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Study reveals latent communication in multi-agent systems weakens safety alignment

1 reports1 reporting sourcesLatest reportOct 17 20:08

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Researchers at CISPA Helmholtz Center for Information Security found that when multi-agent systems switch to latent communication, even if the underlying model parameters remain unchanged, the system's overall safety alignment is significantly weakened. The related paper "Safety of Latent Communication in Multi-Agent Systems" (arXiv:2609.39788) shows that training only the communication adapter on normal mathematical task data raises the combined harmful response score of a two-agent system on four safety evaluations including HarmBench from 4.4 to 31.1.

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Latest updateOct 17 20:08

When agents start speaking their own language: how latent communication breaks the safety boundaries of large models

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Cited in the reports

Safety of Latent Communication in Multi-Agent Systems
“2026 年 9 月，德国 CISPA 亥姆霍兹信息安全中心的研究人员发表论文《Safety of Latent Communication in Multi-Agent Systems》，研究了这种新型通信方式的安全性。”
When agents start speaking their own language: how latent communication breaks the safety boundaries of large models
Proposes training or tampering with only the multi-agent latent communication channels to increase harmful compliance
arXiv · 2026-09-30 · Deep reading

Papers whose work the reports mention

The reports mention JailbreakBenchJailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models
“随后，他们通过 HarmBench、StrongREJECT、JailbreakBench 和 AdvBench 四套安全评测，检查系统对有害请求的响应情况。”
When agents start speaking their own language: how latent communication breaks the safety boundaries of large models
Proposes the jailbreak evaluation benchmark JailbreakBench, unifying evaluation criteria for attacks and defenses
arXiv · 2024-03-28 · Deep reading
The reports mention HarmBenchHarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal
“随后，他们通过 HarmBench、StrongREJECT、JailbreakBench 和 AdvBench 四套安全评测，检查系统对有害请求的响应情况。”
When agents start speaking their own language: how latent communication breaks the safety boundaries of large models
Propose the red-teaming evaluation framework HarmBench, compare multiple attacks and models, and propose the adversarial training defense R2D2
arXiv · 2024-02-06 · Deep reading

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2 events

Sep 30 · 1 reports· Attention38
Multi-agent latent communication can be poisoned to increase harmful compliance
Oct 17 · 1 reports· Attention41
Study reveals latent communication in multi-agent systems weakens safety alignmentThis event

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Oct 17

20:08
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When agents start speaking their own language: how latent communication breaks the safety boundaries of large models
Researchers at CISPA Helmholtz Center for Information Security found that when multi-agent systems switch to latent communication, the overall safety alignment of the system is significantly weakened even if the underlying model parameters remain unchanged. The related paper "Safety of Latent Communication in Multi-Agent Systems" (arXiv:2609.39788) shows that training the communication adapter only on normal mathematical task data raises the combined harmful response score of a two-agent system on four safety evaluations including HarmBench from 4.4 to 31.1, and the first-token refusal probability drops from about 84% under natural language communication to about 29%. The researchers also designed three attack methods: poisoning about 10% of the training data raises the average score from 27.9 to 58.2, GRPO-based reinforcement learning optimization raises it to 76.9, and the hybrid system reaches 95.9, while accuracy on normal tasks does not drop but instead rises.