🐉 Vishap

🐉 Vishap — 6-stage LLM pipeline for analyzing engineering materials (code, spec, logs, config, data).

Vishap takes a file as input → returns a structured Markdown report with risks, STRIDE threat model, refactoring options and audit trail with PII detection and compliance flags.

🌐 Live demo

⚠️ Free tier: first request may take 30–50 s (cold start).

🎉 What's new in v7.2.2

• JavaScript / TypeScript support: detect_language (.js/.jsx/.mjs/.cjs/.ts/.tsx), js_parser.scan_js_patterns (XSS, SQLi, RCE, path traversal, command injection), LLM hints in build_prompt.

Audit evidence (#1–#8)

• #1 context_thread — wires metrics / cwe / evidence → activates #3 / #5 / #6
• #2 compute_critique_verdict — NEEDS_VERIFICATION / REQUIRES_REVIEW / SAFE; critical risk_nodes / security → REQUIRES_REVIEW
• #3 fidelity_index — 0.0–1.0; bias passed when in [0.3, 0.95]
• #4 negative_evidence — checked-but-not-found CWEs with reason
• #5 reproducer — runnable python -c + verified via sys.executable
• #6 bias_check — critical_ratio, FI-range, RCE-without-critical
• #7 CWE_MAPPING — 15 keys
• #8 report_date / valid_until — ISO + 90 days

UI (ui-next/)

• LLMSettings: provider = custom | harness (DeepSeek via custom)
• Report v7.2 block: Fidelity / Bias check / Reproducer / Negative evidence / Report date / valid_until
• CORS fix: app.add_middleware(CORSMiddleware, ...) — Settings form works in browser
• mojibake fix: em-dash —, ellipsis …, emoji 🐉

Backend

• async_chat — respects VISHAP_LLM_PROVIDER=custom (was harness-only)
• Docker: bind 0.0.0.0 — -p 5001:5001 smoke-test passes
• CI: ruff F401 cleanup; ensure_harness probe fallback; services.postgres fix
• Version sync: 7.2.2 (pyproject.toml, version.py, package.json, main.py, layout.tsx)

🛠️ Features

• 6-stage pipeline: Ingestion → Taint Analysis → Dataflow → Risk Assessment → Verdict → Report.
• Multi-language: Python, Java, C, JavaScript / TypeScript (AST + taint patterns).
• Multi-provider LLM: DeepSeek (custom), DSH Harness, or any OpenAI-compatible endpoint (Groq, OpenAI, Mistral, OpenRouter, Together).
• Async API: FastAPI + Celery workers (optional in-process fallback).
• Security: HMAC-SHA256 for API keys (with pepper), Fernet for secrets.
• Observability: Structlog (JSON), Prometheus /metrics, OpenTelemetry traces.
• CI/CD: GitHub Actions (tests, lint, docs deploy).
• Desktop: Tauri app for Windows.

📦 Quick Start

Backend (Docker)

docker build -t vishap .
docker run --rm -p 5001:5001 \
  -e VISHAP_DEV=true \
  -e VISHAP_LLM_PROVIDER=custom \
  -e CUSTOM_LLM_URL=https://api.deepseek.com/v1 \
  -e CUSTOM_LLM_MODEL=deepseek-chat \
  -e CUSTOM_LLM_KEY=sk-... \
  vishap

## 🧠 Claude Desktop extension

Install **Vishap Security Analysis** in Claude Desktop (Windows/macOS):

1. Download [`claude-plugin/claude-plugin.mcpb`](claude-plugin/claude-plugin.mcpb).
2. Claude → **Settings** → **Extensions** → **Install extension** → select `.mcpb`.
3. **Configure**:
   - `LLM Base URL` — `https://api.deepseek.com/v1` (or OpenAI / Groq / Ollama / any OpenAI-compatible)
   - `LLM API Key` — your key (empty for local Ollama)
   - `LLM Model` — `deepseek-chat` / `gpt-4o-mini` / `llama3.1` / ...
4. **Enable** → ask Claude: *"Analyze this code for SQL injection: ..."*

Source: [`claude-plugin/`](claude-plugin/) · `.mcpb` built with `mcpb pack`.