AI Pentesting Services
Securexocean's AI pentesting service delivers a structured security assessment of artificial intelligence systems, LLM-powered applications, and machine learning pipelines — identifying vulnerabilities that conventional penetration testing does not cover.
SERVICE INTRODUCTION
AI systems introduce attack surfaces — including prompt injection, training data extraction, and model manipulation — that traditional network and application testing does not address. As organizations integrate AI into critical business processes, validating the security of those integrations becomes an operational necessity.
Securexocean assesses AI applications and ML infrastructure from the input layer through the model to backend integrations and training data handling, following OWASP LLM Top 10, NIST AI RMF, and MITRE ATLAS frameworks to identify risks specific to your AI architecture.
THREAT LANDSCAPE
Traditional security controls struggle to detect and prevent AI-specific vulnerabilities. Prompt injection can bypass authentication, model inversion can expose training data, and compromised AI agents can be manipulated to execute unauthorized actions across connected enterprise systems.
Threat actors have begun actively targeting AI deployments, seeking to manipulate model output for fraud, exfiltrate private training data, or gain footholds in enterprise environments through insecure AI integrations. AI security assessment converts the unknown risks of your model ecosystem into a prioritized remediation roadmap.
WHAT AI PENTESTING IDENTIFIES
Prompt injection enabling attackers to override system instructions and manipulate LLM behavior
Indirect prompt injection through external data sources consumed by AI agents and pipelines
Insecure output handling allowing LLM responses to trigger unintended downstream actions
Training data extraction and membership inference exposing sensitive information used during model training
Model inversion attacks reconstructing input data from model outputs
Adversarial input manipulation causing model misclassification or bypassing AI-driven security controls
Model theft through repeated inference queries reconstructing proprietary model behavior
Insecure plugin and tool integrations with insufficient access controls
Excessive agency granted to AI agents operating within automated workflows
Vulnerable ML infrastructure including exposed model serving endpoints and insecure data pipelines
Model types, application interfaces, data sources, and integrations defined. Rules of engagement confirmed before testing begins. NDA executed prior to architecture disclosure.
AI-specific threat actors, attack scenarios, and high-value targets identified. MITRE ATLAS techniques mapped to your architecture to prioritize assessment focus.
Manual and structured testing covering prompt injection, jailbreaking, indirect injection, and instruction override across authenticated and unauthenticated interfaces.
Evaluation of model exposure to extraction, inversion, and membership inference. Training data handling, model storage security, and supply chain integrity assessed where accessible.
Assessment of model serving infrastructure, APIs, plugin integrations, and connected systems for authentication weaknesses, access control failures, and data exposure.
Severity-prioritized report with exploitation evidence and AI-specific remediation guidance. Post-remediation retesting and closure report issued.
TOOLS AND TECHNIQUES
Our team uses LLM security testing frameworks for structured prompt injection assessment, adversarial machine learning tooling for input manipulation and model robustness testing, API security tools for inference endpoint assessment, model scanning utilities for open-source component vulnerabilities, and conventional security tools applied to AI-adjacent systems. Manual adversarial testing is central to every engagement.
Executive summary covering AI security posture for leadership and product stakeholders
Technical findings organized by AI attack surface layer including model, application, and infrastructure.
CVSS v3.1 scores alongside AI-specific risk ratings for model and data layer findings.
Compliance mapping against OWASP LLM Top 10, NIST AI RMF, and ISO 27001.
Remediation guidance specific to your AI architecture and deployment environment.
Post-remediation retest report for governance and compliance documentation.
BUSINESS IMPACT
An exploited LLM application can be manipulated to bypass content controls, exfiltrate data from connected systems, or execute unauthorized actions through integrated tools. For fintech and healthcare organizations where AI drives decision-making, failures carry compliance consequences alongside operational risk. AI systems handling personal data introduce additional GDPR and DPDP Act exposure when model vulnerabilities result in unauthorized data access.
COMPLIANCE RELEVANCE
Asset management, access control, and vulnerability management controls apply to AI systems and ML infrastructure within organizational scope.
Model vulnerabilities enabling unauthorized inference or training data extraction create direct regulatory exposure under both frameworks.
Findings are mapped to NIST AI RMF outcomes supporting organizational AI governance and risk management programs.
Engagements validate coverage across all ten LLM risk categories including prompt injection, insecure output handling, and excessive agency.
FREQUENTLY ASKED QUESTIONS
Enterprise-grade VAPT, GRC advisory, compliance consulting, and AI-assisted threat management for modern businesses.
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