LLM Application Vulnerability Assessment and Runtime Protection

Assesss and mitigates security risks in LLM applications across development and runtime, aligning controls to known LLM vulnerability categories and protecting deployed apps from emerging threats.

The Problem

“LLM Application Vulnerability Assessment and Runtime Protection”

Organizations face these key challenges:

1

Security teams lack LLM-specific testing coverage beyond standard AppSec tools

2

Developers ship prompts, agents, and retrieval pipelines without clear threat models

3

Runtime attacks can arrive through user input, retrieved documents, plugins, or tools

4

Manual red teaming does not scale across frequent prompt and model changes

Impact When Solved

Reduce prompt injection and jailbreak exposure before production releaseBlock or contain unsafe tool execution and sensitive data exfiltration at runtimeContinuously align controls to known LLM vulnerability categories such as OWASP LLM risksShorten security review cycles with automated assessment and policy-based enforcement

The Shift

Before AI~85% Manual

Human Does

  • •Review LLM application designs and identify likely security risks manually
  • •Run periodic security checks before release and document findings
  • •Prioritize remediation work and decide release readiness
  • •Investigate incidents and update controls after issues are found

Automation

  • •Execute standard code and dependency scans
  • •Apply predefined security rules and alert on known issues
  • •Collect logs and security events for analyst review
With AI~75% Automated

Human Does

  • •Approve security policies, risk thresholds, and release exceptions
  • •Review high-severity findings and decide remediation priorities
  • •Authorize containment actions for sensitive runtime incidents

AI Handles

  • •Continuously assess LLM applications against known vulnerability categories
  • •Run adversarial testing and surface prioritized weaknesses before release
  • •Monitor prompts, retrieved content, outputs, and tool use for runtime threats
  • •Enforce runtime protections by blocking, filtering, or containing unsafe behavior

Operating Intelligence

How it works

AI watches every signal continuously.

Humans investigate what it flags.

False positives train the next watch cycle.

Confidence83%
ArchetypeMonitor & Flag
Shape6-step linear
Human gates1
Autonomy
67%AI controls 4 of 6 steps

Who is in control at each step

Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

Loop shapelinear

Step 1

Observe

Step 2

Classify

Step 3

Route

Step 4

Exception Review

Step 5

Record

Step 6

Feedback

AI lead

Autonomous execution

1AI
2AI
3AI
5AI
gate

Human lead

Approval, override, feedback

4Human
6↺ Loop
AI-led step
Human-controlled step
Feedback loop
TL;DR

AI observes and classifies continuously. Humans only engage on flagged exceptions. Corrections sharpen future detection.

The Loop

6 steps

1 operating angles mapped

Operational Depth

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