AI agent security is the discipline of protecting autonomous LLM-based systems — which plan, decide and act through tools, MCP and plugins — against tool abuse, excessive agency and prompt injection. Berghem combines Red Team, sandboxing, least privilege and continuous monitoring to contain what each agent can do.
An AI agent is a system that uses an LLM as its reasoning engine to plan and execute tasks autonomously, invoking external tools, APIs, databases and other agents. That autonomy widens the attack surface: every connected tool, every MCP connector and every plugin becomes a path an attacker can use to divert the agent's behavior.
Unlike a chatbot that only returns text, an agent acts in the world — it sends emails, moves money, changes records, runs code. When an agent is induced to act against the business, the impact is operational and immediate, not just informational. Protecting agents means treating the LLM as an untrusted component and constraining what it can actually do.
Agent threats come from pairing a non-deterministic LLM with the power to act. The categories below account for most real-world incidents.
When an agent can reach powerful tools without clear limits, an attacker can chain them for unintended effects — exfiltrating data, running commands or triggering transactions. Risk grows with the number and privilege of the tools exposed to the agent.
Excessive agency means granting the agent more permissions, autonomy or functionality than the task requires. It is an OWASP Top 10 for LLM category and the root cause of many incidents: the agent does something destructive simply because it had access.
Malicious content hidden in an email, web page, document or API response can reprogram the agent mid-execution (indirect prompt injection). The agent reads the data as instruction and starts serving the attacker — with no visible sign to the user.
Every MCP server, plugin or connector adds third-party code and new permissions to the agent. A compromised or misconfigured connector can leak credentials, widen privilege or introduce hostile tools into the execution chain.
In multi-agent architectures, one agent's output becomes another's input. Without validation, an injection in one link propagates across the chain, and implicit trust between agents turns a local flaw into systemic compromise.
Knowledge bases, long-term memory and RAG pipelines can be poisoned with content that only acts later, when retrieved. The agent pulls the planted instruction and runs it as legitimate, well after the moment of attack.
There is no single control. Agent security is defense in depth: each layer reduces the damage possible when the previous one fails.
Each tool gets only the permissions it strictly needs, with scopes, allowlists and segregated credentials. Lowering the privilege of each action limits the damage when the agent is subverted.
Code execution, browsing and tool calls run in isolated environments, with no direct access to secrets, the internal network or production systems. The sandbox contains the blast radius of a malicious action.
High-impact actions — payments, deletions, external data sends — require explicit human confirmation. Manual approval breaks automatic abuse chains before they produce an irreversible effect.
Tool inputs and external data are treated as untrusted; agent outputs pass through filters before becoming action. Separating data from instructions mitigates direct and indirect prompt injection.
Every decision, tool call and result is logged and watched in real time. Telemetry detects behavioral drift, enables fast response and supports post-incident investigation.
Clear policies define which tools each agent may use, under what conditions and with what oversight — aligned to frameworks such as ISO 42001 and the OWASP Top 10 for LLM.
We take the agent surface into AI Red Team and AI pentest, and anchor the controls in governance and in our own security agents.
We simulate real adversaries against your agents: prompt injection, tool abuse and MCP escalation, with end-to-end impact evidence.
Technical testing focused on agents, tools and integrations, mapped to the OWASP Top 10 for LLM and MITRE ATLAS.
Structure policies, roles and controls across your agents' full lifecycle, from design to decommissioning.
Readiness for the ISO/IEC 42001 AI management system (AIMS), with controls applicable to autonomous agents.
Our own autonomous security agents, operated under the same least-privilege and human-oversight principles we recommend to clients.
Red Team and penetration testing across the whole organization, beyond the AI layer.
In a 30-minute conversation we map your agent surface — tools, MCP, plugins and integrations — and design the right testing and hardening scope.