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// CASE FILE 04 — CODENAME: GAMMA FILES — M.SC. DISSERTATION · DEVSECOPS

MAP THE BLAST RADIUS, BLASTR.

Graph-aware RAG security analysis for cross-layer DevSecOps (M.Sc. dissertation) — parses Terraform/IaC into NetworkX attack graphs, propagates "blast radius" across cloud layers, and pairs a LangGraph reasoning agent with FAISS + Sentence-Transformers RAG to explain and prioritise security misconfigurations. Know exactly what goes green before it goes boom.

Inspired By
Hulk
Role
Creator · M.Sc.
Domain
DevSecOps
Fine-Tune
LoRA / QLoRA
Parse IaCBuild the GraphPropagate the BlastExplainPrioritise Parse IaCBuild the GraphPropagate the BlastExplainPrioritise
FILE 01 — THE MISSION

A MISCONFIG IS NEVER JUST ONE RESOURCE

Security scanners hand you a flat list of findings. What they don't tell you is the Hulk question: if this one thing breaks open, how far does the damage spread?

BlastR answers it structurally — parsing Terraform into NetworkX attack graphs and propagating blast radius across cloud layers, so one weak security group is scored by everything it exposes. A LangGraph reasoning agent grounded in FAISS + Sentence-Transformers RAG then explains each misconfiguration and prioritises what to fix first — in plain, cited language.

"Severity isn't the finding. Severity is the blast radius."

  • FILE SNAPSHOT
  • Input — Terraform / IaC
  • Graph — NetworkX attack graphs
  • Radius — propagated across cloud layers
  • Reasoning — LangGraph agent + FAISS RAG
  • SLM — Qwen2.5-Coder-1.5B, LoRA/QLoRA
  • Ships as — Typer CLI · FastAPI · React dashboard
FILE 02 — THE GAMMA PIPELINE

FROM HCL TO PRIORITISED FIXES

01

Parse the IaC

Terraform is parsed into a NetworkX attack graph — resources become nodes, trust and reachability become edges.

02

Propagate the Blast

"Blast radius" propagates across cloud layers — a single misconfiguration is scored by everything it can reach, not just what it is.

03

Reason & Ground

A LangGraph reasoning agent paired with FAISS + Sentence-Transformers RAG explains each misconfiguration and prioritises remediation with grounded citations.

04

Fine-Tune the Specialist

A domain SLM — Qwen2.5-Coder-1.5B, fine-tuned with LoRA/QLoRA via HuggingFace Transformers + PEFT — trained on Terraform labelled with Checkov and Trivy.

05

Benchmark & Track

The fine-tune is benchmarked against a zero-shot baseline and tracked in LangSmith — the gains are measured, not assumed.

06

Ship Three Ways

A Typer CLI for pipelines, a FastAPI service for integration, and a React + TypeScript dashboard for humans.

FILE 03 — WHY IT HITS HARD

STRONGEST THERE IS, PROVABLY

🕸️

Graph-Aware, Not List-Based

NetworkX Attack Graphs

Findings are ranked by cross-layer reachability — the graph knows that a public bucket feeding a privileged role is not a "low".

NetworkXCross-Layer
🧠

Grounded Explanations

LangGraph Agent + FAISS RAG

Every priority call comes with a retrieved, cited explanation — security review that reads like a senior engineer wrote it.

LangGraphFAISSSentence-Transformers
🧬

A Domain Specialist SLM

Qwen2.5-Coder-1.5B · LoRA/QLoRA · PEFT

Small model, gamma-boosted: fine-tuned on Checkov/Trivy-labelled Terraform so it speaks IaC security natively.

LoRA/QLoRAHuggingFace + PEFT
📊

Measured, Not Vibed

Zero-Shot Baseline · LangSmith

Benchmarked against zero-shot and tracked in LangSmith — dissertation-grade rigour behind every claim.

BenchmarksLangSmith
3
Interfaces — CLI · API · Dashboard
2
Scanners Labelling the Training Set
100%
Priorities Backed by Graph + Citations
FILE 04 — THE BLUEPRINT

CONTAINMENT ARCHITECTURE

Terraform / IaC

Source of Truth

Checkov + Trivy

Labelled Training Data

▼ ▼ ▼
Attack Graph Engine

NetworkX · Blast-Radius Propagation Across Layers

▼ ▼ ▼
LangGraph Agent

Reasoning + Prioritisation

FAISS RAG

Sentence-Transformers Grounding

Fine-Tuned SLM

Qwen2.5-Coder-1.5B · LoRA/QLoRA

▼ ▼ ▼
Typer CLI

Pipelines

FastAPI

Service

React + TS Dashboard

Humans

M.Sc. dissertation — benchmarked against zero-shot baseline, tracked in LangSmith
END OF FILE

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