Services: AI Security Testing

Secure your AI before it’s exploited

AI systems introduce new attack paths, from prompt injection and data leakage to model abuse and supply-chain risks. We test your GenAI, LLM applications, and ML pipelines to uncover weaknesses early and help you deploy with confidence.

Why AI security testing matters

Traditional penetration testing is essential, but AI introduces risks that sit outside classic web and infrastructure boundaries.

prompt injection and agent manipulation

Prompt injection & agent manipulation

Unexpected actions, instruction hijacking, and unsafe tool use in AI agents and copilots.
sensitive data exposure

Sensitive data exposure

Leakage from prompts, outputs, retrieval sources, conversation state, and logs.
cost and availability abuse

Cost and availability abuse

Token flooding, automation misuse, and resource exhaustion targeting AI endpoints.
data poisoning and integrity

Data poisoning & integrity

Training, fine-tuning, and pipeline weaknesses that compromise model reliability.
model theft and IP leakage

Model theft & IP leakage

Extraction, inversion, and intellectual property risks for proprietary models.

What we test

Four testing layers covering the full AI attack surface from application logic to operational controls.

GenAI / LLM Apps
RAG & Data Surfaces
Models & ML Pipeline
MLOps / LLMOps

GenAI / LLM applications

Systems where an LLM or GenAI capability is part of the application such as chat assistants, copilots, RAG search, AI agents, and workflow automations.
  1. Prompt injection and instruction-hijacking resistance
  2. Data leakage via prompts, outputs, logs, and conversation state
  3. Unsafe tool use and agent behaviour
  4. Output handling risks (downstream injection, code/markup rendering)
  5. Authentication & authorisation around AI features
GenAI/LLM applications

RAG, retrieval & data interaction surfaces

Where the AI system reads from documents, APIs, databases, or knowledge stores — testing the trust boundaries around connected data.
  1. Retrieval access control and permission boundaries
  2. Prompt injection through documents and untrusted content
  3. Sensitive data exposure from repositories and embeddings
  4. Data minimisation, logging hygiene, and redaction controls
  5. Abuse paths via connectors (internal systems, email, storage)
RAG, retrieval and data interaction surfaces

Models & ML pipeline risks

For teams training, fine-tuning, or managing models — including third-party and hosted models. Applicable when models are part of scope.
  1. Model misuse and privacy risks (extraction/inversion concerns)
  2. Training and fine-tuning data integrity checks (poisoning risks)
  3. Robustness testing for high-impact models (adversarial edge cases)
  4. Model configuration and access controls (keys, endpoints, tenancy)
Models and ML pipeline risks

MLOps / LLMOps operational controls

The AI-specific operational layer that supports your AI system in production — runtime security, abuse prevention, and incident readiness.
  1. Model endpoint security and access paths (keys, auth, rate limits)
  2. Abuse and cost controls (token flooding, anomaly detection)
  3. Guardrails, policy enforcement, and safety monitoring effectiveness
  4. CI/CD and AI supply-chain exposure (dependencies, images, configs)
  5. Observability and incident readiness for AI-specific events
MLOps/LLMOps operational controls

How we work

A threat-driven approach across the AI lifecycle — then validated with realistic abuse cases.

1

Scope & Review

AI architecture, models, data flows, tools, and deployment mapping

2

Threat Model

Misuse cases, abuse paths, and trust boundary analysis

3

Hands-on Testing

AI app + retrieval + pipeline + operational control testing

4

Risk Prioritisation

Impact, likelihood, and exploitability assessment

5

Remediate & Retest

Fix verification, closure support, and readiness sign-off

What you receive

Four testing layers covering the full AI attack surface from application logic to operational controls.

Executive summary

Executive summary

Clear risks and actions for leadership.
Technical report

Technical report

Proof, steps, and fixes for your team.
AI control recommendations

AI control recommendations

Guardrails tailored to your business and AI system.
Reusable test cases

Reusable test cases

Scenarios ready for SDLC and CI.
Assess

Deployment checklist

Go-live readiness, no surprises.
Risk prioritisation matrix

Risk prioritisation matrix

Clear ranking of vulnerabilities based on impact and exploitability.

Engagement options

Choose the approach that fits your maturity and timeline.

AI security assessment

AI Security Assessment

2-4 Weeks

Rapid risk discovery with prioritised fixes. Ideal for teams looking for a clear snapshot of their AI security posture.
AI red team

AI Red Team

Time-boxed engagement

Adversary-style testing against agreed objectives. Simulates real-world attack scenarios on your AI systems.
Pre-production security check

Pre-Production Security Check

Pre-launch validation

Security testing before launch. Catch critical issues before your AI system reaches production.
Continuous AI testing

Continuous AI Testing

Ongoing coverage

Periodic reassessment as prompts, data sources, and models evolve over time.

Ideal for

Common AI use cases we secure and test.

Internal copilots & chat assistants
Internal copilots & chat assistants
RAG knowledge bases & AI search
RAG knowledge bases & AI search
Customer-facing AI (support, and advice)
Customer-facing AI (support, and advice)
AI agents with tools & plugins
AI agents with tools & plugins
ML models (fraud, scoring, analytics)
ML models (fraud, scoring, analytics)
AI workflows & automation
AI workflows & automation

Frequently asked questions

Is this the same as penetration testing?

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Do you only test generative AI?

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Can you test our web/mobile app too?

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How long does an AI security assessment take?

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What do we receive at the end?

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Ready to strengthen your AI security?

Whether you're building an internal copilot or deploying customer-facing AI, we'll help you reduce real-world abuse risk and protect data.
Talk to an expert
Enterprise Cybersecurity Solutions in Malaysia
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