AI Multi Agent SecurityAI Multi Agent SecurityTesting Services
AI agents don't just generate text. They take actions: calling APIs, modifying databases, sending emails, executing code. When an autonomous agent goes wrong, it doesn't just produce a bad output. It does something harmful. We test single-agent and multi-agent systems for tool misuse, unauthorised actions, privilege escalation, and coordination failures.
Agent Framework Expertise
We test LangChain, LangGraph, CrewAI, OpenAI Assistants, Autogen, and custom agent implementations
Autonomous Behaviour Testing
We verify what your agents can actually do vs. what they should be allowed to do, including tool misuse, unauthorised actions, and privilege escalation
Multi-Agent Coordination
We test agent-to-agent communication, delegation chains, and shared resource access for exploitation paths that emerge in multi-agent setups
Why AI Agents Need Specialised Security Testing
Traditional AI applications take an input and produce an output. AI agents are different. They reason, plan, use tools, and take actions in the real world. An agent connected to your email can send messages. An agent with database access can modify records. An agent with code execution can run arbitrary commands. The security implications go far beyond prompt injection.
Multi-agent systems add another layer of complexity. When agents delegate tasks to each other, share context, and coordinate actions, a vulnerability in one agent can cascade across the entire system. An attacker who compromises the "planner" agent can direct "executor" agents to carry out harmful actions. Traditional security testing doesn't model these interaction patterns.
Whether you're running a single AI assistant with tool access or a fleet of specialised agents working together, the question is the same: what's the worst thing your agents could do, and have you tested for it?
Our Testing Services
Single Agent Security
We test individual AI agents for the full range of autonomous behaviour risks: Can the agent be tricked into invoking tools it shouldn't? Can it be manipulated into taking destructive actions? Does it properly validate permissions before acting? We assess agents built on LangChain, LangGraph, OpenAI Assistants, CrewAI, Autogen, and custom frameworks.
Multi-Agent Coordination Security
In multi-agent systems, agents delegate tasks, share context, and pass instructions to each other. We test these interaction patterns for delegation abuse, context poisoning between agents, privilege escalation through agent chains, and scenarios where a compromised agent can manipulate other agents in the system.
Agent Tool Use & Action Safety
Agents that can call APIs, run code, modify files, or interact with external services have real-world impact. We test what happens when those capabilities are abused: unauthorised API calls, destructive file operations, data exfiltration through tool outputs, and actions that can't be undone once executed.
Agent Memory & State Security
Agents with persistent memory, conversation history, or shared state can be attacked through those channels. We test whether an attacker can inject instructions into stored memory, manipulate conversation state to alter future behaviour, or extract sensitive information that the agent has remembered from previous interactions.
Orchestration Framework Security
The framework running your agents is an attack surface too. We assess LangChain, LangGraph, CrewAI, Autogen, and custom orchestration layers for configuration vulnerabilities, insecure defaults, dependency risks, and weaknesses in how they manage agent permissions, tool access, and execution boundaries.
AI Agent Security Testing Checklist
Industry Applications
Enterprise Automation
Agents that manage workflows, process approvals, modify CRM/ERP records, and coordinate between departments. A rogue agent could approve unauthorised transactions, modify records at scale, or leak internal data through external tool calls.
Software Development
Coding agents that write code, deploy to production, manage repositories, and run CI/CD pipelines. An exploited agent could introduce vulnerabilities, push malicious code, or access secrets stored in deployment environments.
Customer Operations
Support agents with access to order systems, refund tools, and customer databases. An attacker using prompt injection could trigger mass refunds, extract customer PII, or modify account details.
Finance & Trading
Agents that execute trades, process payments, or manage portfolios. Unauthorised actions could result in financial losses, regulatory violations, or market manipulation if agent boundaries aren't properly enforced.
Autonomous Agents, Autonomous Risk
An LLM that produces bad text is one thing. An agent that takes bad actions is something else entirely. When autonomous agents have access to real systems, databases, APIs, and external services, a security flaw doesn't just leak data. It triggers actions. Actions that can be destructive, irreversible, and hard to detect until the damage is done. The EU AI Act specifically classifies certain autonomous AI systems as high-risk, requiring documented security assessments.
Your agents can send emails, modify records, call APIs, and execute code. Have you tested what happens when someone tells them to do something they shouldn't?
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