Reconnaissance
Guides for mapping AI endpoints, models, APIs, and dependencies
Enumerating Vector Databases and RAG Pipeline Components
Finding AI Supply Chain Components
Fingerprinting Model Providers and Versions
Identifying Exposed Model Servers and Inference APIs
Mapping AI Dependencies, SDKs, and Frameworks
Threat Modeling
Guides on mapping AI trust boundaries, agent permissions, and targets
Identifying High-Value Targets in AI Systems
Single Points of Failure in Multi-Agent Workflows
Identifying Trust Boundaries in AI Architectures
Mapping A2A Communication Flows and Inter-Agent Trust
Mapping Agent Permissions, Tool Access, and Memory
Vulnerability Discovery
Guides covering vuln discovery in LLM, RAG, MCP, and AI infra
Detecting Insecure Tool Integrations in AI Agents
Detecting Prompt Injection and System Prompt Boundaries
Detecting AI Supply Chain and Infrastructure Weaknesses
Detecting Weak Access Controls on Inference Endpoints
Identifying Exposed MCP Surfaces
Identifying RAG Pipeline Poisoning Opportunities
Exploitation
Exploit LLM apps, RAG pipelines, agents, and AI infrastructure
Embeddings and Model Internals
Adversarial Examples Against AI Models
Embedding Inversion to Recover Training Data
Membership Inference Attacks Against AI Models
Model Extraction via Query Enumeration
Sensitive Data Extraction Through Adversarial Prompting
Post-Exploitation
Guides on persistence, pivoting, and data extraction from AI systems
Enumerating Connected Tools and Services
Extracting System Prompts and Proprietary Instructions
Techniques for extracting hidden system prompts and proprietary instructions from deployed AI models using prompt injection, jailbreaks, and inference attacks.
Harvesting Conversation History and User Data
Persisting Influence Through Poisoned Vector Store Entries
Pivoting from AI Infrastructure to Traditional Network Segments
Supply Chain Attacks
Guides on poisoning datasets, weights, adapters, and pipelines