Penetration testing for large language models, before attackers prompt them.
AI is now business-critical, and it introduces an attack surface traditional testing was never built for. SubRosa's AI red team probes your LLM applications for prompt injection, data leakage, and model and plugin abuse, the way a real adversary would.
Prompt injection · Data leakage · Model manipulation · Plugin abuse
What is LLM penetration testing?
LLM penetration testing is a hands-on security assessment of applications built on large language models, testing the model, its prompts, its data access, and its plugins and integrations for the ways an attacker could abuse them: prompt injection that hijacks behavior, jailbreaks that bypass guardrails, leakage of training data or system prompts, and plugin chains that reach systems the model should never touch. It goes beyond a model benchmark to prove real, exploitable impact in your deployment.
The AI attack surface.
We test every way an attacker could abuse an LLM application, from the prompt to the plugins.
Prompt injection & jailbreaks
Direct and indirect prompt injection and jailbreak techniques that hijack the model's behavior, bypass guardrails, or exfiltrate its instructions.
Data & prompt leakage
Testing for exposure of training data, system prompts, and other users' data through the model and its context window.
Model manipulation
Adversarial inputs that degrade, bias, or manipulate model outputs into producing harmful or unauthorized actions.
Plugin & integration security
Assessment of the tools, plugins, and integrations the model can call, where an injected prompt can pivot into real systems and data.
Offensive security, applied to AI.
AI red team expertise
Our offensive team tests LLM applications with the same adversarial mindset we bring to networks and apps, adapted to how AI actually fails.
Mapped to OWASP LLM Top 10
Findings are mapped to the OWASP Top 10 for LLM Applications, so your risk is framed against the emerging industry standard.
Real deployment context
We test your real deployment, its prompts, data access, and integrations, not a generic model, so the results reflect the risk you actually carry.
From red team to remediation.
Your LLM pen test findings land in Sable, mapped to the OWASP LLM Top 10, prioritized, assigned, and tracked from open to retested, so AI risk becomes a managed program instead of a one-off report.
- CriticalOpenIndirect prompt injection via docLLM01
- HighIn progressSystem prompt disclosureLLM06
- HighRetestedPlugin call reaches internal APILLM07
- MediumOpenGuardrail bypass via role-playLLM01
Secure your AI before attackers prompt it.
Book an LLM penetration test and find out exactly how an attacker could abuse your AI, and how to shut it down.