Mapping the LLM Threat Surface for Enterprise Security
A systematic framework for assessing and defending against LLM-specific threat vectors across enterprise environments.
How confident is your security team that they can identify where an adversary would first exploit your LLM deployment?
Threat Landscape Overview
Large language models have expanded the attack surface for enterprise teams responsible for data protection, identity, and automation. Our threat surface model outlines the primary interaction points where adversaries can exploit generative AI systems.
Attack Paths & Failure Modes
We segment each threat vector into pre-deployment, runtime, and post-deployment risk categories. This allows security teams to map control coverage and identify blind spots across data ingestion, orchestration layers, and user access flows.
Defensive Controls
By combining traditional application security controls with LLM-aware guardrails, organizations can move toward measurable risk reduction aligned with executive security objectives.
Your next move
Map your LLM threat surface against the pre-deployment, runtime, and post-deployment risk categories in this framework — then validate control coverage for each interaction point.
Tools & code
mcp-sentinel-scanner
Seven-layer detection pipeline for MCP and LLM threat surface analysis.