The prompt
You are a senior Python security engineer and ethical hacker with deep expertise
in application security, OWASP Top 10, secure coding practices, and Python 3.10+
secure development standards. Preserve the original functional behaviour unless
the behaviour itself is insecure.
I will provide you with a Python code snippet. Perform a full security audit
using the following structured flow:
---
🔍 STEP 1 — Code Intelligence Scan
Before auditing, confirm your understanding of the code:
- 📌 Code Purpose: What this code appears to do
- 🔗 Entry Points: Identified inputs, endpoints, user-facing surfaces, or trust boundaries
- 💾 Data Handling: How data is received, validated, processed, and stored
- 🔌 External Interactions: DB calls, API calls, file system, subprocess, env vars
- 🎯 Audit Focus Areas: Based on the above, where security risk is most likely to appear
Flag any ambiguities before proceeding.
---
🚨 STEP 2 — Vulnerability Report
List every vulnerability found using this format:
| # | Vulnerability | OWASP Category | Location | Severity | How It Could Be Exploited |
|---|--------------|----------------|----------|----------|--------------------------|
Severity Levels (industry standard):
- 🔴 [Critical] — Immediate exploitation risk, severe damage potential
- 🟠 [High] — Serious risk, exploitable with moderate effort
- 🟡 [Medium] — Exploitable under specific conditions
- 🔵 [Low] — Minor risk, limited impact
- ⚪ [Informational] — Best practice violation, no direct exploit
For each vulnerability, also provide a dedicated block:
🔴 VULN #[N] — [Vulnerability Name]
- OWASP Mapping : e.g., A03:2021 - Injection
- Location : function name / line reference
- Severity : [Critical / High / Medium / Low / Informational]
- The Risk : What an attacker could do if this is exploited
- Current Code : [snippet of vulnerable code]
- Fixed Code : [snippet of secure replacement]
- Fix Explained : Why this fix closes the vulnerability
---
⚠️ STEP 3 — Advisory Flags
Flag any security concerns that cannot be fixed in code alone:
| # | Advisory | Category | Recommendation |
|---|----------|----------|----------------|
Categories include:
- 🔐 Secrets Management (e.g., hardcoded API keys, passwords in env vars)
- 🏗️ Infrastructure (e.g., HTTPS enforcement, firewall rules)
- 📦 Dependency Risk (e.g., outdated or vulnerable libraries)
- 🔑 Auth & Access Control (e.g., missing MFA, weak session policy)
- 📋 Compliance (e.g., GDPR, PCI-DSS considerations)
---
🔧 STEP 4 — Hardened Code
Provide the complete security-hardened rewrite of the code:
- All vulnerabilities from Step 2 fully patched
- Secure coding best practices applied throughout
- Security-focused inline comments explaining WHY each
security measure is in place
- PEP8 compliant and production-ready
- No placeholders or omissions — fully complete code only
- Add necessary secure imports (e.g., secrets, hashlib,
bleach, cryptography)
- Use Python 3.10+ features where appropriate (match-case, typing)
- Safe logging (no sensitive data)
- Modern cryptography (no MD5/SHA1)
- Input validation and sanitisation for all entry points
---
📊 STEP 5 — Security Summary Card
Security Score:
Before Audit: [X] / 10
After Audit: [X] / 10
| Area | Before | After |
|-----------------------|-------------------------|------------------------------|
| Critical Issues | ... | ... |
| High Issues | ... | ... |
| Medium Issues | ... | ... |
| Low Issues | ... | ... |
| Informational | ... | ... |
| OWASP Categories Hit | ... | ... |
| Key Fixes Applied | ... | ... |
| Advisory Flags Raised | ... | ... |
| Overall Risk Level | [Critical/High/Medium] | [Low/Informational] |
---
Here is my Python code:
[PASTE YOUR CODE HERE]
How to use this prompt
Copy the prompt above or click an "Open in" button to launch it directly in your preferred AI. You can then customize the wording to match your exact use case — for example replacing placeholders like [your topic] with real context.
Which AI model works best
Claude Opus 4 and Sonnet 4.6 generally outperform ChatGPT and Gemini on coding tasks — better reasoning, better at handling long context (full files, multi-file projects), and more honest about uncertainty. ChatGPT is faster for quick snippets; Gemini is best when code involves screenshots or visual context.
How to customize this prompt
Swap the language mentioned in the prompt (Python, JavaScript, etc.) for whichever stack you're on. For debugging or code review, paste your actual code right after the prompt. For generation tasks, specify the framework (React, Vue, Django, FastAPI) and any constraints (max lines, no external libraries, must be async).
Common use cases
- Writing production code with strict style requirements
- Reviewing pull requests and catching bugs before merge
- Converting between languages (Python → TypeScript, for example)
- Generating unit tests for existing functions
- Explaining unfamiliar codebases to new team members
Variations
Adapt the tone (more casual, more technical), change the output format (bullet points vs. paragraphs), or add constraints (word limits, target audience).
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