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

Quality assessment: Strong market-fit concept addressing a genuine multimodal AI safety gap with concrete technical architecture and real-world attack vectors (Meta glasses example), but the artifact is incomplete (truncated MVP scope and pitch), lacks depth on novel attack generation methods, and needs clearer differentiation from existing adversarial testing frameworks.