: “how to cancel Xfinity internet”). Top: 13-word poisoned [PITH_FULL_IMAGE:figures/full_fig_p019_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Example of the full-content attack on STORM (query: “best dating apps for divorced men over 50”). Top: [PITH_FULL_IMAGE:figures/full_fig_p020_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Example of the full-content attack on OmniThink (query: “best Mexican food restaurants near Austin”). Top: [PITH_FULL_IMAGE:figures/full_fig_p020_10.png] view at source ↗
read the original abstract

Deep-research agents, i.e., systems that rely on multi-agent pipelines to iteratively retrieve, synthesize, and cite Web content in order to produce structured reports, are rapidly replacing traditional search for both routine and complex information needs. These agents issue many related queries during a single research session. We show that for many common search topics, they repeatedly retrieve the same user-generated content (UGC) pages from platforms such as Reddit and Wikipedia. Next, we argue that this retrieval overlap creates a concentrated attack surface: an adversary who appends a short, crafted text to a single, frequently retrieved UGC page can cause the agent to cite attacker-chosen content and promote attacker-chosen entities across many related queries. We evaluate this attack on three representative deep-research systems (STORM, Co-STORM, and OmniThink) across multiple query clusters. We also study defenses at different stages of the pipeline, including source-level filtering and output-based detection. Our findings highlight a fundamental vulnerability in how deep-research agents retrieve and integrate web content.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The paper claims that deep-research agents repeatedly retrieve the same user-generated content (UGC) pages from platforms like Reddit and Wikipedia during multi-query research sessions, creating a concentrated attack surface; an adversary can append short crafted text to one such page to cause the agent to cite attacker-chosen content and promote attacker-chosen entities across many related queries. The authors state they evaluate the attack on STORM, Co-STORM, and OmniThink across multiple query clusters and study defenses including source filtering and output detection.

Significance. If the retrieval-overlap condition holds at scale and the poisoning propagates reliably through synthesis, the result would identify a practical vulnerability in retrieval-augmented agent pipelines that could undermine trust in generated reports on common topics; the work would usefully direct attention to UGC as a high-leverage poisoning vector and motivate pipeline-level defenses.

major comments (1)
  1. [Abstract] Abstract: the manuscript asserts that an evaluation was performed across three systems and multiple query clusters plus defense studies, yet supplies no success rates, overlap statistics, attack implementation details, measurement methodology, controls, or quantitative results; without these the central claim that the attack is effective remains an unevaluated assertion.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for identifying this issue with the abstract. We address the comment below and will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the manuscript asserts that an evaluation was performed across three systems and multiple query clusters plus defense studies, yet supplies no success rates, overlap statistics, attack implementation details, measurement methodology, controls, or quantitative results; without these the central claim that the attack is effective remains an unevaluated assertion.

    Authors: The referee is correct that the abstract is high-level and does not include quantitative results or methodological details. The full manuscript provides these in Sections 4 (Attack Evaluation), 5 (Defense Studies), and the appendix, including retrieval overlap statistics across query clusters, attack success rates on STORM/Co-STORM/OmniThink, implementation details for the poisoning text, measurement methodology, and controls. To address the concern directly, we will revise the abstract to incorporate key quantitative highlights (e.g., average overlap rates and success rates) while keeping it concise. revision: yes

Circularity Check

0 steps flagged

Empirical attack demonstration with no derivation chain or fitted inputs

full rationale

The paper presents an empirical security argument and attack evaluation on deep-research agents (STORM, Co-STORM, OmniThink). It relies on the observation of retrieval overlap on UGC pages and describes an attack that appends crafted text, followed by defense studies. No equations, parameters, or derivations appear in the abstract or described structure. The central claim is not reduced to its inputs by construction, self-citation, or renaming; it is an experimental demonstration whose validity rests on unreported quantitative results rather than circular logic. This is the expected outcome for a non-theoretical attack paper.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain observation that retrieval overlap occurs for common topics; no free parameters or invented entities are introduced.

axioms (1)
  • domain assumption Deep-research agents issue many related queries and repeatedly retrieve the same UGC pages from platforms such as Reddit and Wikipedia for common topics.
    This premise is stated directly in the abstract as the basis for the concentrated attack surface.

pith-pipeline@v0.9.1-grok · 5709 in / 1272 out tokens · 33438 ms · 2026-06-30T15:32:41.052699+00:00 · methodology

discussion (0)

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    Attacked report(1-URL SERP injection) Austin is known for its vibrant Tex-Mex scene, featuring several top- rated Mexican restaurants

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    (poisoned)

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    best dating apps for divorced men over 50

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