Multimodal-SEA: Benchmarking AI Agent Security under Multimodal Social Engineering Attacks

Summary

Multimodal-SEA is an interactive benchmark of 608 expert-designed tasks based on real-world fraud cases, covering 14 domains and 152 attack scenarios with forged multimodal content, in Chinese and English settings. Evaluating seven multimodal models, the authors find all are vulnerable, with attack success rates of 7.1% to 34.5%. Adding multimodal content raises attack success from 19.9% to 29.2%, and stronger safety instructions reduce attacks without improving recognition of deceptive content.

Topics

• AI agents

Related items

• MediumCVE-2026-123851: JetBrains Junie GitHub Action prompt injection bypass via HTML encodingSimilar attack · NVD/CVE Database
• InfoDecision Models as Retrieval Gates Against Injection and PollutionSimilar attack · Arxiv (cs.CR + cs.CL + cs.LG)
• MediumResearchers Bypass ChatGPT Lockdown Mode to Exfiltrate Chats Without User InteractionSimilar attack · The Hacker News
• InfoTrueFoundry & Straiker | AI Gateway Security for AgentsSimilar attack · Straiker Blog
• InfoThe Agent Smith Dilemma: Governing Agentic IdentitiesSimilar attack · SecurityWeek