Red Teaming & Adversarial Testing
What is the practical way to red team an LLM app before going to production?
Run automated adversarial scans first, then targeted manual attacks — one sprint is enough for a first pass. 1. Baseline scan: run Garak (NVIDIA's open-source vulnerability scanner) against your endpoint; its probe library covers injection, jailbreaks, and leakage mapped to OWASP LLM Top 10 risks. 2. Multi-turn attacks: use PyRIT (Microsoft) to orchestrate conversation-level attacks that single-prompt scanners miss. 3. Breadth pass: DeepTeam adds 40+ vulnerability scans and attack methods in a pytest-like workflow. 4. Make it continuous: schedule scans in CI or use a managed service like Mindgard so regressions surface per release, not per audit. Log every finding against MITRE ATLAS technique IDs — that turns your red-team run into audit evidence for EU AI Act and ISO 42001 conversations.
cited_tools
Tools that solve this
OWASP LLM Top 10
Free
Community-maintained checklist of the ten most critical risks for LLM applications.
DeepTeam
Open Source
Open-source LLM red-teaming framework with 40+ vulnerability scans and attack methods.
Mindgard
Enterprise
Continuous automated red teaming and security testing for AI systems.
MITRE ATLAS
Free
Knowledge base of adversarial tactics and techniques against AI systems, modeled on ATT&CK.
Garak
Open Source
NVIDIA's open-source LLM vulnerability scanner with a modular probe library for injection, jailbreaks, and leakage.
PyRIT
Open Source
Microsoft's Python Risk Identification Toolkit for automated generative-AI red teaming.
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