AV - AI Implementors Cybersecurity Brief

Glen Armes
Jan 15
2 min read

2026 Week 3

Attackers are treating LLM infrastructure as a new, profitable perimeter. Misconfigured proxies, exposed endpoints, and weak auth are being actively targeted and scanned at scale.

The core risk isn’t only model theft, it’s unauthorized usage, data leakage through prompts/connectors, and credential/token compromise in integrated workflows; especially when automation platforms like n8n are in the stack.

Key Signals

Systematic probing of misconfigured proxies tied to paid LLM services
Honeypots have confirmed mass scanning of LLM infrastructure
n8n node supply chain token theft (common in AI orchestration environments)

Critical Vulnerabilities

ServiceNow AI impersonation flaw (if leveraging Now Assist / Virtual Agent related components).
n8n critical CVEs + malicious nodes (if used for orchestration / integrations).

Recommended Actions

Inventory & lock down LLM endpoints/proxies: no unauthenticated access; IP allowlists; mTLS where feasible.
Secrets & token controls: scoped OAuth, short TTL, rotation, and detect token access during workflow runs.
Prompt/data governance: block PII/regulated data; log and review tool calls and connector access.

FAIR QuickQuant

Misconfigured proxy exposes paid LLM services / data paths

Loss event: proxy misconfig allows unauthorized LLM usage or data interception → cost/fraud + possible data exposure.
LEF (annual): 0.5–3.0 / year
PLM (annual): $50k–$750k (API spend, IR, remediation)
SLM (annual): $25k–$500k (privacy, contractual, customer trust)
12-month LE: $75k–$1.25M
Key assumptions: LLM endpoints reachable from internet; weak auth; limited logging/alerts. Each company must perform a cyber risk analysis to understand the true impact to their company; however, this is a good start to a loss event scenario.

Controls that Move the Dial

Secure configuration + exposure management for AI endpoints ↓
Token hygiene + connector governance ↓
Observability (usage anomalies, egress, tool calls) ↓