AI Agents Are Powerful. Unsecured, They're a Liability.
Autonomous AI agents can browse the web, write code, call APIs, and take actions on your behalf. Without the right security controls, they can be manipulated, hijacked, or exploited to cause real harm. We help you design and audit agents that are powerful and trustworthy.
Why Agent Security Matters
Autonomous agents operating with real capabilities introduce risks that traditional application security was never designed to address
Prevent Agent Hijacking
A manipulated AI agent can exfiltrate data, take destructive actions, or silently serve an attacker's goals. Our security testing identifies every path an attacker could use to control your agent's behaviour before it reaches production.
Limit Blast Radius
Autonomous agents have broad capabilities by design. We apply least-privilege principles and containment strategies so that even a compromised agent can only do limited, recoverable damage - not cause a full system breach.
Build User & Customer Trust
AI agents making decisions on behalf of your users carry your reputational risk. Demonstrating that your agents are independently security-tested builds confidence with customers, enterprise buyers, and regulators.
What's Included
Comprehensive security testing and hardening for AI agent systems at every layer
Agent Architecture Security Review
We analyse the design of your AI agent - its decision loops, memory systems, tool access, and external integrations - and identify structural weaknesses that could be exploited to hijack or manipulate the agent's behaviour.
Prompt Injection Vulnerability Testing
We attempt to manipulate your agent through malicious instructions embedded in tool outputs, retrieved documents, or user inputs. Direct and indirect prompt injection vulnerabilities are tested systematically across all input surfaces.
Tool-Calling and API Permission Scoping
AI agents that call APIs, query databases, or execute code need precisely scoped permissions. We audit your tool-calling permissions and apply least-privilege principles - ensuring agents can only do exactly what they're designed to do.
Agent Sandboxing & Containment Strategy
We design and validate containment strategies that limit the blast radius of a compromised agent - including network isolation, execution sandboxes, and break-glass controls that prevent agents from taking irreversible or out-of-scope actions.
Multi-Agent Trust Boundary Analysis
When multiple agents communicate and delegate tasks, trust assumptions can be exploited. We assess inter-agent communication, orchestrator/subagent relationships, and shared memory access to ensure no agent can be used as a pivot point into another.
Monitoring & Anomaly Detection for Agentic Systems
We help you instrument your agents with the right observability for security - logging tool calls, tracking execution paths, and setting up anomaly detection that flags deviation from expected agent behaviour in real time.
Our Testing Methodology
A structured adversarial approach to finding and fixing agent vulnerabilities
Threat Model the Agent
We start by mapping your agent's capabilities, data flows, tool integrations, and trust boundaries. A bespoke threat model identifies the highest-risk attack paths specific to your agent's architecture and use case.
Red Team the Agent
Our team conducts adversarial testing against your agent - attempting prompt injection, permission escalation, tool abuse, and indirect manipulation via external data sources. We also test multi-agent scenarios where applicable.
Harden & Report
We deliver a detailed report of all findings with severity ratings, proof-of-concept reproduction steps, and precise remediation guidance. We include architectural recommendations and re-test after fixes are applied.
Why Choose IronProbe for Agent Security?
Built for engineering teams working with LangChain, AutoGen, CrewAI, Semantic Kernel, or custom agentic implementations.
Agentic Security Specialists
We aren't applying traditional pen testing tools to a new problem. Our team has built and broken AI agents - we understand agentic architectures from the inside.
Developer-Ready Findings
Every finding comes with reproduction steps, root cause analysis, and code-level remediation guidance your engineering team can act on immediately.
Outcome: Agents That Behave
Our goal isn't just a findings report. It's agents that do what you intend - and nothing more. We retest after fixes to verify the outcome.
Frequently Asked Questions
Common questions about AI agent security testing
Prompt injection occurs when malicious instructions are introduced into an AI agent's input - either directly by a user or indirectly through data the agent retrieves (a webpage, a document, an API response). Unlike traditional injection vulnerabilities, prompt injection can cause an AI agent to completely change its behaviour: exfiltrate data, call unintended APIs, ignore its safety instructions, or take destructive actions. Because agents can take real-world actions, the consequences of successful injection can be severe.
We work with all major agentic frameworks including LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel, and custom implementations. Our security testing methodology is framework-agnostic - we focus on the security properties of the agent's architecture, not the specific library used to build it. If you're using a bespoke or proprietary framework, we'll adapt our approach accordingly.
We strongly recommend pre-deployment testing, but we can work at any stage. Pre-launch testing typically offers better value because vulnerabilities can be fixed without operational disruption. That said, we regularly conduct security assessments on production agentic systems, particularly when a new attack vector is identified or after a significant capability upgrade. Both approaches result in written findings and verified fixes.
LLM pentesting focuses on the model itself - its susceptibility to jailbreaks, prompt extraction, and data leakage. AI agent security is broader: it covers the entire agentic system, including tool integrations, permission models, orchestration logic, multi-agent communication, and the real-world actions agents can take. Agents introduce unique risks that don't exist in a standard LLM chatbot and require a specialist assessment approach.
Build Agents That Do What You Intend - and Nothing More
Don't deploy an AI agent that's one injected instruction away from a breach. Let us test and harden it first.
Get a Free Agent Security Assessment