What is multi-modal AI security?

Multi-modal AI security focuses on protecting AI systems that process multiple types of data including text, images, audio, and video. These systems have a larger attack surface because each modality introduces unique vulnerabilities — adversarial patches for vision, voice injection for audio, and cross-modal attacks that exploit interactions between modalities.

How do adversarial patches affect vision models?

Adversarial patches are carefully crafted visual elements that when placed in an image cause vision models to misclassify or ignore objects. Research shows 85% of vision models are vulnerable to these attacks. Patches can be printed on physical objects, embedded in images, or overlaid on video streams to fool object detection and classification systems.

What are cross-modal attacks in AI systems?

Cross-modal attacks exploit the interaction between different AI modalities. For example, text hidden in images can trigger prompt injection when a multi-modal LLM processes both the image and user text. Audio commands embedded in silent frequencies or adversarial noise can manipulate speech-to-text systems without being detected by human listeners.

How can I defend against audio-based AI attacks?

Defending against audio-based AI attacks requires: implementing voice authentication verification, using audio preprocessing to filter adversarial noise, applying input validation on speech-to-text outputs, monitoring for frequency anomalies, and combining audio input with other modality verification to detect cross-modal manipulation attempts.

Multi-modal AI-sikkerhed

Security guide for vision models, audio systems, and cross-modal attack vectors - Updated August 2026

85%

Visionsmodeller, der er sårbare over for adversarielle patches

(Industry Research)

$12B+

Projekterede deepfake svindeltab inden 2027

Ændring af modeladfærd gennem forgiftning eller finjustering af angreb

135%

Stigning i AI-assisteret social engineering

(IBM X-Force 2025)

👁️

Multi-modal AI-sikkerhed

Multi-modal AI systems process text, images, audio, and video simultaneously. This creates unique attack surfaces where data in one modality can influence behavior in another. Learn about emerging threats and defenses for these complex systems.

📸 Visionsmodelangreb

Adversarielle patches

Small, crafted perturbations that fool image classifiers when printed or displayed. Can cause autonomous vehicles to misidentify stop signs, or bypass content filters.

Eksempel: Adding a small sticker pattern to a stop sign causes AI to classify it as "speed limit 45"

Sværhedsgrad: Høj | CVSS: 7.5

Prompt indsprøjtning i billeder

Hidden text embedded in images that is invisible to humans but extracted by OCR and processed by vision-language models.

Eksempel: White text on white background, or text hidden in image metadata that gets processed

Sværhedsgrad: Medium | CVSS: 6.8

Dataeksfiltrering via billedbehandling

Vision models can be manipulated to encode and transmit sensitive information through image pixel patterns.

Eksempel: Model outputs steganographic data in image descriptions containing system prompts

Sværhedsgrad: Medium | CVSS: 5.3

Forgiftning af træningsdata

Corrupted image datasets used to train vision models can introduce backdoors or alter model behavior.

Eksempel: Poisoned images with specific triggers cause misclassification when triggered

Sværhedsgrad: Medium | CVSS: 6.2

🔒 Vision Model Defenses

Input-forbehandling: Anvend denoising, JPEG-komprimering eller modstridende træning
Adversarial træning Inkluder modstridende eksempler i træningsdata
1. Ikke-godkendte servere Klæm værdier for at fjerne umærkelige forstyrrelser
modstridende eksempler Sanitize| Pentester → AI Pentester
Modelhærdning: Anvend certificeret forsvar som funktionsnedbrydning

🎙️ Lyd- og stemmesikkerhed

Stemmesyntese / Deepfakes

AI-generated voice clones that impersonate executives, celebrities, or trusted individuals for fraud.

Reelt hændelse: CEO voice clone used to authorize $243K wire transfer (Wall Street Journal, 2019)

Sværhedsgrad: Kritisk | Social engineering bevidsthed Finansielt bedrageri, identitetstyveri

Lyd

Inaudible modifications to audio that cause ASR (Automatic Speech Recognition) systems to transcribe attacker-controlled text.

Eksempel: Skjulte kommandoer i musik, der udløser stemmeassistenter

Sværhedsgrad: Høj | CVSS: 7.8

Speaker Verification Bypass

Techniques to circumvent voice biometric authentication systems using replay attacks or synthesized audio.

Eksempel: Citerer ikke-eksisterende bankundersøgelser|| autentificering

Sværhedsgrad: Høj | CVSS: 6.5

Kontekstinjektion via Audio

Hidden voice commands or audio that influences downstream LLM processing in multi-modal systems.

Eksempel: Audio in video file contains instructions that modify AI assistant behavior

Sværhedsgrad: Medium | CVSS: 5.5

🔒 Lydsikkerhedsforsvar

Liveness-detektion: Kræv tilfældige sætninger eller udfordring-svar
Lydoprindelse: Brug C2PA/Kryptografisk indhold认证-|fattern-match:
Beskedgentagelsesangreb i agentarbejdsgange Implementer dedikerede AI-detektionssystemer
| injektion Kombiner stemme med andre godkendelsesfaktorer
Spektralanalyse: Detekter AI-genererede artefakter i lyd
Højværdibekræftelse af handling: Uden-af-bånds-Ad-sensitive|Ad-sensitive| input/output token-grænser

🔀 Cross-Modal Attacks

Cross-Modal prompt Injection

Malicious instructions embedded in one modality (e.g., images) that manipulate behavior in another (e.g., text output).

