Services / Red Team

Find what attackers will find before your customers do.

Adversarial testing for AI-enabled products, agents, copilots, RAG systems, tools, workflows, and guardrails, scoped to produce findings, retest plans, and evidence your engineering team can use.

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Prompt InjectionRAG LeakageTool AbuseJailbreaksUnsafe ActionsGuardrail BypassEvidence PacksRetest Plans

Adversarial scenarios

Prompt Injection

Direct, indirect, and role-confusion attacks against instruction handling.

RAG Document Attack

Poisoned docs, retrieval abuse, and context laundering against RAG paths.

Tool Abuse

Unauthorized actions, excessive agency, and misused connectors.

Data Exfiltration

Secrets, PII, logs, and context leakage through prompts or tools.

Policy Bypass

Override, refusal bypass, and guardrail escape routes.

Adversarial

Test Engine

Scenario execution
evidence capture

Evidence completeness 92%

Evidence & outcomes

Findings

What broke and why

Evidence

Steps, logs, traces, screenshots

Retest

Minimal retest set and verification

Control Map

Gap mapped to controls and owners

Surfaces Services Flow Outputs System Next Step

Adversarial surfaces

AI systems fail across prompts, retrieval, tools, memory, and action paths.

Modern AI products do not fail only at the model boundary. They fail where user input, retrieved context, tools, files, identifiers, workflows, and approvals intersect. Red-team work needs to follow those paths end to end.

Surfaces

Prompt Layer

Injection, jailbreaks, role confusion, and unsafe instruction following.

Surfaces

Retrieval Layer

Poisoned documents, cross-tenant chunks, and stale permissions.

Surfaces

Tool Layer

Unauthorized actions, unsafe calls, and approval bypass.

Surfaces

Data Layer

Secrets, PII, customer data, logs, and context leakage.

Surfaces

Workflow Layer

Agent chaining, delegated action, sends, updates, and escalation loops.

Surfaces

Governance Layer

Policy gaps, missing evidence, unsupported claims, and retest failures.

Service formats

Three ways to run the work.

The catalog is structured as a red-team offering plus the adjacent safety and threat-modeling services teams usually need right after the first engagement.

Flagship

Red TeamAvailable

assessment

AI Red Team & Adversarial Testing

Evidence-driven adversarial assurance for AI-enabled products, agents, copilots, RAG systems, and automation workflows. The work tests realistic misuse, prompt injection, data exposure, jailbreak pathways, tool abuse, unsafe autonomy, cross-tenant leakage, and control bypasses.

Outcome

4 deliverables

Best for

CISO, Product Security, Red Team, AI Engineering Lead

•Prompt injection, jailbreak, and policy bypass testing
•RAG data exposure and authorization abuse testing
•Tool/function abuse and excessive agency testing

Duration: 3-6 weeksScoped in discovery call

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Flagship

Red TeamAvailable

implementation

AI Guardrails & Evals Review

Review the controls, tests, monitoring, and fallback paths that keep LLMs, RAG systems, copilots, and agents safe in production. The work covers policy boundaries, refusal behavior, retrieval constraints, eval design, regression tests, output monitoring, abuse detection, escalation paths, and fallback handling.

Outcome

4 deliverables

Best for

AI Product Lead, Product Security, Trust and Safety, Engineering Lead

•Guardrail architecture and refusal/fallback review
•Eval set and abuse case design
•Regression testing strategy

Duration: 3-6 weeksScoped in discovery call

View service Review guardrails and evals See people fit

Standard

Red TeamAvailable

workshop

Threat Modeling Sprint

A focused engagement to map realistic threats, abuse cases, trust boundaries, and security controls before launch or redesign. The work is designed for product and engineering teams that need practical risk discovery, not academic diagrams.

Outcome

4 deliverables

Best for

Product Security, Engineering Lead, Security Architect, Product Manager

•System scoping, asset, actor, and trust-boundary mapping
•Misuse, abuse-case, STRIDE-style, or custom modeling
•Risk prioritization

Duration: 1-3 weeksScoped in discovery call

View service Schedule threat modeling sprint See people fit

Delivery flow

Scoped like a test plan. Delivered like engineering evidence.

The engagement is meant to feel like a clear sequence of security work, not an abstract advisory cycle.

Scope the target

Define product paths, users, data, tools, retrieval sources, allowed actions, and safety boundaries.

Build the attack plan

Select scenarios that matter: prompt injection, indirect injection, RAG leakage, tool abuse, jailbreaks, unsafe action, policy bypass.

Execute and capture evidence

Run tests, collect screenshots, logs, and traces, classify failures, and preserve reproducibility.

Rank and map controls

Prioritize findings by impact, exploitability, affected users, and missing controls.

Retest and package

Define the minimum retest set, verify fixes, and package findings into a usable evidence pack.

Outputs

Leave with artifacts your teams can act on.

The output package is intentionally operational: findings, evidence, control mapping, and retest criteria that can feed the backlog or customer conversation.

Attack Plan

Test scope, allowed actions, target systems, safety boundaries, and scenario list.

Findings Memo

Ranked failures with reproduction steps, impact, risk, and owners.

Evidence Pack

Screenshots, traces, logs, prompts, retrieved context, tool calls, and result proof.

Retest Plan

Minimal verification set after fixes, including regression checks.

Control Map

Findings mapped to guardrails, logging, evals, approvals, and governance controls.

Executive Readout

A concise summary that a leader can use without translating the technical details again.

Connected system

Red-team work connects into the workbench.

The service page should point to the parts of the site that turn a one-time engagement into a repeatable operating model.

SecEng Adversarial Range

Run scenario execution, failure mapping, and evidence generation through the attack range.

Open SecEng Adversarial Range

AIPSA Labs

Turn red-team patterns into hands-on practitioner training and lab completion.

Open AIPSA Labs

AIPSA Evidence Packs

Convert scoped findings into governance and sales-support evidence.

Open AIPSA Evidence Packs

Workshops

Use jumpstarts to scope, execute, or operationalize the work with cross-functional teams.

Open Workshops

Next step

Start with the system you are most worried about.

We can scope the attack surface, run the highest-value test paths, and package the evidence into something engineering and leadership can use.

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