AI Platform VAPT — LLM & ML Penetration Testing
Offensive testing for LLM apps, RAG pipelines, agents and MLOps platforms.
Overview
Specialist vulnerability assessment and penetration testing for AI platforms — generative AI applications, RAG pipelines, autonomous agents, fine-tuned and self-hosted models, and the MLOps infrastructure behind them. Aligned to the OWASP Top 10 for LLM Applications, MITRE ATLAS, NIST AI RMF and ISO/IEC 42001 controls, our AI red team probes prompt injection, jailbreaks, model abuse, data leakage, supply-chain risk and agent misuse with proof-of-concept exploits and remediation guidance your engineering team can ship.
Process flow, compliance checklist and benefits.
A visual breakdown of how the engagement runs, what evidence we leave behind, and the business outcomes you can defend at the board.
How we deliver AI Platform VAPT — LLM & ML Penetration Testing.
- 01Threat Modelling
Map model inventory, data flows, agent tools and trust boundaries against OWASP LLM Top 10 and MITRE ATLAS tactics.
- 02Adversarial Testing
Prompt injection (direct and indirect), jailbreaks, system-prompt leakage, training-data extraction, model DoS, RAG corpus poisoning and tool/function-call abuse.
- 03Platform & Pipeline
Pentest the surrounding stack — vector DBs, inference endpoints, fine-tuning pipelines, MLOps tooling, model registries and identity boundaries.
- 04Reporting
Risk-ranked report with PoCs, screen captures, repeatable payload library and CVSS-aligned scoring.
- 05Re-test
Validation of remediated findings and tuning of guardrails, evaluators and detection rules.
What auditors and regulators expect to see.
Every item below is part of an audit-ready AI Platform VAPT — LLM & ML Penetration Testing programme — what regulators, certification bodies and enterprise buyers expect to see.
- Scope and applicability statement
Confirmed boundaries for AI Platform VAPT — LLM & ML Penetration Testing across entities, locations and systems.
- Gap assessment report
Current-state diagnostic with prioritised, owner-tagged findings.
- Policy and procedure suite
Approved by top management, version-controlled and communicated to staff.
- Risk register and treatment plan
Threats, controls, residual risk and accepted exceptions documented.
- Awareness and role-based training
Attendance, content and assessment evidence retained.
- Evidence repository
Central, auditor-accessible, timestamped artefacts per control.
- Internal audit and management review
Independent assurance run before any external assessment.
- Continuous improvement log
Findings, corrective actions and re-test evidence tracked to closure.
What you walk away with.
Frequently asked questions.
Which AI systems do you test?+
LLM chatbots, RAG applications, autonomous agents with tool use, fine-tuned and self-hosted models (Llama, Mistral, Falcon), embedded copilots, Azure OpenAI / Bedrock / Vertex deployments and traditional ML scoring services.
How is this different from a normal web pentest?+
We cover all the usual application and API attack surface, plus AI-specific threats: prompt injection, jailbreaks, training-data and system-prompt extraction, model and vector-DB poisoning, agent tool abuse, and inference-cost denial-of-wallet attacks.
Do you align to OWASP LLM Top 10 and MITRE ATLAS?+
Yes — every finding is mapped to OWASP LLM Top 10 (2025), MITRE ATLAS techniques and the relevant NIST AI RMF / ISO 42001 control so the evidence pack drops into your governance programme.
Can your report be used for ISO 42001, EU AI Act or SOC 2 AI add-ons?+
Yes. Reports are written to satisfy ISO/IEC 42001 evaluation evidence, EU AI Act high-risk testing obligations, and enterprise AI assurance questionnaires from BFSI and healthcare buyers.
Will testing leak our prompts or fine-tuned weights?+
No. Engagements run under NDA with payload sandboxing; any extracted system prompts, embeddings or weights are reported then securely destroyed. We can test fully air-gapped against a customer-controlled environment.
How long does an AI platform pentest take?+
Single LLM application: 7 to 12 working days. Multi-agent or RAG-heavy platforms: 3 to 5 weeks. Full red team against an AI-enabled product line: 4 to 8 weeks.
Do you cover the MLOps and training pipeline?+
Yes — model registries, CI/CD for models, vector databases (Pinecone, Weaviate, pgvector), feature stores and inference gateways are in scope when present. We test supply-chain risk in third-party models and datasets.
Can this be part of an existing VAPT or red team retainer?+
Yes. AI platform testing slots into our standard VAPT retainer and feeds the same reporting, attestation and re-test workflow your security team already uses.
The AI Platform VAPT Checklist.
A 12-section, practitioner-grade checklist used by our AI red team — covering prompt injection, RAG, agents and tool calling, MLOps, supply chain and governance mapping to OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF and ISO/IEC 42001.
Choose how you'd like to engage on AI Platform VAPT — LLM & ML Penetration Testing.
Related services buyers of this engagement pair with.
Most clients combine this engagement with one or more of the services below — a natural next step once foundations are in place.
- ExploreAudit & AssuranceVAPT — Vulnerability Assessment & Penetration Testing
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- ExploreAI Governance & RiskAI Governance & ISO 42001
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Standards and regulations this service maps to.
Direct links into the relevant clauses, controls and regulator obligations covered by this engagement.
- ExploreStandardISO/IEC 42001 — AI Management System
AI Governance
- ExploreStandardISO/IEC 27001 — Information Security
Info & Cyber Security
- ExploreRegulationISO/IEC 42001 — AI Management System
International Organization for Standardization · Global / International
- ExploreRegulationNIST AI Risk Management Framework
National Institute of Standards and Technology · Global / International
- ExploreRegulationCBUAE AI / ML Guidance
Central Bank of the UAE · United Arab Emirates
- ExploreRegulationSDAIA AI Ethics Principles
Saudi Data & AI Authority · Saudi Arabia
Briefings, playbooks and checklists on this topic.
Long-form analysis from our practice leads — read alongside this service to brief your team or audit committee.
- ExploreBriefingData governance for LLM products: PII, IP and provenance.
Reference architecture for training-data lineage, opt-out and red-teamed evaluation.
- ExplorePlaybookEvidence packs for model evaluation, bias and safety.
What ISO 42001 auditors actually want to see for evaluation cadence, datasets and rollback decisions.
- ExploreChecklistAn AI risk register that survives contact with a model.
Risk taxonomy, scoring and treatment options aligned to ISO 23894 and the NIST AI RMF.
- ExploreField noteISO 42001 in practice: governing AI without slowing it down.
Three early adopters on building an AI management system auditors trust and product teams will actually use.
- ExploreChecklistModern web-app pentest checklist (OWASP ASVS L2/L3).
A test plan template grounded in ASVS, with sample evidence and reporting structure.
- ExplorePlaybookAPI security testing for payments and open-banking APIs.
BOLA, broken auth, rate-limit bypass — the OWASP API Top 10 grounded in real Gulf engagements.
- ExploreBriefingRed team vs purple team — what your detection team actually needs.
When to buy a red team engagement and when a purple team partnership delivers more uplift.
- ExploreField noteScoping cloud pentests across AWS, Azure and OCI.
What's in scope, what's the provider's problem, and what your contract needs to allow.
Explore every facet of AI Platform VAPT — LLM & ML Penetration Testing.
Methodology, deliverables, week-by-week timeline, pricing models, industry context, tooling and extended FAQs — each on its own page for fast reference and deep linking.
Ready to scope your AI Platform VAPT engagement?
Tell us a little about your business — a senior consultant will reach out within one business day.