AI Platforms:
Vulnerability Discovery
Guides covering vuln discovery in LLM, RAG, MCP, and AI infra
Detecting Insecure Tool Integrations in AI Agents
Identifies security flaws in AI agent tool integrations, covering authentication gaps, input validation failures, and unsafe API connections that expose systems to exploitation.
Detecting Prompt Injection and System Prompt Boundaries
Identify prompt injection vulnerabilities in AI systems by probing instruction boundaries, testing context isolation, and detecting system prompt leakage or override weaknesses.
Detecting AI Supply Chain and Infrastructure Weaknesses
Identifies vulnerabilities in AI supply chains including model dependencies, training pipelines, third-party components, and infrastructure misconfigurations across AI platforms.
Detecting Weak Access Controls on Inference Endpoints
Identify misconfigured AI inference endpoints exposing unauthenticated or over-permissioned access through API probing, header analysis, and permission enumeration techniques.
Identifying Exposed MCP Surfaces
Techniques for discovering exposed Model Context Protocol endpoints, authentication gaps, and misconfigured AI platform attack surfaces during reconnaissance.
Identifying RAG Pipeline Poisoning Opportunities
Techniques for identifying attack surfaces in RAG pipelines, including document ingestion vectors, embedding manipulation, and retrieval mechanism abuse for security assessment.
Vulnerability discovery for AI systems confirms specific weaknesses before moving to exploitation. The goal is to prove a condition is wrong , not just that a surface exists , without yet using it for access or impact. AI vulnerability discovery spans multiple layers: the model's input handling, the retrieval pipeline's access controls, the tool integration's permission scope, and the infrastructure's network exposure.
Many AI vulnerabilities are configuration issues rather than code bugs: a vector database with no authentication, a retrieval endpoint that ignores tenant isolation, an agent granted write access to production systems, or a fine-tuning pipeline that loads models from public registries without verification. These conditions are confirmed through targeted probing that stops short of exploitation, producing evidence of the weakness without triggering the full impact.