AI/LLM Vulnerability Discovery Methodology
Overview
A structured approach to identifying, classifying, and exploiting weaknesses in large-language-model systems. Unlike traditional software security, LLM vulnerabilities often reside in prompt-engineering, reasoning logic, or data leakage rather than memory corruption.
Core Methodology Phases
- Reconnaissance: Mapping the attack surface, including model capabilities, training data sources, and integration points API Security.
- Threat Modeling: Identifying specific risks such as Prompt Injection, jailbreaking, data poisoning, and supply chain vulnerabilities.
- Zero-Trust Integration for Agents: Applying Zero Trust principles specifically to AI Agents to mitigate escalating cyber risks. Key insights from the Anthropic Zero Trust Playbook for AI Agent Security Summary include:
- Framework Adoption: Utilizing Anthropic’s “Zero Trust for AI Agents” playbook to establish robust security boundaries for autonomous systems.
- Risk Mitigation: Addressing the unique threat vectors introduced by agentic workflows, where traditional perimeter security is insufficient.
- Operational Security: Implementing strict verification and least-privilege access controls for agent interactions with external tools and data sources.