PromptGuard — Multimodal AI Safety Testing Platform

Ship vision-language AI without shipping vulnerabilities

PromptGuard stress-tests your multimodal models against cross-modal adversarial attacks before they reach production—the same attack vectors that recently exposed Meta's AI glasses to privacy violations. Our automated testing platform generates thousands of adversarial image-text combinations daily, validating safety across robotics platforms, web agents, and wearable AI where a single bypass could mean regulatory action or brand damage. Get compliance-ready safety reports that map directly to emerging multimodal AI regulations.

Key Benefits:

- Automated generation of cross-modal adversarial test cases combining image perturbations with prompt injections—catching vulnerabilities that single-modality testing misses

- Compliance-ready safety validation reports mapping attack resistance to regulatory frameworks, essential as AI glasses and robotics face increasing scrutiny

- Pre-production vulnerability detection using dual-modality adversarial training techniques from latest multimodal safety research, preventing costly post-deployment incidents

MVP Scope: MVP provides automated testing for vision-language models against cross-modal adversarial attacks. Core features: (1) Adversarial test case generation combining image perturbations with prompt injections, (2) Safety validation against generated attacks with pass/fail reporting, (3) Web dashboard showing vulnerability findings and compliance status, (4) Integration with 2-3 popular VLMs (GPT-4V, Claude Vision, Gemini), (5) CSV export of test results for audit trails. Scope excludes: custom model fine-tuning, real-time monitoring, advanced quantization analysis.

Tech Stack: Python, PyTorch, FastAPI, PostgreSQL, React, Docker, Kubernetes

Components:

- Adversarial Generation Engine

- Multimodal Safety Validator

- Attack Vector Dashboard

- Model Integration Layer

- Compliance Reporting Module

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