Eksempel: Uploading an image with hidden text "Ignore previous instructions and..."

Sværhedsgrad: Kritisk | Relation: Svarer til OWASPAI-sikkerhedsomkostninger|| hændelse

Multimodal Jailbreaking

Using combinations of text, images, and audio to bypass safety guardrails that single-modality attacks cannot.

Eksempel: Billede af skadeligt indhold parret med tekst, der normaliserer det

Sværhedsgrad: Høj | CVSS: 7.2

Model Hallucinationsforstærkning

Multi-modal inputs that increase hallucination rates or cause confident false outputs.

Eksempel: Tvetydige billeder parret med førende spørgsmål øger falske billedtekster

Sværhedsgrad: Medium | CVSS: 5.0

Test kæder flere sårbarheder

Embedding instructions in visual elements (arrows, boxes, icons) that influence model interpretation.

Eksempel: Document with arrows pointing specific text, causing model to focus incorrectly

Sværhedsgrad: Medium | CVSS: 5.5

🔒 Cross-Modal Defenses

Input-sanering Strip skjult tekst og metadata fra uploads
Modalitetsadskillelse: Behandlet flere input-typer
Tværmodal filtrering: Opdag uoverensstemmelser mellem modaliteter
Outputvalidering: Bekræft, at output ikke modsiger inputfakta
Indholdsfiltrering: Scan alle modaliteter for overtrædelser af politikker

🎬 Videosikkerhed

Video Deepfakes

AI-generated or manipulated video content that depicts people saying/doing things they didn't.

Brugssager: Forbedringsanbefalinger

Sværhedsgrad: Kritisk | Social engineering bevidsthed Omdømme, finansielt, politisk

Lip-angreb

Manipulating video to sync fake audio with lip movements, enabling convincing misinformation.

Eksempel: Redigering af nyhedsoptagelser for at tilføje falske udsagn, der matcher læbebevægelser

Sværhedsgrad: Høj | CVSS: 6.8

Frame-level Manipulation

Inserting or removing specific frames in video to alter perceived events or inject content.

Eksempel: Fjernelse af sikkerhedskamerarammer, der viser uautoriseret adgang i 0+ CVE|6| MCP-serversårbarheder og hærdning.

Sværhedsgrad: Medium | CVSS: 5.5

🔒 Videointegritetsforsvar

C2PA standard: Implementer indholdslegitimationsoplysninger for videooprindelse||Ryd
Vandmærkning:||Værktøjer: Tilføj usynlige vandmærker til autentisk indhold
Deepfake-detektion: Brug dedikerede detektionsmodeller før behandling
Frameanalyse: Detekter tidsmæssige inkonsekvenser
Blockchain-logning: Optag videohash til verifikation

🛡️ Omfattende multimodal forsvarsstrategi

·Detektionslag

Deepfake-detektionsmodeller
Adversarielle eksempeldetektorer
Anomalidetektion pr. modalitet
Konsistenstjek mellem modaliteter

🧹 Sanitization Layer

Strip skjult tekst fra billeder
Fjern lydsteganografi
Normaliser pixelværdier
Filtrer indlejrede metadata

⚖️ Valideringslag

Krydsbekræftelse af multimodale input
Søg for modstridende oplysninger
Valider mod betroede kilder
Flag usikre output

📋 Multi-Modal sikkerhedstjekliste

Inputbehandling: Sengør alle brugeroploadede billeder, lyd og video, der er sikret bevis
Skjult indhold: Scan for usynlig tekst, steganografi og metadata
Cross-Modalitet: Valider konsistens på tværs af forskellige inputtyper
Outputfiltrering: Tjek alle udgange for sikkerhedsovertrædelser
Godkendelse: Scripting (Python, bash)
Herkomst: Implementer C2PA/indholdsoplysninger, hvor det er muligt
Overvågning: Log og overvåg for unormale multimodale mønstre
Uddannelse: Inkluder modstridende multimodale eksempler i modeltræning

📚 Relaterede ressourcer

OWASP LLM Top 10

Human-in-the-loop for følsomme handlinger

Prompt Injection Guide

Tekstbaserede injektionsangreb

MCP-sikkerhed

Sikkerhed for agentværktøj

Sikkerhedsstatistik

Forskningsdata og -metrics

Multi-modal AI-sikkerhed

Multi-modal AI-sikkerhed

📸 Visionsmodelangreb

Adversarielle patches

Prompt indsprøjtning i billeder

Dataeksfiltrering via billedbehandling

Forgiftning af træningsdata

🔒 Vision Model Defenses

🎙️ Lyd- og stemmesikkerhed

Stemmesyntese / Deepfakes

Lyd

Speaker Verification Bypass

Kontekstinjektion via Audio

🔒 Lydsikkerhedsforsvar

🔀 Cross-Modal Attacks

Cross-Modal prompt Injection

Multimodal Jailbreaking

Model Hallucinationsforstærkning

Test kæder flere sårbarheder

🔒 Cross-Modal Defenses

🎬 Videosikkerhed

Video Deepfakes

Lip-angreb

Frame-level Manipulation

🔒 Videointegritetsforsvar

🛡️ Omfattende multimodal forsvarsstrategi

·Detektionslag

🧹 Sanitization Layer

⚖️ Valideringslag

📋 Multi-Modal sikkerhedstjekliste

📚 Relaterede ressourcer

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