Threat Landscape & Frameworks

Overview

AI threats don't fit neatly into traditional cybersecurity taxonomies. They span the entire ML pipeline — from training data to inference output — and require frameworks designed specifically for machine learning systems.

Threat Actor Profiles

ActorMotivationTypical AttacksResources
Script kiddieCuriosity, bragging rightsKnown jailbreaks, copy-paste injectionLow — public tools only
Red teamerAuthorized testingFull methodology, custom toolingMedium-High — scoped access
CybercriminalFinancial gainAI-powered phishing, deepfakes, fraudMedium — cloud compute, social engineering
CompetitorIP theftModel extraction, training data theftHigh — funded research teams
Nation-stateEspionage, disruptionData poisoning, supply chain, influence opsVery High — custom labs, insider access
InsiderVariesTraining data manipulation, model backdoorsHigh — direct pipeline access

Key Frameworks

Two frameworks matter most for AI red teaming:

OWASP LLM Top 10

Focuses on application-level vulnerabilities in LLM deployments. Best for scoping pentests and communicating risk to developers.

→ OWASP LLM Top 10 Deep Dive

MITRE ATLAS

Focuses on adversarial tactics and techniques across the ML lifecycle. ATT&CK-style matrix for machine learning. Best for threat modeling and mapping attack paths.

→ MITRE ATLAS Deep Dive

Mapping to the Kill Chain

Cyber Kill Chain PhaseAI-Specific Activity
ReconnaissanceFingerprint model, extract system prompt, enumerate tools
WeaponizationCraft adversarial prompts, build injection payloads, fine-tune attack model
DeliveryPlant indirect injection in documents, web pages, emails
ExploitationExecute prompt injection, jailbreak, trigger backdoor
InstallationAchieve persistence via poisoned RAG source, tool manipulation
Command & ControlExfiltrate data via tool calls, establish ongoing injection channel
Actions on ObjectivesData theft, unauthorized actions, model compromise, disinformation