Analysis
Adversarial Attacks on Multi-Modal LLMs: How Image, Audio, and Video Inputs Get Weaponized and How to Defend Against It
First reported · Discovered neuralstackms.tech
Page published
Earliest dated coverage: 22 Sep 2026 · First observed: 14 Oct 2026 · Latest dated coverage: 22 Sep 2026
Coverage timeline
Single-source analysis — one report is available.
Why it matters
Multimodal LLMs in production (support bots, meeting assistants, browsing agents) open image, audio, and video as new injection channels that text-trained safety alignment often fails to catch, which defenders must account for.
An explainer by Manuela Schrittwieser maps the adversarial attack surface across image, audio, and video inputs to multimodal LLMs, covering the modality gap, preprocessing-layer payloads, and the lack of a content/instruction boundary, then surveys defensive patterns at the input, model, and architecture layers. The piece references OWASP's extension of its LLM01 prompt-injection category to cover multimodal injection and classical adversarial techniques (FGSM, PGD) adapted to vision-language models.