SecJev：为 System One 决策模型引入安全领域专业知识

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Abstract:Security workflows need models that turn complex observations and explicit policies into decisions. System One models introduced by Jev return typed predictions and probabilities; security specialization supplies the domain expertise behind those predictions. We introduce SecJev, to our knowledge the first family of Jev-like decision models specialized for security, spanning 0.8B to 9B parameters. Built on Kev's single-pass candidate scorer, SecJev learns Boolean, choice, and ordered decisions from text, telemetry, and observation histories. We develop SecJev-Corpus to unify source-label prediction and explicit-policy evaluation across 14 tasks and eight sources. It covers tool outputs, traffic, federated updates, consensus, authentication, and vehicle messages. Scene-weighted training adapts the models across these domains while preserving a shared typed decision interface. Security specialization improves every model in the family; SecJev-0.8B outperforms general Kev-9B by 20.51 percentage points in task-macro accuracy. Comparisons with answer-only generative fine-tuning show close accuracy and latency with lower peak inference memory. Tests on new source groups reproduce gains over Kev in prompt-injection and traffic decisions, with capture-dependent false alarms. We release adapters, decision heads, SecJev-Corpus, and training and inference code.

Comments:	22 pages, 1 figure
Subjects:	Cryptography and Security (cs.CR); Computation and Language (cs.CL)
Cite as:	arXiv:2610.03073 [cs.CR]
	(or arXiv:2610.03073v1 [cs.CR] for this version)
	https://doi.org/10.48550/arXiv.2610.03073 arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Zheng Chen [view email]
[v1] Fri, 2 Oct 2026 09:57:05 UTC (286 KB)