Independent security research

Security research,
grounded in
observation.

We study exposed AI systems and build defensive tools that turn unexpected behavior into evidence you can inspect.

FIELD WORK / 01INPUT → EVIDENCE
From an exposed surface to a reviewable record An architectural illustration. Incoming requests cross a capture boundary into a preserved record, then move to human review. It does not display live traffic. CAPTURE BOUNDARY REQUEST RECEIVED PRESERVED RECORD REVIEW OBSERVE FIRST. INTERPRET CAREFULLY.
Conceptual workflowHoneyglass / LLM exposure
01 AI exposure02 Defensive systems03 Software trust↓

TOOLS FOR THE INVESTIGATION

Built to look closer.

Practical systems that make evidence visible and consequential actions deliberate.

02 / DEFENSIVE ENGINEERING

VoidRMM

Remote management with signed agent updates, auditable actions, and explicit operator control. A continuing engineering focus on trust across a fleet.

Engineering on GitHub

THE RESEARCH NOTEBOOK

Methods before
conclusions.

Notes on what we can observe, what we can infer, and where the evidence stops.

HOW WE WORK

Keep the boundaries explicit.

Research is useful when someone else can understand the method and assess the limits.

Observe precisely.

Distinguish submitted input, application behavior, and verified outcomes. Make the capture limits part of the evidence.

Bound capabilities.

Design around what untrusted input can reach. Treat permissions, network access, and recovery as part of the system.

Review before action.

Use enrichment as context. Tie external reporting to a considered reading of the evidence, with a person in control.

OPEN A CONVERSATION

Compare
notes.

Research questions, corrections, or relevant security findings? Get in touch with Kaspryx.

reports@kaspryx.com

Please leave credentials and sensitive raw captures out of an initial email.