LLM Red Teaming Development Services

LLM red teaming is adversarial testing with engineering deliverables, not a checklist workshop. The work includes mapping how prompt injection, RAG poisoning, tool abuse, and multi-turn manipulation could compromise your product, building probe libraries that run in CI, running manual exercises automation cannot replicate, and producing an evidence log auditors expect.

That is different from application penetration testing. Web pen tests rarely cover agent tool chains, retrieval permission boundaries, or model-specific jailbreak classes. It is also different from guardrails implementation: red teaming finds the holes; guardrails close them in production.

We map exercises to frameworks buyers already reference, including the OWASP Top 10 for LLM Applications and the NIST AI Risk Management Framework, without turning the engagement into slideware.

Six attack classes we probe on every engagement. Your product may emphasize three of them; we still document the rest so procurement questionnaires get honest answers.

Security review without evidence

Product shipped a copilot demo. Enterprise procurement asked for adversarial test results. Engineering ran informal jailbreak attempts in ChatGPT and called it done. We run the Adversarial Coverage Readiness Test, then deliver probe repos, CI gates, and a findings log mapped to OWASP LLM risks.

Agent with dangerous tool chain

An internal agent can read tickets, query billing, and send email because prototyping was faster that way. We exercise argument injection, chained tool misuse, and privilege escalation paths, then pair findings with guardrail recommendations your platform team can implement.

Model upgrade without adversarial regression

Engineering swapped model versions for cost savings. Quality evals passed. Security incidents rose. We wire adversarial probes into the same CI pipeline as LLM evaluation engineering so model changes cannot merge without retest evidence.

RAG document upload surface

Customers upload PDFs that become retrieval context. Indirect injection via hidden instructions in documents was never tested. We build poisoned-document cases and cross-tenant bleed probes aligned with your ingestion permissions.

Annual exercise cadence missing

Automated scans run in CI but no one performs manual multi-turn chains quarterly. Compliance wants external-style depth without hiring a full internal red team. We deliver the manual exercise, executive summary, and retest playbook your team can repeat.

Agents without observability on attacks

Security cannot tell whether a probe failure reached production users. We integrate probe results with your tracing stack or recommend AI agent observability hooks so denied attacks are visible in incident workflows.

Attack surface mapping runs the Adversarial Coverage Readiness Test with security, product, and platform stakeholders. If Q3 fails, we fix the evidence log schema before writing probes.

Probe library design catalogs injection, RAG, tool, and multi-turn cases per workflow. Cases live in version control with severity tags and OWASP class mappings.

CI integration wires automated probes into pull request or release pipelines using frameworks such as Promptfoo or custom harnesses. Failed probes block merge when configured as required checks.

Manual red team exercise targets chains automation misses: slow trust escalation, novel tool combinations, and business-logic abuse specific to your domain.

Remediation support pairs findings with engineering recommendations. We retest after fixes land and update probe baselines so regressions fail CI.

Evidence handoff delivers findings report, executive summary, probe repos, retest log, and GRC-ready export fields. Your team owns the cadence after launch. Retainer probe maintenance is available when models or regulations shift.

A four- to five-person squad is the usual shape. The adversarial ML engineer and compliance reviewer are the roles vendors skip to win on price. They are also the roles that keep a probe suite from becoming a stale script library nobody trusts.

For ongoing probe maintenance across multiple product lines, the same squad can run as a dedicated AI development team. For one security-minded engineer inside your platform group, staff augmentation on an existing AI squad is often the better entry point after the initial engagement.

Outsource when

• Launch is blocked on adversarial testing evidence and you cannot hire AI security plus LLM engineering in one hiring cycle.
• Multiple squads ship agents without shared probe suites and security wants CI gates this quarter.
• You need manual exploit depth plus automated regression before an enterprise security review or regulator meeting.
• Tool-using agents need abuse cases your application team has not modeled before.

Keep it in-house when

• You already operate a mature AI red team with quarterly manual exercises and CI probe coverage.
• The workload is an internal prototype with no external users and no compliance deadline.
• A single senior engineer can maintain Promptfoo probes for one chat surface in a sprint.

Red-team SaaS dashboards can flag vulnerabilities. They rarely own the probe code in your repository, agent runtime tests, or retest workflow. Traditional pen-test vendors understand web apps. They often miss RAG permission boundaries and multi-step agent tool abuse without LLM-specific methodology.

The situation

Crestview Mutual sells property and casualty insurance through a member portal used by regional agents. They built a copilot that answers policy coverage questions from uploaded endorsements and internal rate manuals. Sales loved the demo. Security paused enterprise rollout when test users coaxed the model into summarizing claimant details from unrelated policy files in the same retrieval index.

Engineering had run informal jailbreak attempts in a sandbox. There was no versioned probe library, no CI gate on prompt changes, and no evidence log procurement could file. Tool access to the document store was read-only yet unscoped by member tenant.

What we would deliver

A ten-week project with a five-person squad: AI security lead, adversarial ML engineer, backend engineer, eval engineer, and part-time compliance reviewer.

• Attack surface map with tenant-scoped retrieval abuse cases and indirect injection via uploaded PDFs.
• Automated probe suite in CI covering jailbreak, PII exfiltration, and off-topic medical advice classes.
• Manual multi-turn exercise targeting slow trust escalation and refund manipulation prompts.
• Findings mapped to OWASP LLM risks with remediation tickets and retest evidence.
• Executive summary and GRC export schema for carrier procurement questionnaires.

Expected outcomes in a scenario like this: enterprise security review unblocked, named owners for remediation, and reproducible adversarial evidence without freezing the product roadmap. Follow-on guardrails work would implement the controls probes identified.

False confidence from automated scans alone. Mitigation: mandatory manual exercise for high-risk workflows plus probes automation cannot replicate.

Probe suites that rot after one engagement. Mitigation: probes in git, CI gates on model and prompt changes, and documented ownership for quarterly refresh.

Findings without remediation owners. Mitigation: every finding ships with severity, suggested fix, named engineering owner, and retest case before we close the engagement.

Testing production data in probes. Mitigation: synthetic and anonymized fixtures by default; production trace sampling only with explicit approval and PII scrubbing.

Evidence logs auditors reject. Mitigation: compliance reviewer signs off on export fields mapping methodology, model IDs, findings, fixes, and retest status.

Red team work disconnected from guardrails. Mitigation: handoff package references control recommendations and pairs naturally with guardrail implementation when you want us to build the fixes.