AI Red Teamer / Adversarial Tester

Role Overview

The AI Red Teamer proactively tests AI systems — especially LLMs and generative AI — for security vulnerabilities, safety risks, biases, and failure modes through adversarial simulation. This is the newest role in the AI governance taxonomy: Microsoft formed the first dedicated AI Red Team in 2018 under Siva Kumar, but the field exploded after 2023 with the rise of LLMs and was further catalyzed by the White House Executive Order on AI (October 2023).

Title variations in active listings include “AI Red Team Specialist,” “LLM Red Teamer,” “Adversarial ML Engineer/Researcher,” “AI Security Researcher,” “AI Safety Tester,” “AI Vulnerability Researcher,” and “ML Threat Operations Specialist.” The role sits within Security, AI Safety, Trust & Safety, or AI/ML Research departments.

Microsoft’s AI Red Team is notably interdisciplinary, including cybersecurity experts, a neuroscientist, a linguist, and national security specialists. They have red-teamed over 100 generative AI products and published a whitepaper on their methodology (“Lessons From Red Teaming 100 Generative AI Products,” January 2025). This interdisciplinary model is becoming the norm: Mercor lists “psychology, acting, or writing backgrounds for unconventional adversarial thinking” among desirable qualifications, reflecting the creative dimension of modern AI red-teaming.

Industries hiring include tech companies (Microsoft, Google, NVIDIA, OpenAI), AI security startups (HiddenLayer, 10a Labs, Mindgard), defense and government contractors, financial services, and consulting firms. Only 14% of organizations believe they have the necessary AI security talent (World Economic Forum, 2025), signaling massive unmet demand.

Career Compensation Ladder

The verified range for mid-level AI Red Teamers is $120K to $160K base salary, consistent with our 20-Role Table. Salary data is sparse because the title is so new — most estimates are inferred from traditional red teamer salaries plus AI premiums.

Entry-level (startups, 0 to 2 years): $60,000 to $90,000. 10a Labs explicitly hires entry-level candidates at $60K–$70K with just a bachelor’s degree and demonstrated interest in AI safety. This is one of the few AI governance roles with genuine entry-level openings.

Contract/hourly (freelance): $54 to $111/hour ($112K to $231K annualized). Mercor listings on Remotive confirm this range for remote AI Red Teamer positions.

Mid-level (2 to 5 years): $120,000 to $220,000. TechJackSolutions AI Security Careers Hub reports AI Red Teamers at $150K to $250K citing Glassdoor and ZipRecruiter data. General red teamer averages on Glassdoor sit at $124K (25th-to-75th: $93K–$173K). The AI specialization adds a meaningful premium.

Senior / Lead (5+ years): $180,000 to $280,000+. AI Red Team Leads and Heads of AI Security at major technology companies. Levels.fyi comparables: Security Software Engineer median $233K, AI Engineer median $154K.

Director+ and CISO with AI Focus: $250,000 to $500,000+. The career ceiling is exceptionally high because AI security leadership is in extreme demand and short supply.

What You Will Do Day to Day

Daily work from Microsoft, HiddenLayer, and 10a Labs listings: developing and executing adversarial test suites (manual and scripted) for LLMs and image/video models, crafting multilingual jailbreak prompts targeting policy edge cases, running automated scanning with PyRIT and Garak, analyzing AI outputs and triaging failures, writing vulnerability reports with actionable remediation recommendations, contributing to internal tooling (prompt libraries, scenario generators, dashboards), and briefing product teams and leadership on risk findings.

Work cycles typically follow model release cadences: pre-deployment red-teaming is most intensive, followed by ongoing monitoring and periodic re-testing. You shift between deep-focus adversarial sessions (crafting novel attack vectors), collaborative threat modeling with engineering teams, and report-writing for leadership and compliance documentation.

AI-specific red-teaming tools represent a critical differentiator: Microsoft PyRIT (Python Risk Identification Tool for automated red-teaming of generative AI — Microsoft’s core internal tool), NVIDIA Garak (LLM vulnerability scanner described as “Nmap for LLMs,” probing for hallucination, data leakage, prompt injection, jailbreaks, toxicity), Microsoft Counterfit (CLI tool for automated ML security assessment), IBM Adversarial Robustness Toolbox (ART) (comprehensive attack/defense library), DeepTeam by Confident AI (open-source LLM red-teaming with 40+ vulnerability types), and Promptfoo (LLM testing and red-teaming with CI/CD integration).

