
Multimodal Prompt Injection: Attacks in Images, Audio, and Video
How attackers bypass text-based guardrails by embedding malicious instructions in images and audio, and the layered defenses required to counter them.
AI agents that plan, use tools, persist memory, and coordinate with other agents are reaching production systems faster than most security teams can evaluate their risks. Their security model differs fundamentally from standalone LLMs, introducing new attack surfaces across tools, memory, planning loops, and agent-to-agent interaction. I’ve been writing about these emerging threats and the defense patterns needed to secure agentic systems as the ecosystem evolves.
If you need practical support, I offer agentic AI security consulting to help trace attack paths and design defense architectures tailored to your environment.

How attackers bypass text-based guardrails by embedding malicious instructions in images and audio, and the layered defenses required to counter them.

AI agents create a third class of lateral movement, bridging previously isolated systems through natural language, tool access, and execution autonomy.

How attackers plant instructions targeting agentic AI systems today that execute weeks later, and the defense architecture that stops them.

Why your sanitized user queries don’t protect you when the threat enters through your knowledge base.

Why the Model Context Protocol needs a new security mental model, and how to build it.

A scenario-driven workflow for tracing attack paths in agentic AI systems using a five-zone navigation lens, attack trees, and OWASP’s threat taxonomy and playbooks.

How the shift from single-model LLM integrations to agentic AI systems amplifies prompt injection into a multi-step attack chain.