Services · LLM Red-Teaming
LLM Red-Teaming
LLM red-teaming is structured adversarial testing of large language model applications to surface failure modes before an attacker or a regulator finds them. We probe deployed and pre-deployment systems the way a motivated adversary would, then document findings with reproduction steps and severity classification.
What LLM red-teaming covers
Prompt injection
Direct and indirect injection testing, including injection through retrieved documents, tool outputs, and user-supplied content that reaches the model context.
Jailbreak resistance
Systematic evaluation of guardrail durability against known and novel bypass techniques, including multi-turn escalation, role-play framing, encoding, and instruction hierarchy confusion.
System prompt extraction
Testing whether proprietary instructions, business logic, or configuration embedded in the system prompt can be surfaced by an end user.
Training and retrieval data leakage
Probing for exposure of sensitive data through model outputs, including data reachable via a RAG pipeline that the user should not have permission to access.
Agentic tool-use abuse
For systems with tool access, testing whether the model can be induced to invoke tools outside intended scope, chain actions destructively, or exfiltrate data through legitimate tool channels.
Harmful output generation
Evaluation against defined risk categories relevant to the deployment context and applicable policy.
How we approach it
Every engagement starts with a threat model specific to your deployment. A customer service assistant and a clinical decision support tool have almost nothing in common in their risk surface, and generic red-team checklists produce generic findings. We define what an adversary would want from your system, then test for those outcomes.
Testing combines automated adversarial suites with manual exploration. The automated layer provides coverage and repeatability; the manual layer finds the failures that only emerge when a human follows an unexpected thread. Both are necessary — automation alone misses novel attack paths, and manual testing alone cannot be re-run as a regression suite.
What you receive
A documented finding set with severity classification, full reproduction steps for each finding, and remediation guidance. Where applicable, we deliver the automated portion as a runnable evaluation suite your team can execute against future releases, so red-teaming becomes a regression gate rather than a one-time exercise.
Who this is for
Organizations shipping LLM-backed applications to external users, teams deploying AI in regulated contexts, and vendors who need independent testing evidence for enterprise procurement or regulatory review.
Frequently asked questions
How is red-teaming different from penetration testing?
Penetration testing targets infrastructure and application security — authentication, injection, access control. LLM red-teaming targets model behavior: whether the system can be induced to say or do something it should not, through natural language rather than through code. Both are necessary for an AI application, and they find entirely different classes of problem.
Can red-teaming be automated?
Partially. Automated adversarial suites provide breadth and repeatability against known attack patterns, and should be part of any mature program. But novel attack paths are found by humans following unexpected threads, and the most consequential findings in most engagements come from manual exploration. Automation alone produces a false sense of coverage.
How long does an engagement take?
Scope depends on system complexity, tool access, and the number of distinct user roles and risk categories in play. A focused engagement against a single application typically runs weeks rather than months. We scope after a threat modeling session rather than quoting from a template.