Prompts
System instructions, user input, and indirect instructions entering through external content.
Evidence-led assurance
We combine structured adversarial coverage with manual validation, so your team receives reproducible evidence instead of an unfiltered scan.
Trust boundaries
It appears where data, authority, instructions, and generated output cross into the surrounding application.
System instructions, user input, and indirect instructions entering through external content.
Documents, indexes, permissions, provenance, and untrusted content returned to the model.
What different users can ask, see, influence, and carry across sessions.
What the system can discover, call, change, or send through connected services.
What persists, who can alter it, and whether context crosses users or approved boundaries.
How generated content reaches browsers, tickets, data stores, and operational workflows.
The workflow
Document the target, roles, data boundaries, exclusions, rate limits, and written authorization.
Run the relevant adversarial scenarios across the application trust boundaries.
Reproduce important behavior manually and remove noise before it becomes a finding.
Connect the observed control gap to practical mitigation and expected secure behavior.
Repeat focused test cases to verify agreed fixes against the original evidence.
What changes
Leadership can see which validated behaviors matter and why.
Each important finding connects evidence to mitigation and expected secure behavior.
The report distinguishes what was tested, what was excluded, and where assumptions remain.
Why this approach
Scroll horizontally to compare
| Capability | Aspexa assessment | Scan-only review | Internal ad-hoc testing |
|---|---|---|---|
| Application-specific scope | yes | limited | variable |
| Structured automated coverage | yes | yes | variable |
| Manual validation | yes | no | variable |
| Payloads and reproducible evidence | yes | limited | variable |
| Mitigation guidance | yes | limited | variable |
| Focused retest | yes | no | variable |