AI systems don't have perimeters. They have semantic surfaces—every input, every retrieved document, every tool call is a potential attack vector. ZIVIS maps them before attackers find them.
STRIDE assumes deterministic systems with hard trust boundaries—network perimeters, service accounts, defined data flows. AI systems don't have those. A prompt that crosses a "trust boundary" looks identical to a legitimate message. A document retrieved from a knowledge base has the same influence as your system instructions. The attack surface is semantic, not structural.
ZIVIS models AI threats the way they actually manifest: through endpoints, intent surfaces, fuzzy boundaries, and emergent behaviors that no component diagram can capture.
From cloud LLMs to edge devices, across every layer where AI receives, processes, and acts on input
Prompt injection, jailbreaking, system prompt extraction, and semantic boundary violations in cloud and fine-tuned models.
Tool chain exploitation, intent drift, excessive agency, and multi-agent coordination failures where small inputs produce large actions.
Retrieval manipulation, document injection attacks, context window exploitation, and knowledge base poisoning.
Chatbots, copilots, and AI-enhanced products. Semantic input surfaces, output manipulation, and session-level persistence.
On-device model security, hardware attack surfaces, firmware manipulation, and physically-accessible AI deployments.
MCP servers, tool APIs, middleware, and the plumbing connecting AI to systems it can act on.
Endpoint-first analysis built for how AI systems actually fail
Map every surface the AI can receive input from—not just user prompts, but tool outputs, retrieved documents, memory, external APIs, and inter-agent messages.
Identify where trust assumptions change—the fuzzy lines between system and user, authored and retrieved, trusted and untrusted—and how attackers can blur them.
Model the AI's decision-making exposure: what goals can be redirected, what behaviors can be triggered, what constraints can be circumvented through semantic manipulation.
Convert findings into concrete, architecture-specific attack scenarios—the input sequences and document payloads that exploit the identified exposures.
The threat model doesn't end with a document. It ends with proven security.
Understand your exposure
Map semantic surfaces, boundaries, and attack scenarios specific to your architecture.
Concrete test cases
Each identified threat becomes a targeted test case—prompt sequences, document payloads, tool abuse chains.
Prove exploitability
Run the scenarios. Confirm what's vulnerable, quantify blast radius, produce evidence.
Stay ahead of drift
As your AI evolves, your threat model evolves. Continuous monitoring via the ZIVIS platform or embedded team.
Actionable artifacts—not just a report, but a path to verified security
Architecture-specific documentation of semantic attack surfaces, identified exposures, and risk ratings.
Concrete, runnable test cases tied to your endpoints, tools, and data flows—ready for red team execution.
Prioritized controls, architectural changes, and implementation guidance ordered by risk and effort.
How to maintain your threat model as your AI evolves—what to re-test, what to monitor, what changes trigger reassessment.
Threat modeling is the foundation. These services build on it.
AI systems have a new kind of attack surface. ZIVIS is built to find it.