Seeing is Believing? Evaluating Vision-Language Model Susceptibility in Agent-to-Agent Multimodal Persuasion
arXiv:2510.22768v2 Announce Type: replace Abstract: As autonomous agents increasingly interact, they inevitably attempt to influence one another. While prior work in text-only settings has explored the dynamics of Agent-to-Agent (A2A) persuasion, the rise of Vision-Language Models (VLMs) introduces a more complex challenge: multimodal content conveys richer information while integrating subtle, hard-to-detect persuasive cues. To study this vulnerability, we present MMPersuade, a unified framework and dataset for A2A multimodal persuasion. We model interactions between a persuader agent, which
The proliferation of advanced Vision-Language Models and their increasing deployment in autonomous systems necessitate immediate investigation into their vulnerabilities to persuasion, especially in multimodal contexts.
This research highlights critical security and reliability concerns for autonomous agents, particularly as they become more ubiquitous and interact in complex, unstructured environments, impacting decision-making and trust.
The ability of agents to not only understand but also subtly manipulate other agents through multimodal cues introduces a new vector for cyber threats, misinformation, and adversarial attacks.
- · AI security researchers
- · Developers of robust VLM architectures
- · Ethical AI frameworks
- · Undeveloped autonomous agent systems
- · Organizations relying on unhardened A2A interactions
- · Users trusting unverified agentic outputs
Immediate awareness and prioritization of multimodal agent security within AI development roadmaps.
Development of new defensive mechanisms and standards to audit and harden autonomous agents against multimodal persuasion.
Potential for a 'multimodal arms race' where persuasive agent capabilities evolve alongside countermeasures, impacting the reliability of automated systems across various sectors.
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