Quantigence: A Multi-Agent Framework for Post-Quantum Security Analysis on Commodity Hardware
About
The migration to post-quantum cryptography (PQC) forces security teams to synthesize a fast-moving literature spanning lattice theory, implementation security, and shifting NIST policy. We present Quantigence, a multi-agent framework that structures this analysis as a supervisor that decomposes a query into sub-tasks and dispatches them to four specialist agents (cryptographic analysis, threat modeling, standards compliance, and risk assessment), each grounded in live external tools: the arXiv and NVD APIs and a local retrieval index over the NIST PQC standards. Agents run serially on a single commodity GPU using a 4-bit-quantized 9B model, keeping the peak memory footprint within an 8 GB budget. The framework computes a Quantum-Adjusted Risk Score (QARS), an automated operationalization of the risk model of Grigali\=unas and Br\=uzgien\.e, to prioritize assets. On a 40-query benchmark of atomic, machine-checkable questions, tool access is what raises accuracy, from 57% (zero-shot) to 98% (single agent), and a single tool-using agent is, as expected, a strong baseline that multi-agent decomposition does not beat on single-fact lookups. On a second benchmark of ten complex, multi-faceted queries (the setting orchestration is designed for), the supervisor/worker decomposition instead improves rubric coverage from 78% to 89% and judged answer quality. We report both, and further measure robustness to retrieval-corpus poisoning and behavior across model scales. The implementation and benchmark are released as open source.