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Developer Survey: AI Prompt Injection & LLM Security

Use this template to measure developer awareness of prompt injection in LLM apps, capture mitigation practices, and identify AI security gaps. 6-8 minutes.

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

Sample Survey Items

Q1
Multiple Choice
Which area best describes your primary role?
  • Back-end engineering
  • Front-end engineering
  • Full-stack
  • Machine learning / data science
  • DevOps / SRE
  • Security engineering
  • QA / Test automation
Q2
Multiple Choice
Do you currently build or maintain features that integrate LLMs?
  • Yes — I currently build/maintain LLM-integrated features
  • Not yet — planning to integrate in the next 6 months
  • No — not working with LLMs
Q3
Multiple Choice
Which threat vectors do you consider when designing LLM features? Select all that apply.
  • Prompt injection (malicious instructions)
  • Indirect prompt injection via external content sources
  • Jailbreaks/model override of system policies
  • Data exfiltration or leakage via prompts/responses
  • Tool misuse or over-permissioned actions
  • Training data poisoning
  • Prompt leakage or version exposure
Q4
Matrix
For each mitigation, indicate current adoption status.
RowsFully implementedPartially implementedPlannedNot plannedNot sure
Harden system prompts and templates•••••
Validate inputs and filter or allowlist outputs•••••
Retrieval grounding with content filtering•••••
Restrict tool/action permissions and use sandboxes•••••
Rate limit and monitor for anomalies•••••
Separate roles/contexts and enforce least privilege•••••
Verify content provenance/trust (e.g., signed sources)•••••
Network/model isolation for untrusted inputs•••••
Q5
Opinion Scale
How confident are you in your understanding of prompt injection risks?
Range: 1 – 10
Min: Not confidentMid: ModerateMax: Very confident
Q6
Multiple Choice
In the last 6 months, have you encountered suspected prompt injection or jailbreak activity in your systems?
  • Yes — confirmed incident
  • Possibly — suspicious behavior, not confirmed
  • No
Q7
Long Text
Briefly describe what happened and how it was handled. Please omit sensitive data.
Max 600 chars
Q8
Multiple Choice
Attention check: To confirm attention, please select “Not sure.”
  • Yes
  • No
  • Not sure
Q9
Rating
Overall, how aware are you of prompt injection as a security threat?
Scale: 10 (star)
Min: Not awareMax: Very aware
Q10
Ranking
Rank the following by your current level of concern (top = highest concern).
Drag to order (top = most important)
  1. Direct prompt injection via user input
  2. Indirect prompt injection via content sources
  3. Over-permissive tools/actions
  4. Data leakage via prompts/responses
  5. Model override/jailbreak to ignore policies
  6. Training-time poisoning
  7. Third-party prompt/plugin supply chain risk
Q11
Dropdown
Where have you learned about prompt injection 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
Q12
Multiple Choice
How often do you evaluate for prompt injection or jailbreak risks?
  • Weekly
  • Every 2–3 weeks
  • Monthly
  • Quarterly
  • Less often or never
Q13
Multiple Choice
Which methods do you use to test for prompt injection weaknesses? Select all that apply.
  • Adversarial red teaming by engineers
  • Automated evaluation suites/checklists
  • Unit or integration tests for prompts
  • Canary/honeytoken detection
  • Shadow deployment with monitoring/alerts
  • External penetration testing
  • We do not currently test for this
Q14
Multiple Choice
What is your default response policy when LLM input may be unsafe?
  • Allow but sanitize/validate content
  • Block and ask for clarification
  • Escalate to human review
  • Varies by context
  • Not sure
Q15
Dropdown
Which types of tools or platforms do you use for mitigation? Select all that apply.
  • LLM gateway/proxy with policy enforcement
  • Content moderation/safety APIs
  • Vector database with filtering/access controls
  • Open-source guardrails libraries
  • Cloud provider built-in safety features
  • Custom internal middleware/services
  • None
Q16
Constant Sum
Allocate 100 points to the metrics you prioritize when mitigating prompt injection. Total must equal 100.
Total must equal 100
Min per option: 0Whole numbers only
Q17
Short Text
What is your biggest obstacle to managing prompt injection risk today?
Max 100 chars
Q18
Multiple Choice
Total years of professional software experience
  • Less than 1 year
  • 1–3 years
  • 4–6 years
  • 7–10 years
  • More than 10 years
Q19
Multiple Choice
Organization size (employees)
  • 1–10
  • 11–50
  • 51–200
  • 201–1,000
  • 1,001–5,000
  • 5,001+
Q20
Multiple Choice
Where are you primarily located?
  • Africa
  • Asia
  • Europe
  • Latin America
  • Middle East
  • North America
  • Oceania
Q21
Multiple Choice
Primary industry
  • Technology/software
  • Finance
  • Healthcare
  • Retail/e-commerce
  • Manufacturing
  • Education
  • Government/nonprofit
  • Other
Q22
Long Text
Any additional feedback or context you’d like to share? Optional.
Max 600 chars
Q23
AI Interview
AI Interview: 2 Follow-up Questions on Prompt Injection Practices
AI InterviewLength: 2Personality: Expert InterviewerMode: Fast
Q24
Chat Message
Thanks for participating! Your responses help improve secure LLM development.
Q25
Chat Message
Welcome! This survey asks about your work context, your awareness of prompt injection risks, and the safeguards you use. It should take about 6–8 minutes.

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