Cyberdudebivash

Building an AI-Powered Vulnerability Analysis Tool — By CyberDudeBivash

Executive summary

Goal: a production-ready system that ingests code, containers, SBOMs, and cloud configs; enriches with threat intel; then uses AI + rules to (1) detect issues, (2) predict exploitability/business risk, and (3) generate actionable, prioritized fixes for developers and SecOps.

1) Architecture (high level)

Sources → Ingestion → Scanners → AI Engine → Risk & Fixes → Delivery

  • Sources: GitHub/GitLab repos, CI artifacts, containers/VM images, SBOMs (CycloneDX/SPDX), package registries, cloud & IaC (Terraform/K8s), ticketing history.
  • Ingestion: webhooks + schedulers, message queue (e.g., RabbitMQ), object store (S3/GCS), metadata DB (Postgres).
  • Scanners (baseline):
    • SAST: Semgrep, Bandit (py), ESLint security (js/ts).
    • SCA: Syft→SBOM, Grype/Trivy→CVE match.
    • Secrets: Gitleaks.
    • IaC/Cloud: Checkov/tfsec, kube-bench.
  • AI Engine (see §3): LLM + ML microservices for dedup, exploit-likelihood, business impact, fix drafts.
  • Risk & Fixes: scoring service combining CVSS, exploit signals, blast-radius, and compensating controls.
  • Delivery: PR comments, Jira tickets, Slack/Teams, dashboard, export (PDF/JSON), CERT-In-ready report template (for Indian orgs).

2) Data model (core objects)

  • Asset (repo, image, function, microservice, cloud resource)
  • Finding {cve/cwe, location, evidence, scanner, severity}
  • Context {SBOM deps, ownership, runtime presence, network egress, data sensitivity tag}
  • Signals {EPSS-like score, exploit POC seen?, commit cadence, package popularity/staleness}
  • Recommendation {fix steps, patch version, code patch draft}
  • Ticket {status, SLA, owner}

3) AI components (what the “AI” actually does)

  1. Finding consolidation
    • LLM (code-aware) clusters duplicates across scanners; NER to extract CWE, component, version, file, function.
  2. Exploitability prediction
    • Gradient-boosted model (XGBoost/LightGBM) with features: CVSS vector, textual embeddings (BERT/MPNet) of advisory, package popularity, release freshness, presence in runtime, internet-exposed?, known POC indicators, historical MTTR.
  3. Business impact estimation
    • Rules + ML using data classification tags (PII/financial), asset criticality, user count, blast radius.
  4. Fix generation & review
    • LLM drafts patch diffs / config changes; guardrail policy: never auto-merge, require human review; include unit tests where possible.
  5. Explanations
    • Short, developer-friendly rationale (“Why this matters”) + ATT&CK mapping + references.
  6. RAG knowledge
    • Vector store of advisories, internal runbooks, past incident notes. Retrieval-augmented answers keep guidance current and org-specific.

Guardrails: strict prompt-escaping, allow-lists for tools, reproducible prompts, red-team tests for prompt injection, and output diff linting.

4) Risk score (prioritization)

Risk = f(Exploitability, BusinessImpact, Exposure, CompensatingControls, TimeSinceDisclosure)

Example weights to start:

  • Exploitability 0.4, Business Impact 0.3, External Exposure 0.2, Controls −0.1, Age +0.1.
    Tune with historical incidents to minimize mean time to risk reduction (MTRR).

5) Developer workflow (CI/CD + IDE)

  • Pre-commit/PR: run light SAST/secret scans; the bot comments with 1-line summary + quick-fix patch.
  • CI: full SCA + IaC; fail only on risk>threshold (not raw severity) to avoid alert fatigue.
  • IDE: extension shows “why, how to fix, and sample code,” linked to docset.
  • Auto-ticketing: one Jira per service/epic; SLAs tied to risk; reminders in Slack.

