mlsanityData to 14 Oct 2026

Out of Sight, Not Out of Mind: Unveiling Latent Attack in Latent-based Multi-Agent Systems

arXiv 2605.28214 ↗First posted 27 May 2026cs.CR cs.LG cs.MAcited by 4

Not on the map. Why?

Abstract (arXiv)

Latent-based multi-agent systems replace parts of explicit inter-agent communication with hidden representations, offering a new direction for efficient and flexible agent collaboration. However, moving coordination into latent space may also move attacks beyond the reach of visible-text inspection. In this paper, we study whether latent states can carry attack-associated information that remains effective during clean executions. To examine this question, we introduce a latent attack framework that reactivates attack-induced effects through latent interventions without reusing adversarial text. Extensive experiments show that the resulting latent attacks can substantially degrade task performance in clean executions, especially when applied to inter-agent KV-cache handoffs rather than local hidden states. Further control analyses indicate that this degradation cannot be reduced to arbitrary perturbations or invalid generation. Overall, our findings suggest that latent-based collaboration does not remove attack risk. It shifts part of the risk into less observable execution states, calling for safeguards beyond visible-text inspection.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 17 of its 55 references that are in the map. First posted on arXiv 27 May 2026.

Builds on

17 of 55 references.

References · 17

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  3. 2025Qwen3 Technical Reportcited by 8,820
  4. 2023Universal and Transferable Adversarial Attacks on Aligned Language Modelscited by 3,104
  5. 2023Representation Engineering: A Top-Down Approach to AI Transparencycited by 1,336
  6. 2023Steering Language Models With Activation Engineeringcited by 922
  7. 2022Ignore Previous Prompt: Attack Techniques For Language Modelscited by 832
  8. 2023Prompt Injection attack against LLM-integrated Applicationscited by 605
  9. 2024Agent-SafetyBench: Evaluating the Safety of LLM Agentscited by 306
  10. 2024Compressed Chain of Thought: Efficient Reasoning Through Dense Representationscited by 201
  11. 2024SafeAgentBench: A Benchmark for Safe Task Planning of Embodied LLM Agentscited by 116
  12. 2025A Survey on Latent Reasoningcited by 81
5 more
  1. 2025Latent Collaboration in Multi-Agent Systemscited by 73
  2. 2026The Latent Space: Foundation, Evolution, Mechanism, Ability, and Outlookcited by 43
  3. 2025Enabling Agents to Communicate Entirely in Latent Spacecited by 34
  4. 2026MemMark: State-Evolution Attribution Watermarking for Agent Long-Term Memory Systemscited by 16
  5. 2026From Backward Spreading to Forward Replay: Revisiting Target Construction in LLM Parameter Editingcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2026LCGuard: Latent Communication Guard for Safe KV Sharing in Multi-Agent Systems
  2. 2025Exposing Weak Links in Multi-Agent Systems under Adversarial Prompting
  3. 2026Insider Attacks in Multi-Agent LLM Consensus Systems
  4. 2026Defending LLM-based Multi-Agent Systems Against Cooperative Attacks with Sentence-Level Rectification
  5. 2026Beyond the Transcript: Detecting Covert Co ordination in Latent Multi-Agent Communication
  6. 2024AgentMonitor: A Plug-and-Play Framework for Predictive and Secure Multi-Agent Systems
  7. 2026Beyond tokens: a unified framework for latent communication in LLM-based multi-agent systems
  8. 2025Demonstrations of Integrity Attacks in Multi-Agent Systems