Request a quote

Your agent can act.
Should it be allowed to?

An agent is more than a prompt. Its tools, permissions, memory, APIs, and approval steps determine the damage a bad instruction can cause. Start with the boundaries around real actions.

Discuss your agent’s security →

Security assessments for AI-agent builders

InstructionPermissionAction

What a useful assessment looks like

  1. Map the authority

    Identify what the agent can read, change, send, or execute.

  2. Test the boundary

    Use a controlled environment and synthetic data to assess trust assumptions.

  3. Verify the control

    Confirm the application enforces permissions even when the model makes a bad choice.

“What happens if untrusted text becomes an instruction?”

An illustrative support agent reads a customer message and can update a record. The meaningful question is not whether the model follows the message. It is whether the system lets it exceed the user’s authority.

Choose a stage to see the decision and what to prepare. This is an illustrative scenario, not a client case study.

A document is data, not permission.

123
  1. Untrusted input
  2. Tool permission
  3. Action result
Follow the boundary from a message to a permitted action.
Example input
A retrieved customer message
Trust decision
Content does not grant authority
Why this matters

Map external messages, retrieved documents, and tool responses. Identify where untrusted content could influence the agent’s next action.

Authorization belongs outside the model.

123
  1. Untrusted input
  2. Tool permission
  3. Action result
Follow the boundary from a message to a permitted action.
Example action
Update a customer record
Required check
Caller can change this record
Why this matters

Check the tool gateway and application APIs. Keep credentials narrowly scoped and require human approval for consequential actions.

Test the denied path too.

123
  1. Untrusted input
  2. Tool permission
  3. Action result
Follow the boundary from a message to a permitted action.
Test request
A different account’s record
Expected result
Denied and logged
Why this matters

Use synthetic cross-account requests and an explicit deny condition. Confirm the action is blocked, logged, and understandable to the operator.

Scope the work around your situation.

Abhimanyu has performed multiple AI-agent security audits. Agent-specific coverage is agreed per system; this is not a claim of exhaustive model testing or a guarantee against prompt injection.

Explore the methodology →
  • Web and API foundationsAuthentication, tenant isolation, exposed endpoints, and sensitive data paths around the agent.
  • Agent-specific trust boundariesReview tool permissions, prompt/data separation, approval steps, and retrieval boundaries according to the agreed scope.
  • Code and integration reviewInspect the code that grants authority, handles secrets, and decides which actions require a human.

Understand the risk before the call.

Why correct code can still create unsafe workflows.

Read the guide →

Ready to share the context?

Discuss your agent’s security →