6) MVP to v1 roadmap

Week 0–2 (MVP)

  • Repos & containers ingestion; Syft→SBOM; Grype/Trivy; Semgrep; Gitleaks.
  • Simple risk formula (CVSS + exposure flags).
  • Dashboard + PR comments; exports (PDF/JSON).

Week 3–6

  • LLM dedup/summarize findings; CWE mapping; fix text suggestions.
  • IaC scanning; cloud misconfig baselines.
  • RAG store of advisories & internal runbooks.

Week 7–10

  • Exploitability ML (train on historical CVEs, EPSS-style features, PoC sightings).
  • Business impact model using asset tags & data classes.
  • Policy engine for CERT-In/DPDP reporting fields (timestamps, logs, contact POCs).

Week 11–14 (v1)

  • Code-patch drafts for top ecosystems (JS/TS, Python, Java).
  • Role-based RBAC, audit logs, org SSO (OIDC/SAML).
  • Multi-tenant SaaS hardening; rate limits; per-tenant encryption.

7) Tech stack (suggested)

  • Backend: Python (FastAPI), workers (Celery/Redis), Postgres, S3/GCS.
  • ML/AI: PyTorch/Transformers, XGBoost/LightGBM, sentence-transformers, FAISS/pgvector.
  • Scanners: Semgrep, Bandit, Trivy/Grype, Syft, Gitleaks, Checkov/tfsec, kube-bench.
  • Queue/Events: RabbitMQ/Kafka.
  • Frontend: React + Tailwind; charts (Recharts).
  • Deploy: Docker + Kubernetes; observability (OpenTelemetry, Prometheus, Grafana).
  • Security: Vault for secrets; OPA/Gatekeeper for policy.

8) Example API (sketch)

POST /ingest/sbom            # upload CycloneDX/SPDX
POST /scan/repo?url=...      # enqueue repo scan
GET  /findings?asset_id=...  # list normalized findings
POST /score                  # compute risk score for payload
POST /fix/draft              # get LLM patch suggestion (human review required)

Risk scoring pseudo-code

def risk_score(exp, impact, exposure, controls, age_days):
    return 0.4*exp + 0.3*impact + 0.2*exposure - 0.1*controls + 0.1*min(age_days/90,1.0)

9) Data & evaluation

  • Labels: historical exploitation (yes/no), MTTR, incident attribution, whether a finding led to a hotfix.
  • Metrics: precision@k for “critical first,” reduction in open risk, developer acceptance rate of fixes, PR cycle time.
  • Offline→Online: start with offline CVE corpora; then A/B on teams to prove fewer “false-urgent” tickets.

10) Compliance & India specifics

  • CERT-In: ensure the report export includes incident type, affected systems, indicators, timestamps, and a 24×7 contact field.
  • DPDP: tag datasets and findings that touch personal data; support breach-assessment workflows.
  • Auditability: store prompts, model versions, and decision traces for every recommendation.

11) Pricing & GTM (quick sketch)

  • Free tier: 1 repo, weekly scans, dashboard.
  • Pro: per-seat or per-asset; CI checks, PR comments, Jira/Slack.
  • Enterprise: on-prem/air-gapped, SSO/SIEM, custom models, CERT-In report pack.
  • Add-on: “Fix Assist” (LLM patch drafts) billed per successful merge.

12) Risks & mitigations

  • Hallucinated fixes → require tests + gated review; limit LLM to read-only code unless diff is approved.
  • Noise/duplication → AI dedup + “merge similar findings” across scanners.
  • Supply-chain of the AI itself → pin model weights/images; MBOM/SBOM for the tool; signed releases.
  • Data leakage → tenant-scoped stores; PII redaction; no training on customer code without explicit consent.

13) What we can build first (fast win)

  • GitHub App that:
    1. Generates SBOM with Syft,
    2. Runs Trivy/Grype + Semgrep,
    3. Calls our LLM summarizer to post one prioritized PR comment with: risk score → “why it matters” → fix snippet → link to CERT-In export.

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