Frameworks: The OWASP Top 10 for LLMs (2025 edition) covers prompt injection, sensitive information disclosure, supply chain attacks, data/model poisoning, improper output handling, excessive agency, system prompt leakage, vector/embedding weaknesses, misinformation, and unbounded consumption. The MITRE ATLAS framework (15 adversarial tactics against AI systems, modeled after ATT&CK) provides the strategic attack taxonomy. The OWASP GenAI Red Teaming Guide (released January 2025) is the emerging methodological standard.

Supporting tools include Python (essential), Bash scripting, PyTorch/TensorFlow/Keras, Hugging Face Transformers, Burp Suite, and Metasploit.

Skills Deep Dive

Technical skills blend traditional offensive security with ML-specific attack vectors. Core adversarial ML: evasion attacks, data poisoning, model extraction, membership inference. Prompt injection and jailbreaking: direct/indirect injection, multi-turn attacks, encoding-based injections, image jailbreaks. Understanding LLM internals: transformer architecture, attention mechanisms, tokenization, embeddings, fine-tuning, and RAG systems.

Knowledge architecture follows four tiers. Primary/core knowledge: adversarial ML techniques, prompt injection and jailbreaking methodology, OWASP Top 10 for LLMs, and MITRE ATLAS framework. Supplementary knowledge: traditional penetration testing, threat modeling for AI systems, ML model internals, NLP fundamentals, and web application security. Specialized expertise: automated red-teaming tools and frameworks, AI-specific attack vectors (backdoor attacks, training data extraction), safety evaluation frameworks (Google SAIF, NIST AI RMF), agentic AI security (tool use, autonomous decision-making vulnerabilities), and multi-modal attacks. Nice-to-know: transformer internals at implementation level, RLHF/RLAIF mechanisms, differential privacy security implications, and ML supply chain security.

Soft skills: clear vulnerability reporting with actionable remediation guidance, ability to explain complex technical risks to non-security stakeholders, creative and adversarial thinking, structured methodology under ambiguity, and collaboration with engineering teams on remediation.

Certifications That Move the Needle

The field values demonstrated skills over formal certifications — CTF rankings, published research, and open-source contributions carry significant weight. That said, certifications accelerate entry from adjacent fields.

Priority 1 (offensive security gold standard): OffSec OSCP+ ($1,749 PEN-200 + exam with 90-day lab access; 23 hour 45 minute proctored practical exam; OSCP is lifetime, OSCP+ expires 3 years with $799 recertification). Foundational credibility for any red-teaming role.

Priority 2 (AI governance complement): IAPP AIGP ($799/$649 member; 100 MCQ, 2 hours 45 minutes; 20 CPE biennially). Bridges security expertise with governance vocabulary — increasingly valued as AI red-teaming expands beyond pure security into compliance.

Priority 3 (AI-specific security): CAISP (Practical DevSecOps) ($999; hands-on LLM vulnerability detection, MITRE ATLAS defenses). Purpose-built for AI security practitioners. Alternatively, HackTheBox AI Red Teamer Job Role Path (in collaboration with Google, aligned with SAIF framework; covers prompt injection, model privacy attacks, adversarial AI, supply chain risks — hands-on, lab-based).

Priority 4 (enterprise penetration testing): GIAC GPEN ($999 exam only; SANS SEC560 training $7,640; web-based proctored with CyberLive components; 73% to pass; 4-year renewal with 36 CPE credits). More widely recognized by enterprise security organizations than OSCP.

Priority 5 (broad ethical hacking): EC-Council CEH v13 ($950–$1,199 exam + $100/attempt admin fee; 4 hours, 125 MCQ plus optional practical; 3-year renewal with 120 ECE credits, $80/year membership). More recognized by HR departments, less respected by practitioners than OSCP.

Learning Roadmap

Free training (highest-impact starting point): Microsoft AI Red Teaming 101 (free, 10-episode training series covering fundamentals, attack techniques, PyRIT automation). This is the single strongest free on-ramp into the field.

Courses: Coursera’s AI Security Specialization (Edureka), “AI Security: Security in the Age of AI” (includes MITRE ATLAS and PyRIT labs), and “AI for Cybersecurity” (Johns Hopkins). HackTheBox Academy AI Red Teamer path offers hands-on, lab-based training aligned with Google’s SAIF framework.

Essential reading: Microsoft’s whitepaper “Lessons From Red Teaming 100 Generative AI Products” (January 2025), the OWASP GenAI Red Teaming Guide (January 2025), “Universal and Transferable Adversarial Attacks on Aligned Language Models” (Zou et al., 2023), and “Security Engineering” by Ross Anderson (foundational security text).

