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Research

We publish on the things we sell.

Applied security research co-authored at St. John's University. It is the same work the firm does for clients, with the findings written down and mapped to frameworks you're likely already audited against.

Each of these is collaborative academic work; co-authors are credited in full. Summaries below are written for this site rather than excerpted from the papers.

The team generated administrative scripts from neutral prompts — database querying, file integrity hashing, and Active Directory user provisioning — then put them through the same static analysis as functionally equivalent human-written scripts sourced from vetted repositories.

The AI output failed in consistent, recognisable ways. A prompt asking for a user-lookup script produced an f-string SQL query with unsanitised input concatenated straight into the statement; Bandit flagged it as B608 and mapped it to CWE-89. A prompt for an Active Directory provisioning script produced Invoke-Expression wrapping a user-supplied name, which PSScriptAnalyzer flagged immediately — a command injection path that Microsoft's own guidance has warned against for years.

Both were refactored — parameterised queries in the Python case, direct cmdlet invocation in the PowerShell case — and rescanned clean. The practical conclusion is the one that carries into client work: static analysis belongs in the CI/CD pipeline as a gate, not as an audit afterwards, and AI-generated code deserves at least the scrutiny human code gets.

Tooling
BanditPSScriptAnalyzerPythonPowerShellKali Linux
Mapped to
CWENIST SP 800-53MITRE ATT&CK
Co-authors
Brandon Spence, Tyrell Freeman, Vincent Trujillo, Peter Andrews, Brandon Jodhan

The AI code you're shipping deserves the same scan.

If your team is generating code with an assistant and nothing is gating it before merge, that's a review worth booking. We do it as a fixed-scope assessment.