LLM01 · Input
Prompt injection & jailbreaks
Direct and indirect injection through user input, documents, web pages, emails and tool results — including multi-turn and encoded payloads that bypass guardrails.
AI & LLM security testing
AI RED TEAMING.BREAK IT BEFORE THEY DO.
VEXTRACE red-teams large language models, RAG pipelines and autonomous AI agents the way real adversaries attack them — prompt injection, jailbreaks, data exfiltration and tool abuse — and hands your engineers a prioritized, reproducible remediation plan mapped to the OWASP Top 10 for LLM Applications, MITRE ATLAS and the NIST AI Risk Management Framework.
01
What we test
Every surface where a model reads untrusted input, touches data or takes an action.
LLM02 · LLM07 · Data
Sensitive data & system-prompt leakage
Extraction of system prompts, secrets, PII and proprietary context from chat surfaces, logs and retrieval layers.
LLM08 · Retrieval
RAG & vector database security
Knowledge-base poisoning, embedding manipulation, access-control bypass across tenants and context-filter evasion.
LLM06 · Agents
Agentic AI & tool-use abuse
Excessive agency, privilege escalation through tools and APIs, unsafe code execution and multi-agent chains (LangGraph, AutoGen, CrewAI, MCP servers).
LLM05 · Output
Insecure output handling
Model output that reaches browsers, shells, SQL or downstream systems without validation — XSS, SSRF and command injection via the model.
LLM09 · LLM10 · Behaviour
Misinformation & resource abuse
Hallucination under adversarial pressure, harmful-content bypass, denial-of-wallet and unbounded consumption.
02
Methodology
Human-led adversarial testing, accelerated by our own automation — never a scanner report with a logo on it.
01Threat modelMap the model, data flows, tools, users and trust boundaries.
02ReconFingerprint the model, guardrails, retrieval and agent permissions.
03AttackManual and automated campaigns across the OWASP LLM Top 10 and MITRE ATLAS.
04ChainCombine findings into realistic attack paths with business impact.
05ReportReproducible evidence, severity, fixes and guardrail recommendations.
06RetestVerify every fix and regression-test the prompts that broke it.
03
Frameworks we map to
Findings translate directly into the language your auditors, regulators and engineers already use.
| Framework | How it is used |
|---|---|
| OWASP Top 10 for LLM Applications (2025) | Coverage checklist and finding taxonomy (LLM01–LLM10). |
| MITRE ATLAS | Adversary tactics and techniques against AI systems, used to build attack paths. |
| NIST AI RMF 1.0 & Generative AI Profile (AI 600-1) | Risk language for executive reporting and governance programs. |
| EU AI Act · ISO/IEC 42001 | Evidence of adversarial testing for high-risk and general-purpose AI obligations. |
04
Frequently asked questions
What security and AI teams ask before an engagement.
What is AI red teaming?
AI red teaming is adversarial testing of an AI system: specialists attack the model, its data and the tools it can use — the way a real attacker would — to find failures such as prompt injection, data leakage or unsafe actions before they reach production.
How is it different from a normal penetration test?
A classic pentest targets code and infrastructure. LLM applications fail in new ways: the "input" is natural language, behaviour is probabilistic and an agent can act on your systems. We test both layers — the AI behaviour and the application and infrastructure around it.
Which models and platforms do you test?
Commercial APIs (OpenAI, Anthropic, Google, Mistral and others), open-weight models you host yourself, RAG stacks built on common vector databases, and agent frameworks including MCP-based tool integrations.
How long does an AI red team take?
A focused assessment of one LLM feature usually takes one to two weeks; a full program covering several agents, data sources and integrations typically runs three to six weeks, including retest.
Will you need our training data or model weights?
No. Most engagements are black- or grey-box against your deployed application. Access to system prompts, architecture diagrams and tool permissions makes testing deeper and faster, and is handled under NDA and our data-handling policy.
SHIP AI YOU CAN DEFEND.
Tell us what the model can see and do. We return a scoped AI red-team plan within 48 hours.