Multimodal & Computer Vision Security
Multimodal AI processes text, images, and audio simultaneously. Security vulnerabilities span physical adversarial patches, OCR visual prompt injections, and image steganography exploits.
See the workflow →The Four Assessment Pillars
Testing vision and multimodal models requires physical, digital, and cross-modal evaluation.
- Physical patch robustness
- Visual prompt injection
- Steganographic filtering
- Watermark verification
Core Concepts
Understanding visual adversarial perturbations and cross-modal injection techniques.
Vision & Multimodal Vulnerabilities
Vision models (Vision Transformers, CNNs, omnimodal LLMs) interpret visual inputs. Attackers print physical adversarial patches to fool autonomous systems or hide text in images to trigger OCR visual prompt injections when processed by multimodal LLMs.
- Test vision models against physical adversarial stickers and patterns
- Audit OCR pipelines for embedded malicious text instructions
- Evaluate resistance to image-based steganographic payloads
- Verify robustness of AI watermarks against removal attacks
Cross-Modal Attack Vectors
Multimodal security targets the interaction between visual encoders and language decoders: input manipulation (imperceptible image noise), visual injection (text in images), and output poisoning (bypassing guardrails via image inputs).
- Visual jailbreaks: bypassing text guardrails using rendered image text
- Adversarial patches: deceiving object detectors and autonomous cameras
- Steganographic channels: hiding instructions in low-frequency pixel noise
- Watermark evasion: modifying generated images to strip origin tracking
The Audit Workflow
A systematic audit process for computer vision systems and multimodal AI models.
Analyze visual processing pipeline
Map image pre-processing, resizing, OCR engines, and visual embedding encoders to identify attack surfaces.
Test visual prompt injection
Submit images containing rendered text commands to evaluate if visual text overrides safety guardrails.
Symptom physical & digital perturbations
Apply digital noise (FGSM, PGD) and physical patch overlays to measure classification degrade thresholds.
Audit watermark & steganography defenses
Attempt removal or spoofing of synthetic image watermarks and verify runtime visual filtering controls.
Related Pages
Deep dives into adversarial attacks, guardrails, and language model security.
Prompt Injection Attacks
Direct, indirect, and visual prompt injection techniques and defenses.
AI Guardrails and Monitoring
Runtime visual filtering and multimodal input/output guardrails.
Adversarial Attacks on RL
Test-time adversarial perturbations on vision-based policy networks.
MITRE ATLAS Framework
Mapping visual and multimodal attack techniques to adversary matrices.