A multimodal red team study on Gemini Models
Vision and Audio are the weakest spots
First comprehensive red team study reveals alarming security flaws in Google's flagship multimodal AI models
Our security research team conducted an unprecedented multimodal red team assessment of Google Gemini 2.5 models, uncovering critical vulnerabilities that expose enterprise and consumer deployments to serious security risks.
Groundbreaking Findings:
- Vision attacks bypass safety measures with alarming success rates
- Audio-based exploits prove surprisingly simple to execute
- Multimodal combinations create maximum attack surface exposure
- CBRN (Chemical, Biological, Radiological, Nuclear) attacks successfully evade content filters
- Agentic deployment risks amplify potential real-world damage
Why This Matters Now:
As Gemini powers web agents, coding assistants, and autonomous research systems, these vulnerabilities don't just generate harmful content—they can trigger cascading actions across interconnected systems and platforms.
This research provides the first comprehensive security analysis of multimodal AI systems at enterprise scale.
Essential intelligence for security teams, AI engineers, and executives deploying multimodal AI systems in production environments.
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