AI security
AI penetration testing and LLM red teaming
Certified ethical hackers test your chatbots, LLM applications and AI agents the way an attacker would. We try to make them leak data, ignore their rules and misuse the tools they can reach, then show you how to fix what we find.
Human-led testing
Certified ethical hackers who go beyond automated scans
Recognized frameworks
OWASP Top 10 for LLM Applications and MITRE ATLAS
Retesting after you fix
Remediation validation after you apply fixes
Why AI needs its own test
AI applications fail in ways traditional tests miss
A language model treats instructions and data in the same stream of text. That means a crafted message, a hidden line in a document or a poisoned web page can change what your AI application does. Standard application testing checks the code around the model. It doesn’t check how the model behaves when someone tries to talk it into misbehaving.
The risk grows once an AI system can act. Agents that call APIs, read files, send email or query databases can be steered into doing those things for an attacker. AI red teaming services test that behavior directly, with the same rigor we bring to network and application penetration testing.
LLM penetration testing
What we test
Each test is scoped to your AI system, its data and the tools it can reach.
Prompt injection testing
Direct and indirect prompt injection through user input, uploaded files, retrieved documents and web content, to override your instructions.
Jailbreaks and content-policy bypass
Attempts to get the model to ignore its safety rules, produce prohibited content or act outside its intended role.
Data leakage
Exposure of system prompts, training or retrieval data, other users’ information, credentials and secrets through model output.
Tool and agent abuse
Tricking agents into calling APIs, running actions or moving data they shouldn’t, and testing whether permissions follow least privilege.
Retrieval and embeddings
Weaknesses in the documents and vector stores your application draws on, including poisoned content and access that crosses user boundaries.
Output handling and integration
How your application uses model output, including injection into web pages, code or downstream systems, plus API authentication and rate limits.
Methodology
Grounded in OWASP and MITRE ATLAS
We map our testing to the OWASP Top 10 for LLM Applications, published by the OWASP Gen AI Security Project, which covers risks such as prompt injection, sensitive information disclosure, improper output handling, excessive agency and system prompt leakage.
We also draw on MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems), a knowledge base of real attacker tactics and techniques against AI systems. Traditional components of your AI application are tested against OWASP, PTES and NIST SP 800-115, the same methodologies we use for every penetration test.
How it works
How an AI penetration test runs
Pre-engagement and threat model
We learn what your AI system does, who uses it, what data and tools it can reach and what outcomes would hurt you most.
Test
Our testers run manual and tool-assisted attacks against the model, prompts, retrieval layer, agents and surrounding application.
Report
You get an executive summary, detailed findings with evidence and a prioritized remediation plan written for your developers.
Retest
Once fixes are in place, we retest to confirm they work and that new guardrails didn’t open other gaps.
Deliverables
What you receive
FAQ
AI penetration testing questions
Related services
Related services
Book a scoping call
Test your AI before attackers do
Tell us about the AI applications and agents you run. A tester will reply by email to scope the engagement.
Prefer email? Write to [email protected] or call (858) 712-0040.