It’s been two weeks since I launched KYLO, my AI‑powered CLI tool that audits code for real security risks. In just a short span, the feedback has been amazing — devs running it on real projects, catching hidden flaws, and asking for new features.
In this update, I’ll walk through:
- What Kylo is doing now and how it’s growing.
- – How it works under the hood — the security model, prompts, workflow.
- – Real-world examples and user feedback.
- – What’s coming next — roadmap & how you can get involved.
If you’re a dev, AI tool user, or building developer tools — here’s how KYLO helps you ship safely.
🧰 What KYLO Does (Today)
KYLO is your security code companion. You install it with:
pip install kylo
Then run:
kylo secure path/to/file.py
What it checks (and flags) includes:
- Hardcoded secrets (API keys, tokens, passwords)
- Missing authentication or authorization logic
- Unsafe SQL / string concatenation
- Sensitive files committed to git
- Poor error handling / raw exceptions exposed
- Unrestricted file uploads
- Insecure cryptographic functions / outdated algorithms
- Missing or flawed role-based checks (RBAC)
- Lack of rate limiting / brute-force protection
- No HTTPS enforcement / SSL missing headers
These are the core rules of KYLO’s security model but it seriously does more than this.
The idea: don’t just lint your code — get reasoned feedback, context-aware suggestions, and a safety net before you deploy.
⚙️ Under the Hood: How KYLO Thinks (Security + AI Model)
KYLO isn’t a dumb rules engine — it harnesses the power of an AI model we literally trained ans fine tune to understands how hacker and pen testers thinks plus custom logic to audit code with context and reasoning.
- Chunk & Context.
- The tool reads your `.py` file and splits it into logical units: functions, classes, imports, endpoints, etc.
- – It preserves context (imports, local variables) so the model “sees” enough to make decisions.
2. Prompt Engineering.
- Each chunk is paired with a prompt like:
- . > “You are a Python security auditor. Analyze this function and list security vulnerabilities, severity, and suggestions.”
- The prompt includes instructions, allowed exceptions, and examples to reduce hallucination.
3. Parsing & Merging.
- Our Model returns a review: issues, code lines, suggestions.
- KYLO parses that into structured JSON / text, merges duplicate reports, and orders them by severity.
4. CLI Report.
- You see a terminal report with color coding: red for critical, yellow for warnings, green for safe.
- – Each finding shows file name, line numbers, description, recommendation.
5. Future: Auto‑Fix Suggestions.
- Down the road, I’m planning `kylo fix` modes: small safe refactors (e.g. replace string concatenation with parameterized queries)
- – Integration support (CI, pre-commit, GitHub Actions) so your code is audited on every commit.
đź“Š Real Examples & Community Feedback
Some of the early testers ran KYLO on real codebases and found surprising flaws:
> “Kylo flagged a secret key I had in a test file I forgot about.”
> “It caught an endpoint missing role‑checks that I didn’t realize was wide open.”
> “I’m using AI code snippets everywhere — KYLO is now my safety check before push.”
One sample snippet flagged:
python
def login(user, passcode):
. if user == “admin” and passcode == “hard!code123”:
. return { “token”: “XXX_SECRET” }
. else:
. return { “error”: “wrong credentials” }
Kylo flagged:
- Missing hashing or salt.
- Hardcoded credentials.
- Exposed token literal
- – Lack of input validation.
Users say the suggestions are easy to act on, and the guard it provides gives confidence.
🛡️ Kylo’s Security Rulebook (Simplified)
Below is a condensed version of what KYLO enforces. Over time, this grows and evolves.
| Rule | What it detects | Why it matters |
| Hardcoded secrets | API keys, tokens in source | Leaked secrets = breaches |
| Auth / Authorization gaps | No role checks, public endpoints | Unauthorized access |
| Unsafe SQL | String concatenation, no parameterization | SQL injection |
| Sensitive git files | `.env`, `.pem` in repo | Accidentally public secrets |
| Insecure error messages | Raw traces in responses | Information leakage |
| File upload risks | No validation or scanning | Malware or path traversal |
| Weak crypto | MD5, static keys | Broken encryption |
| No rate limits | Unlimited login attempts | Brute-force attack |
| No HTTPS enforcement | HTTP in production | Man-in-the-middle vulnerabilities |
I also regularly review false positives and fine-tune prompts so KYLO becomes smarter over time.
🔮 What’s Next / Roadmap
Here’s what’s coming in the next phases:
• Auto-fix mode (`kylo fix`) for safe refactors
- Language expansion: support for JavaScript, Go, others.
- Web dashboard / UI to view audits across projects.
- GitHub / CI integration— audit pull requests.
- Plugin / extension system— allow users to write custom rules.
- Better caching & performance for large codebases.
I’m also building a security rules publishing page on my domain, so you can always read what rules KYLO reasons by (and use it as reference).
📢 Getting Started (You Use It Today)
- Install:
. pip install kylo
2. Run a security scan:
. kylo secure path/to/your_module.py
3. Review findings, fix, rerun.
. Integrate in CI or your git workflow.
Check out the full rules and documentation: https://kylo.pxxl.click
Follow me (Leon Musk / legally Alabi Muiz) for updates, tutorials, and behind-the-scenes.
Let’s help developers ship safely with AI-assisted security in every build.
🧠Why This Matters
AI-generated code is powerful — but dangerous if unchecked. Models often focus on function over security. They may forget auth, expose secrets, or miss edge-case logic. As we rely more on auto‑generated code, tools like KYLO become essential.
My mission is simple: blend the speed of AI with the safety of expert auditing. You’re not just shipping faster — you’re shipping safer.
Thanks to everyone who’s tried it, given feedback, or shared ideas. We’re just getting started. Let’s build a more secure dev world together.
