# SecJev: Bringing Security Expertise to System One Decision Models

arXiv: https://arxiv.org/abs/2610.03073 · HTML: https://mlsanity.com/paper/2610.03073/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 2 Oct 2026.

## Builds on (most cited first, 10 of 10)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [SciRIFF: A Resource to Enhance Language Model Instruction-Following over Scientific Literature](https://mlsanity.com/paper/2406.07835/) (2024)
- [Decision Hijacking: Prompt Injection Attacks on Jev's Typed Probabilistic Decisions](https://mlsanity.com/paper/2609.28613/) (2026)
- [LLaMA-E: Empowering E-commerce Authoring with Object-Interleaved Instruction Following](https://mlsanity.com/paper/2308.04913/) (2023)
- [Federated TON_IoT Windows Datasets for Evaluating AI-based Security Applications](https://mlsanity.com/paper/2010.08522/) (2020)
- [Data Analytics-enabled Intrusion Detection: Evaluations of ToN_IoT Linux Datasets](https://mlsanity.com/paper/2010.08521/) (2020)
- [ByzFL: Research Framework for Robust Federated Learning](https://mlsanity.com/paper/2505.24802/) (2025)
- [A Systemic IoT-Fog-Cloud Architecture for Big-Data Analytics and Cyber Security Systems: A Review of Fog Computing](https://mlsanity.com/paper/1906.01055/) (2019)
- [Punching Above Their Weight: Classification-Head Fine-Tuning of Tiny Language Models (TLMs) for Verifiable Multiple-Choice Tasks](https://mlsanity.com/paper/2607.03801/) (2026)

## Built on by (most cited first, 1 of 1)

- [Correct Choices, Failed Sequences: Choice2Seq Benchmarks Correctness Retention Across Output Interfaces](https://mlsanity.com/paper/2610.16191/) (2026)

## Abstract (arXiv)

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-style 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. At 0.8B and 4B, SecJev achieves accuracy and latency close to answer-only generative fine-tuning with lower peak allocated GPU memory. SecJev-4B improves on Kev-4B and security-trained generation in prompt-injection and traffic transfer tests. We release adapters, decision heads, SecJev-Corpus, and training and inference code.