FORMATION IA
Red Team IA : Tests Adversariaux et Sécurité
Acquérez les compétences pour attaquer, sonder et sécuriser les systèmes d'IA face aux menaces adversariales réelles.
Ce qu'elle couvre
Ce bootcamp de niveau praticien forme les ingénieurs en sécurité et les équipes IA à attaquer et défendre méthodiquement les déploiements de grands modèles de langage. Les participants travaillent sur des laboratoires pratiques couvrant l'injection de prompt, les jailbreaks, l'empoisonnement de modèle, l'exfiltration indirecte de données et l'intégralité de l'OWASP LLM Top 10. Le programme associe simulations d'attaques structurées et conception de patterns défensifs, permettant aux équipes d'intégrer le red-teaming dans leur cycle de développement IA. Les livrables comprennent un plan de test adversarial réutilisable et un ensemble de garde-fous de prompt appliqués aux systèmes en production.
À l'issue, vous saurez
- Execute a structured prompt injection campaign against a live LLM API and document exploitable attack surfaces
- Reproduce at least five OWASP LLM Top 10 vulnerabilities in a sandboxed environment and propose mitigations for each
- Design and implement an input/output guardrail layer that reduces jailbreak success rate by a measurable threshold
- Produce a reusable AI red-team test plan aligned to an organisation's threat model and AI deployment architecture
- Integrate adversarial test cases into a CI/CD pipeline to catch regressions before model updates reach production
Sujets abordés
- OWASP LLM Top 10: full walkthrough and exploitation labs
- Prompt injection attacks — direct, indirect, and multi-turn
- Jailbreak techniques and bypass pattern taxonomy
- Model and data poisoning vectors in fine-tuning pipelines
- Data exfiltration via LLM outputs and embeddings
- Adversarial evaluation frameworks and automated fuzzing
- Defensive guardrails: input/output filtering, sandboxing, privilege separation
- Embedding red-teaming into MLOps and secure SDLC
Modalité
Typically delivered as a 3-to-5-day in-person or live-virtual bootcamp with a 70/30 hands-on-to-theory ratio. Each session uses a shared lab environment (cloud-hosted, pre-provisioned) with real LLM endpoints. Participants receive an attack playbook, a defensive patterns reference guide, and post-training access to an updated vulnerability library. Remote delivery uses breakout rooms for attack-simulation pairs. In-person delivery is preferred for red-team role-play exercises involving multi-team adversarial scenarios.
Ce qui fait que ça marche
- Running adversarial drills against a staging clone of the actual production LLM stack rather than generic demo models
- Establishing a shared vulnerability taxonomy between security and AI teams before the bootcamp begins
- Scheduling a 30-day post-bootcamp follow-up to review whether mitigations held against new attack variants
- Embedding at least one trained red-team practitioner into each AI product squad as a standing security champion
Erreurs fréquentes
- Treating prompt injection as a purely theoretical risk and skipping production-realistic lab environments
- Focusing only on external jailbreaks while ignoring insider-threat and supply-chain poisoning vectors
- Implementing guardrails as a one-time fix rather than a continuously tested, version-controlled component
- Assigning red-teaming solely to security teams without involving the AI engineers who build the pipelines
Quand NE PAS suivre cette formation
This bootcamp is not the right fit for teams that have not yet deployed any LLM-based feature to production; foundational AI literacy or a prompt-engineering course should come first, as participants without real deployment context cannot meaningfully scope a threat model or interpret attack results.
Fournisseurs à considérer
Sources
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