University of Toronto

Researchers at Georgian and the University of Toronto ran Vercel Labs' open-source DeepSec harness with 24 model–runner configurations on a production TypeScript codebase whose vulnerabilities they say were non-public at scan time. No configuration covered all 265 confirmed security root causes, and moving from a minimal prompt to an engineered prompt with CodeQL-derived call-graph context raised criticality-weighted yield by 20–86% for three smaller or cheaper models but by 2% for GPT-5.5. The paper is published in the 2026 CAMLIS proceedings (PMLR 349).

A team from 2077AI and several universities presents CyberArena, in which two model-harness entrants host identical vulnerable services in isolated sandboxes and, over three 20-minute rounds, try to capture rotating flags from the opponent while patching their own service under health checks. They report standings for 12 agents over 466 valid games on 40 challenges, with GPT-5.6 Sol at an expected score of 78.6 and Claude Opus 5 at 73.8, and say contested play lowers capture rates relative to solo play. An LLM-run audit of all trajectories reports recurring defender errors such as partial patches and repair claims resting on stale evidence; the authors note it is not human verification of every finding.