Skip to content
#

attack-graph

Here are 27 public repositories matching this topic...

aapp-mart

AAPP-MART (AI‑Autonomous Attack Path Prediction & Multi‑Agent Red Team Simulation Engine) is a security engine for AI-powered attack-path prediction, autonomous multi-agent red-team simulation, threat modeling, adversary emulation, risk assessment, and MITRE ATT&CK aligned.

  • Updated Jul 15, 2026
  • Python

Enterprise cloud attack path analysis platform using Neo4j graph databases, Cypher queries, and AI to map cloud assets, uncover lateral movement paths, identify toxic privilege combinations, and accelerate cloud security investigations.

  • Updated Jun 24, 2026
  • Python

Lightweight Autonomous Intrusion Detection and Response System — real-time packet capture, adaptive baseline learning, micro-honeypot deception layer, causal attack graphs, and explainable automated blocking for small-scale networks.

  • Updated Jun 26, 2026
  • Python

Neuro-symbolic RL agent that learns to pentest networks it has never seen — GPT-4o compiles CVE preconditions into a Z3 action mask over a GraphSAGE PPO policy. Zero-shot attack-graph transfer, negatives disclosed.

  • Updated Jun 4, 2026
  • Python

Hybrid LSTM-Markov attack chain forecasting for MITRE ATT&CK. Learns from 4,849 campaign chains + 8,437 real intrusion traces. Generates 26,051 risk-ranked multi-step attack futures via constrained beam search. 86% next-step accuracy, 0.76 Pearson correlation with NCISS severity. SECRYPT 2026 submission.

  • Updated Jun 23, 2026
  • Python

Improve this page

Add a description, image, and links to the attack-graph topic page so that developers can more easily learn about it.

Curate this topic

Add this topic to your repo

To associate your repository with the attack-graph topic, visit your repo's landing page and select "manage topics."

Learn more