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Every customer-facing LLM application has failure modes that standard QA won't find. QA tests whether the system does what it should. Red-teaming tests whether the system can be made to do what it shouldn't, and in adversarial conditions, it often can.
Prompt injection vulnerabilities let users override your system prompt. Jailbreak paths let users circumvent your output guardrails. Data leakage risks let users extract information they should not have access to. None of these show up in happy-path testing. They show up when the first curious or malicious user finds them.
We run a systematic, documented red-team audit: every finding comes with a minimal reproduction case so you can verify it, a severity rating, and a specific remediation recommendation. You get a written report you can share with your security team, compliance reviewers, or enterprise customers.
Tell us what you're building.
Four attack categories that every customer-facing LLM application should be tested against before launch.
Can a user inject instructions that override your system prompt? We test direct injection via user input, indirect injection via documents or tool call outputs, and instruction-hijacking via adversarial data in retrieved context.
Can users get the model to produce content outside its defined guardrails? We test role-playing attacks, hypothetical framing, multi-turn escalation, and token-level manipulation, the categories that succeed most often in practice.
Can the system be induced to reveal your system prompt, training data, retrieval content from other users, or sensitive information from its context window? We test extraction via direct questions, indirect probing, and context confusion attacks.
Does the system produce materially different outputs for semantically identical inputs? Instability in safety-relevant behaviors is a risk indicator. It means the guardrails are not applied consistently and can be bypassed by rephrasing.
Every finding is reproducible. We don't report theoretical vulnerabilities. We report demonstrated ones.
01
We map every input vector: user messages, file uploads, tool call outputs, retrieved documents, conversation history. Each input vector is a potential attack surface. We scope the test coverage based on which vectors exist in your system.
02
We design test scenarios for each attack category across each input vector. Scenarios are drawn from published research, known attack taxonomies, and adversarial creativity. We document every scenario before running it.
03
We run the test scenarios against your system. Every finding gets a minimal reproduction case (the exact input that produces the failure) so you can reproduce it yourself before we discuss remediation.
04
We deliver a written report with every finding, severity rating (Critical / High / Medium / Low), reproduction steps, and specific remediation recommendations. We discuss the report in a review call and answer questions about the fixes.
Every finding in the report is rated on a four-level severity scale so you can prioritize remediation.
The public jailbreak lists test the model. Your actual risk usually lives in the integration around it: the documents it reads, the tools it can call, and the boundaries it is trusted to keep. That is where we concentrate.
Your own data can be the attacker. In a system that retrieves documents or reads tool outputs, an instruction hidden inside the content the model ingests can hijack its behavior. The attacker writes the wiki page or the support ticket your agent later reads. We craft injection payloads against your real retrieval and tool paths, not just the chat box, because that is the vector most teams never test.
Tenant and data boundaries are the high-stakes target. The failures that end deals and trigger breach notifications are cross-boundary: one customer's data surfacing for another, the system prompt leaking, credentials or context escaping. We probe those boundaries directly, shaped to your actual tenancy model, because a boundary that holds in the demo is not the same as one that holds under a deliberate probe.
Guardrails erode over a conversation. A refusal that holds on turn one often crumbles by turn six under rephrasing, role-play framing, and incremental pressure. We test multi-turn erosion, not just single prompts, because real users and real attackers do not give up after one try, and a guardrail that only survives the first attempt is not a guardrail.
No test proves safety, but it catches regression. We are honest about what an audit is: a snapshot that demonstrates the known attack classes fail today. It cannot prove your system is unbreakable, because nobody can. Its lasting value is the reproducible test set we leave behind, so when you change a prompt or swap a model, you can re-run it and catch a reopened hole before a user finds it.
Red-teaming is a point-in-time audit. It is not the right tool for every situation.
Teams building internal tools with no external user input
If your LLM application is only accessible to trusted internal users who do not have a motive to probe it adversarially, the risk profile is different. You may still want an audit for compliance reasons, but the urgency is lower than a public-facing application.
Teams who need ongoing continuous monitoring
A red-team audit is a snapshot. If your application changes frequently or you need continuous adversarial coverage, you need a different solution, either automated red-teaming tooling or a recurring audit cadence. We can scope that as a separate engagement.
Tell us what the application does, who has access to it, and your launch timeline. We'll reply within one business day with a surface area assessment and rough engagement scope.