Evidence First
Technical claims supported by reproducible artifacts, traces, and experiments.
SECUSKKU explores vulnerabilities, digital evidence, binary behavior, and AI-assisted security analysis through reproducible, evidence-driven research.
Technical claims supported by reproducible artifacts, traces, and experiments.
Security analysis across software, toolchains, binaries, and operational workflows.
Careful validation, coordinated disclosure, and practical defensive outcomes.
Security research at the intersection of software analysis, cyber forensics, automation, and emerging AI capabilities.
Analysis of software, development toolchains, attack surfaces, unsafe assumptions, and security-relevant implementation flaws.
DISCOVERY · VALIDATION · DISCLOSUREEvidence-oriented examination of project files, system artifacts, application traces, and forensic workflows.
ARTIFACTS · TIMELINES · EVIDENCEEvaluation of AI-assisted security analysis, model reliability, context sensitivity, and trustworthy automation.
LLM · EVALUATION · AUTOMATIONStatic and structural analysis of compiled software, cryptographic implementations, functions, and low-level behaviors.
STATIC ANALYSIS · ML · REVERSE ENGINEERINGWe focus on security questions that can be tested, reproduced, and translated into concrete engineering or policy decisions.
research.pipeline$ define threat_model
✓ assets, trust boundaries, assumptions
$ collect evidence
✓ artifacts, binaries, traces, datasets
$ validate findings
✓ controlled experiments + reproducibility
$ communicate responsibly
✓ disclosure, publication, mitigation
For research collaboration, technical discussions, or responsible security communication, contact us by email.