Adversarial AI testing
Garak, PyRIT, promptfoo-style corpora, WardenBot attack libraries, and adaptive attacker loops where the tier supports them.
Methodology
External AI testing with evidence, not vibes.
WardenBot AI tests approved web, API, chatbot, RAG, and agent surfaces from the outside. The method is designed for two outcomes: verified audit findings before launch and useful monitoring alerts after launch.
- Scope, ownership, and test boundaries
- Browser-driven surface and chatbot discovery
- Script-first checks for canaries, facts, schemas, and known exploit signals
- AI red-team probes for prompt, retrieval, tool, and refusal failure modes
- Evidence capture, grading, and human review where impact is high
- Agent-ready remediation, retest criteria, and monitoring regression checks
Technology
Built around the actual failure surface.
Traditional AppSec tools still matter, but they do not know whether a chatbot leaked a canary, invented a refund policy, or let a retrieved document steer an agent into unsafe behavior.
Web and API testing
OWASP ZAP with WardenBot extensions, Nuclei, SQLmap guardrails, FFUF, Playwright, GraphQL crawling, WebSocket fuzzing, and OAST verification.
Layered grading
Deterministic checks first, guarded LLM judges for semantic cases, and human calibration for safety-critical scoring.
We drive real widgets
Playwright-based browser automation talks to chatbot widgets and AI flows the way customers do, without requiring SDK instrumentation.
We prefer deterministic proof
Canary leaks, exact facts, numeric ranges, schema checks, and callbacks are scored with scripts before any LLM judge is involved.
We stop at remediation guidance
WardenBot produces agent-ready Markdown, validation steps, and retest criteria. Customer teams still review and deploy fixes.