Towards Cybersecurity SuperIntelligence (CSI): What's the best harness for cybersecurity?
For cybersecurity AI researchers, this work demonstrates that combining structurally diverse scaffolds yields higher coverage than any single harness, addressing the problem of scaffold interoperability and selection.
The paper introduces a meta-scaffold (CSI) that unifies heterogeneous LLM-driven cybersecurity agent harnesses, achieving 57.6% solve rate on cybench challenges via a blackboard multi-agent architecture, a 27% relative gain over the best individual scaffold (45.5%) with 25% faster runtime.
What is the best harness for cybersecurity AI? Cybersecurity systems are converging on a single execution scaffold per agent, an iterative shell loop driven by a Large Language Model (LLM). However, scaffolds are not interchangeable, rarely interoperable, and no single scaffold dominates across all challenge types. In our path towards researching Cybersecurity SuperIntelligence (CSI), we present a meta-scaffold that unifies heterogeneous agent harnesses under a common orchestration layer, enabling any LLM-driven scaffold to be deployed, benchmarked, and composed within the same infrastructure. Using CSI, we benchmark five scaffolds (CSI::Claude, CSI::Codex, CSI::GCAI, CSI::Mistral, CSI::CAI) on the 33 cybench challenges, holding the model fixed at alias2-mini. The best individual scaffolds solve 15/33 (45.5%); the four-scaffold union solves 17/33 (51.5%), with the fifth (CSI::Mistral, 10/33) contributing one exclusive solve. We find that no single scaffold is the best harness: it is the combination of structurally heterogeneous scaffolds that yields the highest coverage. We validate this through CSI's blackboard-based multi-agent architecture, in which scaffold-specialised agents run in parallel and exchange intermediate findings via a shared substrate (a blackboard). The blackboard solves 19/33 (57.6%), a 27% relative gain over CSI::Claude, one of the best individual scaffolds (15/33, 45.5%), 25% faster (20.2 h vs. 26.8 h), at comparable cost ($5,480 vs. $5,122).