CTF competitions are essential for this role: The AI Village CTF at DEF CON (annual, on Kaggle; tasks include evading, poisoning, stealing, and fooling AI models, co-organized by NVIDIA AI Red Team) is the flagship event. HackTheBox AI Red Teaming CTF (scenario-based LLM jailbreak challenges) and general platforms like HackTheBox and TryHackMe build practical skills. CTF rankings serve as portfolio credentials for this role more than any other in AI governance.

Communities: AI Village at DEF CON is the central community. OWASP Slack (#team-llm-redteam channel) and the OWASP AI Red Teaming Initiative (biweekly calls standardizing methodologies) connect practitioners. Apart Research runs alignment hackathons. Key conferences: DEF CON (Las Vegas, world’s largest hacking conference with dedicated AI Village), Black Hat (AI security tracks), and NeurIPS (adversarial ML workshops).

Career Pathways

From zero (2 to 3 year timeline): Build CS/security foundation including Python, networking, Linux administration (6–12 months). Security basics via CompTIA Security+, basic pentesting, and CTF competitions (6–12 months). ML fundamentals through Andrew Ng’s courses (3–6 months). Offensive security specialization via OSCP and HackTheBox labs (6–12 months). Bridge to AI security through OWASP Top 10 for LLMs, MITRE ATLAS, HackTheBox AI Red Teamer path, and practice with Garak/PyRIT (3–6 months). Build portfolio through AI Village CTF and open-source contributions. Land first AI red team role.

From adjacent roles: Penetration Testers have the most direct path — add AI/ML vulnerability knowledge, learn MITRE ATLAS, and practice with PyRIT and Garak. Security Engineers and Researchers add adversarial ML skills. ML Engineers add security methodology and offensive thinking. Trust & Safety Analysts transition into content-safety red-teaming, which is one of the fastest-growing sub-specializations.

Career progression: Entry-Level AI Red Teamer ($60K–$90K) → AI Red Teamer ($120K–$160K) → Senior AI Red Teamer ($150K–$220K) → AI Red Team Lead ($180K–$250K) → Head of AI Security/Director of AI Safety ($200K–$280K+) → CISO with AI Focus ($250K–$500K+).

Experience expectations: Because this field is so new, experience requirements are notably lower than traditional senior security roles. 10a Labs explicitly hires entry-level candidates with just a bachelor’s degree (in CS, data science, linguistics, or international studies), basic Python, and “demonstrated interest” in AI safety — no specific years of experience required, at $60K–$70K. Mid-level roles (2–4 years) require comfort with scripting and report writing. HiddenLayer’s mid-senior listing requires 3+ years penetration testing with at least 1 year focused on AI systems, deep understanding of ML attack techniques, and hands-on experience with adversarial ML tools (Foolbox, CleverHans, ART).

The field values demonstrated skills over years of tenure. Valued portfolio elements: published vulnerabilities or responsible disclosures, CTF rankings (AI Village CTF, HackTheBox), research papers or blog posts on AI security, open-source contributions to Garak/PyRIT/ART, and bug bounty experience.

Market Context

Employer landscape: Tech companies (Microsoft, Google, NVIDIA, OpenAI), AI security startups (HiddenLayer, 10a Labs, Mindgard), defense/government contractors, financial services, and consulting firms. Microsoft’s free 10-episode AI Red Teaming training series provides an immediate on-ramp, and their publication of PyRIT as open-source demonstrates the field’s maturation.

Resume expectations: CTF rankings and competition results, published vulnerabilities or responsible disclosures, open-source contributions to AI security tools, blog posts or research papers on adversarial ML, and demonstrated proficiency with PyRIT/Garak/ART. For mid-senior roles: penetration testing track record, vulnerability assessment reports, and cross-functional collaboration experience.

Market signals: In 2026, an estimated 60% of organizations will be using AI red-teaming per Practical DevSecOps projections. The EU AI Act creates mandatory testing obligations for high-risk AI systems. The White House EO on AI (October 2023) catalyzed government investment in AI security testing. AI-related job postings have grown by over 35% in the past three years, with security roles growing even faster. Microsoft’s warning that “skilled LLM security practitioners are already in high demand and low supply” reflects the market reality — making this one of the most accessible AI governance roles for career changers from either security or ML backgrounds.

Related Roles

• AI Systems Safety Manager – broader safety scope including evaluation design and deployment decisions
• AI Security Specialist – overlapping threat assessment with defensive rather than offensive focus
• AI Model Validator – overlapping evaluation methodology with performance and fairness focus
• AI Bias Mitigation Specialist – overlapping fairness testing methodology
• AI Auditor – overlapping assessment methodology with broader compliance scope