@inproceedings{choi-etal-2026-membrane,
title = "Membrane: A Self-Evolving Contrastive Safety Memory for {LLM} Agent Defense",
author = "Choi, Minseok and
Yang, Seungbin and
Kim, Dongjin and
Kim, Subin and
Son, Jungmin and
Lee, Yunseung and
Choo, Jaegul and
Kwak, Youngjun",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.2651/",
pages = "60320--60354",
ISBN = "979-8-89176-459-0",
abstract = "Despite advances in safety alignment, large language models remain vulnerable to continuously evolving jailbreaks. Existing fine-tuned safety classifiers cannot adapt to these evolving attacks, while adaptive memory-based guardrails tend to over-refuse benign queries that resemble stored attacks. We propose Membrane, a self-evolving guardrail built on Contrastive Safety Memory (CSM): each cell pairs the conditions for blocking a harmful query with those for permitting a superficially similar benign request. Without retraining, Membrane evolves CSM by distilling each harmful interaction and its benign counterpart into a contrastive cell indexed by the underlying attack strategy, so that one cell generalizes across topical variants of the same mechanism. At inference, retrieved cells serve as grounding context for precise safety decisions. Across model-level safety on HarmBench and agent-level safety on AgentHarm, Membrane achieves the highest F1 on all six modern jailbreak attacks. Notably, benign refusal on AgentHarm stays at 7-14{\%}, well below the 28-85{\%} range of prior guards. Memory cells also retain 87-88{\%} F1 under cross-attack transfer and remain stable under memory poisoning."
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<abstract>Despite advances in safety alignment, large language models remain vulnerable to continuously evolving jailbreaks. Existing fine-tuned safety classifiers cannot adapt to these evolving attacks, while adaptive memory-based guardrails tend to over-refuse benign queries that resemble stored attacks. We propose Membrane, a self-evolving guardrail built on Contrastive Safety Memory (CSM): each cell pairs the conditions for blocking a harmful query with those for permitting a superficially similar benign request. Without retraining, Membrane evolves CSM by distilling each harmful interaction and its benign counterpart into a contrastive cell indexed by the underlying attack strategy, so that one cell generalizes across topical variants of the same mechanism. At inference, retrieved cells serve as grounding context for precise safety decisions. Across model-level safety on HarmBench and agent-level safety on AgentHarm, Membrane achieves the highest F1 on all six modern jailbreak attacks. Notably, benign refusal on AgentHarm stays at 7-14%, well below the 28-85% range of prior guards. Memory cells also retain 87-88% F1 under cross-attack transfer and remain stable under memory poisoning.</abstract>
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%0 Conference Proceedings
%T Membrane: A Self-Evolving Contrastive Safety Memory for LLM Agent Defense
%A Choi, Minseok
%A Yang, Seungbin
%A Kim, Dongjin
%A Kim, Subin
%A Son, Jungmin
%A Lee, Yunseung
%A Choo, Jaegul
%A Kwak, Youngjun
%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 choi-etal-2026-membrane
%X Despite advances in safety alignment, large language models remain vulnerable to continuously evolving jailbreaks. Existing fine-tuned safety classifiers cannot adapt to these evolving attacks, while adaptive memory-based guardrails tend to over-refuse benign queries that resemble stored attacks. We propose Membrane, a self-evolving guardrail built on Contrastive Safety Memory (CSM): each cell pairs the conditions for blocking a harmful query with those for permitting a superficially similar benign request. Without retraining, Membrane evolves CSM by distilling each harmful interaction and its benign counterpart into a contrastive cell indexed by the underlying attack strategy, so that one cell generalizes across topical variants of the same mechanism. At inference, retrieved cells serve as grounding context for precise safety decisions. Across model-level safety on HarmBench and agent-level safety on AgentHarm, Membrane achieves the highest F1 on all six modern jailbreak attacks. Notably, benign refusal on AgentHarm stays at 7-14%, well below the 28-85% range of prior guards. Memory cells also retain 87-88% F1 under cross-attack transfer and remain stable under memory poisoning.
%U https://aclanthology.org/2026.emnlp-main.2651/
%P 60320-60354
Markdown (Informal)
[Membrane: A Self-Evolving Contrastive Safety Memory for LLM Agent Defense](https://aclanthology.org/2026.emnlp-main.2651/) (Choi et al., EMNLP 2026)
ACL
- Minseok Choi, Seungbin Yang, Dongjin Kim, Subin Kim, Jungmin Son, Yunseung Lee, Jaegul Choo, and Youngjun Kwak. 2026. Membrane: A Self-Evolving Contrastive Safety Memory for LLM Agent Defense. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 60320–60354, Budapest, Hungary. Association for Computational Linguistics.