These prompts turn a chatty AI into a security auditor that actually checks your code instead of agreeing with you.
Generic prompts like "find vulnerabilities" fail because LLMs default to pattern-matching against known CVE write-ups, not your actual runtime context. They hallucinate issues that don't matter and miss the ones that do. Prompt Templates for Security Audits fix this by forcing the model into a structured, evidence-based workflow that works reliably across Claude, GPT-4, Gemini, and DeepSeek.
Why Generic Prompts Fail Here
Ask an LLM "is this code secure?" and you get a list of generic best practices — input validation, SQL injection, XSS. That's not a security audit; that's a compliance checklist.
The real failure mode is confirmation bias without context. The model doesn't know your threat model, your dependencies, or your deployment environment. So it flags theoretical issues while ignoring the actual vulnerability sitting in your auth middleware. These templates fix that by constraining the model to trace data flow, check specific attack surfaces, and produce verifiable output — not vibes.
Template 1: The Data Flow Auditor
This template forces the model to trace untrusted input from entry point to sink. It's the most useful for web applications.
You are a senior application security engineer. Analyze the code below for injection and data-flow vulnerabilities.
Context:
- Language/Framework: {LANGUAGE_FRAMEWORK}
- Entry points: {LIST_ROUTES_OR_FUNCTIONS}
- Data stores: {DATABASE_OR_CACHE}
- Auth mechanism: {AUTH_METHOD}
Instructions:
1. Trace every user-controlled input from entry point to database query, file write, or eval() call.
2. For each path, identify the exact sanitization step (or lack thereof).
3. Output a table with: Entry Point | Sink | Sanitization Present? | Vulnerability Type | Severity (CVSS)
4. Only report issues where you can trace the complete data path. Do NOT guess.
5. If no vulnerability exists in a path, state "CLEAN" explicitly.
Code:
{PASTE_CODE}
Placeholders: Replace {LANGUAGE_FRAMEWORK} with "Python/Flask", {LIST_ROUTES_OR_FUNCTIONS} with actual route names, and {PASTE_CODE} with the target file. The CVSS requirement prevents the model from giving vague "medium" ratings.
Template 2: The Dependency Risk Assessor
Most real breaches come from outdated dependencies, not custom code. This template targets supply chain risk with a different structure.
You are a dependency security auditor. Given the manifest file below, produce a prioritized remediation plan.
Constraints:
- Use only the exact versions listed. Do not suggest "latest" without checking compatibility.
- Cross-reference each package against known CVE databases from your training data.
- For each vulnerable package, provide:
a) The exact CVE ID and CVSS score
b) The fix version (if known)
c) Whether the vulnerable function is actually reachable in this codebase
d) A one-line exploit scenario
Prioritization:
- CRITICAL: Directly reachable, remote exploitable
- HIGH: Reachable but requires local access
- MEDIUM: Not reachable but should be patched
- LOW: No known exploit
Manifest (package.json):
{PASTE_MANIFEST}
Also analyze this call-site usage:
{PASTE_IMPORT_USAGE}
The key difference here is the "reachable" requirement. Most LLMs will flag every outdated package as critical. This template forces them to check whether the vulnerable code path is actually used.
Template 3: The Logic Flaw Hunter
Logic flaws — IDOR, race conditions, broken access control — are where LLMs typically fail hardest. They require understanding business rules, not syntax.
You are a penetration tester analyzing business logic. The code below implements {FEATURE_DESCRIPTION}.
Threat model for this feature:
- Attacker role: {ATTACKER_ROLE}
- Sensitive operations: {LIST_SENSITIVE_OPS}
- Expected access levels: {ROLE_PERMISSIONS_MATRIX}
Analyze for:
1. IDOR: Can a user access/modify resources belonging to another user? Trace the object ID handling.
2. Race conditions: Are there TOCTOU (time-of-check-time-of-use) gaps in state transitions?
3. Broken function-level access control: Can a lower-privilege user invoke admin endpoints directly?
4. State machine violations: Can the application reach an invalid state through unexpected request ordering?
For each finding, provide:
- The exact code line where the flaw exists
- A concrete HTTP request or function call sequence that exploits it
- The business impact (not just "high" — specify what data is exposed)
Code:
{PASTE_CODE}
This template works because it gives the model a specific threat model and demands an exploit sequence, not just a description. You'll get actionable findings instead of "this looks risky."
How to Adapt These for Your Own Codebase
First, strip out any code that isn't relevant to the audit. Feeding the model 5,000 lines of boilerplate dilutes its attention — give it the auth module, not the entire repository.
Second, add your actual dependency versions and framework specifics. LLMs trained before your framework's latest release will hallucinate APIs. Correct them upfront.
Third, iterate. The first pass will miss things. Feed the model's own output back with "Now check the edge cases you missed" — this second pass catches 30-40% more issues.
Do These Prompts Work With Any LLM?
Yes, but with caveats. Claude 3.5 Sonnet and GPT-4 Turbo handle the structured output formats best. Gemini tends to ignore the CVSS scoring unless you explicitly demand it. DeepSeek works but needs the "output a table" instruction repeated — it defaults to prose. All four will produce usable results if you keep the context window tight and the instructions explicit. For critical production code, use these prompts to find candidate vulnerabilities, then verify manually — no LLM replaces a human auditor.
The single adjustment that improves these prompts the most: add "If you cannot find a vulnerability, say 'NO ISSUE FOUND' explicitly" to every template. This kills the LLM's bias toward finding problems that don't exist, which means when it does flag something, you'll actually pay attention.