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Learning Roadmap

How to Become a AI Responsible Disclosure Specialist

A step-by-step, phase-based learning path from beginner to job-ready AI Responsible Disclosure Specialist. Estimated completion: 7 months across 5 phases.

5 Phases
30 Weeks Total
High Entry Barrier
Advanced Difficulty
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  1. Foundations: AI Systems & Security Mindset

    6 weeks
    Goals
    • Understand transformer architecture, LLM training pipelines, and common failure modes
    • Learn core cybersecurity principles: threat modeling, attack surfaces, responsible disclosure
    • Master Python for ML experimentation and security scripting
    Resources
    • Stanford CS324 - LLMs course materials
    • OWASP Top 10 for LLM Applications (2025 edition)
    • CERT Coordination Center's Guide to Coordinated Vulnerability Disclosure
    • Fast.ai Practical Deep Learning course
    Milestone

    You can articulate how LLMs work, identify basic failure modes, and explain the CVD lifecycle with examples.

  2. AI Attack Techniques & Red-Teaming

    8 weeks
    Goals
    • Master prompt injection, jailbreaking, data extraction, and system prompt leakage techniques
    • Learn adversarial ML fundamentals: evasion, poisoning, model extraction, inversion attacks
    • Practice hands-on red-teaming against open-source models using Garak and PyRIT
    Resources
    • Microsoft PyRIT documentation and tutorial notebooks
    • NCC Group's Garak LLM vulnerability scanner GitHub repo
    • Simon Willison's blog and LLM vulnerability research archives
    • AdvML course by Bo Li (UIUC) or similar adversarial ML materials
    Milestone

    You can independently red-team an LLM application, discover at least 3 distinct vulnerability classes, and document findings reproducibly.

  3. Disclosure Craft & Stakeholder Coordination

    6 weeks
    Goals
    • Learn to write professional-grade vulnerability disclosure reports and security advisories
    • Understand CVE assignment process, CVSS scoring, and AI-specific severity frameworks
    • Practice multi-stakeholder coordination simulations with timelines and escalation paths
    Resources
    • FIRST.org - Vulnerability Disclosure Policy templates and CVSS v4 calculator
    • Google Project Zero disclosure policy case studies
    • CISA's coordinated vulnerability disclosure playbook
    • NIST AI Risk Management Framework (AI RMF 1.0)
    Milestone

    You can write a complete disclosure report for a discovered AI vulnerability, manage a 90-day disclosure timeline, and communicate effectively with vendor security teams.

  4. Regulatory Landscape & AI Governance

    4 weeks
    Goals
    • Map international AI regulations relevant to disclosure obligations (EU AI Act, US executive orders)
    • Understand ISO/IEC 42001 AI management system requirements
    • Learn how legal frameworks interact with voluntary disclosure norms
    Resources
    • EU AI Act official text and implementation guidance
    • NIST AI RMF Playbook and companion resources
    • ISO/IEC 42001:2023 standard overview
    • Future of Privacy Forum AI incident reporting resources
    Milestone

    You can advise an organization on disclosure obligations under current and upcoming AI regulations and design compliant disclosure workflows.

  5. Advanced Specialization & Portfolio Building

    6 weeks
    Goals
    • Conduct original vulnerability research and submit findings to AI bug bounty programs
    • Build a public portfolio of disclosed and resolved AI vulnerabilities
    • Develop automated disclosure workflow tools and contribute to open-source safety projects
    Resources
    • OpenAI, Google, Anthropic, and HuggingFace bug bounty / security research programs
    • arXiv AI safety and security preprints
    • Conference talks from DEF CON AI Village, Black Hat, NeurIPS Safety Track
    • Your own GitHub repository of disclosure templates and tooling
    Milestone

    You have at least 2 publicly credited AI vulnerability disclosures, a professional portfolio, and are ready for mid-level roles or consulting engagements.

Practice Projects

Apply your skills with hands-on projects. Ordered by difficulty.

LLM Red-Team Playbook & Vulnerability Catalog

Intermediate

Build a structured, open-source catalog of LLM attack techniques organized by OWASP LLM Top 10 categories. Each entry includes attack description, reproduction steps, affected models, severity rating, and suggested mitigations. Test against at least 3 open-source models.

~40h
Adversarial ML techniquesTechnical report writingSeverity scoring

Automated AI Vulnerability Scanner with Garak + Custom Probes

Intermediate

Extend Garak's probe library with custom probes targeting a specific vulnerability class (e.g., system prompt extraction, PII leakage). Build a pipeline that runs scans on schedule, aggregates results, and generates severity-ranked reports.

~35h
Python security toolingCI/CD integrationAutomated testing

Simulated Coordinated Disclosure Workflow

Beginner

Create a realistic end-to-end disclosure scenario: discover a vulnerability in an open-source AI project, draft a professional disclosure report, simulate vendor communication via email templates, manage a 90-day timeline, and produce a public advisory. Document the entire process.

~25h
Disclosure process masteryStakeholder communicationSecurity advisory writing

AI Supply Chain Vulnerability Assessment Tool

Advanced

Build a tool that traces the provenance of AI model weights, datasets, and fine-tuning pipelines to identify potential supply chain risks. Integrate with HuggingFace model metadata, scan for known vulnerable dependencies, and flag models with incomplete provenance information.

~50h
Supply chain securitySoftware composition analysisAPI integration

Multimodal Injection Attack Research

Advanced

Conduct original research on indirect prompt injection through multimodal inputs - test image-based, audio-based, and document-based injection vectors against leading multimodal models. Publish findings with a responsible disclosure case study.

~60h
Multimodal attack techniquesOriginal research methodologyDisclosure coordination

AI Incident Disclosure Tracker & Dashboard

Intermediate

Build a public dashboard that aggregates and categorizes AI safety incidents, disclosures, and CVEs from multiple sources. Include filtering by severity, vendor, vulnerability class, and timeline. Useful for the community and demonstrates ecosystem awareness.

~30h
Data aggregationWeb developmentInformation architecture

Bias Disclosure Case Study & Framework

Intermediate

Select a publicly available AI model and conduct a systematic bias audit across protected attributes. Develop a disclosure framework specific to bias findings, including quantitative metrics, disparate impact analysis, and recommended evaluation benchmarks for the vendor.

~35h
Bias auditingFairness metricsQuantitative analysis

AI Red-Team Capture The Flag (CTF) Challenge Set

Advanced

Design and build a set of CTF challenges that teach AI vulnerability discovery skills. Each challenge simulates a different attack vector against a sandboxed LLM. Include progressive difficulty, solution write-ups, and scoring rubrics suitable for a conference workshop.

~45h
Teaching and curriculum designSecurity challenge designLLM deployment and sandboxing

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