Dataconsultant evaluates AI applications, large language models and agents against realistic misuse, manipulation and boundary cases. We combine threat modelling, controlled attack simulation, evidence capture and remediation planning to help product, technology, security and risk teams identify weaknesses before they become operational, customer, regulatory or reputational problems.
Adversarial Testing Service is a controlled assessment of how an AI system behaves when users, attackers, compromised content or connected tools attempt to bypass its intended rules. It is commonly commissioned by AI product owners, technology leaders, security teams, risk functions and compliance teams. Typical outputs include a threat model, attack scenarios, reproducible evidence, severity ratings, control observations and a remediation backlog. Business value comes from earlier risk visibility and stronger release decisions. Results remain limited to the tested versions, access, data, scenarios and operating conditions.
The service can be scoped as a pre-release evaluation, focused red-team exercise, independent assurance review, remediation validation or recurring adversarial-testing programme.
We map intended use, users, data, model boundaries, connected tools, prohibited behaviours and credible abuse paths. Inputs include architecture, policies, risk criteria and authorised access. Outputs include rules of engagement, threat scenarios, test priorities and evidence requirements.
Specialists run structured attacks across prompts, retrieved content, model context, interfaces, identities and agent actions. Tests are documented for repeatability, with escalation controls for sensitive findings and clear separation between observations and confirmed risks.
Findings are classified by impact, exploitability, exposure and control maturity. We support response planning, control redesign, acceptance criteria, retesting and knowledge transfer. The client remains responsible for business decisions, legal interpretation and production change approval.
Identify misuse paths and control gaps before broader deployment, external exposure or expansion into a higher-risk use case.
Provide product and risk owners with reproducible evidence, severity context, residual-risk notes and defined retest criteria.
Connect observed failures to practical improvements in prompts, retrieval, permissions, monitoring, approvals and user experience.
Create test libraries, evaluation procedures and reporting methods that internal teams can extend across models and releases.
Conventional quality assurance confirms whether a system works as designed. Adversarial testing asks how it fails when instructions conflict, users manipulate context, data is hostile, or connected capabilities are abused.
Attackers may override system instructions, exploit indirect content, use obfuscation or combine multi-turn techniques. We build representative test chains, record successful and unsuccessful attempts, and assess the effectiveness of instruction, filtering and escalation controls.
Models may reveal hidden prompts, retrieved documents, personal data, secrets or information from another user context. Testing examines access boundaries, retrieval behaviour, output filtering, logging and the practical limits of model-side protections.
AI agents can trigger external systems, transactions, messages or data changes. We test permission boundaries, approval gates, tool selection, argument validation, action confirmation and recovery behaviour under manipulated instructions.
Systems may generate prohibited, discriminatory, harmful, misleading or non-compliant outputs under unusual framing. Testing uses domain-relevant scenarios and evaluates both model behaviour and surrounding human, procedural and technical controls.
Organisations may lack logs, test traceability, ownership or thresholds for escalation. We identify evidence gaps and propose reporting, acceptance and retesting practices proportionate to the use case.
Suitable for organisations building, buying or operating AI systems where misuse, unsafe outputs, data exposure or autonomous actions could create material business risk.
Test prompt injection, policy bypass, hallucinated commitments, personal-data leakage and escalation behaviour before public release.
Challenge document permissions, retrieval boundaries, indirect prompt injection, source attribution and cross-user information separation.
Evaluate whether manipulated instructions can trigger unauthorised emails, records, payments, code execution or workflow changes.
Run regression attacks after changing a model, prompt stack, safety layer, retrieval service or hosting provider.
Test manipulation, bias-sensitive scenarios, unsupported recommendations, confidence communication and human-override controls.
Assess exposed interfaces and available controls to support due diligence, acceptance criteria and compensating-control decisions.
Define assets, actors, trust boundaries, intended and prohibited uses, likely attack paths and business consequences. Inputs include architecture, user roles, data classifications, model information, tool permissions and incident history. Outputs include a scoped threat model, scenario catalogue, rules of engagement and prioritised test plan.
Test direct and indirect prompt injection, jailbreaks, encoding and obfuscation, multi-turn manipulation, context-window pressure, system-prompt extraction, retrieval poisoning scenarios, source confusion and permission-boundary failures. Coverage is adapted to languages, channels and user roles.
Examine whether the application reveals secrets, hidden instructions, personal data, restricted documents, training-data-like fragments or another user’s information. Testing considers access controls, retrieval filters, session separation, logging and retention while avoiding unnecessary use of live sensitive data.
Evaluate tool selection, function calling, argument validation, identity propagation, least privilege, approvals, confirmation steps, rate limits, transaction boundaries, monitoring and recovery. The service does not replace infrastructure penetration testing or platform-specific security certification.
