Who Is Your Agent Serving? Provider-Side Indirect Prompt Injection in Proactive Agents
On the map: LLM safety and security → Prompt injection and agent security
Abstract (arXiv)
Proactive personal agents increasingly decide what to recommend, how to personalize advice, and what follow-up assistance to offer, creating a new user-decision attack surface for provider-side indirect prompt injection. We show that an external provider need not access private user context, compromise the agent, or gain additional permissions: by controlling only content associated with its own target, it can redirect an otherwise benign agent to advance that target, recruit legitimately available user context to justify it, and proactively reduce the friction of adoption. We characterize this failure mode through Target Control, Private Binding, and Prospective Support, which respectively steer what the agent advances, how it connects the target to the user, and what target-specific assistance it offers next. Across three proactive-agent environments and six simulated user models, the full attack increases target authorization in all tested environment-user-model combinations, with a macro gain of up to 77.4 percentage points. Controlled replay shows that correct user-target binding is more consequential than additional proposal detail alone, while a multi-turn extension reveals that provider objectives can remain influential even without final authorization by reshaping how the agent responds to user constraints and resistance. These findings expose a broader trust boundary: capabilities designed to serve the user can be redirected toward objectives originating outside the user-agent relationship.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Prompt injection and agent security, one of 12 directions in LLM safety and security. Builds on 24 of its 30 references that are in the map. First posted on arXiv 4 Oct 2026.
Builds on
24 of 30 references.
Closest ancestors · 6
Same direction, newest first
- 2025Prompt Injection Attack to Tool Selection in LLM Agents28 Apr
- 2025WASP: Benchmarking Web Agent Security Against Prompt Injection Attacks22 Apr
- 2024Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents3 Oct
- 2024AgentPoison: Red-teaming LLM Agents via Poisoning Memory or Knowledge Bases17 Jul
- 2024InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents5 Mar
- 2023Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection23 Feb
Other references · 18
- 2024EIA: Environmental Injection Attack on Generalist Web Agents for Privacy Leakagecited by 150
- 2023Co-Writing with Opinionated Language Models Affects Users' Viewscited by 148
- 2025Memory Injection Attacks on LLM Agents via Query-Only Interactioncited by 144
- 2024On the Conversational Persuasiveness of Large Language Models: A Randomized Controlled Trialcited by 109
- 2024Proactive Agent: Shifting LLM Agents from Reactive Responses to Active Assistancecited by 87
- 2024Need Help? Designing Proactive AI Assistants for Programmingcited by 43
- 2024Adversarial Search Engine Optimization for Large Language Modelscited by 40
- 2025Proactive Assistant Dialogue Generation from Streaming Egocentric Videoscited by 28
- 2024Ranking Manipulation for Conversational Search Enginescited by 26
- 2025FingerTip 20K: A Benchmark for Proactive and Personalized Mobile LLM Agentscited by 20
- 2020Digital Nudging with Recommender Systems: Survey and Future Directionscited by 17
- 2026Proactive Agent Research Environment: Simulating Active Users to Evaluate Proactive Assistantscited by 15
6 more
- 2025ProPerSim: Developing Proactive and Personalized AI Assistants through User-Assistant Simulationcited by 11
- 2026PASK: Toward Intent-Aware Proactive Agents with Long-Term Memorycited by 9
- 2026Commercial Persuasion in AI-Mediated Conversationscited by 7
- 2025Contextualizing Recommendation Explanations with LLMs: A User Studycited by 4
- 2026Anticipate and Learn: Unleashing Idle-Time Compute in Proactive Agentscited by 4
- 2025Detecting Malicious AI Agents Through Simulated Interactionscited by 2
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2026SovereignPA-Bench: Evaluating User-Owned Personal Agents under Evolving Intent, Platform Mediation, and Consent Constraints
- 2026π-Bench: Evaluating Proactive Personal Assistant Agents in Long-Horizon Workflows
- 2026When Consent Outlives Context: Residual Authority Replay in Long-Lived Agents
- 2025Indirect Prompt Injections: Are Firewalls All You Need, or Stronger Benchmarks?
- 2026KnowU-Bench: Towards Interactive, Proactive, and Personalized Mobile Agent Evaluation
- 2026CausalArmor: Efficient Indirect Prompt Injection Guardrails via Causal Attribution
- 2026How Vulnerable Are AI Agents to Indirect Prompt Injections? Insights from a Large-Scale Public Competition
- 2025It's a TRAP! Task-Redirecting Agent Persuasion Benchmark for Web Agents