Auto-Scanning in Action
See how KAVACH catches and fixes issues automatically.
import os
from flask import Flask, request
app = Flask(__name__)
@app.route('/read_file')
def read_file():
filename = request.args.get('file')
# 🚨 DANGER: Path Traversal
file_path = os.path.join('/var/www/data', filename)
with open(file_path, 'r') as f:
return f.read()Analysis Report
app.py
Waiting for code changes...
Why AI code needs scanning
LLMs optimize for functionality, not security. Don't let generated code become a liability.
More vulnerabilities
in AI-generated code compared to human-written code (Stanford Research)
Average cost
of a data breach caused by insecure application code (IBM 2023)
AI Apps
contain at least one critical OWASP Top 10 vulnerability on day one.
Security in 3 simple steps
No complex integrations or lengthy setups required.
Paste or Upload
Connect your GitHub repo, paste snippets, or upload files directly into the dashboard.
Get Instant Analysis
Our engine combines Semgrep static analysis with custom LLM evaluation models.
Apply Secure Fixes
Review context-aware fix suggestions and apply them with a single click.
We catch what LLMs miss
AI models are trained on billions of lines of code—including insecure ones. KAVACH acts as a safety net before that code hits production.
SQL Injection
Ensure database queries won't be hijacked by malicious inputs.
Hardcoded Secrets
LLMs often generate dummy API keys that end up in production.
Command Injection
Prevent arbitrary code execution from unsanitized parameters.
XSS Attacks
Stop cross-site scripting in AI-generated React/Vue components.
Insecure Auth
Catch weak hashing algorithms and broken JWT implementations.
Path Traversal
Block access to sensitive system files via user input.
Frequently asked questions
?Does KAVACH support code generated by Cursor?▼
Yes, KAVACH works seamlessly with code generated by Cursor, GitHub Copilot, ChatGPT, Claude, and any other LLM. If it's code, we can scan it.
?How is this different from standard Semgrep?▼
While we use Semgrep's powerful static analysis engine under the hood, KAVACH adds an AI evaluation layer specifically tuned to catch logic flaws common in LLM-generated code that static analysis misses.
?Is my code sent to OpenAI?▼
No. Our AI evaluation layer uses local open-source models (Qwen 2.5 Coder) to analyze logic flaws. Your code never leaves our secure, ephemeral infrastructure and is deleted immediately after scanning.
?What languages do you support?▼
Currently in beta, we support Python, TypeScript/JavaScript, Go, and Java. We are rapidly adding support for Rust, C++, and Ruby.
?When will the paid plans be available?▼
KAVACH is currently 100% free during our beta. Pro plans launching in Q2 2025 with advanced AI, PDF reports, and team features.