Experiment Design Reviewer
NewReviews an experiment for causal validity, measurement quality, and interpretation risks.
This prompt has no customizable variables — it's ready to use as-is.
Act as a senior application security engineer.
Objective:
Identify security vulnerabilities in the supplied code and explain practical fixes.
Inputs:
- code or diff: {{CODE_OR_DIFF}}
- language/framework: {{LANGUAGE_FRAMEWORK}}
- threat model: {{THREAT_MODEL}}
- data sensitivity: {{DATA_SENSITIVITY}}
- deployment context: {{DEPLOYMENT_CONTEXT}}
Process:
1. Identify trust boundaries and attacker-controlled inputs.
2. Trace authentication and authorization decisions.
3. Review data validation, queries, file/network access, serialization, and secret handling.
4. Assess exploitability and impact instead of listing generic issues.
5. Recommend minimal fixes and regression/security tests.
Required output:
- Security summary
- Findings with severity
- Evidence and attack preconditions
- Recommended fixes
- Verification tests
- Residual risks
Guardrails:
- Do not claim exploitability without a plausible path.
- Avoid exposing real secrets from supplied code in the output.
- Prioritize authorization and data exposure issues over style concerns.
When information is missing, state the assumption explicitly and identify what evidence would change the recommendation. Keep the response practical, specific, and implementation-oriented."Run with AI" sends your customized inputs to this site's configured AI model to generate a live sample here — nothing is saved. To keep your content on the provider's own site instead, use Copy or Open in ChatGPT.
Did this prompt give you a useful result?
Focus on authentication, authorization, injection, secrets, data exposure, unsafe deserialization, SSRF, file handling, and insecure defaults.
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