Building Your Security Portfolio
Intermediate7 min readUpdated 2026-03-13
Practical guide to building a portfolio that demonstrates AI red teaming skills to employers, including project ideas, documentation standards, responsible disclosure, and online presence.
A strong portfolio is the single most effective tool for getting hired in AI red teaming. Unlike traditional software engineering where coding interviews dominate, AI security hiring relies heavily on evidence of practical ability. Your portfolio is that evidence.
Portfolio Structure
Essential Components
| Component | Purpose | Priority |
|---|---|---|
| Technical blog | Demonstrate research and communication skills | Must have |
| GitHub repositories | Show code quality and tool-building ability | Must have |
| CTF/bounty writeups | Prove hands-on vulnerability discovery | Strongly recommended |
| Conference talks/workshops | Establish community presence | Recommended |
| Research papers | Demonstrate depth (not required for most roles) | Nice to have |
Project Ideas by Skill Level
Beginner Projects
| Project | Skills Demonstrated | Estimated Time |
|---|---|---|
| Prompt injection taxonomy on 5 chatbots | Systematic testing, documentation | 2-3 weekends |
| Guardrail bypass comparison across providers | Filter analysis, comparative testing | 2-3 weekends |
| Simple attack automation script | Python, API interaction, tooling | 1-2 weekends |
| Lab writeup series (from this wiki) | Technical writing, methodology | Ongoing |
Intermediate Projects
| Project | Skills Demonstrated | Estimated Time |
|---|---|---|
| Custom red team tool (e.g., prompt fuzzer) | Software engineering, security tooling | 2-4 weeks |
| RAG poisoning proof-of-concept | ML pipeline attacks, end-to-end exploitation | 2-3 weeks |
| Agent exploitation case study | Agentic AI security, tool abuse | 2-3 weeks |
| Defense bypass research on a specific guardrail product | Deep technical analysis, reverse engineering | 3-4 weeks |
Advanced Projects
| Project | Skills Demonstrated | Estimated Time |
|---|---|---|
| Novel attack discovery and responsible disclosure | Original research, disclosure ethics | Varies |
| Open-source tool contribution (Garak, PyRIT) | Production code quality, community engagement | Ongoing |
| Training pipeline attack research | ML engineering, data poisoning | 4-8 weeks |
| Multimodal attack chain development | Cross-domain expertise | 3-6 weeks |
| Security evaluation framework or benchmark | Evaluation design, rigor | 4-8 weeks |
Writing High-Quality Findings
Blog Post Template for AI Security Research
# Title: [Descriptive, specific title]
## Summary
[2-3 sentences: what you found, why it matters]
## Background
[Context the reader needs: what system, what the expected behavior is]
## Methodology
[How you approached the testing -- what tools, what process]
## Findings
[For each finding:]
### Finding N: [Title]
- **Severity:** [Critical/High/Medium/Low]
- **Steps to reproduce:** [Exact steps, payloads, screenshots]
- **Root cause:** [Why the vulnerability exists]
- **Impact:** [What an attacker could achieve]
## Remediation Recommendations
[Specific, actionable fixes for each finding]
## Responsible Disclosure
[Timeline and communication with vendor, if applicable]
## Conclusion
[Key takeaways and broader implications]Responsible Disclosure for AI Systems
AI security findings require additional disclosure considerations beyond traditional vulnerability disclosure.
| Consideration | Guidance |
|---|---|
| Model behavior vs. infrastructure bugs | Model behavior issues (jailbreaks) are often lower urgency than infrastructure vulnerabilities (data access) |
| Disclosure timeline | Standard 90-day timeline. For critical issues, coordinate shorter timelines |
| What to publish | Describe the technique and impact. For widely exploitable techniques, consider withholding full automation |
| Vendor contact | Most AI companies have security@company.com or bug bounty programs. Check for AI-specific programs |
| Academic publication | If publishing academically, follow venue ethics guidelines and consider responsible AI research principles |
AI Bug Bounty Programs (2026)
| Company | Program | AI-Specific Scope |
|---|---|---|
| OpenAI | Bug bounty via Bugcrowd | Model behavior, API security |
| Google VRP | Gemini safety, AI product security | |
| Meta | Meta Bug Bounty | Llama-related findings |
| Microsoft | MSRC | Copilot, Azure AI |
| Anthropic | Responsible disclosure | Claude safety findings |
| HuggingFace | Responsible disclosure | Hub security, model safety |
Building Online Presence
Platform Strategy
| Platform | Use For | Frequency |
|---|---|---|
| Personal blog / website | Long-form research, portfolio hub | 1-2 posts/month |
| GitHub | Code, tools, proof-of-concepts | Ongoing |
| Twitter/X | Short insights, community engagement, networking | Daily-weekly |
| Professional networking, job searching | Weekly | |
| YouTube / podcast | Talks, tutorials, demonstrations | Monthly (if applicable) |
Content Calendar for First 6 Months
| Month | Blog Post | GitHub | Community |
|---|---|---|---|
| 1 | "My First Prompt Injection: What I Learned" | Lab environment setup scripts | Join OWASP LLM project Slack |
| 2 | "Comparing Guardrails Across 5 Chatbots" | Guardrail probing tool | Comment on 5 relevant posts |
| 3 | "Building a Prompt Fuzzer in Python" | Prompt fuzzer repository | Attend AI Village meetup |
| 4 | "RAG Poisoning: A Practical Guide" | RAG poisoning PoC | Submit talk to local meetup |
| 5 | "Deep Dive: [Specific Technique]" | Contribution to Garak/PyRIT | Present at local meetup |
| 6 | "Responsible Disclosure Case Study" | Updated portfolio README | Apply to speak at conference |
Portfolio Review Checklist
Before sharing your portfolio with potential employers, verify:
- All findings have clear reproduction steps
- Code repositories have README files and documentation
- No sensitive information (API keys, credentials) is committed
- Responsible disclosure has been followed for any real-world findings
- Blog posts are technically accurate and well-edited
- GitHub profile is clean and professional
- Portfolio demonstrates breadth (multiple attack types) and depth (at least one deep dive)
- Contact information is easy to find
For career planning context, see AI Red Teaming Career Guide and Specialization Paths.
Related Topics
- AI Red Teaming Career Guide -- career overview and entry strategies
- Specialization Paths -- choosing a focus area to showcase
- Industry Certifications & Training -- credentials that complement portfolio work
- Ethics & Responsible Disclosure -- disclosure guidelines for portfolio findings
References
- "How to Build a Cybersecurity Portfolio" - SANS Institute (2024) - Practical guidance on structuring security portfolios for job seekers
- "Responsible Disclosure Guidelines for AI Systems" - Partnership on AI (2024) - Ethical guidelines for publishing AI vulnerability research in portfolios
- "Bug Bounty Programs for AI Systems" - HackerOne (2024) - Platforms and programs where AI security findings can be responsibly disclosed and showcased
Knowledge Check
What distinguishes a great AI security blog post from a merely good one?