Instruction boundaries
System prompts, user instructions, retrieved content, tool descriptions, hidden instructions, role separation, and instruction precedence.
AI and Agent Security
Secure agents before they act.
Review instructions, memory, retrieval, tools, permissions, and approvals before agent authority expands.
Make autonomy observable, bounded, and accountable.
System map
The surface is mapped before the work begins.
Each Solutions page uses the same operating view: define the trust surface, identify the review loop, and make the evidence usable for builders and leaders.
AI and Agent Security
Trust map
Instructions
Identity
Tools
Retrieval
Memory
Evidence
Review loop
Frame
Map
Inspect
Evidence
Context
The model is only one part of the attack surface.
Agent security is often reduced to prompt injection or output filtering. Those problems matter, but they represent only part of the system.
Practical exposure can emerge from the instructions an agent receives, the information it retrieves, the tools it can call, the credentials available to it, the actions it can perform, the approvals it can bypass, and the evidence left after a decision.
A model may behave exactly as designed while the surrounding system still creates an unacceptable outcome.
What SecureSpace examines
Agent identity
How an agent is represented, authenticated, scoped, delegated, revoked, and associated with a human or organisational owner.
Tool access
Which tools are exposed, what arguments are accepted, what resources are reachable, and how dangerous actions are constrained.
Retrieval and provenance
Where context originates, whether it can be manipulated, how evidence is attributed, and which sources influence decisions.
Memory
What information is retained, how long it persists, who can access it, and whether memory can introduce cross-user or cross-task leakage.
Permissions and authority
The difference between what the agent can observe, recommend, request, modify, approve, or execute.
Human approval
When approval is required, whether the reviewer has sufficient context, and whether approval becomes a meaningful control rather than a repeated click.
Monitoring and evidence
Logs, tool traces, model decisions, approval records, identity context, failure signals, and the ability to reconstruct what happened.
Common situations and failure patterns
01
Prompt injection through user input
02
Hidden instructions inside retrieved content
03
Poisoned tool descriptions
04
Excessive tool permissions
05
Agent access to secrets
06
Uncontrolled memory retention
07
Cross-user context leakage
08
Weak separation between read and write authority
09
Agents executing irreversible actions
10
Approval fatigue
11
Missing ownership
12
Incomplete logging
13
Agent-to-agent trust without verification
14
Unsafe fallback behaviour
15
Output used as trusted instructions
16
Failure to contain repeated autonomous actions
How SecureSpace approaches the work
01
Define the system
Identify the model, agent, tools, data sources, workflows, environments, identities, and human stakeholders.
02
Map context and authority
Document what the agent can know, what it can request, what it can change, and who remains accountable.
03
Identify trust boundaries
Separate trusted instructions, untrusted content, internal data, external services, tool outputs, and human decisions.
04
Model realistic abuse
Test how a malicious user, compromised source, unsafe tool, mistaken instruction, or over-permissioned agent could change system behaviour.
05
Review controls
Examine isolation, permissions, validation, approval, monitoring, retention, rate limits, sandboxing, and failure containment.
06
Create evidence
Produce a clear record of system assumptions, risks, decisions, control ownership, and recommended next steps.
What the work can produce
AI-system threat model
Agent trust-boundary map
Tool-risk inventory
Prompt and instruction review
Permission and authority analysis
Retrieval and provenance review
Memory-risk assessment
Approval-workflow recommendations
Adversarial test scenarios
Control-gap analysis
Prioritised remediation plan
Leadership summary
Research questions requiring deeper study
Who it is for
Teams that need clarity without slowing the build.
Teams launching AI agents
AI-first SaaS companies
Developer-tool companies
Enterprise AI teams
Security organisations reviewing internal agents
Research laboratories
Regulated teams introducing AI workflows
Platform teams connecting models to internal systems
Mintos AI
Agent-aware context is central to the Mintos AI direction.
Mintos AI is being designed around the idea that intelligent systems need security context across instructions, identities, tools, data, permissions, actions, approvals, and evidence.
SecureSpace's AI and agent security work helps test which parts of this model are operationally useful. The public product architecture remains intentionally limited until the foundation is ready.
What this work should not overclaim
An AI-security review cannot guarantee safe model behaviour under every possible condition.
Results depend on scope, access, system maturity, test coverage, model behaviour, third-party systems, and the ability to observe real workflows.
SecureSpace does not describe a system as secure merely because it passes a limited set of prompt-injection tests.
Mintos AI is still being developed. Only features explicitly labelled available should be treated as live.
Questions teams usually ask
Is this only prompt-injection testing?
No. Prompt injection is one part of a broader system involving context, tools, permissions, retrieval, memory, identity, approval, and external actions.
Can SecureSpace review an internal enterprise agent?
Yes, subject to scope, access, data-handling requirements, and confidentiality.
Does this replace model evaluation?
No. Model evaluation, application security, agent security, and operational governance address different parts of the system.
Can SecureSpace test third-party models?
SecureSpace can assess how third-party models are integrated and governed. Model-provider internals may remain outside the available scope.
Does this automatically make an AI system compliant?
No. Security review and compliance are separate processes.
Is Mintos AI already providing these controls?
Mintos AI is still being developed. Only features explicitly labelled available should be treated as live.
Continue from here
Next step
Start with the system, not the category label.
Tell us what you are building, which decision is becoming difficult, and where the security boundary feels unclear.