Temporal Attack Pattern Detection in Multi-Agent AI Workflows: An Open Framework for Training Trace-Based Security Models

📝 Original Info

• Title: Temporal Attack Pattern Detection in Multi-Agent AI Workflows: An Open Framework for Training Trace-Based Security Models
• ArXiv ID: 2601.00848
• Date: 2025-12-29
• Authors: Ron F. Del Rosario

📝 Abstract

We present the first openly documented methodology for fine-tuning language models to detect temporal attack patterns in multi-agent AI workflows using OpenTelemetry trace analysis. Our lean experimentation approach demonstrates that focused, iterative refinement can achieve substantial performance gains without massive computational resources or proprietary infrastructure. We curate a dataset of 80,851 examples from 18 public cybersecurity sources plus 35,026 synthetic OpenTelemetry traces, then apply iterative QLoRA fine-tuning on resource-constrained ARM64 hardware. Through three training iterations with strategic augmentation, we improve accuracy from 42.86% to 74.29% on our custom benchmark-a statistically significant 31.4-point gain (p < 0.001). Our iterative approach shows that targeted examples addressing specific knowledge gaps outperform indiscriminate scaling. Key contributions include: (1) synthetic OpenTelemetry trace generation methodology for multi-agent attacks and regulatory violations, (2) demonstration that training data composition fundamentally determines behavior-our attack-focused dataset causes high false positive rates resistant to prompt engineering, and ( 3 ) complete open release of d...

📄 Full Content

...(본문 내용이 길어 생략되었습니다. 사이트에서 전문을 확인해 주세요.)