# Adversarial Feeds Steer LLM Agent Decisions Against Their Defaults

arXiv: https://arxiv.org/abs/2606.00914 · HTML: https://mlsanity.com/paper/2606.00914/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Social simulation and multi-agent debate, one of 16 directions in LLM agents and tool use.
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 30 May 2026.

## Placed in

- LLM agents and tool use / [Social simulation and multi-agent debate](https://mlsanity.com/agents/social-simulation-and-multi-agent-debate/)

## Builds on (most cited first, 11 of 11)

- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents](https://mlsanity.com/paper/2403.02691/) (2024)
- [AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases](https://mlsanity.com/paper/2407.12784/) (2024)
- [Formalizing and Benchmarking Prompt Injection Attacks and Defenses](https://mlsanity.com/paper/2310.12815/) (2023)
- [PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models](https://mlsanity.com/paper/2402.07867/) (2024)
- [Adaptive Attacks Break Defenses Against Indirect Prompt Injection Attacks on LLM Agents](https://mlsanity.com/paper/2503.00061/) (2025)
- [Control Illusion: The Failure of Instruction Hierarchies in Large Language Models](https://mlsanity.com/paper/2502.15851/) (2025)
- [Ranking Manipulation for Conversational Search Engines](https://mlsanity.com/paper/2406.03589/) (2024)
- [Counterfactual Trace Auditing of LLM Agent Skills](https://mlsanity.com/paper/2605.11946/) (2026)
- [Evaluating the Effect of Retrieval Augmentation on Social Biases](https://mlsanity.com/paper/2502.17611/) (2025)
- [Rational Synthesizers or Heuristic Followers? Analyzing LLMs in RAG-based Question-Answering](https://mlsanity.com/paper/2601.06189/) (2026)

## Abstract (arXiv)

LLM agents increasingly decide from evidence assembled by upstream systems: retrievers choose documents, recommenders choose posts, and memory systems choose prior events. Existing evaluations usually hold this evidence fixed, missing failures in which individually ordinary items form a systematically one-sided context. We introduce a counterfactual evidence audit: expose an agent to two mirrored sets of five documents, measure the difference in six downstream decisions, and use that contrast to predict its response to disjoint 45-document contexts. The protocol was frozen before testing three held-out open-weight model families. Across 18 held-out model-task cells, five-document effects predict full-context effects with Spearman rho=.855 (p<.001), reduce mean absolute prediction error by 62% relative to a zero-effect predictor, and recover the direction of 12 of 13 material effects. A reviewer-requested post-hoc task-mean baseline is also substantially weaker (MAE .369 versus .167). Matched controls show that selecting one-sided ordinary items, rather than merely reordering identical items, causes the shift in a susceptible model. Across seven open-weight families, susceptibility transfers from an interactive feed to a static RAG dossier (rho=.750, exact p=.033), while a provenance warning does not reliably mitigate it. A separate study of three deployed Codex agent tiers finds strong audit-to-full ranking (rho=.951, p<.001) but no individually significant full-context effect after correction. Within this single synthetic remote-work domain, the result supports a domain-specific triage procedure, not a universal steering claim: evidence selection must be evaluated as part of the composed agent system.