Capture reproducible inputs, outputs, system conditions and control observations. Findings are rated using agreed criteria and translated into practical actions across application logic, model configuration, prompts, retrieval, permissions, monitoring, policy, training and operating procedures.
Final deliverables are agreed during discovery and reflect the authorised scope, system maturity and evidence needs.
| Deliverable | What it includes | Format | Stage | Client input required | Primary owner |
|---|---|---|---|---|---|
| Rules of engagement | Scope, authorisation, environments, exclusions, escalation and stop conditions | Controlled document | Mobilisation | Approvals, contacts, access constraints | Joint |
| AI threat model | Assets, actors, trust boundaries, attack paths and business impacts | Diagram and register | Discovery | Architecture, data flows, intended use | Dataconsultant |
| Adversarial test library | Prioritised scenarios, prompts, payloads, preconditions and expected controls | Structured test pack | Design | Policies, risk criteria, user roles | Dataconsultant |
| Evidence register | Reproducible steps, outputs, screenshots, logs and system conditions | Restricted evidence pack | Execution | Test access and logging | Dataconsultant |
| Findings and risk report | Severity, impact, exploitability, affected controls, limitations and ownership | Report and briefing | Analysis | Risk calibration and factual review | Joint |
| Remediation backlog | Prioritised application, model, data, access, monitoring and process actions | Action register | Response | Feasibility and ownership decisions | Joint |
| Retest report | Closure status, residual observations, regressions and acceptance evidence | Assurance report | Validation | Implemented changes and release version | Dataconsultant |
The sequence is adapted to the system, release stage, risk profile and access available. No fixed timeline is assumed before discovery.
Confirm objectives, systems, environments, owners, access, prohibited actions, escalation routes and evidence handling.
Map misuse paths, sensitive assets, control boundaries, business impacts and relevant regulatory or policy expectations.
Create prioritised scenarios, attack chains, personas, payload variants, success criteria and quality-review checkpoints.
Run authorised tests, preserve reproducible evidence, protect sensitive information and escalate critical observations promptly.
Validate findings, remove duplicates, rate risk, document limitations and connect failures to affected controls and decisions.
Support prioritisation, define acceptance criteria, retest agreed changes and transfer reusable methods to internal teams.
Testing is vendor-neutral and can work across custom applications and major hosted AI platforms, subject to contracts, provider policies and authorised access.
Generative AI applications, RAG systems, AI agents, model APIs, custom machine-learning services, chat interfaces, workflow automation and internal copilots.
Test harnesses, prompt and response logging, evaluation datasets, model monitoring, tracing, policy engines, access management, ticketing and evidence repositories.
Relevant references may include ISO/IEC 42001, NIST AI RMF, OWASP guidance for LLM applications, MITRE ATLAS, ISO/IEC 27001, ISO/IEC 27701, GDPR, the DPDP Act and applicable AI regulations.
| Model | Best for | Client involvement | Flexibility | Billing approach | Main advantage | Main limitation |
|---|---|---|---|---|---|---|
| Fixed-scope assessment | One system or release gate | Moderate | Defined scope | Fixed estimate | Clear deliverables and boundaries | Less suitable for changing systems |
| AI red-team project | Deep attack simulation | High during setup and triage | Scenario-led | Project based | Realistic multi-step testing | Requires strong authorisation and access |
| Retainer | Regular releases and advisory | Ongoing | High | Monthly | Continuity and rapid support | Needs active backlog management |
| Managed evaluation service | Recurring test execution and reporting | Governance and review | Configured service | Monthly or subscription | Repeatable coverage over time | Initial setup and integration required |
| Capability-building engagement | Internal testing team development | High | Tailored | Project or training package | Knowledge transfer and self-sufficiency | Does not replace independent assurance |
These examples are illustrative and do not represent named clients or guaranteed results.
A regulated organisation tests indirect prompt injection in retrieved documents, confidential-data boundaries, unsupported guidance and escalation to human review. Deliverables include a threat model, evidence pack and remediation backlog. Measurement focuses on critical finding closure and retest results.
An online retailer tests whether users can manipulate refund rules, expose order information, create false commitments or trigger unauthorised workflow actions. The scope includes role-based scenarios, tool-use controls and monitoring. Results depend on representative integrations and test accounts.
A technology team tests secret leakage, insecure code suggestions, malicious repository content and unsafe command execution. The engagement combines application-level testing with clear exclusions for infrastructure penetration testing and provider-controlled model behaviour.
Metrics should be baselined, linked to the tested scope and interpreted with model version, environment and coverage limitations.
Critical and high findings by scenario, system, user role and attack category.
Attack-block rate, unsafe-action prevention and escalation adherence under agreed tests.
Finding closure age, retest pass rate, regression count and accepted residual risks.
