01THREAT MODELING
Map the human attack surface.
Identify where perception, attention, fatigue, persuasion, or interface design could create exploitable behavior in trusted workflows.
EARLY-STAGE VENTURE / SEEKING INVESTORS + STRATEGIC PARTNERS
SECURE THE LASTTRUSTED LAYER.
Watch the reconstruction59 seconds▶CAPABILITIESRESEARCH → TEST → DEFEND
WHAT WE DO
MAKE THE HUMAN LAYER HARDER TO EXPLOIT.
We identify how adaptive systems could exploit perception, attention, fatigue, and trust—then turn verified findings into practical defenses.
02ADVERSARIAL TRAINING
Test people and workflows safely.
Run controlled red-team pilots and scenario-based training to expose weak handoffs, warning failures, and authorization habits.
03HARDENING
Turn findings into controls.
Define interface requirements, operating procedures, assurance tests, and training that reduce human-layer risk.
Evidence-led research. Controlled evaluation. Practical security controls.
INCIDENT RECONSTRUCTION 03 / HYPOTHETICAL ATTACK PATHTHE HUMAN ATTACK CHAIN.
00:59 / ELEVENLABS NARRATION / CHAPTEREDINCIDENT DECODED / HYPOTHETICAL ATTACK PATH
HOW THE ATTACK WORKS.
A malicious video reaches a trusted user.
It arrives through an ordinary message and appears safe to open.
The visual signal causes a medical emergency.
During the user's confused recovery, the clip repeats a false instruction: ignore the next warning and allow access.
The user approves the AI agent.
The request is accepted inside a legitimate session, giving the agent access without bypassing the system's technical controls.
Visually triggered seizures are real. A reliable implanted command remains a hypothesis to be tested.
EVIDENCEKNOWN EFFECTS → ATTACK HYPOTHESIS → CONTROLS
START WITH WHAT IS REAL.
The presentation can unsettle. The evidence has to survive adversarial review.
01
KNOWN / SENSORY INPUT
A screen can cross into the body.
Rapid flicker and certain high-contrast patterns can provoke seizures in susceptible people. That is not speculation. It is proof that pixels can become a physical input.
02
KNOWN / OPERATOR ATTENTION
Visible does not mean noticed.
When attention is occupied, people can miss an unexpected object—or a major change—directly in front of them. The eyes receive it. Awareness does not.
03
OPEN / ADAPTIVE ADVERSARY
Can adaptive AI turn a human limitation into an attack path?
That attack path has not been demonstrated. We research whether a model could vary cues, observe responses, and personalize the sequence into a repeatable security risk.
METHODCYBERSECURITY × ADAPTIVE AI × HUMAN FACTORS
MNEMONIC ARCHITECT'S WORK
MAP.BREAK.HARDEN.
Map the human attack surface.
Identify plausible sensory, attentional, and decision-making failure modes, then validate the threat model through evidence review and controlled evaluation.
Test trusted workflows.
Use adversarial training and controlled red-team pilots to expose weaknesses in handoffs, warnings, and authorization—with appropriate oversight.
Convert findings into safeguards.
Turn validated findings into interface constraints, assurance tests, operating procedures, and role-specific training.
INVESTMENT + PARTNERSHIPSSEED CAPITAL / RESEARCH / PILOT PARTNERS
BACK THE RESEARCH.BUILD THE DEFENSE.
Mnemonic Architect is building a repeatable human attack-surface security method spanning threat modeling, controlled red-team pilots, and hardening guidance. We are seeking aligned capital and strategic design partners to validate the method and shape the first commercial products and services.
Direct contact / hello@mnemonicarchitect.com