KVigil
Kyyba
AI Governance · Adversarial Testing

AI RED
TEAMING

Deliberately attacking your AI system — before adversaries, accidents, or employees do. The adversarial testing infrastructure that makes AI deployment safe to scale.

50+
Adversarial Test Cases
6
Attack Vectors Covered
3
Frameworks: NIST · OWASP · MITRE
Six Attack Vectors We Test
🔓
PII Leakage
SSNs, health data, constituent information exposed through AI interactions.
💉
Prompt Injection
Malicious input hijacks model output or extracts system-level instructions.
🌀
Hallucination Risk
Model fabricates facts in eligibility or legal guidance decisions.
⬆️
Role Escalation
Users query data beyond clearance level via crafted prompts.
⚖️
Bias & Disparate Impact
Protected classes treated unequally in AI-assisted decisions.
🌐
Data Residency Breach
Sensitive state data processed outside approved data perimeters.
The Process
1
Build Scenarios
Curated adversarial prompts matched to government workflows and 6 attack vectors.
2
Execute Against Live System
Fire scenarios systematically. Automated via KVigil SLM. Every result logged.
3
Score & Risk-Tier
Findings rated on likelihood, impact, detectability — prioritized risk register.
4
Remediate with Controls
Findings drive DLP, human-in-the-loop policies, and access restrictions.
Grounded in NIST AI RMF OWASP LLM Top 10 MITRE ATLAS Data Residency Policy