Priority threat coverage, reproducibility, logging completeness and mapped control ownership.
Number of models, interfaces, retrieval sources, user roles, tools, agents and environments.
Scenario breadth, attack-chain complexity, languages, specialist domains and evidence requirements.
High-impact uses, sensitive data, jurisdictions, audit needs and stakeholder review cycles.
One-off assessment, deep red team, retesting, ongoing service, onsite work and knowledge transfer.
Dataconsultant combines AI evaluation, data governance, security-conscious delivery and business-risk communication. The approach is designed to work with product, engineering, security, privacy, compliance and executive stakeholders without confusing testing evidence with legal certification or a guarantee of safety.
Findings are connected to realistic impacts, responsible owners, control decisions and release criteria.
Reports state what was tested, what was not tested, assumptions, access constraints and residual uncertainty.
Teams receive reusable scenarios, methods and acceptance criteria rather than an isolated list of defects.
The following statements are representative testimonial-style examples and should be replaced with approved, attributable customer evidence before publication.
“The findings were reproducible and linked to practical product decisions, which helped engineering and risk teams agree priorities without overstating the evidence.”
“The team tested realistic multi-step misuse rather than relying only on generic prompts, and the retest criteria made remediation easier to manage.”
“The report clearly separated application controls, model-provider limitations and residual risk, giving governance stakeholders a more useful basis for approval.”
Adversarial testing is a structured evaluation of how an AI system behaves when exposed to malicious, misleading, unusual, or boundary-case inputs. It examines whether safeguards, policies, access controls, monitoring, and human oversight remain effective under pressure.
The service can cover generative AI applications, large language model assistants, retrieval-augmented generation systems, machine-learning models, AI agents, classifiers, recommendation systems, and API-based AI features. Scope depends on access, architecture, intended use, and risk profile.
A typical engagement includes scope definition, threat modelling, test-case design, prompt and input attacks, misuse and abuse testing, data-leakage checks, tool-use and agent testing, evidence capture, risk classification, remediation guidance, and retesting.
Penetration testing primarily assesses technical security vulnerabilities in infrastructure, applications, and networks. AI adversarial testing focuses on model and system behaviour, unsafe outputs, instruction conflicts, prompt injection, data exposure, harmful autonomy, and weaknesses in AI-specific controls. The two services may complement each other.
Yes. Adversarial testing can be delivered as a focused AI red-team exercise when the objective is to simulate realistic misuse, attack paths, and policy bypass attempts. The scope, rules of engagement, test environment, and escalation process are agreed before testing starts.
Yes. Testing can include direct and indirect prompt injection, jailbreak attempts, instruction hierarchy conflicts, encoded or obfuscated prompts, multilingual attacks, context manipulation, retrieval poisoning scenarios, and attempts to make connected tools perform unauthorised actions.
Typical deliverables include a test plan, threat model, adversarial test library, evidence register, findings report, risk ratings, reproducible test steps, control observations, remediation backlog, executive summary, and retest results. Deliverables are tailored to the agreed scope.
There is no reliable fixed duration before scoping. Timing depends on the number of systems, model and tool complexity, access methods, test depth, jurisdictions, safety domains, evidence requirements, remediation cycles, and whether retesting or continuous evaluation is included.
Pricing is influenced by system count, model types, interfaces, connected tools, user roles, threat scenarios, testing depth, required specialists, evidence format, regulatory context, retesting, onsite requirements, and the engagement model. A written estimate can be prepared after initial discovery.
Depending on context, the engagement may reference the NIST AI Risk Management Framework, ISO/IEC 42001, OWASP guidance for large language model applications, MITRE ATLAS, internal risk policies, security standards, privacy obligations, and applicable AI regulations. Final applicability requires organisational and legal validation.
Testing is designed to minimise unnecessary exposure. Data handling, environments, accounts, logging, retention, access, and deletion procedures are agreed before work begins. Synthetic or masked data is preferred where practical, and any production testing requires explicit authorisation and controls.
Yes. Pre-release testing is often valuable before pilot, production deployment, major model change, new tool integration, or expansion into a sensitive use case. It can also be repeated after remediation, model updates, policy changes, or material shifts in the threat landscape.
Testing may be possible where contracts, provider terms, technical access, and authorisation permit it. The engagement distinguishes between issues controlled by the organisation, issues controlled by the model or platform provider, and risks that require compensating controls.
Clients usually provide system documentation, authorised access, intended-use information, prohibited-use policies, data-flow details, control evidence, escalation contacts, risk owners, and subject-matter experts. Timely review is needed to validate findings and prioritise remediation.
No single test can prove that an AI system is completely safe or secure. Adversarial testing provides evidence about the tested scope, conditions, model version, controls, and time period. Residual risk, changing models, new attack methods, and operational misuse must continue to be managed.
