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.
Safe next step
Talk through your AI agent scope.
No commitment required.
Share the tools, workflows, and approval gates you care about. We will outline a scoped assessment and provide a fixed quote if you want one.
Start a conversationor View a sample report first