Missionloops · research seat (open) · the hacker mindset
How a white-hat hacker actually thinks.
And whether that mind can be trained, not just found. This is a research seat for the scientist who studies how hackers, reverse engineers, and red-teamers reason. The people we want to train are not programmers but regular Canadians up against a lawsuit or a bureaucracy far larger than they are, and the question is whether the reasoning transfers to them. It comes with an instrument being built for exactly this question.
A way of reasoning these fields share
The practitioner is handed a system that will not explain itself, and refuses to take it at its word. Instead of following the rules it advertises, they work out the rules it runs on, and look for the requirement its designers never thought to question. The procedure a system publishes and the logic it runs on are not the same thing, and this is the reasoning that tells them apart.
Software is where this reasoning has been studied best, because the answer is checkable. The reasoning itself is not about software. A person facing an unfair decision from an institution that will not explain itself needs the same move: work out the rules it actually runs on, fast, and find the angle of approach that sits outside the frame its own people are standing in. White-hat means what it means in security. The move works inside the law. The aim is the position the other side has to accept, not the one it fails to catch.
What is that reasoning? Is it one capacity or several? And can it be taught to someone who does not already have it? Those are the questions this seat is open to study.
What the literature already shows
A research literature studies this move directly, by watching skilled practitioners do it and writing down how. Much of it comes from the usable-security line of Daniel Votipka, Michelle Mazurek, and colleagues. It is one line among several. A reader in reverse engineering or attribution will have findings of their own to set beside it. The findings below are specific and recur across studies. Each links to its paper.
- The expert does not trust the system's account of itself. They probe it, watch how it responds, and build a working model from the traces it leaves, the way a reverse engineer reconstructs a program's logic from its behaviour rather than its documentation.
- The commonest gap is conceptual. In the one study that counted, simple slips were the rarest class, and when present they were nearly always found. The common failure is the unintuitive requirement the builder's own mental model never represented: something the designer could not see, because they were standing inside their own assumptions.
- Inside access can blind the holder. The person holding the full internal picture inherits the builder's blind spots, while the outsider, forced to reconstruct the system from outside, is often the one who notices the assumption the insider cannot.
- Practitioners credit real targets over formal training. In interviews the skill shows up built case by case, from a stock of prior encounters the practitioner reasons from by analogy.
- Two ingredients make it teachable rather than tacit: metacognition (predict what the system will do, then check against what it did) and transfer (being taught when and why a technique applies, not only how to run it). Solo practice supplies neither reliably.
That last finding is why the platform has a second person in it. The design pairs every operator with a guide, a person the operator already trusts, whose job is to ask for the prediction before the check and to name the principle behind the move. Whether the pairing supplies both ingredients is one of the things this seat exists to test.
The open questions
Start with the one everything else depends on: is rule-discovery a skill the platform trains, or a trait it only reveals? Our proposal leans hard on the first reading. Its whole innovation and dual-use case assume ordinary operators can be taught to find a hidden ruleset and redesign their position against it, and that is the claim we cannot judge for ourselves. If the wins come instead from people who would have solved it anyway, the case studies are survivorship stories, and the claim has to soften from "trains" to "selects for and showcases." Trained, untrained, and placebo arms on a held-out problem tell the two apart, and settle it against us if that is where the evidence goes.
Your field has a strong claim on this question. Hacking and troubleshooting give a hard binary outcome: the exploit lands or it does not, and the rule yields or it does not. That is the ground truth the trained, untrained, and placebo design above is built to use. Three more questions extend the reasoning this seat studies. They are the last three studies in the menu below.
Why the platform is the instrument
The evidence base for how adversarial reasoners think is built almost entirely on small samples, observed through think-aloud and interview, because the people doing this reasoning under real stakes are few, busy, and hard to instrument.
Missionloops is being built as a real-stakes operator network for reverse-engineering real systems: a regulator's classification logic, a procurement rule that contradicts itself, a dispute with a party that can afford more lawyers, a hard negotiation, a Northern build-out stuck on a rule no one in the field controls. The telemetry is designed content-blind, built to record loop depth, revisions, where the operator got stuck and how they got unstuck, without ever reading the problem itself. Once built, it would not replace the think-aloud work. It would put structural traces at scale beside the deep maps the field already has, against problems where the rule either gives way or holds.
The studies this seat can run
Take these as a starting menu: recast any toward your own published line, or bring a question we did not anticipate.
- Rule-Discovery as Innovation Is rule-discovery trained or only revealed? The study with a hard yes-or-no outcome.
- Trained Judgment Under AI Denial and Compromise The white-hat literature assumes a system that answers honestly. Does trained judgment hold when the AI assisting the operator is denied, or quietly turned against them?
- Symmetric Adoption in Negotiation Reconstructing how the other side reasons has a cooperative twin: modelling what a counterpart values, to find the move that is cheap for you and worth a lot to them. Does real-stakes practice train it, and does it show up on both sides of a negotiation?
- Veteran Integration and Network Hardening The reasoning that finds a flaw in a system can spot an attempt to work one on a person. Does a network seeded with people who carry that pattern recognition resist intrusion better than one without them?
The seat, and the terms
The seat is open, and the ask is a conversation. The terms are a free option: tell us the question you would bring, and commit only if the work is funded.
We are looking for the researcher in Canada whose work already is this: reverse engineering and attribution (the craft of reading what a system or its makers left behind), usable security and human factors, red-team and threat-model reasoning, the cognitive science of expertise, or the evaluation methodology that decides whether a claimed skill was measured. If that describes your work, the platform is being built as the substrate this question needs.
scott (at) missionloops (dot) ca
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