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K | KYRO

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GENERATIVE AI & AGENTIC SYSTEMS

Think. Build. Secure. | Currently local-first

01 / 07

KYRO

Think. Build. Secure.

Autonomous software security

and engineering intelligence

Evidence-backed findings. Approved changes.

Independent verification.

RUNS LOCALLY THROUGH DOCKER

Night Raid

FOUR MEMBERS / B.TECH CSE / THIRD YEAR

Gaurav Wakle

TEAM LEAD

Rudransh Jaiswal

MEMBER

Savinay Pathak

MEMBER

Pranad Bhale

MEMBER

G. S. Mandal’s Marathwada Institute of Technology

Chhatrapati Sambhajinagar (Aurangabad)

Team ID: Not issued

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PROBLEM STATEMENT

A finding still needs an engineering investigation.

Developers need to turn scanner output into a relevant, tested, reviewable change.

Think. Build. Secure. | Currently local-first

02 / 07

01

Fragmented evidence

Source rules, dependency advisories and repository context must be reviewed together.

02

Uncertain relevance

Severity alone does not establish whether affected functionality is referenced or reachable.

03

Risky remediation

A patch can remove a finding and still break behavior. Builds, tests and rescans are essential.

WHO BENEFITS

Developers • AppSec teams • Open-source maintainers • Small engineering teams

Practical need: prioritize work, preserve application behavior, and leave evidence for review.

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PROPOSED SOLUTION

One controlled workflow from finding to evidence.

Repository context + mature scanners + a bounded agent + an independent verifier.

Think. Build. Secure. | Currently local-first

03 / 07

01

Connect

Authorized GitHub

repository + branch

02

Understand

Index code, imports

and dependencies

03

Investigate

Scan, normalize

and explain risk

04

Approve

Review a model plan

and exact scope

05

Remediate

Docker patch,

build and tests

06

Verify

Rescan + report;

PR API integrated

Evidence-aware prioritization

Preserve scanner provenance; show references, confidence and analysis limits.

Human control

Approval binds file scope and hashes. Tool, time, retry and budget limits remain enforced.

Separate verification

Recorded builds, tests and rescans decide outcomes; missing evidence stays inconclusive.

MEASURED FIXTURE: 22 → 21 findings • build + 4 tests pass • 6 regression tests pass • 0 new findings

21 findings remain. Expected benefits will be evaluated by time-to-verified-fix and review effort; no comparative gain claimed.

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ARCHITECTURE & DATA FLOW

A local modular monolith with isolated execution.

Think. Build. Secure. | Currently local-first

04 / 07

React workspace

Authenticated UI • API requests • SSE

Persistent storage

SQLite / PostgreSQL

Evidence, jobs, audits

GitHub API

Authorized snapshot

Branch metadata

Draft PR API

FastAPI backend

Owner checks + CSRF

Secrets stay on backend

Plans, approval and reports

DB-backed worker

Persistent state + bounded retries

Sequential resource-heavy jobs

Docker workspaces

Semgrep • OSV • Trivy

Patch, build and test containers

Independent rescan

Context selection

File / symbol / dependency context

Repository content is untrusted

Local Qwen model

Ollama / llama.cpp

Structured output validation

AI PIPELINE

Relevant context → model plan → human approval

TEST/PATCH POLICY

No host scripts or credentials.

Non-root; network denied;

CPU / memory / process limits.

Local-only mode blocks external model calls. GitHub/advisory access is separate. Docker socket requires a trusted dedicated host.

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TECHNOLOGY STACK

Small components. Real integrations. Local inference.

Think. Build. Secure. | Currently local-first

05 / 07

FRONTEND

React 19 + TypeScript + Vite

TanStack Query • React Router • Lucide • CSS design tokens

BACKEND & DATA

FastAPI + Pydantic + SQLAlchemy + Alembic

SQLite local default; PostgreSQL migration/constraint acceptance tested

CODE INTELLIGENCE

Python AST + JavaScript / TypeScript Tree-sitter

File hashes, symbols, imports, dependency references and selected context

SECURITY

Semgrep 1.184.0 • OSV-Scanner 2.7.0 • Trivy 0.76.2

Actual container adapters; OSV / NVD / EPSS / CISA KEV enrichment

AI & CONTROL

Local Qwen via Ollama / llama.cpp

qwen2.5-coder:7b tested; Qwen3 8B Q4 compatible; local-only enforcement

DELIVERY & QA

Docker Compose + GitHub API + GitHub Actions

Pytest • Vitest • Playwright • Ruff • mypy • dependency / secret checks

RESOURCE CHOICE

One worker, sequential scanners, one small local model. Optional cloud adapter has no successful live acceptance.

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WORKING APPLICATION & DEMO

Currently runs locally through Docker.

Think. Build. Secure. | Currently local-first

06 / 07

NO PUBLIC APPLICATION DEPLOYMENT

Actual controlled-test workspace capture • selected remediation succeeded and verified

RECORDED CHECKS / 07 NOV 2026

132

backend tests

9

UI unit tests

14

browser tests

LIVE REPOSITORY AUDIT

19 authorized repos processed; 17 indexed.

8 complete scans; 9 inconclusive; 2 empty.

347 normalized findings preserved.

PUBLIC SOURCE + SETUP

Demo video: to be recorded (2–3 minutes).

Live product-generated remediation PR: unrun.

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SCALABILITY & ROADMAP

Start on one laptop. Expand with measured evidence.

Think. Build. Secure. | Currently local-first

07 / 07

ROUND 2

Verify a real-repository fix and PR

Public URL import; approved fix and actual draft PR.

Broader scanner rules and regression fixtures.

FINALE

Broaden reliability and collaboration

Fork contribution flow; stronger reachability; team roles.

Concurrent dispatch and dedicated runner isolation.

SCALABILITY STRATEGY

Now: one worker, sequential scanners, incremental file indexing.

Next: measured queue limits, PostgreSQL deployment, retention and dedicated runners.

USERS & FEASIBILITY

Open-source local workflow for developers and maintainers.

Managed team hosting is a future option; no revenue or market validation claimed.

Thank you. Let’s review the evidence.

Q&A