As enterprises integrate generative AI into high-trust workflows, they expose a new attack surface with unique failure modes. AI pentesting is no longer a niche exercise. It is now part of core risk governance for product teams, CISOs, and compliance leaders.
Unlike traditional application testing, LLM security requires layered adversarial testing across prompt handling, retrieval pipelines, plugin actions, model output trust boundaries, and downstream business logic. A secure AI deployment must prevent data leakage, harmful output execution, privilege escalation, and policy evasion under realistic attacker behavior.
The OWASP Top 10 for LLMs
Prompt Injection
Forcing the AI to bypass its safety filters and leak system data.
Insecure Output Handling
When the AI's output is directly executed as code or HTML, leading to XSS.
Training Data Poisoning
Manipulating the AI's knowledge base to provide biased or malicious responses.
Model Inversion
Extracting private training data through targeted queries.
Excessive Agency
Abusing tool integrations so the model performs actions beyond intended permissions.
Sensitive Data Exposure
Leaking tokens, credentials, or customer data through retrieval and generation paths.
How We Run an AI Pentest
Context Mapping
Map system prompts, retrieval connectors, action tools, and trust boundaries.
Adversarial Simulation
Execute red-team prompts for jailbreaks, data exfiltration, and unsafe actions.
Control Validation
Test guardrails, moderation, role isolation, and policy enforcement under load.
Fix Roadmap
Provide prioritized remediation with developer-ready guidance and retesting support.
AI Governance (ISO 42001)
Beyond technical bugs, we assess whether your AI deployment aligns with emerging governance standards such as ISO 42001, sector-specific regulations, and internal accountability controls. Security, privacy, explainability, and operational resilience must be designed as one system.
High-maturity organizations treat AI security as a lifecycle discipline: threat modeling before launch, active attack simulation during operation, and governance checkpoints tied to change management. This creates measurable risk reduction and stronger audit readiness.
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