# Formal Models and Convergence Analysis for Context-Aware Security Verification

arXiv: https://arxiv.org/abs/2510.12440 · HTML: https://mlsanity.com/paper/2510.12440/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 30 references that are in the map.
- First posted on arXiv 14 Oct 2025.

## Builds on (most cited first, 7 of 7)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [Code Llama: Open Foundation Models for Code](https://mlsanity.com/paper/2308.12950/) (2023)
- [Spectrally-normalized margin bounds for neural networks](https://mlsanity.com/paper/1706.08498/) (2017)
- [PentestGPT: An LLM-empowered Automatic Penetration Testing Tool](https://mlsanity.com/paper/2308.06782/) (2023)
- [Getting pwn'd by AI: Penetration Testing with Large Language Models](https://mlsanity.com/paper/2308.00121/) (2023)
- [Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity](https://mlsanity.com/paper/1810.07770/) (2018)

## Abstract (arXiv)

Traditional security scanners fail when facing new attack patterns they haven't seen before. They rely on fixed rules and predetermined signatures, making them blind to novel threats. We present a fundamentally different approach: instead of memorizing specific attack patterns, we learn what makes systems genuinely secure. Our key insight is simple yet powerful: context determines vulnerability. A SQL query that's safe in one environment becomes dangerous in another. By modeling this context-vulnerability relationship, we achieve something remarkable: our system detects attacks it has never seen before. We introduce context-aware verification that learns from genuine system behavior. Through reconstruction learning on secure systems, we capture their essential characteristics. When an unknown attack deviates from these patterns, our system recognizes it, even without prior knowledge of that specific attack type. We prove this capability theoretically, showing detection rates improve exponentially with context information I(W;C). Our framework combines three components: (1) reconstruction learning that models secure behavior, (2) multi-scale graph reasoning that aggregates contextual clues, and (3) attention mechanisms guided by reconstruction differences. Extensive experiments validate our approach: detection accuracy jumps from 58 percent to 82 percent with full context, unknown attack detection improves by 31 percent, and our system maintains above 90 percent accuracy even against completely novel attack vectors.