[ 01 ] — Product — AI Red Teaming
Attack your models before someone else does.
Specialist researchers probe LLMs, agents and AI features for jailbreaks, prompt injection, data leakage and harmful output.
What is AI red teaming?
AI red teaming is adversarial testing of AI systems — chatbots, LLM features and agents — to find ways they can be manipulated. Testers try prompt injection, jailbreaks, data leakage, unsafe tool calls and harmful outputs, then report reproducible issues with fixes.
[ 02 ] — Capabilities
Safety & security
Covers both misuse (harmful content) and exploitation (data exfiltration, tool abuse).
Agent testing
Tool-calling, RAG pipelines and autonomous workflows in scope.
OWASP LLM Top 10
Findings mapped to OWASP LLM and MITRE ATLAS.
Custom harms taxonomy
Define what unacceptable output means for your product.
Reproducible artifacts
Every finding includes prompts, transcripts and model versions.
Regulatory evidence
Supports EU AI Act and NIST AI RMF documentation.
[ 04 ] — Questions people ask
What is prompt injection?+
Prompt injection is when crafted input — typed by a user or hidden in a document, web page or email — overrides an AI model's instructions. It is the top risk in the OWASP Top 10 for LLM Applications.
Which frameworks does AI red teaming map to?+
Findings can be mapped to the OWASP Top 10 for LLM Applications, MITRE ATLAS and the NIST AI Risk Management Framework.
When should we red team an AI feature?+
Before launch, after any change to its tools, data access or model, and continuously once it handles customer data.
Do you use AI to red team AI?+
Specialist researchers lead AI red teaming. AutoVAPT AI adds autonomous testing of the surrounding apps and APIs.
[ ∞ ] — Next step