Attack your own AI, on a schedule.
On-demand adversarial campaigns across five attack families, plus scheduled weekly probes — with findings mapped to frameworks and exportable as evidence.
From target to evidence in five stages.
- 01
SELECT
Pick a target model, endpoint or agent and choose the attack families to exercise.
- 02
MUTATE
Generate bounded, deterministic attack batches — no stochastic spray-and-pray.
- 03
RUN
Execute probes, score every response and capture the prompt, output and reasoning.
- 04
REPORT
Findings are triaged by severity, mapped to frameworks and stored in the evidence locker.
- 05
EXPORT
Bundle the campaign, results and audit hash into an exportable evidence package.
What it does.
On-demand campaigns
Launch a full adversarial test whenever you ship a new model, policy or integration.
Scheduled weekly probes
Standing probes run automatically and alert you when a previously passing control starts failing.
Per-finding severity and evidence
Every finding includes severity, reproduction payload, response snapshot and recommended remediation.
Framework mapping on every finding
Controls are linked to OWASP LLM Top 10, MITRE ATLAS and NIST AI RMF clauses automatically.
Campaign history and trend
Track pass rates, failure modes and drift over time to prove improvement to auditors.
Export to the evidence locker
Campaigns become tamper-evident artifacts ready for board packs, auditors and regulators.
Findings written to the audit chain
Each result is hashed and signed so the timeline of discovery cannot be altered after the fact.
A campaign in one block.
policy > "Run OWASP LLM01 and LLM06 probes against the customer-support assistant every Monday at 06:00 UTC"
campaign: "weekly-llm01-llm06"
target: "assistant/customer-support"
families: ["prompt_injection", "excessive_agency"]
probes: 240
blocked: 236 / 240 (98.3%)
failures: 4 — severity HIGH
mapped_to: ["OWASP LLM01", "OWASP LLM06", "MITRE ATLAS:LLM-FIN:001"]
evidence_id: "ev-2026-0830-rt-7d1a…e03b"
Findings are mapped to the frameworks your auditor already uses.
Five ways adversaries test your AI.
Prompt injection
Direct and indirect instructions that try to override system behaviour or reveal the system prompt.
Jailbreak
Persona, encoding and refusal-breaking attacks designed to bypass safety and policy boundaries.
Data exfiltration
Probes that coerce the model into returning training data, secrets or context from prior sessions.
Tool abuse
Adversarial tool-call sequences that exploit ordering, ambiguity or excessive agency in agents.
PII extraction
Targeted attempts to elicit names, account numbers, credentials and other regulated information.
We publish our own adversarial pass-rate. Most vendors do not.