Guides
Definitive, evergreen guides to the attack surfaces of agentic AI. The deep references behind the daily research.
MCP Security: The Complete Guide
MCP security is the practice of securing the Model Context Protocol, the open standard that lets AI agents discover and call external tools, data sources, and APIs through MCP servers. The core risk is that an MCP server is untrusted input to the agent: its tool definitions, schemas, and runtime outputs all enter the model context and can carry instructions, so a malicious or compromised server can redirect the agent's behavior, exfiltrate data, or abuse the agent's existing privileges. Securing MCP means treating every connected server as a hostile boundary and constraining what the agent is allowed to do on the other side of it.
Prompt Injection Testing: A Practical Guide
Prompt injection testing is the practice of attacking an LLM application with adversarial inputs to find where untrusted text can override the system's intended instructions. It treats the model as a non-deterministic target, so you run many variations of each attack and measure how often the application follows the attacker instead of its own rules. Done well, it produces a reproducible corpus of attacks and a success rate per attack class, not a single pass or fail.
Agentic AI Red Teaming: A Practitioner's Guide
Agentic AI red teaming is the adversarial testing of AI systems that plan, make decisions, and take actions over many steps using external tools, rather than answering a single prompt. The goal is to find sequences of inputs and conditions that drive an autonomous agent to misuse its tools, exceed its authority, leak data, or cause real-world impact, then prove those paths end to end. It treats the agent as an attack surface that holds state and credentials across a session, not as a stateless text box.
AI Agent Security: Threats and Defenses Guide
AI agent security is the practice of protecting LLM-driven agents that can take actions in the world: agents that call tools, run code, query databases, send messages, and persist memory across sessions. It matters because the failure mode changes when you give a model autonomy. A jailbroken chatbot says something wrong; a jailbroken agent with tool access executes code, exfiltrates data, or modifies records. AI agent security treats the model as an untrusted component inside a system and applies access control, sandboxing, output validation, monitoring, and human oversight around it.