LLM and generative AI security testing
New models, new attack surface.
A chatbot that can be talked out of its instructions, or a RAG pipeline that returns another customer's documents, won't show up in a standard web application test. We attack your LLM applications the way an adversary would and show you exactly what got through.
What we test.
Prompt injection
Test for direct and indirect prompt injection attacks that can manipulate model behavior.
Jailbreak testing
Evaluate resistance to jailbreaking techniques that bypass safety guardrails.
RAG security
Assess retrieval-augmented generation systems for data poisoning and leakage risks.
AI red teaming
Open-ended adversarial testing that chains techniques together to find security and safety failures.
Output filtering
Test content moderation and output filtering mechanisms for bypass vulnerabilities.
Data protection
Identify risks of training data extraction and sensitive information disclosure.
What a test tells you
Prompt injection
Whether a user, or a document your application reads, can override the system prompt and make the model act against you.
Jailbreaks and guardrail bypasses
How far your guardrails hold up when someone deliberately tries to talk the model past them.
RAG data exposure
Whether your retrieval-augmented generation pipeline hands users documents they shouldn't see, or can be fed content that manipulates answers.
Pre-deployment red teaming
Adversarial testing before launch, so security and safety issues surface in a report rather than in production.
Output filtering
Whether your content filters and safety mechanisms catch what they are meant to, and how easily they are bypassed.
Data leakage
Whether training data, PII or other sensitive information can be extracted through ordinary-looking prompts.
How we test
Scoping and threat modeling first, then hands-on attack work, then specific fixes.
Scope and objectives
Agree which LLM applications are in scope and which security concerns matter most to you.
Threat modeling
Identify attack vectors relevant to your LLM implementation and use cases.
Prompt injection testing
Test for direct and indirect prompt injection across every input the model reads.
Adversarial testing
Red team exercises to test guardrails, safety mechanisms, and edge cases.
Data security analysis
Assess risks of data leakage, training data extraction, and PII exposure.
Remediation guidance
Deliver findings with specific recommendations for hardening your LLM systems.
What you get.
Vulnerability report
Every LLM vulnerability we found, with a severity rating and the evidence behind it.
Attack scenarios
Documentation of successful attack chains and exploitation techniques used during testing.
Remediation guide
Specific recommendations for hardening your LLM systems against identified threats.
Security roadmap
A plan for ongoing LLM security work and monitoring after the test.
Find out what your model will say to an attacker.
Tell us what the application does, which data it can reach, and whether it uses retrieval or tools. We'll scope a test around that.