AI agent security testing

We assess how agents use tools, memory, and approvals in real workflows. Scoped, non-disruptive testing that shows where controls hold and where they need tightening.

Clear scope, agreed boundaries, and practical findings your team can act on.

The agent threat model

AI agents blend LLM reasoning with the ability to call tools, write code, and act on real systems. That mix creates new ways for an attacker to steer outcomes by shaping what the agent sees or remembers.

We use this model to design tests that check tool policies, approval gates, and memory boundaries under realistic workflows.

Instruction injection

Malicious or misleading instructions embedded in data the agent processes.

Tool abuse

Guiding the agent to use legitimate tools for unintended actions.

Memory poisoning

Altering stored context so future decisions drift from intended behavior.

Confirmation bypass

Inducing the agent to route around human-in-the-loop checks.

Privilege escalation

Chaining low-privilege steps into higher-impact outcomes.

What We Test

Agents are most often steered through the tools they can call, the approvals they rely on, and the memories they keep. We model those behaviors in controlled workflows to confirm which guardrails hold and where they need tightening.

Tool Invocation Controls

Testing whether agents can be guided to invoke tools outside intended scope.

Unauthorized tool invocation
Parameter manipulation
Tool chaining abuse
Scope boundary drift

Human-in-the-Loop Controls

Testing the effectiveness of confirmation dialogs and approval workflows.

Confirmation bypass techniques
Social engineering via agent outputs
Approval fatigue exploitation
Emergency override abuse

Memory & Context Manipulation

Testing how persistent memory and context windows can be seeded to alter behavior.

Memory injection attacks
Context poisoning
History manipulation
State corruption

Multi-Step Attack Chains

Testing sequences where low-risk actions combine into higher-impact outcomes.

Privilege escalation chains
Lateral movement patterns
Data staging and exfiltration
Persistence mechanisms

Example findings from AI agent assessments

We document where controls are dependable and where they can be steered off-course, with clear remediation guidance and a path to re-test.

Tool scope drift through ambiguous parameters

An agent follows a legitimate request but expands tool parameters beyond intended boundaries when inputs are loosely validated.

Resolution: Tighten tool schemas, enforce allow-listed parameters, and log denied invocations for review.

Approval fatigue in repeated confirmations

Human-in-the-loop prompts become repetitive and vague, making it easier to approve higher-impact actions without full context.

Resolution: Require explicit action summaries, rate-limit repeated approvals, and enforce step-level confirmations for sensitive tools.

Memory contamination across workflows

Persistent memory carries over unintended context that alters later decisions in unrelated tasks.

Resolution: Segment memory by workflow, expire sensitive context, and reset state on task boundaries.

Multi-step escalation from low-risk actions

A sequence of minor actions combines into a higher-impact outcome that was not reviewed as a whole.

Resolution: Model chain-level permissions, add cumulative risk checks, and require approvals for action bundles.

FAQ: AI agent security testing

What do we receive at the end of the assessment?

A clear report that ties findings to specific agent workflows and tools, with evidence, impact notes, and practical fix guidance your team can review internally.

How is scope defined for an agent engagement?

We document the exact workflows, tools, data sources, and approval gates in scope, plus what is explicitly out of scope. That scope is agreed before testing begins.

Do you need source code, or can this be black-box?

Both are possible. Black-box testing is often realistic for agent behavior, while white-box access can improve coverage. We decide the mix upfront.

Where do you test, and how do you keep it safe?

We prefer staging or sandbox environments with test accounts. If production testing is needed, it is limited, explicitly approved, and designed to avoid disruption.

Can you re-test after fixes are made?

Yes. We can validate remediations and update the report so you have a clean, defensible record of what was resolved.

Explore AI security testing

Related AI security services and resources

Move from AI security concepts into testing scope, agent risks, prompt injection, MCP exposure, and practical assessment paths.

Service

AI & MCP Security Testing

Product security testing for AI apps, agent workflows, MCP tools, prompts, and connected data sources.

Service

LLM Integration Security Testing

Security testing for LLM features, RAG workflows, prompt handling, tool calls, and connected data exposure.

Service

MCP Server Security Testing

Scoped testing for transport security, tool safety, prompt injection, OAuth hygiene, and access boundaries.

Glossary

AI Red Teaming

Adversarial testing for AI-enabled product behavior, tools, retrieval, agents, and workflows.

Guide

AI Red Teaming for LLM Applications

How to scope adversarial testing for LLM apps, RAG, agents, tools, MCP, and workflow actions.

Guide

AI Red Teaming vs AI Security Testing

How adversarial AI behavior testing fits with broader product and system security testing.

Glossary

LLM Security

Risks and controls for LLM applications, RAG systems, embeddings, and model-connected workflows.

Glossary

Prompt Injection

How malicious instructions enter prompts through users, documents, retrieved content, and tool output.

Glossary