Product · Pillar I · Assess
AdversarialScan finds out how badly your AI breaks.
Red teaming and vulnerability assessment across text, conversations, and images, run against break-goals you define, with every finding scored by severity. You get a ranked account of what an adversary can actually do to your system, not a pass/fail stamp.
Text, conversations, and images: all first-class targets.
Most red-teaming tools are text tools. AdversarialScan treats image generation and image understanding as primary attack surfaces, because that is where the highest-consequence failures now happen.
Text prompts
Direct and indirect prompt injection, policy evasion, data extraction, and instruction override, all aimed at your system rather than a public benchmark.
Multi-turn conversations
Attacks that build across turns: trust-establishment, context poisoning, role drift, and slow-walked policy erosion that single-prompt tests never catch.
Image generation
Prompt-level and composition-level attacks that push image models past content policy. This is the failure mode that ends payment and platform relationships.
Image understanding
Adversarial inputs against vision components: embedded instructions, obfuscated content, and inputs engineered to be misread.
Tested against your definition of failure.
A generic jailbreak list tells you little about your risk. Every engagement starts by defining break-goals with your team: the specific outputs, behaviors, and disclosures that would constitute a business problem for you. Then we attack toward them.
Industry-shaped
Vulnerability sets reflect the rules your reviewers care about: payment-network policy, underwriting criteria, platform content rules, sector regulation.
System-specific
Attacks target your deployed system (your model choices, your prompts, your tools), not a reference model in a lab.
Repeatable
The same break-goals re-run after remediation, so improvement is measured against the original findings instead of asserted.
Severity-scored findings, ranked for action.
Each finding carries an exploitability score, the evidence trail that produced it, and its mapped business impact, so your team fixes what matters first and can show its work.
| ID | Finding | Severity | Business impact |
|---|---|---|---|
| F-014 | Multi-turn roleplay sequence elicits prohibited financial advice | Critical0.94 | Regulatory exposure; card-network policy breach |
| F-027 | Image model produces brand-unsafe composition under indirect prompt | High0.81 | Platform policy violation; partner escalation |
| F-033 | System prompt partially recoverable via repeated probing | Medium0.57 | IP disclosure; downstream attack enablement |
Rows above are illustrative. Real findings include reproduction steps, transcripts or image evidence, and remediation guidance, delivered in the Evidence Pack.
A world-model engine drives the attacks.
AdversarialScan is powered by a proprietary engine that builds a working model of the system under test: how it responds, where it hesitates, which pressures move it. Attack campaigns are generated and adapted against that model, turn by turn, toward your break-goals. We do not publish the methodology. Adversaries read papers too.
What the engine does
Plans multi-turn strategies, composes image and text attacks, and escalates where your system shows give. It hunts your break-goals, then documents the path.
What we will show you
Every finding is fully evidenced: the inputs, the outputs, the reproduction path. You never have to take a score on faith.
What we won't
The generation strategy behind the attacks. Keeping it unpublished keeps it working for you, not against you.
Findings become protection, then proof.
Scope an AdversarialScan.
Tell us what you are deploying or evaluating and what failure would mean for it. We come back with break-goals, a scan plan, and a timeline.
Ask about the Evidence Pack
Leave your email and we'll walk you through what an Evidence Pack contains for your use case: severity-scored findings, business-impact mapping, and the approval record.