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    <abstract>Modern LLM agents persist state across sessions via two channels: long-lived memory files silently prepended to the system prompt, and scheduled tasks waking the agent without user input. A one-time indirect prompt injection committed to either channel can re-fire across sessions long after the attacker leaves, threats we term carryover attacks. Existing prompt-injection benchmarks evaluate attacks only in the session where malicious content is first observed, leaving the gate governing when and how often a planted payload later re-fires unmeasured. We present CarryoverBench: 12 personal-assistant scenarios and 101 attack tasks across three trigger channels: Instant, same-turn IPI; Autonomous, heartbeat-scheduled; and Latent, memory-resurfaced. A rule-based verifier scores planting and triggering as independent stages. Across five frontier models, planted payloads re-fire without measurable decay over five rounds; conditional on successful planting, the trigger stage often provides little additional protection. Porting the Latent channel to a second runtime, Hermes, attenuates but does not eliminate the effect, suggesting that carryover defense requires runtime-layer controls in addition to prompt-level interventions.</abstract>
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%0 Conference Proceedings
%T After the Attacker Leaves: Benchmarking Carryover Attacks on LLM Agents
%A Xu, Zhiyue
%A Wang, Xukai
%A Huang, Zhiyuan
%A Zhan, Mingjie
%A Zhao, Jian
%A Zhang, Xu-Yao
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F xu-etal-2026-attacker
%X Modern LLM agents persist state across sessions via two channels: long-lived memory files silently prepended to the system prompt, and scheduled tasks waking the agent without user input. A one-time indirect prompt injection committed to either channel can re-fire across sessions long after the attacker leaves, threats we term carryover attacks. Existing prompt-injection benchmarks evaluate attacks only in the session where malicious content is first observed, leaving the gate governing when and how often a planted payload later re-fires unmeasured. We present CarryoverBench: 12 personal-assistant scenarios and 101 attack tasks across three trigger channels: Instant, same-turn IPI; Autonomous, heartbeat-scheduled; and Latent, memory-resurfaced. A rule-based verifier scores planting and triggering as independent stages. Across five frontier models, planted payloads re-fire without measurable decay over five rounds; conditional on successful planting, the trigger stage often provides little additional protection. Porting the Latent channel to a second runtime, Hermes, attenuates but does not eliminate the effect, suggesting that carryover defense requires runtime-layer controls in addition to prompt-level interventions.
%U https://aclanthology.org/2026.emnlp-main.1996/
%P 45456-45476

Markdown (Informal)

[After the Attacker Leaves: Benchmarking Carryover Attacks on LLM Agents](https://aclanthology.org/2026.emnlp-main.1996/) (Xu et al., EMNLP 2026)

• After the Attacker Leaves: Benchmarking Carryover Attacks on LLM Agents (Xu et al., EMNLP 2026)

ACL

• Zhiyue Xu, Xukai Wang, Zhiyuan Huang, Mingjie Zhan, Jian Zhao, and Xu-Yao Zhang. 2026. After the Attacker Leaves: Benchmarking Carryover Attacks on LLM Agents. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 45456–45476, Budapest, Hungary. Association for Computational Linguistics.