Measures developer awareness of prompt injection threats, captures current security mitigation practices, and identifies gaps in LLM application defense. Designed for engineering teams building or evaluating LLM-integrated features.
What's Included
AI-Powered Questions
Intelligent follow-up questions based on responses
Automated Analysis
Real-time sentiment and insight detection
Smart Distribution
Target the right audience automatically
Detailed Reports
Comprehensive insights and recommendations
Template Overview
24
Questions
AI-Powered
Smart Analysis
Ready-to-Use
Launch in Minutes
This professionally designed survey template helps you gather valuable insights with intelligent question flow and automated analysis.
Sample Survey Items
Q1
Chat Message
Welcome! Thank you for participating in this survey about your experiences with LLM application development and security.
Your participation is entirely voluntary, and you may stop at any time. There are no right or wrong answers — we are interested in your honest perspectives and practices. All responses are confidential and will be reported only in aggregate.
This survey takes approximately 6–8 minutes to complete.
Q2
Multiple Choice
Which area best describes your primary role?
Back-end engineering
Front-end engineering
Full-stack engineering
Machine learning / data science
DevOps / SRE
Security engineering
QA / Test automation
Other (please specify)
Q3
Multiple Choice
Which of the following best describes your current involvement with LLM-integrated features?
I currently build or maintain LLM-integrated features
I am planning to integrate LLMs in the next 6 months
I am not currently working with LLMs
Q4
Opinion Scale
How confident are you in your personal understanding of prompt injection risks and mitigations?
Range: 1 – 7
Min: Not at all confidentMid: NeutralMax: Extremely confident
Q5
Opinion Scale
How well-prepared is your team or organization to defend against prompt injection attacks on your LLM-integrated applications?
Range: 1 – 7
Min: Not at all preparedMid: NeutralMax: Extremely well-prepared
Q6
Multiple Choice
Where have you learned about prompt injection risks and mitigations? Select all that apply.
Vendor or framework documentation
OWASP Top 10 for LLM Applications
Academic papers or preprints
Security blogs or newsletters
Conference talks or workshops
Internal training or peer guidance
Social media or forums
I have not sought out information on this topic
Q7
Multiple Choice
Which of the following threat vectors do you consider when designing or reviewing LLM features? Select all that apply.
Prompt injection (malicious instructions in user input)
Indirect prompt injection via external content sources
Jailbreaks / model override of system policies
Data exfiltration or leakage via prompts or responses
Tool misuse or over-permissioned agent actions
Training data poisoning
Prompt leakage or system prompt version exposure
None of these
Q8
Ranking
Rank the following LLM security concerns from highest to lowest priority for your work.
Drag to order (top = most important)
Direct prompt injection via user input
Indirect prompt injection via external content
Data leakage via prompts or responses
Over-permissive tool or agent actions
Model override / jailbreak to bypass policies
Q9
Multiple Choice
Which of the following prompt injection mitigations has your team adopted? Select all that apply.
Input validation and sanitization of user prompts
System prompt hardening (e.g., instruction hierarchy, delimiters)
Output filtering or content safety checks
Role-based access controls for LLM tool/action permissions
Monitoring and logging of LLM interactions
Canary tokens or honeypots in system prompts
Sandboxing or isolation of LLM execution environments
None of these
Not sure
Q10
Multiple Choice
How often does your team evaluate for prompt injection or jailbreak risks?
Weekly or more often
Every 2–3 weeks
Monthly
Quarterly
Less often or never
Q11
Multiple Choice
Which methods does your team use to test for prompt injection vulnerabilities? Select all that apply.
Adversarial red teaming by engineers
Automated evaluation suites or checklists
Unit or integration tests for prompts
Canary or honeytoken detection
Shadow deployment with monitoring and alerts
External penetration testing
We do not currently test for this
Q12
Multiple Choice
What is your team's default response policy when LLM input may be unsafe?
Allow but sanitize or validate content
Block and ask the user for clarification
Escalate to human review
Varies by context or risk level
Not sure
Q13
Multiple Choice
Which types of tools or platforms does your team use to mitigate prompt injection risks? Select all that apply.
What is your biggest obstacle to managing prompt injection risk today?
Max chars
Q18
AI Interview
We'd like to explore your experience with LLM security practices in a bit more depth. An AI moderator will ask a couple of follow-up questions based on your earlier responses.
AI InterviewLength: 2Personality: [Object Object]Mode: Fast
Reference questions: 7
Q19
Long Text
Based on your responses in this survey, please share any additional thoughts or experiences related to LLM security and prompt injection that we haven't covered. (Optional)
Max chars
Q20
Multiple Choice
How many years of professional software development experience do you have?
Less than 1 year
1–3 years
4–6 years
7–10 years
More than 10 years
Q21
Multiple Choice
Approximately how many employees are in your organization?
1–10
11–50
51–200
201–1,000
1,001–5,000
5,001+
Q22
Multiple Choice
Where are you primarily located?
Africa
Asia
Europe
Latin America
Middle East
North America
Oceania
Q23
Multiple Choice
Which industry best describes your organization?
Technology / Software
Finance / Banking
Healthcare
Retail / E-commerce
Manufacturing
Education
Government / Nonprofit
Other (please specify)
Q24
Chat Message
Thank you for completing this survey! Your responses will contribute to a better understanding of LLM security practices across the developer community and help improve secure development guidance.
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