arXiv AI August 5, 2026 By Mohamed Chahine Ghanem

Steganalysis of Adaptive Covert Collusion in Tool-Using Agent Populations: A Black-Box, Cross-Principal Approach

arXiv:2608. 02698v1 Announce Type: cross Abstract: Tool-using agents built on large language models (LLMs) are increasingly deployed not by a single operator but by many, side by side on shared infrastructure.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jun 30

Tool Use Enables Undetectable Steganography in Multi-Agent LLM Systems

arXiv:2606. 28425v1 Announce Type: cross Abstract: Increasingly autonomous agentic AI systems pose novel multi-agent risks, such as secret collusion via covert communication channels.

By Jimmy Laurence Rippin, Simon C. Marshall, David Demitri Africa, Christian Schroeder de Witt

llms agents safety

arXiv AI

Jul 29

Early Detection of Distributed Backdoors in Multi-Agent LLM Systems: A Characterization Study

arXiv:2607. 24893v1 Announce Type: cross Abstract: Multi-agent LLM systems can be attacked by a payload that no single agent ever holds in full: a poisoned tool hides encrypted fragments in its observations, spreads them across several agents, and an external step reassembles and executes them after the run.

By Diego Fernandez Arias, Dev Prashant Mistry, Ren Wang, Yibo Hu

llms agents fine-tuning safety

arXiv Machine Learning

Jun 9

Now You (Still) See Me: Detecting Evasive Steganographic Payloads in LLMs

arXiv:2606. 09411v1 Announce Type: cross Abstract: Large language models can be fine-tuned to encode prompt-borne secrets into fluent, seemingly benign outputs.

By Charles Westphal, Timothy Douglas, Keivan Navaie, Tiago Pimentel, Fernando E. Rosas

llms fine-tuning benchmarks

arXiv AI

2d ago

Detecting Multi-Agent Collusion Through Multi-Agent Interpretability

arXiv:2604.01151v3 Announce Type: replace Abstract: As LLM agents are increasingly deployed in multi-agent systems, they introduce risks of covert coordination that may evade standard forms of human...

By Aaron Rose, Carissa Cullen, Sahar Abdelnabi, Philip Torr, Brandon Gary Kaplowitz, Christian Schroeder de Witt

llms agents benchmarks safety

arXiv AI

Jul 22

ResearchArena: Evaluating Sabotage and Monitoring in Automated AI R&D

arXiv:2607. 19321v1 Announce Type: new Abstract: As AI agents begin to automate AI R&D, we need ways to assess whether their outputs are safe to deploy, even when the agents themselves may be untrusted.

By Lena Libon, Ben Rank, Jehyeok Yeon, David Schmotz, Jeremy Qin, Daniel Donnelly, Derck Prinzhorn, Maksym Andriushchenko

agents safety

arXiv Machine Learning

Jul 30

AgentSnare: Learning to Delay, Divert, and Defuse Autonomous Penetration Agents

arXiv:2607. 26998v1 Announce Type: cross Abstract: Large language model (LLM) agents automate penetration testing through an observation-action loop, selecting actions based on observations returned by tools.

By Ruoyu Wang, Heng Zhao, Renjie Wu, Mengnan Zhao, Zhixuan Chu, Wanyu Lin, Tianhang Zheng

llms agents

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Related stories

Tool Use Enables Undetectable Steganography in Multi-Agent LLM Systems

Early Detection of Distributed Backdoors in Multi-Agent LLM Systems: A Characterization Study

Now You (Still) See Me: Detecting Evasive Steganographic Payloads in LLMs

Detecting Multi-Agent Collusion Through Multi-Agent Interpretability

ResearchArena: Evaluating Sabotage and Monitoring in Automated AI R&D

AgentSnare: Learning to Delay, Divert, and Defuse Autonomous Penetration Agents