AI Agent Security Testing
Your agents act. We test what they can be made to do.
AI agents don't just answer, they act. We test the tools, permissions, and autonomy behind your agents: MCP servers, over-permissioned coding agents, and the injection paths that turn an agent against your own systems.
Overview
An AI agent is a language model with hands. It connects to tools, calls functions, reads and writes to your systems, and increasingly acts on its own across multiple steps. That autonomy is what makes agents useful, and what makes them dangerous when an attacker can influence them. We test agents adversarially: recovering system prompts to enumerate the tools they can call, abusing connections that carry more access than the job needs, exploiting the MCP servers that broker that access, and planting instructions in the content agents process. The coding agents reviewing your pull requests and the chat agents wired into your internal tools are where we start, because that's where access and autonomy concentrate.
What We Test
We target eight areas where an agent's ability to act becomes an attacker's ability to act through it.
Tool & Function Abuse
Agents connect to real tools: repositories, ticketing, cloud APIs, internal services. We manipulate the agent into misusing those connections, making calls and taking actions well beyond its intended job.
MCP Server Security
Model Context Protocol servers hand agents their tools. We evaluate and exploit that layer against the OWASP MCP Top 10: tool poisoning and tool-description injection (line-jumping), shadow and over-permissioned servers, supply-chain tampering, and injection through the data an MCP tool returns.
Over-Permissioned Agents
Coding agents that review pull requests, check code quality, or triage vulnerabilities often run with write access, secrets, or repo scope they never needed. We find the gap between what the agent does and what it can do.
System Prompt & Tool Enumeration
We jailbreak the agent to recover its system prompt, then enumerate the tools and functions it can call. That map becomes the target list for the rest of the test.
Indirect Prompt Injection
Agents act on content they process: a pull request, an issue, a document, a web page. We plant instructions in that content to hijack the agent's next action without ever touching the chat window.
Autonomy & Action Chains
Multi-step and multi-agent workflows let one manipulated decision cascade. We test where a single injection turns into a chain of unauthorized actions, and whether a human is actually in the loop where it counts.
Memory & Context Poisoning
Agents that keep memory or pull from shared context can be poisoned. We plant content that persists across turns or sessions to steer the agent's later decisions, including shared-memory poisoning in multi-agent setups.
Inter-Agent Communication & Identity
Where agents call and trust other agents, we test the messages between them and the identities behind them: spoofed or impersonated agents, unauthenticated inter-agent calls, and a rogue agent injected into the workflow.
Our Approach
We test agents the way we test any target, with an attacker's mindset, adapted to the tools, permissions, and autonomy that make agents different.
Recon & Tool Mapping
We map the agent's capabilities, connected tools, MCP servers, and data sources. What the agent can reach defines what an attacker will aim it at.
Access & Enumeration
We jailbreak guardrails to recover system prompts and enumerate the available tools and functions, building the attack surface from the inside out.
Exploitation
We abuse over-permissioned tools, chain actions across steps and agents, and exploit MCP connections to prove real impact: unauthorized changes, data access, and actions the agent was never meant to take.
Post-Exploitation & Pivoting
An abused agent is a foothold. From there we do what an attacker would: use the agent's credentials and tool access to escalate privileges, reach systems beyond the agent's mandate, and move laterally into your repositories, cloud, and internal services. The point is how far a compromised agent reaches, not just that it can be manipulated.
Documentation
Every finding comes with reproduction steps, evidence, severity, and remediation, mapped to the OWASP Top 10 for Agentic Applications (2026) and MITRE ATLAS techniques.
Common Findings
These are issues we frequently discover during ai agent security testing engagements:
Over-Permissioned Code Agent
CriticalA PR-review or code-quality agent running with write access to the repo, CI secrets, or production systems it never needed. One injection turns it into an insider.
Exploitable MCP Connection
HighAn MCP server the agent trusts implicitly, exposing tools with broader reach than the task requires or returning attacker-controlled data the agent acts on without question.
System Prompt & Tool Disclosure
HighThe agent's system prompt and tool list recovered through jailbreaking, handing an attacker the full map of what to target next.
Indirect Injection via Processed Content
HighInstructions planted in a pull request, issue, or document that the agent reads and acts on, redirecting its behavior with no access to the chat interface.
Missing Human-in-the-Loop
HighHigh-impact actions (merging code, moving money, changing configuration) executed autonomously, with no approval gate an attacker has to get past.
Cascading Multi-Agent Action
HighOne manipulated agent instructing another, so a single injection propagates into a chain of actions no single guardrail is positioned to catch.
Common Questions
How is agent testing different from LLM penetration testing?
Do you test coding agents and PR-review bots?
What is MCP and why does it matter?
Do you need production access to test our agents?
Related Testing
AI & LLM Penetration Testing
Adversarial testing of the underlying model behind your agents.
AI Application Security Review
Review the application and integration your agents live inside.
OWASP AI Security Guide
The OWASP Top 10 for LLM and Agentic Applications we map findings to.
Other AI Security Options
AI & LLM Penetration Testing
We test your deployed AI systems adversarially (prompt injection, jailbreaking, agent manipulation, data extraction) using the same attacker mindset we bring to every engagement.
AI Application Security Review
We review how your applications integrate AI -- data flows to models, output handling, agent connections, RAG pipelines -- finding the vulnerabilities that live at integration points.
AI Security Assessment
We inventory every AI tool in your environment -- chatbots, copilots, agents, embedded SaaS AI -- and assess governance, access, and data handling against current frameworks.
Ready to Strengthen Your Defenses?
Schedule a free consultation with our security experts to discuss your organization's needs.
Or call us directly at (445) 273-2873