The first task is to identify what an attacker can influence and what the AI system can affect. We map user roles, system instructions, retrieved documents, tool descriptions, browser or file inputs, memory, approvals, secrets, and downstream side effects. A generic prompt list cannot capture these trust boundaries. The useful threat model connects each controllable input to a business impact such as cross-tenant access, unauthorized actions, sensitive output, or corrupted workflow state.
Enterprise-Ready AI Security
Find the AI failure paths before a buyer—or attacker—does.
LLM red teaming services for production AI agents and RAG. Test prompt injection, tool misuse, and tenant boundaries; ship fixes and regression tests.
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30 minutes. No deck. Leave with a clear next step.
System-level interventionProduction-shaped diagnosisInspectable verification
The production trigger
Recognize the failure before naming the service.
Find reproducible AI attack paths, ship priority fixes, and keep the regression suite.
Your product blocks the obvious prompt injection test. The risk begins after twenty turns, through retrieved content, tool output, role escalation, and state carried across a workflow.
Trigger detail 1We attack the real product surface, document reproducible findings, implement priority fixes, and leave a regression suite your team can run again.
Trigger detail 2System diagnosis
Find where the failure actually lives.
The visible symptom is rarely the whole problem. The first pass follows it through the production path until the controllable boundary is clear.
We review the controls already present before launching attacks. Input filters, model refusals, tool policies, retrieval permissions, output validation, and human approvals each cover different failure modes. Their order matters, as does what happens across multiple turns and retries. This prevents the exercise from reporting a model response as a critical product issue when downstream authorization blocks the action, or overlooking a mild-looking response that becomes dangerous once a tool executes it.
The scope is calibrated to the deployed product, supported identities, and permitted testing environment. We agree on test accounts, data handling, rate limits, stop conditions, and contacts before execution. A production-adjacent environment with realistic tools and policies is usually more informative than a toy clone. Traditional application and infrastructure penetration testing remain separate; this engagement concentrates on AI-mediated behavior and the controls around it.
The intervention
Change the critical path, not the surrounding theatre.
Each workstream targets a different failure boundary. Together they connect product behavior, infrastructure, controls, and verification.
Threat model and attack surface
Build abuse cases from the product's roles, tools, data, and high-impact actions. We identify direct and indirect prompt injection paths, privilege transitions, cross-tenant boundaries, memory poisoning, output-to-action chains, and controls that depend only on model compliance. Each scenario names a precondition and expected safety property so testing is tied to the system rather than a catalog headline.
Multi-turn adversarial execution
Run controlled conversations that vary framing, turn count, retrieved context, tool results, and retry behavior. We capture prompts, intermediate state, model and tool responses, policy decisions, and environmental conditions needed to reproduce a result. Automated harnesses provide breadth, while manual sequences explore stateful paths and business logic that a single-turn scanner cannot understand.
Impact validation and severity
Separate suspicious text from exploitable product behavior. A finding is validated through the full path to data exposure, unauthorized action, policy bypass, or integrity loss, without causing avoidable harm. Severity considers access required, repeatability, tenant reach, user interaction, control coverage, and recoverability. Uncertain observations are labeled as such instead of being promoted into dramatic claims.
Remediation and regression
Fix priority issues at the correct layer: authorization, tool scoping, retrieval filtering, instruction boundaries, confirmation, validation, or model behavior. The regression suite encodes both the attack and legitimate neighboring behavior so a block does not quietly break the product. Findings outside the current intervention receive a specific mitigation, responsible team, and retest condition.
Inspectable changes
See what changes in the system.
- Product-specific threat model and attack plan
- Multi-turn jailbreak and role-escalation scenarios
- Indirect prompt-injection tests through tools and retrieval
- Agent tool-misuse and data-exfiltration findings
- Severity-ranked written report mapped to OWASP LLM Top 10
- Priority fixes implemented in your repository
- Repeatable adversarial regression suite
Engineering judgment
The decisions that determine whether the change holds.
Control placement
Decide which safety properties must be enforced deterministically outside the model. Identity, tenant authorization, secret access, payment-like actions, and destructive tools generally need code-level policy even when prompts also discourage misuse. We document where model judgment is acceptable and where a refusal alone cannot carry the risk.
Realism versus containment
Choose the closest safe testing environment to production. Mocked tools can hide authorization and side-effect failures, while unrestricted production testing creates unnecessary exposure. We define representative identities, seeded data, instrumented dependencies, and stop conditions that preserve realistic control paths without placing customer data or live operations at avoidable risk.
Regression release policy
Set which adversarial cases block a release, which require manual review, and which are monitored as known limitations. Thresholds account for nondeterminism and include repeated runs where needed. The policy also defines who refreshes attacks after a model, prompt, retrieval source, tool, or permission boundary changes.
Strong fit
- Your AI product is live in front of customers.
- A buyer or security lead needs evidence beyond a single jailbreak test.
Probably not the right fit
- Your AI product is not live yet.
- You want a box-tick without actionable findings.
Scope, timing, and ownership
Know the commercial shape before the call.
A bounded engagement with a service-specific delivery sequence, an agreed price, and artifacts that stay under your control.
Day 1
Threat model
Map user roles, tools, retrieval paths, secrets, and high-impact failure modes.
Day 2
Adversarial run
Execute multi-turn jailbreak, indirect injection, tool misuse, and escalation scenarios.
Day 3
Fix and handoff
Reproduce findings, ship priority remediations, and walk through the regression suite.
Typical fixed scope
Mid-four to low-five figures
Most engagements fall in this planning range. Your exact fixed price is agreed in writing after we review the production surface, access needs, and success criteria. The range is guidance, not a quote.
Commercial terms
No open-ended consulting meter.
- Fixed scope agreed in writing
- Fixed timeline agreed in writing
- Fixed price agreed before work starts
- Client-owned deliverables from day one
- No hourly meter or surprise overages
Client ownership
The implementation remains yours.
- Your repository
- Your infrastructure
- Your evals and evidence
- Yours from commit one
Book a 30-minute consultation
30 minutes. No deck. Leave with a clear next step.
AI Red-Teaming
Questions worth settling before the call
Technical, commercial, and handoff questions answered before you book.
Find the attack path before it becomes a customer incident.
Bring the failing workflow, current evidence, and the constraints your engineers cannot ignore.
Book a 30-minute consultation
30 minutes. No deck. Leave with a clear next step.