Set the rules of engagement
Name the assets, identities, data, techniques, timing, observers, stop conditions, and evidence route before testing begins.
Authorized offensive research. Engineered remediation.
We test software, infrastructure, and AI systems that we own or have written permission to assess. We reproduce exploitable paths under controlled conditions, repair the root cause, and retest the original path and its nearest variants.
The exploit-to-patch loop
Our sequence follows the core logic of a technical security assessment: plan the test, conduct it, analyze the evidence, mitigate the weakness, and validate the mitigation. A scanner can start the search; it cannot finish the proof.
Name the assets, identities, data, techniques, timing, observers, stop conditions, and evidence route before testing begins.
Trace the violated trust boundary and build the smallest controlled proof that establishes impact without expanding the agreed scope.
Change the broken invariant in code, configuration, identity, or architecture. Where possible, remove the whole defect class instead of one symptom.
Replay the exploit, test realistic variants, and save regression evidence. The result states what was tested, what changed, and what remains outside scope.
Research and engineering
The deliverable is a defensible change: a reproducible trigger, an impact boundary, a root-cause repair, and evidence from the retest.
Source-assisted and black-box investigation of new failure modes, exploit primitives, trust-boundary mistakes, and assumptions that routine checks do not exercise.
Bounded proofs with explicit prerequisites, observable effects, blast-radius controls, and evidence that separates a theoretical weakness from a usable attack path.
Defensive code, identity and configuration changes, reproduction harnesses, regression tests, and a retest of both the original trigger and practical variants.
Prompt injection, tool and API abuse, excessive agency, memory poisoning, data exfiltration, model and dependency supply-chain risk, and failures in human approval boundaries.
AI as an amplifier
We use AI to inspect more code, correlate signals, generate test cases, and challenge assumptions. It does not authorize access, expand scope, or declare a system safe. People remain accountable for those decisions and for every action that reaches a real system.
[scope] target and stop conditions recorded [prove] trust boundary violation reproduced [repair] authorization invariant enforced [retest] original and adjacent paths replayed [result] exploit blocked; evidence preserved
Research boundaries
Public reference points
These sources inform our operating model. They are reference points, not certifications or endorsements.
Start with authorization
We begin with authorization, scope, stop conditions, evidence handling, and the route from a confirmed exploit to a verified repair.
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